Post-training quantization (PTQ) offers an efficient approach to compressing large language models (LLMs), significantly reducing memory access and computational costs. Existing compensation-based weight calibration methods often rely on a second-order Taylor expansion to model quantization error, under the assumption that the first-order term is negligible in well-trained full-precision models. However, we reveal that the progressive compensation process introduces accumulated first-order deviations between latent weights and their full-precision counterparts, making this assumption fundamentally flawed. To address this, we propose FOEM, a novel PTQ method that explicitly incorporates first-order gradient terms to improve quantization error compensation. FOEM approximates gradients by performing a first-order Taylor expansion around the pre-quantization weights. This yields an approximation based on the difference between latent and full-precision weights as well as the Hessian matrix. When substituted into the theoretical solution, the formulation eliminates the need to explicitly compute the Hessian, thereby avoiding the high computational cost and limited generalization of backpropagation-based gradient methods. This design introduces only minimal additional computational overhead. Extensive experiments across a wide range of models and benchmarks demonstrate that FOEM consistently outperforms the classical GPTQ method. In 3-bit weight-only quantization, FOEM reduces the perplexity of Llama3-8B by 17.3% and increases the 5-shot MMLU accuracy from 53.8% achieved by GPTAQ to 56.1%. Moreover, FOEM can be seamlessly combined with advanced techniques such as SpinQuant, delivering additional gains under the challenging W4A4KV4 setting and further narrowing the performance gap with full-precision baselines, surpassing existing state-of-the-art methods.

Magnetic resonance imaging (MRI) relies on physiological triggering for cardiac gating, typically using the R-peak of an electrocardiogram (ECG). However, ECG-based triggering faces limitations in MRI environments due to challenges in electrode placement, magnetic field interference, and RF-induced heating. Alternatively, contact photoplethysmography (PPG) offers a feasible solution; however, it suffers from reduced accuracy and requires the fixation of a probe on the finger tip. To overcome these limitations, this paper proposes an MRI-compatible system for contactless forehead PPG using bundled fiber-optic guides. The proposed approach eliminates electrical interference and ensures safety. A low-power sensor node is proposed to investigate the trade-offs among signal fidelity, energy efficiency, and system latency in on-device PPG. By combining programmable optical sources, an analog front-end, and BLE connectivity, the platform enables reproducible MRI experimentation. It fully processes the PPG data in just 2.8 ms onboard, utilizing a low-power ARM Cortex-M33 core running at 128 MHz. A feasibility study involving 8 subjects was conducted to evaluate the proposed system and demonstrate the effectiveness of the MRI-compatible contactless PPG using green/red light. Several fiducial points of the PPG waveform - foot, onset, and peak - were evaluated for trigger generation. Despite a physiological delay of ∼100-150 ms relative to the R-peak of a reference ECG, the PPG-based R-peak point is reliably estimated with a jitter of 5.14 ms. The sensor node demonstrated the efficiency of the proposed solution operating with a 520 mAh battery for over 23 hours and integrates custom adapters for 2 m optical guides, ensuring safe electronic placement outside the MRI bore. These results confirm that our system can enable prospective gating of MRI measurements without the practical challenges of securing ECG leads or a fingertip PPG. The proposed system paves the way for a safe, contactless, low-power, self-contained sensor node with onboard processing and an interference-free gating method, with the potential to redefine physiological monitoring workflows in MRI environments, enabling precise synchronization without electromagnetic interference.

Diffusion models have received wide attention in generation tasks. However, the expensive computation cost prevents the application of diffusion models in resource-constrained scenarios. Quantization emerges as a practical solution that significantly saves storage and computation by reducing the bit-width of parameters. However, the existing quantization methods for diffusion models still cause severe degradation in performance, especially under extremely low bit-widths (2-4 bit). The primary decrease in performance comes from the significant discretization of activation values at low bit quantization. Too few activation candidates are unfriendly for outlier significant weight channel quantization, and the discretized features prevent stable learning over different time steps of the diffusion model. This paper presents MPQ-DM, a Mixed-Precision Quantization method for Diffusion Models. The proposed MPQ-DM mainly relies on two techniques: (1) To mitigate the quantization error caused by outlier severe weight channels, we propose an Outlier-Driven Mixed Quantization (OMQ) technique that uses Kurtosis to quantify outlier salient channels and apply optimized intra-layer mixed-precision bit-width allocation to recover accuracy performance within target efficiency. (2) To robustly learn representations crossing time steps, we construct a Time-Smoothed Relation Distillation (TRD) scheme between the quantized diffusion model and its full-precision counterpart, transferring discrete and continuous latent to a unified relation space to reduce the representation inconsistency. Comprehensive experiments demonstrate that MPQ-DM achieves significant accuracy gains under extremely low bit-widths compared with SOTA quantization methods. MPQ-DM achieves a 58% FID decrease under W2A4 setting compared with baseline, while all other methods even collapse.

Biomechanical data acquisition in sports demands sub-millisecond synchronization across distributed body-worn sensor nodes. This study evaluates and characterizes the Enhanced ShockBurst (ESB) protocol from Nordic Semiconductor under controlled laboratory conditions for wireless, low-latency command broadcasting, enabling fast event updates in multi-node systems. Through systematic profiling of protocol parameters, including cyclic-redundancy-check modes, bit-rate, transmission modes, and payload handling, we achieve a mean Device-to-Device (D2D) latency of$504.99 \pm 96.89 \mu \mathrm{s}$and a network-to-network core latency of$311.78 \pm 96.90 \mu \mathrm{s}$using a onebyte payload with retransmission optimization. This significantly outperforms Bluetooth Low Energy (BLE), which is constrained by a 7.5 ms connection interval, by providing deterministic, submillisecond synchronization suitable for high-frequency$(500 \text{Hz}$to 1000 Hz) biosignals. These results position ESB as a viable solution for time-critical, multi-node wearable systems in sports, enabling precise event alignment and reliable high-speed data fusion for advanced athlete monitoring and feedback applications.

CUPIDO (Circuit for Unobtrusive Palpebral Interpretation and Detection Optimization) is an ultra-low-power electrostatic sensor able to convert eye blinks into digital events with a detection sensitivity up to 90.5 %. It can be easily integrated into the rims of smart glasses allowing for contactless interaction without compromising comfort and privacy (since no camera is used). Thanks to its extremely low power consumption (385.1 μW at peak during the blink), CUPIDO can extend battery life in smart glasses, allowing for continuous and real-time (detection latency of approximately 1 ms) monitoring applications like hands-free glasses control, assistive technologies, augmented reality, and drowsiness monitoring while driving.

With the advancement of diffusion models (DMs) and the substantially increased computational requirements, quantization emerges as a practical solution to obtain compact and efficient low-bit DMs. However, the highly discrete representation leads to severe accuracy degradation, hindering the quantization of diffusion models to ultra-low bit-widths. This paper proposes a novel weight binarization approach for DMs, namely BinaryDM, pushing binarized DMs to be accurate and efficient by improving the representation and optimization. From the representation perspective, we present an Evolvable-Basis Binarizer (EBB) to enable a smooth evolution of DMs from full-precision to accurately binarized. EBB enhances information representation in the initial stage through the flexible combination of multiple binary bases and applies regularization to evolve into efficient single-basis binarization. The evolution only occurs in the head and tail of the DM architecture to retain the stability of training. From the optimization perspective, a Low-rank Representation Mimicking (LRM) is applied to assist the optimization of binarized DMs. The LRM mimics the representations of full-precision DMs in low-rank space, alleviating the direction ambiguity of the optimization process caused by fine-grained alignment. Comprehensive experiments demonstrate that BinaryDM achieves significant accuracy and efficiency gains compared to SOTA quantization methods of DMs under ultra-low bit-widths. With 1-bit weight and 4-bit activation (W1A4), BinaryDM achieves as low as 7.74 FID and saves the performance from collapse (baseline FID 10.87). As the first binarization method for diffusion models, W1A4 BinaryDM achieves impressive 15.2x OPs and 29.2x model size savings, showcasing its substantial potential for edge deployment.

Post-training quantization (PTQ) is an effective technique for compressing large language models (LLMs). However, while uniform-precision quantization is computationally efficient, it often compromises model performance. To address this, we propose SliM-LLM, a salience-driven mixed-precision quantization framework that allocates bit-widths at the group-wise with high accuracy. Our approach leverages the observation that important weights follow a structured distribution and introduces two key components: 1) Salience-Determined Bit Allocation adaptively assigns bit-widths to groups within each layer based on their salience; and 2) Salience-Weighted Quantizer Calibration optimizes quantizer parameters by incorporating element-level salience, retain essential information. With its structured group-wise partitioning, SliM-LLM provides a hardware-friendly solution that matches the efficiency of uniform quantization methods while significantly improving accuracy. Experiments show that SliM-LLM achieves superior performance across various LLMs at low bit-widths. For example, a 2-bit quantized LLaMA-7B model reduces memory usage by nearly 6x compared to the floating-point baseline, decreases perplexity by 48% compared to state-of-the-art gradient-free PTQ methods, and maintains GPU inference speed. Additionally, the extended version, SliM-LLM+, which incorporates gradient-based quantization, further reduces perplexity by 35.1%. Our code is available at https://github.com/Aaronhuang-778/SliM-LLM.

Diffusion transformers (DiT) have demonstrated exceptional performance in video generation. However, their large number of parameters and high computational complexity limit their deployment on edge devices. Quantization can reduce storage requirements and accelerate inference by lowering the bit-width of model parameters. Yet, existing quantization methods for image generation models do not generalize well to video generation tasks. We identify two primary challenges: the loss of information during quantization and the misalignment between optimization objectives and the unique requirements of video generation. To address these challenges, we present **Q-VDiT**, a quantization framework specifically designed for video DiT models. From the quantization perspective, we propose the *Token aware Quantization Estimator* (TQE), which compensates for quantization errors in both the token and feature dimensions. From the optimization perspective, we introduce *Temporal Maintenance Distillation* (TMD), which preserves the spatiotemporal correlations between frames and enables the optimization of each frame with respect to the overall video context. Our W3A6 Q-VDiT achieves a scene consistency score of 23.40, setting a new benchmark and outperforming the current state-of-the-art quantization methods by **1.9$\times$**.

Autonomous racing presents a complex environment requiring robust controllers capable of making rapid decisions under dynamic conditions. While traditional controllers based on tire models are reliable, they often demand extensive tuning or system identification. Reinforcement Learning (RL) methods offer significant potential due to their ability to learn directly from interaction, yet they typically suffer from the Sim-to-Real gap, where policies trained in simulation fail to perform effectively in the real world. In this paper, we propose RLPP, a residual RL framework that enhances a Pure Pursuit (PP) controller with an RL-based residual. This hybrid approach leverages the reliability and interpretability of PP while using RL to fine-tune the controller's performance in real-world scenarios. Extensive testing on the F1TENTH platform demonstrates that RLPP improves lap times of the baseline controllers by up to 6.37 %, closing the gap to the State-of-the-Art (SotA) methods by more than 52 % and providing reliable performance in zero-shot real-world deployment, overcoming key challenges associated with the Sim-to-Real transfer and reducing the performance gap from simulation to reality by more than 8 -fold when compared to the baseline RL controller. The RLPP framework is made available as an open-source tool, encouraging further exploration and advancement in autonomous racing research. The code is available at: www.github.com/forzaeth/rlpp.

Compact quadrupedal robots are proving increasingly suitable for deployment in real-world scenarios. Their smaller size fosters easy integration into human environments. Nevertheless, real-time locomotion on uneven terrains remains challenging, particularly due to the high computational demands of terrain perception. This paper presents a robust reinforcement learning-based exteroceptive locomotion controller for resource-constrained small-scale quadrupeds in challenging terrains, which exploits real-time elevation mapping, supported by a careful depth sensor selection. We concurrently train both a policy and a state estimator, which together provide an odometry source for elevation mapping, optionally fused with visual-inertial odometry (VIO). We demonstrate the importance of positioning an additional time-of-flight sensor for maintaining robustness even without VIO, thus having the potential to free up computational resources. We experimentally demonstrate that the proposed controller can flawlessly traverse steps up to 17.5 cm in height and achieve an 80% success rate on 22.5 cm steps, both with and without VIO. The proposed controller also achieves accurate forward and yaw velocity tracking of up to 1.0 m/s and 1.5 rad/s respectively. We open-source our training code at github.com/ETH-PBL/elmap-rl-controller.

Ensuring robust and real-time obstacle avoidance is critical for the safe operation of autonomous robots in dynamic, real-world environments. This paper proposes a neural network framework for predicting the time and collision position of an unmanned aerial vehicle with a dynamic object, using RGB and event-based vision sensors. The proposed architecture consists of two separate encoder branches, one for each modality, followed by fusion by self-attention to improve prediction accuracy.To facilitate benchmarking, we leverage the ABCD [8] dataset collected that enables detailed comparisons of single-modality and fusion-based approaches.At the same prediction throughput of 50Hz, the experimental results show that the fusion-based model offers an improvement in prediction accuracy over single-modality approaches of 1% on average and 10% for distances beyond 0.5m, but comes at the cost of +71% in memory and + 105% in FLOPs. Notably, the event-based model outperforms the RGB model by 4% for position and 26% for time error at a similar computational cost, making it a competitive alternative.Additionally, we evaluate quantized versions of the event-based models, applying 1- to 8-bit quantization to assess the trade-offs between predictive performance and computational efficiency.These findings highlight the trade-offs of multi-modal perception using RGB and event-based cameras in robotic applications.

Achieving Simultaneous Localization and Mapping (SLAM) in an unfamiliar environment is a crucial challenge, especially for robots that rely on efficient on-device processing. While accurate mapping is achievable on high-end robotic systems, it still faces substantial challenges due to hardware and latency constraints, especially on smaller robots with limited power budget. Although machine learning is proving highly effective for robot perception, there is a growing need for lightweight solutions in terms of computation and sensing. This paper presents Nano VS, a lightweight monocular perception layer supporting semantic mapping with less than 1 M parameters. We propose a family of quantized and efficient models integrating emerging attention layers and weight-sharing in a multi-task neural network. Experimental results demonstrate multiple tasks within a single model, including Semantic Segmentation (SS), Feature Detection and Description (FDD), and Visual Place Recognition (VPR). Our findings indicate that multi-tasking effectively reduces computational overhead by eliminating the need for multiple networks. Nano VS achieves 70% classwise mIoU with the cityscapes benchmark and 66% Recall@1 in the Pitts30k challenge on tiny images (120x160 pixels). Finally, this paper implements and evaluates Nano VS on a novel milli-watt multi-core RISC-V Microcontroller (MCU), running the full semantic front-end in as little as 52 ms, consuming only 9 mJ per inference. This work represents a significant step towards making advanced SLAM capabilities accessible to tiny robots, or even faster and energy-efficient SLAM on high-end processors.

Generating overtaking trajectories in autonomous racing is a challenging task, as the trajectory must satisfy the vehicle’s dynamics and ensure safety and real-time performance running on resource-constrained hardware. This work proposes the Fast and Safe Data-Driven Planner to address this challenge. Sparse Gaussian predictions are introduced to improve both the computational efficiency and accuracy of opponent predictions. Furthermore, the proposed approach employs a bi-level quadratic programming framework to generate an overtaking trajectory leveraging the opponent predictions. The first level uses polynomial fitting to generate a rough trajectory, from which reference states and control inputs are derived for the second level. The second level formulates a model predictive control optimization problem in the Frenet frame, generating a trajectory that satisfies both kinematic feasibility and safety. Experimental results on the F1TENTH platform show that our method outperforms the State-of-the-Art, achieving an 8.93% higher overtaking success rate, allowing the maximum opponent speed, ensuring a smoother ego trajectory, and reducing 74.04% computational time compared to the Predictive Spliner method. The code is available at: https://github.com/ZJU-DDRX/FSDP.

Unrestricted multi-agent racing presents a significant research challenge, requiring decision-making at the limits of a robot's operational capabilities. While previous approaches have either ignored spatiotemporal information in the decision-making process or been restricted to single-opponent scenarios, this work enables arbitrary multi-opponent head-to-head racing while considering the opponents' future intent. The proposed method employs a Kalman Filter (KF)-based multi-opponent tracker to effectively perform opponent Re-Identification (reID) by associating them across observations. Simultaneously, spatial and velocity Gaussian Process Regression (GPR) is performed on all observed opponent trajectories, providing predictive information to compute the overtaking maneuvers. This approach has been experimentally validated on a physical 1:10 scale autonomous racing car achieving an overtaking success rate of up to 91.65% and demonstrating an average 10.13%-point improvement in safety at the same speed as the previous State-of-the-Art (SotA). These results highlight its potential for high-performance autonomous racing.

Autonomous racing has emerged as a crucial testbed for autonomous driving algorithms, necessitating a simulation environment for both vehicle dynamics and sensor behavior. Striking the right balance between vehicle dynamics and sensor accuracy is crucial for pushing vehicles to their performance limits. However, autonomous racing developers often face a trade-off between accurate vehicle dynamics and high-fidelity sensor simulations. This paper introduces$\mathcal{R}-\mathbf{CARLA}$, an enhancement of the CARLA simulator that supports holistic full-stack testing, from perception to control, using a single system. By seamlessly integrating accurate vehicle dynamics with sensor simulations, opponents simulation as Non-Player Characters (NPCs), and a pipeline for creating digital twins from real-world robotic data,$\mathcal{R}-\mathbf{CARLA}$empowers researchers to push the boundaries of autonomous racing development. Furthermore, it is developed using CARLA's rich suite of sensor simulations. Our results indicate that incorporating the proposed digital-twin framework into$\mathcal{R}-\mathbf{CARLA}$enables more realistic full-stack testing, demonstrating a significant reduction in the Sim-to-Real gap of car dynamics simulation by 42% and by 82% in the case of sensor simulation across various testing scenarios.

Perception within autonomous driving is nearly synonymous with Neural Networks (NNs). Yet, the domain of autonomous racing is often characterized by scaled, computationally limited robots used for cost-effectiveness and safety. For this reason, opponent detection and tracking systems typically resort to traditional computer vision techniques due to computational constraints. This paper introduces TinyCenterSpeed, a streamlined adaptation of the seminal CenterPoint method, optimized for real-time performance on 1:10 scale autonomous racing platforms. This adaptation is viable even on OBCs powered solely by Central Processing Units (CPUs), as it incorporates the use of an external Tensor Processing Unit (TPU). We demonstrate that, compared to Adaptive Breakpoint Detector (ABD), the current State-of-the-Art (SotA) in scaled autonomous racing, TinyCenterSpeed not only improves detection and velocity estimation by up to 61.38% but also supports multi-opponent detection and estimation. It achieves real-time performance with an inference time of just 7.88ms on the TPU, significantly reducing CPU utilization 8.3-fold.

Asset tracking solutions have proven their significance in industrial contexts, as evidenced by their successful commercialization (e.g., Hilti On!Track). However, a seamless solution for matching assets with their users, such as operators of construction power tools, is still missing. By enabling asset-user matching, organizations gain valuable insights that can be used to optimize user health and safety, asset utilization, and maintenance. This article introduces a novel approach to address this gap by leveraging existing Bluetooth low energy (BLE)-enabled low-power Internet of Things (IoT) devices. The proposed framework comprises the following components: 1) a wearable device; 2) an IoT device attached to or embedded in the assets; 3) an algorithm to estimate the distance between assets and operators by exploiting simple received signal strength indicator (RSSI) measurements via an extended Kalman filter (EKF); and 4) a cloud-based algorithm that collects all estimated distances to derive the correct asset-operator matching. The effectiveness of the proposed system has been validated through indoor and outdoor experiments in a construction setting for identifying the operator of a power tool. A physical prototype was developed to evaluate the algorithms in a realistic setup. The results demonstrated a median accuracy of 0.49m in estimating the distance between assets and users, and up to 98.6% in correctly matching users with their assets.

This work explores the feasibility of employing ultrasound (US) technology in a wrist-worn Internet-of-Things (IoT) device for low-power, high-fidelity heart rate (HR) extraction. US offers deep tissue penetration and can monitor pulsatile arterial blood flow in large vessels and the surrounding tissue, potentially improving robustness and accuracy compared to photoplethysmogram (PPG). We present an IoT wearable system prototype utilizing a commercial microcontroller (MCU) employing the onboard analogdigital converters (ADC) to capture high-frequency US signals and an innovative low-power US pulser. An envelope filter lowers the bandwidth of the US signal by a factor of >5 x, reducing the systems acquisition requirements without compromising accuracy (correlation coefficient between HR extracted from enveloped and raw signals, r(92)=0.996, p<0.001). The full signal processing pipeline is ported to fixed-point arithmetic for increased energy efficiency and runs entirely onboard. The extracted HR can be transmitted to the cloud via a Bluetooth low energy (BLE) module. The system has an average power consumption of 5.8mW, competitive with commercial PPG based systems, and the HR extraction algorithm requires only 69 kB of RAM and 71 ms of processing time on an ARM Cortex-M4 based MCU. The system is estimated to run continuously on a smartwatch battery for more than 7 days. To accurately evaluate the proposed circuit and algorithm and identify the anatomical location on the wrist with the highest accuracy for HR extraction, we collected a dataset from 10 healthy adults at three different wrist positions. The dataset comprises roughly 5 hours of HR data with an average of 80.6116.3 bpm. During recording, we synchronized the established electrocardiography (ECG) gold standard with our US-based method. The comparisons yield a Pearson correlation coefficient of r(92)=0.99, p<0.001 and a mean error of 0.6811.88 bpm in the lateral wrist position near the radial artery. Moreover, we tested our method while walking and running to assess its robustness to motion artifacts, achieving a heart rate extraction accuracy of 1.9912.80 bpm. The collected dataset and code used in this work have been open-sourced and are available at https://github.com/mgiordy/Ultrasound-Heart-Rate.

Visual Inertial Odometry (VIO) is a widely used computer vision method that determines an agent’s movement through a camera and an IMU sensor. This paper presents an efficient and accurate VIO pipeline optimized for applications on micro- and nano-UAVs. The proposed design incorporates state-of-the-art feature detection and tracking methods (SuperPoint, PX4FLOW, ORB), all optimized and quantized for emerging RISC-V-based ultra-low-power parallel systems on chips (SoCs). Furthermore, by employing a rigid body motion model, the pipeline reduces estimation errors and achieves improved accuracy in planar motion scenarios. The pipeline’s suitability for real-time VIO is assessed on an ultra-low-power SoC in terms of compute requirements and tracking accuracy after quantization. The pipeline, including the three feature tracking methods, was implemented on the SoC for real-world validation. This design bridges the gap between high-accuracy VIO pipelines that are traditionally run on computationally powerful systems and lightweight implementations suitable for microcontrollers. The optimized pipeline on the GAP9 low-power SoC demonstrates an average reduction in RMSE of up to a factor of 3.65x over the baseline pipeline when using the ORB feature tracker. The analysis of the computational complexity of the feature trackers further shows that PX4FLOW achieves on-par tracking accuracy with ORB at a lower runtime for movement speeds below 24 pixels/frame.

Smart glasses with integrated eye tracking technology are revolutionizing diverse fields, from immersive augmented reality experiences to cutting-edge health monitoring solutions. However, traditional eye tracking systems rely heavily on cameras and significant computational power, leading to high-energy demand and privacy issues. Alternatively, systems based on electrooculography (EOG) provide superior battery life but are less accurate and primarily effective for detecting blinks, while being highly invasive. To bridge this gap, the paper introduces ElectraSight, a system built upon a new concept we define as hybrid Electrooculography (hEOG). This approach combines contact and contactless electrodes to create a robust, low-power, and truly non-invasive eye tracking system. To validate our approach, we collected a comprehensive dataset from 20 participants, using a commercial eye-tracker for ground-truth labeling. A lightweight 1D Convolutional Neural Network (CNN), quantized to 4-bit and occupying just 79 kB of memory, performs real-time eye movement classification. Without requiring user-specific calibration, the model achieves 81% accuracy for 10 classes and 92% for 6 classes. Experimental results demonstrate that ElectraSight delivers high accuracy in eye movement and blink classification, with minimal overall movement detection latency (90% within 60ms) and an ultra-low inference time (301 ms). The power consumption settles down to 7.75mW for continuous data acquisition and 46μJ for the tinyML inference. This efficiency enables continuous operation for over 3 days on a compact 175 m A h battery. This work opens new possibilities for eye tracking in commercial applications, offering an unobtrusive solution that enables advancements in user interfaces, health diagnostics, and hands-free control systems.

Deep neural networks for real-time video matting suffer significant computational limitations on edge devices, hindering their adoption in widespread applications such as online conferences and short-form video production. Binarization emerges as one of the most common neural network compression approaches, substantially curbing computational and memory requirements through compact 1-bit parameters and efficient bitwise operations. However, the empirical observation reveals that accuracy and efficiency limitations exist in the binarized video matting network due to its degenerated encoder and redundant decoder. Following a theoretical analysis based on the information bottleneck principle, the limitations are mainly caused by the degradation of prediction-relevant information in the intermediate features and the redundant computation in prediction-irrelevant areas. We presentBiVM, an accurate and resource-efficientBinarized neural network forVideoMatting, where architecture and optimization allow real-time video matting to proceed on edge hardware. First, we present a series of binarized computation structures with elastic shortcuts and evolvable topologies, enabling the constructed encoder backbone to extract high-quality representation from input videos for accurate prediction. Second, we sparse the intermediate feature of the binarized decoder by masking homogeneous parts, allowing the decoder to focus on representation with diverse details while alleviating the computation burden for efficient inference. Furthermore, we construct a localized binarization-aware mimicking framework with the information-guided strategy, prompting matting-related representation in full-precision counterparts to be accurately and fully utilized. Comprehensive experiments show that the proposed BiVM surpasses alternative binarized video matting networks, including state-of-the-art (SOTA) binarization methods, by a substantial margin. For example, BiVM surpasses 16.67 MAD compared to SOTA binarization on the VM dataset. Notably, our approach can even perform comparably to the full-precision counterpart in terms of visual quality. Moreover, our BiVM achieves significant savings of 14.3× and 21.6× in computation and storage costs, respectively. We also evaluate BiVM on ARM CPU hardware, underscoring its potential for deployment in resource-constrained scenarios.

This work introduces SynPF, an MCL-based algorithm tailored for high-speed racing environments. Benchmarked against Cartographer, a state-of-the-art pose-graph SLAM algorithm, SynPF leverages synergies from previous particle-filtering methods and synthesizes them for the high-performance racing do-main. Our extensive in-field evaluations reveal that while Cartogra-pher excels under nominal conditions, it struggles when subjected to wheel-slip-a common phenomenon in a racing scenario due to varying grip levels and aggressive driving behaviour. Conversely, SynPF demonstrates robustness in these challenging conditions and a low-latency computation time of 1.25 ms on on-board computers without a GPU. Using the FITENTH platform, a 1:10 scaled autonomous racing vehicle, this work not only highlights the vulnerabilities of existing algorithms in high-speed scenarios, tested up until$7.6 \text{ms}^{-1}$, but also emphasizes the potential of SynP F as a viable alternative, especially in deteriorating odometry conditions.

We present the WaveFormer, a neural network architecture based on a linear attention transformer to enable long sequence inference for TinyML devices. Waveformer achieves a new state-of-the-art accuracy of 98.8 % and 99.1 % on the Google Speech V2 keyword spotting (KWS) dataset for the 12 and 35 class problems with only 130 kB of weight storage, compatible with MCU class devices. Top-1 accuracy is improved by 0.1 and 0.9 percentage points while reducing the model size and number of operations by 2.5× and 4.7× compared to the state of the art. We also propose a hardware-friendly 8-bit integer quantization algorithm for the linear attention operator, enabling efficient deployment on low-cost, ultra-low-power microcontrollers without loss of accuracy.

Pretrained large language models (LLMs) exhibit exceptional general language processing capabilities but come with significant demands on memory and computational resources. As a powerful compression technology, binarization can extremely reduce model weights to a mere 1 bit, lowering the expensive computation and memory requirements. However, existing quantization techniques fall short of maintaining LLM performance under ultra-low bit-widths. In response to this challenge, we present BiLLM, a groundbreaking 1-bit post-training quantization scheme tailored for pretrained LLMs. Based on the weight distribution of LLMs, BiLLM first identifies and structurally selects salient weights, and minimizes the compression loss through an effective binary residual approximation strategy. Moreover, considering the bell-shaped distribution of the non-salient weights, we propose an optimal splitting search to group and binarize them accurately. BiLLM, for the first time, achieves high-accuracy inference (e.g. 8.41 perplexity on LLaMA2-70B) with only 1.08-bit weights across various LLM families and evaluation metrics, outperforms SOTA quantization methods of LLM by significant margins. Moreover, BiLLM enables the binarization process of a 7-billion LLM within 0.5 hours on a single GPU, demonstrating satisfactory time efficiency. Our code is available at https://github.com/Aaronhuang-778/BiLLM .

The LoRA-finetuning quantization of LLMs has been extensively studied to obtain accurate yet compact LLMs for deployment on resource-constrained hardware. However, existing methods cause the quantized LLM to severely degrade and even fail to benefit from the finetuning of LoRA. This paper proposes a novel IR-QLoRA for pushing quantized LLMs with LoRA to be highly accurate through information retention. The proposed IR-QLoRA mainly relies on two technologies derived from the perspective of unified information: (1) statistics-based Information Calibration Quantization allows the quantized parameters of LLM to retain original information accurately; (2) finetuning-based Information Elastic Connection makes LoRA utilizes elastic representation transformation with diverse information. Comprehensive experiments show that IR-QLoRA can significantly improve accuracy across LLaMA and LLaMA2 families under 2-4 bit-widths, e.g., 4-bit LLaMA-7B achieves 1.4% improvement on MMLU compared with the state-of-the-art methods. The significant performance gain requires only a tiny 0.31% additional time consumption, revealing the satisfactory efficiency of our IR-QLoRA. We highlight that IR-QLoRA enjoys excellent versatility, compatible with various frameworks (e.g., NormalFloat and Integer quantization) and brings general accuracy gains. The code is available at https://github.com/htqin/ir-qlora .

Nano-sized unmanned aerial vehicles (UAVs) are well-fit for indoor applications and for close proximity to humans. To enable autonomy, the nano-UAV must be able to self-localize in its operating environment. This is a particularly-challenging task due to the limited sensing and compute resources on board. This work presents an online and onboard approach for localization in floor plans annotated with semantic information. Unlike sensor-based maps, floor plans are readily-available, and do not increase the cost and time of deployment. To overcome the difficulty of localizing in sparse maps, the proposed approach fuses geometric information from miniaturized time-of-flight sensors and semantic cues. The semantic information is extracted from images by deploying a state-of-the-art object detection model on a high-performance multi-core microcontroller onboard the drone, consuming only 2.5mJ per frame and executing in 38ms. In our evaluation, we globally localize in a real-world office environment, achieving 90% success rate. We also release an open-source implementation of our work1.

Low-latency sensing and decision-making processing are critical requirements for the highly dynamic control and perception applications often found in Unmanned Areal Systems (UASs). Novel sensors such as Dynamic Vision Sensors (DVSs) are enhancing the pure performance of the perception component with orders of magnitude lower latency. However, they are typically not optimally integrated with the computing hardware, which effectively reduces the potential in both latency and power consumption. In addition to the non-optimal integration, low latency processing of such high data rate generating sensors is often challenging on resource-constrained platforms. Here, Field Programmable Gate Arrays (FPGAs) platforms offer a promising set of features, including low-level access to hardware and memories, as well as high-speed interfaces. On the other hand, FPGA platforms are not as popular on UASs due to their complex programming and development environments, steep initial learning curve, and challenges in achieving optimal performance. To accelerate future development, this paper presents SwiftEagle, an open-source, cutting-edge 720g FPGA based UAS based on a custom hardware design including a dual camera interface RGB/DVSs on a multi-sensors subsystem and an initial software and firmware stack designed for high precision recording of machine learning datasets in-flight with sub-micro-second time resolution and on-FPGA rendering of DVS event frames. Utilizing this developed platform, as proof-of-concept the end-to-end latency of a novel, just released DVS is shown to be below 210µs as a worst-case scenario, enabling future cutting-edge autonomous algorithms.

To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential. While Light Detection and Ranging (LiDAR) sensors have set the benchmark for high-performance systems, the appeal of camera-only solutions lies in their cost-effectiveness. Notably, despite the prevalent use of Radio Detection and Ranging (RADAR) sensors in automotive systems, their potential in 3D detection and tracking has been largely disregarded due to data sparsity and measurement noise. As a recent development, the combination of RADARs and cameras is emerging as a promising solution. This paper presents Camera-RADAR 3D Detection and Tracking (CR3DT), a camera-RADAR fusion model for 3D object detection, and Multi-Object Tracking (MOT). Building upon the foundations of the State-of-the-Art (SotA) camera-only BEVDet architecture, CR3DT demonstrates substantial improvements in both detection and tracking capabilities, by incorporating the spatial and velocity information of the RADAR sensor. Experimental results demonstrate an absolute improvement in detection performance of 5.3% in mean Average Precision (mAP) and a 14.9% increase in Average Multi-Object Tracking Accuracy (AMOTA) on the nuScenes dataset when leveraging both modalities. CR3DT bridges the gap between high-performance and cost-effective perception systems in autonomous driving, by capitalizing on the ubiquitous presence of RADAR in automotive applications. The code is available at: https://github.com/ETH-PBL/CR3DT.

Ultra-wideband (UWB) technology has emerged as one of the most promising localization solutions, finding extensive use in industrial settings and a growing presence in the Internet of Things (IoT) and mobile devices, including smartphones. What sets UWB apart in the realm of localization and positioning is its impressive centimeter-level accuracy, enabling the development of highly secure, anti-spoofing applications. However, the primary challenge hindering the adoption of UWB in battery-operated and battery-less devices is its relatively high power consumption, especially during idle listening. Current approaches often adopt duty cycling, aiding in power conservation at the cost of increased latency. This paper presents a wake-up radio integrated circuit with addressing capabilities specifically designed for ultra-wideband communication and ranging. This wake-up radio seamlessly functions with any standard commercial UWB transceiver, generating on-off keying messages, eliminating the need for a separate radio. Our proposed solution achieves an impressive sensitivity of −86dBm while consuming an average of 36nW of power in waiting-for-wake-up mode and 1mW during signal sampling. The system supports full asynchronous communication, ensuring millisecond-level latency and maintaining an always-on capability. The digital interface of the design allows one to choose the desired trade-off between latency and power consumption, promising substantial energy savings for applications with strict power constraints.

The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in applications ranging from surveillance and first-aid missions to industrial automation involving cooperation with other machines or humans. To maximize area coverage and reduce mission latency, swarms of collaborating drones have become a significant research direction. However, this approach requires open challenges in positioning, mapping, and communications to be addressed. This work describes a distributed mapping system based on a swarm of nano-UAVs, characterized by a limited payload of 35 g and tightly constrained onboard sensing and computing capabilities. Each nano-UAV is equipped with four 64-pixel depth sensors that measure the relative distance to obstacles in four directions. The proposed system merges the information from the swarm and generates a coherent grid map without relying on any external infrastructure. The data fusion is performed using the iterative closest point algorithm and a graph-based simultaneous localization and mapping algorithm, running entirely onboard the UAV’s low-power ARM Cortex-M microcontroller with just 192 kB of memory. Field results gathered in three different mazes with a swarm of up to 4 nano-UAVs prove a mapping accuracy of 12 cm and demonstrate that the mapping time is inversely proportional to the number of agents. The proposed framework scales linearly in terms of communication bandwidth and onboard computational complexity, supporting communication between up to 20 nano-UAVs and mapping of areas up to 180 m2 with the chosen configuration requiring only 50 kB of memory.

Autonomously navigating robots need to perceive and interpret their surroundings. Currently, cameras are among the most used sensors due to their high resolution and frame rates at relatively low energy consumption and cost. In recent years, cutting-edge sensors, such as miniaturized depth cameras, have demonstrated strong potential, specifically for nano-size unmanned aerial vehicles (UAVs), where low power consumption, lightweight hardware, and low computational demand are essential. However, cameras are limited to working under good lighting conditions, while depth cameras have a limited range. To maximize robustness, we propose to fuse a millimeter form factor 64 pixel depth sensor and a low-resolution grayscale camera. In this work, a nano-UAV learns to detect and fly through a gate with a lightweight autonomous navigation system based on two tinyML convolutional neural network models trained in simulation, running entirely onboard in 7.6 ms and with an accuracy above 91%. Field tests are based on the Crazyflie 2.1, featuring a total mass of 39 g. We demonstrate the robustness and potential of our navigation policy in multiple application scenarios, with a failure probability down to 1.2 ˙ 10-3 crash/meter, experiencing only two crashes on a cumulative flight distance of 1.7 km.

Smart and unobtrusive mobile sensor nodes that accurately track their own position have the potential to augment data collection with location-based functions. To attain this vision of unobtrusiveness, the sensor nodes must have a compact form factor and operate over long periods without battery recharging or replacement. This article presents a self-sustaining and accurate ultrawideband (UWB)-based indoor location system with conservative infrastructure overhead. An event-driven sensing approach allows for balancing the limited energy harvested in indoor conditions with the power consumption of UWB transceivers. The presented tag-centralized concept, which combines heterogeneous system design with embedded processing, minimizes idle consumption without sacrificing functionality. Despite modest infrastructure requirements, high-localization accuracy is achieved with error-correcting double-sided two-way ranging and embedded optimal multilateration. Experimental results demonstrate the benefits of the proposed system: the node achieves a quiescent current of 47nA and operates at 1.2$\mu \text{A}$while performing energy harvesting and motion detection. The energy consumption for position updates, with an accuracy of 40 cm (2-D) in realistic nonline-of-sight conditions, is 10.84mJ. In an asset tracking case study within a 200m2 multiroom office space, the achieved accuracy level allows for identifying 36 different desk and storage locations with an accuracy of over 95%. The system’s long-time self-sustainability has been analyzed over 700 days in multiple indoor lighting situations.

Perceiving and mapping the surroundings are essential for autonomous navigation in any robotic platform. The algorithm class that enables accurate mapping while correcting the odometry errors present in most robotics systems is Simultaneous Localization and Mapping (SLAM). Today, fully onboard mapping is only achievable on robotic platforms that can host high-wattage processors, mainly due to the significant computational load and memory demands required for executing SLAM algorithms. For this reason, pocket-size hardware-constrained robots offload the execution of SLAM to external infrastructures. To address the challenge of enabling SLAM algorithms on resource-constrained processors, this paper proposes NanoSLAM, a lightweight and optimized end-to-end SLAM approach specifically designed to operate on centimeter-size robots at a power budget of only 87.9mW. We demonstrate the mapping capabilities in real-world scenarios and deploy NanoSLAM on a nano-drone weighing 44g and equipped with a novel commercial RISC-V low-power parallel processor called GAP9. The algorithm, designed to leverage the parallel capabilities of the RISC-V processing cores, enables mapping of a general environment with an accuracy of 4.5cm and an end-to-end execution time of less than 250ms.

Nano-size unmanned aerial vehicles (UAVs) hold enormous potential to perform autonomous operations in complex environments, such as inspection, monitoring or data collection. Moreover, their small size allows safe operation close to humans and agile flight. An important part of autonomous flight is localization, a computationally intensive task, especially on a nano-UAV that usually has strong constraints in sensing, processing and memory. This work presents a real-time localization approach with low-element-count multizone range sensors for resource-constrained nano-UAVs. The proposed approach is based on a novel miniature 64-zone time-of-flight sensor from STMicroelectronics and a RISC-V-based parallel ultra-low-power processor to enable accurate and low latency Monte Carlo Localization on-board. Experimental evaluation using a nano-UAV open platform demonstrated that the proposed solution is capable of localizing on a 31.2 m2map with 0.15 m accuracy and an above 95% success rate. The achieved accuracy is sufficient for localization in common indoor environments. We analyze trade-offs in using full and half-precision floating point numbers as well as a quantized map and evaluate the accuracy and memory footprint across the design space. Experimental evaluation shows that parallelizing the execution for 8 RISC-V cores brings a 7x speedup and allows us to execute the algorithm onboard in real-time with a latency of 0.2-30 ms (depending on the number of particles), while only increasing the overall drone power consumption by 3–7%. Finally, we provide an open-source implementation of our approach.

Underwater magnetic induction (MI)-assisted acoustic cooperative multiple-input-multiple-output (MIMO) technology enables reliable communication with high throughput over long transmission distances. The combination of such as technology and underwater wireless sensor networks (UWSNs) is initiating a novel underwater Internet of Things (IoT paradigm): underwater MI-assisted acoustic cooperative MIMO WSNs. The MIMO UWSNs meets the requirements of reliable communication networks connecting underwater sensor nodes and remote surface base station (BS) for the next generation of smart ocean applications. The objective of the paper is to obtain the cooperative MIMO formation scheme in these networks for minimizing the nodes' energy consumption, simultaneously ensuring the connectivity of nodes and surface BS, and improving the coverage of networks. This paper proposes a scaly cooperative MIMO formation (SCMF) scheme based on the greedy algorithm to solve the constrained optimization problem, which is a two-phase method. In the first phase, the members of the cooperative MIMO are preliminarily determined while ensuring that there is only a single coverage hole in each iteration. In the second phase, a new master node (MN) is selected and the transmission power is determined to reduce energy consumption. Simulation results show that our proposed scheme not only fills the gap that the existing algorithm does not consider the network coverage, but also confirms that it can achieve significant energy saving and prolongs the network lifetime considerably.

Activation functions provide the non-linearity to deep neural networks, which are crucial for the optimization and performance improvement. In this paper, we propose a learnable continuous piece-wise nonlinear activation function (or CPN in short), which improves the widely used ReLU from three directions, i.e., finer pieces, non-linear terms and learnable parameterization. CPN is a continuous activation function with multiple pieces and incorporates non-linear terms in every interval. We give a general formulation of CPN and provide different implementations according to three key factors: whether the activation space is divided uniformly or not, whether the non-linear terms exist or not, and whether the activation function is continuous or not. We demonstrate the effectiveness of our method on image classification and single image super-resolution tasks by simply changing the activation function. For example, CPN improves 4.78% / 4.52% top-1 accuracy over ReLU on MobileNetV2_0.25 / MobileNetV2_0.35 for ImageNet classification and achieves better PSNR on several benchmarks for super-resolution. Our implementation is available at https://github.com/xc-G/CPN.

Autonomous racing is a research field gaining large popularity, as it pushes autonomous driving algorithms to their limits and serves as a catalyst for general autonomous driving. For scaled autonomous racing platforms, the computational constraint and complexity often limit the use of Model Predictive Control (MPC). As a consequence, geometric controllers are the most frequently deployed controllers. They prove to be performant while yielding implementation and operational simplicity. Yet, they inherently lack the incorporation of model dynamics, thus limiting the race car to a velocity domain where tire slip can be neglected. This paper presents Model- and Acceleration-based Pursuit (MAP) a high-performance model-based trajectory tracking controller that preserves the simplicity of geometric approaches while leveraging tire dynamics. The proposed algorithm allows accurate tracking of a trajectory at unprecedented velocities compared to State-of-the-Art (SotA) geometric controllers. The MAP controller is experimentally validated and outperforms the reference geometric controller four-fold in terms of lateral tracking error, yielding a tracking error of 0.055 m at tested speeds up to 11 m/s on a scaled racecar. Code: https://github.com/ETH-PBL/MAP-Controller.

The combined use of spiking neural networks and event cameras is gaining momentum in the field of embedded computer vision as they promise to reduce latency and computational resource requests. However, state-of-the-art embedded neuromorphic models show little interest in modifying input data to optimise model performance, memory usage, latency, and power consumption. This work addresses this optimisation trade-off by implementing a neuromorphic model of salient selection, which simultaneously outputs multiple segregated objects of interest detected in an event-based scene. This work extends previous ones and identifies regions of interest as those corresponding to a high spatiotemporal density of events. Without any training and with a limited number of neurons, the proposed model is able to simultaneously detect different objects with a delay of only 14ms at most, and filtered objects maintain 73% of the original data's classification performance. We are thus confident that the method proposed in this paper will allow for improving the subsequent neuromorphic processing of event data on embedded systems. To the best of our knowledge, it is the first neuromorphic model able to simultaneously select multiple objects of interest. Our code can be found here: github.com/amygruel/FoveationStakes_DVS/.

The aim of this paper is to study the influence of locality mechanisms in vision transformers. Transformers originated from machine translation and are particularly good at modelling long-range dependencies within a long sequence. Although the global interaction between the token embeddings could be well modelled by the self-attention mechanism of transformers, what is lacking is a locality mechanism for infor-mation exchange within a local region. In this paper, locality mechanism is systematically investigated by carefully designed controlled experiments. We add locality to vision transformers into the feed-forward network. This seemingly simple solution is inspired by the comparison between feed-forward networks and inverted residual blocks. The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to vision transformers with different architecture designs, which shows the generalization of the locality concept. For ImageNet2012 classification, the locality-enhanced transformers outperform the baselines Swin-T [1], DeiT-T [2] and PVT-T [3] by 1.0%, 2.6 % and 3.1 % with a negligible increase in the number of parameters and computational effort. Code is available at https://github.com/ofsoundof/LocalViT.

Decentralized and autonomous control of Unmanned Aerial Vehicle (UAV) swarms is a key enabler for cooperative systems and infrastructure-less formation flights. However, UAVs often lack reliable heading angle measurements, especially in indoor scenarios, space, and GNSS-denied environments, posing an additional observability challenge on range-based relative localization. We tackle this problem by proposing a novel solution enhancing the classical tag-and-anchor trilateration. The proposed solution relies on Ultra-wideband range measurements and addresses the relative pose estimation between pairs of UAVs under relative motion. Furthermore, it does not require any explicit motion pattern or initialization procedure and leverages an approximate maximum-likelihood algorithm to recursively solve the relative localization problem with constant computational complexity. The method has been implemented and demonstrated through field experiments, where a swarm of nano-UAVs positioned themselves with respect to a leader in a nearly-static formation with an average error of 38.5 cm and a convergence time of 25 s. The achieved formation accuracy is similar to the one achieved by the state-of-the-art EKF-based leader-follower methods.

End-to-end event-based computation has the poten-tial to push the envelope in latency and energy efficiency for edge AI applications. Unfortunately, event-based sensors (e.g., DVS cameras) and neuromorphic spike-based processors (e.g., Loihi) have been designed in a decoupled fashion, thereby missing major streamlining opportunities. This paper presents ColibriES, the first-ever neuromorphic hardware embedded system plat-form with dedicated event-sensor interfaces and full processing pipelines. ColibriES includes event and frame interfaces and data processing, aiming at efficient and long-life embedded systems in edge scenarios. ColibriES is based on the Kraken system-on-chip and contains a heterogeneous parallel ultra-low power (PULP) processor, frame-based and event-based camera interfaces, and two hardware accelerators for the computation of both event-based spiking neural networks and frame-based ternary convolutional neural networks. This paper explores and accurately evaluates the performance of event data processing on the example of gesture recognition on ColibriES, as the first step of full-system evaluation. In our experiments, we demonstrate a chip energy consumption of 7.7 mJ and latency of 164.5 ms of each inference with the DVS Gesture event data set as an example for closed-loop data processing, showcasing the potential of ColibriES for battery-powered applications such as wearable devices and UAVs that require low-latency closed-loop control.

Animal vocalisations serve a wide range of vital functions. Although it is possible to record animal vocalisations with external microphones, more insights are gained from miniature sensors mounted directly on animals' backs. We present TinyBird-ML; a wearable sensor node weighing only 1.4 g for acquiring, processing, and wirelessly transmitting acoustic signals to a host system using Bluetooth Low Energy. TinyBird-ML embeds low-latency tiny machine learning algorithms for song syllable classification. To optimize battery lifetime of TinyBird-ML during fault-tolerant continuous recordings, we present an efficient firmware and hardware design. We make use of standard lossy compression schemes to reduce the amount of data sent over the Bluetooth antenna, which increases battery lifetime by 70% without negative impact on offline sound analysis. Furthermore, by not transmitting signals during silent periods, we further increase battery lifetime. One advantage of our sensor is that it allows for closed-loop experiments in the microsecond range by processing sounds directly on the device instead of streaming them to a computer. We demonstrate this capability by detecting and classifying song syllables with minimal latency and a syllable error rate of 7%, using a light-weight neural network that runs directly on the sensor node itself. Thanks to our power-saving hardware and software design, during continuous operation at a sampling rate of 16 kHz, the sensor node achieves a lifetime of 25 hours on a single size 13 zinc-air battery.

The rapid growth of the Internet of Things paradigm is pushing the need to connect billions of battery-operated devices to the internet and among them. To address this need, the introduction of energy-efficient wireless mesh networks based on Bluetooth provides an effective solution. This paper proposes a testbed setup to accurately evaluate and compare the standard Bluetooth Mesh 5.0 and the emerging energy-efficient Wirepas protocol that promises better performance. The paper presents the evaluation in terms of power consumption, energy efficiency, and transmission latency which are the most crucial features, in a controlled and reproducible test setup consisting of 10 nodes. Experimental results demonstrated that Wirepas has a median latency of 2.83ms in Low-Latency mode respectively around 2s in the Low-Energy mode. The corresponding power consumption is 6.2mA in Low-Latency mode and 38.9µA in Low-Energy mode. For Bluetooth Mesh the median latency is 4.54ms with a power consumption of 6.2mA at 3.3V. Based on this comparison, conclusions about the advantages and disadvantages of both technologies can be drawn.

Smart interaction between autonomous centimeter-scale unmanned aerial vehicles (i.e., nano-UAVs) and Internet of Things (IoT) sensor nodes is an upcoming high-impact scenario. This work tackles precise 3-D localization of indoor edge nodes with an autonomous nano-UAV without prior knowledge of their position. We employ ultrawideband (UWB) and wake-up radio (WUR) technologies: we perform UWB-based ranging and data exchange between the nano-UAV and the nodes, while the WUR minimizes the sensors’ power consumption. UWB-based precise localization requires addressing multiple sources of error, such as UWB-ranging noise and UWB antennas’ uneven radiation pattern. The limited computational resources aboard a nano-UAV further complicate this scenario, requiring real-time execution of the localization algorithm within a microcontroller unit (MCU). We propose a novel UWB-based localization system for nano-UAVs, composed by: 1) a lightweight localization algorithm; 2) an optimal flight strategy; and 3) a ranging-error-correction model. Our 3-D flight policy requires only five UWB measurements to feed the localization algorithm, which bounds the localization error within$\mathrm {28 \, \text {c} \text {m} }$and runs in$\mathrm {1.2 \text {m} \text {s} }$on a Cortex-M4 MCU. Localization accuracy is improved by an additional 25% thanks to a novel error-correction model. Leveraging the WUR, the entire localization/data-exchange cycle costs only$\mathrm {24 \, \text {m} \text {J} }$at the sensor node, which is 50 times more energy efficient than the state of the art with comparable localization accuracy.

Nanosize drones hold enormous potential to explore unknown and complex environments. Their small size makes them agile and safe for operation close to humans and allows them to navigate through narrow spaces. However, their tiny size and payload restrict the possibilities for onboard computation and sensing, making fully autonomous flight extremely challenging. The first step toward full autonomy is reliable obstacle avoidance, which has proven to be challenging by itself in a generic indoor environment. Current approaches utilize vision-based or 1-D sensors to support nanodrone perception algorithms. This article presents a lightweight obstacle avoidance system based on a novel millimeter form factor 64 pixels multizone time-of-flight (ToF) sensor and a generalized model-free control policy. In-field tests are based on the Crazyflie 2.1, extended by a custom multizone ToF deck, featuring a total flight mass of 35 g. The algorithm only uses 0.3% of the onboard processing power (${210}\,{\mu }\mathrm{{s}}$execution time) with a frame rate of 15 f/s. The presented autonomous nanosize drone reaches 100% reliability at 0.5 m/s in a generic and previously unexplored indoor environment.

Digital ultrasound (US) probes integrate the analog-to-digital conversion directly on the probe and can be conveniently connected to commodity devices. Existing digital probes are however limited to a relatively small number of channels, do not guarantee access to the raw US data, or cannot operate at very high frame rates (e.g., due to exhaustion of computing and storage units on the receiving device). In this work, we present an open, compact, power-efficient, 192-channels digital US data acquisition system capable of streaming US data at transfer rates greater than 80 Gbps towards a host PC for ultra-high frame rate imaging (in the multi-kHz range). Our US probe is equipped with two power-efficient Field Programmable Gate Arrays (FPGAs) and is interfaced to the host PC with two optical-link 100G Ethernet connections. The high-speed performance is enabled by implementing a Remote Direct Memory Access (RDMA) communication protocol between the probe and the controlling PC, that utilizes a high-performance Non-Volatile Memory Express (NVMe) interface to store the streamed data. To the best of our knowledge, thanks to the achieved datarates, this is the first high-channel-count compact digital US platform capable of raw data streaming at frame rates of 20 kHz (for imaging at 3.5 cm depths), without the need for sparse sampling, consuming less than 40 W.

Unmanned aerial vehicles (UAVs) are a very active research topic, and especially the nano and micro subclass, characterized by cen-timeter size and minimal on-board computational capabilities, have gained popularity in recent years. These lightweight platforms provide good agility and movement freedom in indoor environments, but it is still a significant challenge to enable autonomous navigation or basic obstacle avoidance capabilities using standard image sensors, due to the limited computational capabilities that can be hosted on-board. This work demonstrates the possibility of using a new multi-zone Time of Flight (ToF) sensor to enhance autonomous navigation with a significantly lower computational load than most common visual-based solutions. Our system proved reliable (>95%) in-field obstacle avoidance capabilities when flying in indoor environments with dynamic obstacles.

Optical flow estimation is crucial for autonomous navigation and localization of unmanned aerial vehicles (UAV). On micro and nano UAVs, real-time calculation of the optical flow is run on low power and resource-constrained microcontroller units (MCUs). Thus, lightweight algorithms for optical flow have been proposed targeting real-time execution on traditional single-core MCUs. This paper introduces an efficient parallelization strategy for optical flow computation targeting new-generation multicore low power RISC-V based microcontroller units. Our approach enables higher frame rates at lower clock speeds. It has been implemented and evaluated on the eight-core cluster of a commercial octa-core MCU (GAP8) reaching a parallelization speedup factor of 7.21 allowing for a frame rate of 500 frames per second when running on a 50MHz clock frequency. The proposed parallel algorithm significantly boosts the camera frame rate on micro unmanned aerial vehicles, which enables higher flight speeds: the maximum flight speed can be doubled, while using less than a third of the clock frequency of previous singlecore implementations.

Cardiovascular disease is a major cause of premature mortality, high healthcare costs, and disability-adjusted life years. Digital interventions such as continuous cardiac monitoring solutions can help to monitor the patient status and provide valuable feedback to clinicians to detect early warning signs and provide effective interventions. This paper presents the evaluation of a novel low-power sensor exploited to measure the electrostatic charge variation in the upper thorax to provide an energy-efficient and accurate detection of the electric activity of the heart. The sensor is investigated for measuring the heart activity in terms of the QRS complex. The paper presents the design of a wearable sensor device, optimization of electrode positions and incorporation into a wearable chest strap that can be integrated seamlessly under clothes. Due to the low power consumption of the sensor, the sensor node consumes only 87.3 µW of power and can provide multiple weeks of operation using a coin cell battery while providing the same functionality as that of commercially available sensors such as photoplethysmography and electrocardiogram (ECG) ICs. In addition, the chest strap was also characterised for different use scenarios during sedentary and activity periods. We evaluated both the signal quality and the power consumption compared with other sensors technology showing a power save of an order of magnitude when compared with photoplethysmography sensors.

The trend in Internet of Things research points toward performing increasingly compute-intensive data analysis tasks on embedded sensor nodes, rather than server centers. Ex-ploiting the technological advances in both energy efficiency, and Tiny Machine Learning algorithms and methods, an increasing number of recognition and classification tasks can be performed by small, low-power, wireless sensor nodes. This paper presents Wide Vision, a wireless, wide-area sensing platform capable of performing on-board person detection with power requirements in the mW range. The WideVision platform integrates seamlessly into the Internet of Things, by coupling a dedicated multi-radio platform, including a LoRa interface, enabling medium-and long-range communication, with a novel parallel RISC- V microcontroller. We evaluate the proposed platform with the GAP8 microcontroller, which includes an 8-core RISC- V cluster, and greyscale camera to perform person detection by training and deploying an advanced, quantized neural network, achieving a statistical accuracy 84.5% for a 5-person detection task with a latency of only 182 ms. Experimental results demonstrate that the WideVision sensor node platform while performing inference at a rate of one image per minute on-board, is capable of lasting 300 days on a 2400 mAh Li-ion battery, and 65 days when evaluating one image per 10 seconds while providing effective surveillance of its perimeter.

Monitoring of physiological parameters support early detection, prevention, and management of adverse health-care related events. In-ear wearable sensor nodes that track changing bio-physiological signals can be advantageous over traditional monitoring methods in being compact, unobtrusive, and inconspicuous, increasing their appeal. However, in-ear ergonomics place rigid constraints on a device's physical design and size, limiting battery lifetime and restricting the incorporation of multiple sensor modules. This work presents the design and implementation of the wireless hearable VitalPod, an in-ear multi-vital sign monitor that measures heart rate (HR), respiratory rate (RR), blood oxygenation (SpO2), skin temperature and activity. The VitalPod is designed with low power and energy efficiency in mind, using novel sensing integrated circuits and processing modules that achieve operational longevity of up to 42 hours using a small form factor 32mAh rechargeable Liion polymer battery. It has a compact design and form factor, similar to many commercial wireless earbuds, featuring hardware required for wireless music playback using a standard balanced armature driver in addition to vital sign monitoring sensors. Experimental results show high performance in estimating vital signs for HR (-0.061± 1.12 beats per minute), RR (0.87±1.48 breaths per minute) and change in SpO2 (0.21±0.82 %). Additionally, the presented work also evaluates the positional placement of sensors in the ear to reduce the effect of motion induced artifacts.

Ultrawideband (UWB) communications have gained popularity in recent years for being able to provide distance measurements and localization with high accuracy, which can enhance the capabilities of devices in the Internet of Things (IoT). Since energy efficiency is of utmost concern in such applications, in this work, we evaluate the power and energy consumption, distance measurements, and localization performance of two types of UWB physical interfaces (PHYs), which use either a low- or high-rate pulse repetition (LRP and HRP, respectively). The evaluation is done through measurements acquired in identical conditions, which is crucial in order to have a fair comparison between the devices. We performed measurements in typical line-of-sight (LOS) and nonline-of-sight (NLOS) scenarios. Our results suggest that the LRP interface allows a lower power and energy consumption than the HRP one. Both types of devices achieved ranging and localization errors within the same order of magnitude and their performance depended on the type of NLOS obstruction. We propose theoretical models for the distance errors obtained with LRP devices in these situations, which can be used to simulate realistic building deployments and we illustrate such an example. This article, therefore, provides a comprehensive overview of the energy demands, ranging characteristics, and localization performance of state-of-the-art UWB devices.

Keyword spotting (KWS) is a crucial function enabling the interaction with the many ubiquitous smart devices in our surroundings, either activating them through wake-word or directly as a human-computer interface. For many applications, KWS is the entry point for our interactions with the device and, thus, an always-on workload. Many smart devices are mobile and their battery lifetime is heavily impacted by continuously running services. KWS and similar always-on services are thus the focus when optimizing the overall power consumption. This work addresses KWS energy-efficiency on low-cost microcontroller units (MCUs). We combine analog binary feature extraction with binary neural networks. By replacing the digital preprocessing with the proposed analog front-end, we show that the energy required for data acquisition and preprocessing can be reduced by$29\times $, cutting its share from a dominating 85% to a mere 16% of the overall energy consumption for our reference KWS application. Experimental evaluations on the Speech Commands Dataset show that the proposed system outperforms state-of-the-art accuracy and energy efficiency, respectively, by 1% and$4.3\times $on a 10-class dataset while providing a compelling accuracy-energy trade-off including a 2% accuracy drop for a$71\times $energy reduction.

Event-based cameras, also called dynamic vision sensors (DVS), inspired by the human vision system, are gaining popularity due to their potential energy-saving since they generate asynchronous events only from the pixels changes in the field of view. Unfortunately, in most current uses, data acquisition, processing, and streaming of data from event-based cameras are performed by power-hungry hardware, mainly high-power FPGAs. For this reason, the overall power consumption of an event-based system that includes digital capture and streaming of events, is in the order of hundreds of milliwatts or even watts, reducing significantly usability in real-life low-power applications such as wearable devices. This work presents FlyDVS, the first event-driven wireless ultra-low-power visual sensor node that includes a low-power Lattice FPGA and, a Bluetooth wireless system-on-chip, and hosts a commercial ultra-low-power DVS camera module. Experimental results show that the low-power FPGA can reach up to 874 efps (event-frames per second) with only 17.6mW of power, and the sensor node consumes an overall power of 35.5 mW (including wireless streaming) at 200 efps. We demonstrate FlyDVS in a real-life scenario, namely, to acquire event frames of a gesture recognition data set.

The novel COVID-19 disease has been declared a pandemic event. Early detection of infection symptoms and contact tracing are playing a vital role in containing COVID-19 spread. As demonstrated by recent literature, multi-sensor and connected wearable devices might enable symptom detection and help tracing contacts, while also acquiring useful epidemiological information. This paper presents the design and implementation of a fully open-source wearable platform called H-Watch. It has been designed to include several sensors for COVID-19 early detection, multi-radio for wireless transmission and tracking, a microcontroller for processing data on-board, and finally, an energy harvester to extend the battery lifetime. Experimental results demonstrated only 5.9 mW of average power consumption, leading to a lifetime of 9 days on a small watch battery. Finally, all the hardware and the software, including a machine learning on MCU toolkit, are provided open-source, allowing the research community to build and use the H-Watch.

In this demo we present a battery-free smart camera that exploits aggressive power management and energy harvesting to achieve face recognition in an energy-neutral fashion. A novel hardware accelerator for Convolution Neural Networks is employed to speed up the inference of the Tiny Machine Learning algorithm. The recognized face, and not the entire image, is sent via LoRa in a sensor network-like scenario. Experimental results demonstrated the capability of the developed sensor node to start and work perpetually with only a small photovoltaic panel array.

Radio Frequency (RF) energy transfer is an emerging technology to supply perpetually the new generation of internet of things devices. The proposed work shows the design and implementation of RF power transmission circuits the possible usages as a power source for batteryless devices. The realized circuits can receive power in the order of 1-10 mW depending on the distance from the transmitter, size, and antenna efficiency, allowing the deployment of these rectification circuits in any low power sensing network that requires a reliable and controllable power source. This paper will introduce and illustrate the preliminary results achieve for the work done for in a life-demo.

Indoor Positioning System (IPS) is a crucial technology that enables medical staff and hospital managements to accurately locate and track persons or assets inside the medical buildings. Among other technologies, Bluetooth Low Energy (BLE) can be exploited for achieving an energy-efficient and low-cost solution. This work presents the design and implementation of an received signal strength indicator (RSSI)-based indoor localization system. The paper shows the implementation of a low complex weighted k-Nearest Neighbors algorithm that processes raw RSSI data from connection-less iBeacon’s. The designed hardware and firmware are implemented around the low-power and low-cost nRF52832 from Nordic Semiconductor. Experimental evaluation with the real-time data processing has been evaluated and presented in a 7.2 m by 7.2 m room with furniture and 5 beacon nodes. The experimental results show an average error of only 0.72 m in realistic conditions. Finally, the overall power consumption of the fixed beacon with a periodic advertisement of 100 ms is only 50 µA at 3 V, which leads to a long-lasting solution of over one year with a 500 mAh coin battery.

Ultra-wide band is one of the most promising localization technologies already and increasingly used in industrial environments. It is also starting to be included in many Internet of Things and mobile devices. The most attractive feature of ultra-wide band for localization and positioning is the achievable centimeter-accuracy that can enable a highly secure spoofing-protected application. On the other hand, the main drawback that still limits its use in battery-operated devices is the high power consumption, especially in idle listening. Today most common approaches use duty cycling, which reduces the overall power consumption at the cost of increased latency. The paper presents a hardware wake-up radio with addressing capability for ultra-wide band communication and ranging. Moreover, the paper introduces a novel protocol that enables the generation of wake-up signals directly with any existing ultra-wide band transceiver, without the requirement of a different radio. The proposed solution achieves −48 dBm of sensitivity with only 100 µW of power consumption. The proposed system enables full asynchronous communication, achieving millisecond latency and always-on capability. Experimental evaluation with a popular commercial transceiver produced by Decawave demonstrates the high energy saving and the effectiveness of the proposed addressing protocol by reducing energy consumption in the order of 100x compared to conventional receivers.

This work proposes a low-power high-accuracy embedded hand-gesture recognition algorithm targeting battery-operated wearable devices using low-power short-range RADAR sensors. A 2-D convolutional neural network (CNN) using range-frequency Doppler features is combined with a temporal convolutional neural network (TCN) for time sequence prediction. The final algorithm has a model size of only 46 thousand parameters, yielding a memory footprint of only 92 KB. Two data sets containing 11 challenging hand gestures performed by 26 different people have been recorded containing a total of 20'210 gesture instances. On the 11 hand gesture data set, accuracies of 86.6% (26 users) and 92.4% (single user) have been achieved, which are comparable to the state of the art, which achieves 87% (10 users) and 94% (single user), while using a TCN-based network that is 7500× smaller than the state of the art. Furthermore, the gesture recognition classifier has been implemented on a parallel ultralow power processor, demonstrating that real-time prediction is feasible with only 21 mW of power consumption for the full TCN sequence prediction network, while a system-level power consumption of less than 120 mW is achieved. We provide open-source access to example code and all data collected and used in this work on tinyradar.ethz.ch.

This work presents InfiniWolf, a novel multi-sensor smartwatch that can achieve self-sustainability exploiting thermal and solar energy harvesting, performing computationally high demanding tasks. The smartwatch embeds both a System-on-Chip (SoC) with an ARM Cortex-M processor and Bluetooth Low Energy (BLE) and Mr. Wolf, an open-hardware RISC-V based parallel ultra-low-power processor that boosts the processing capabilities on board by more than one order of magnitude, while also increasing energy efficiency. We demonstrate its functionality based on a sample application scenario performing stress detection with multi-layer artificial neural networks on a wearable multi-sensor bracelet. Experimental results show the benefits in terms of energy efficiency and latency of Mr. Wolf over an ARM Cortex-M4F micro-controllers and the possibility, under specific assumptions, to be self-sustainable using thermal and solar energy harvesting while performing up to 24 stress classifications per minute in indoor conditions.

Monitoring of renal function can be crucial for patients in acute care settings. Commonly during postsurgical surveillance, urinary catheters are employed to assess the urine output accurately. However, as with any external device inserted into the body, the use of these catheters carries a significant risk of infection. In this paper, we present a non-invasive method to measure the fill rate of the bladder, and thus rate of renal clearance, via an external bioimpedance sensor system to avoid the use of urinary catheters, thereby eliminating the risk of infections and improving patient comfort. We design and propose a 4-electrode front-end and the whole wearable and wireless system with low power and accuracy in mind. The results demonstrate the accuracy of the sensors and low power consumption of only 80μW with a duty cycling of 1 acquisition every 5 minutes, which makes this battery-operated wearable device a long-term monitor system.

Ultra-wideband (UWB) communication is attracting increased interest for its high-accuracy distance measurements. However, the typical current consumption of tens to hundreds of mA during transmission and reception might make the technology prohibitive to battery-powered devices in the Internet of Things. The IEEE 802.15.4 standard specifies two UWB physical layer interfaces (PHYs), with low- and high-rate pulse repetition (LRP and HRP, respectively). While the LRP PHY allows a more energy-efficient implementation of the UWB transceiver than its HRP counterpart, the question is whether some ranging quality is lost in exchange. We evaluate the trade-off between power and energy consumption, on the one hand, and distance measurement accuracy and precision, on the other hand, using UWB devices developed by Decawave (HRP) and 3db Access (LRP). We find that the distance measurement errors of 3db Access devices have at most 12 cm higher bias and standard deviation in line-of-sight propagation and 2-3 times higher spread in non-line-of-sight scenarios than those of Decawave devices. However, 3db Access chips consume 10 times less power and 125 times less energy per distance measurement than Decawave ones. Since the LRP PHY has an ultra-low energy consumption, it should be preferred over the HRP PHY when energy efficiency is critical, with a small penalty in the ranging performance.

This work investigates the capability of unmanned aerial vehicles (UAVs) to find and communicate with wireless sensor nodes positioned at unknown locations. In this scenario, the UAV acts as a mobile gateway that estimates the sensor node position using multiple ultra-wideband (UWB) range measurements, before flying in its vicinity to perform energy-efficient data acquisition. In addition to UWB, we use wake-up radio (WUR) to improve the sensor node's energy efficiency, keeping it in the always-on “low-activity” state when the drone is not nearby. The paper proposes a localization algorithm that consists of an iterative, noise-robust and computationally lightweight approach based on multi-lateration. Experimental evaluations performed on synthetic data demonstrate that our approach achieves a submeter localization accuracy using only three range measurements. We confirm this with an extensive in-field evaluation. The multilateration algorithm runs in 4 ms, in low power microcontrollers such as the ARM Cortex-M4F. The WUR and our energy-efficient algorithm enable the sensor node to consume only 31 mJ during the whole localization-acquisition process. Our solution can be introduced in many other industrial applications where a mobile robot needs to estimate the location of imprecisely positioned objects.

Sound event detection (SED) is a hot topic in consumer and smart city applications. Existing approaches based on deep neural networks (DNNs) are very effective, but highly demanding in terms of memory, power, and throughput when targeting ultra-low power always-on devices.

The growing number of low-power smart devices in the Internet of Things is coupled with the concept of “edge computing” that is moving some of the intelligence, especially machine learning, toward the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-power smart devices, is challenging in terms of hardware (optimized and energy-efficient integrated circuits), algorithmic, and firmware implementations. This article presents a FANN-on-MCU, an open-source toolkit built upon the fast artificial neural network (FANN) library to run lightweight and energy-efficient neural networks on microcontrollers based on both the ARM Cortex-M series and the novel RISC-V-based parallel ultralow-power (PULP) platform. The toolkit takes multilayer perceptrons trained with FANN and generates code targeted to low-power microcontrollers. This article also presents detailed analyses of energy efficiency across the different cores, and the optimizations to handle different network sizes. Moreover, it provides a detailed analysis of parallel speedups and degradations due to parallelization overhead and memory transfers. Further evaluations include experimental results for three different applications using a self-sustainable wearable multisensor bracelet. The experimental results show a measured latency in the order of only a few microseconds and power consumption of a few milliwatts while keeping the memory requirements below the limitations of the targeted microcontrollers. In particular, the parallel implementation on the octa-core RISC-V platform reaches a speedup of 22× and a 69% reduction in energy consumption with respect to a single-core implementation on Cortex-M4 for continuous real-time classification.

Low power and long-range communications are crucial features of the Internet of Things (IoT) paradigm that is becoming essential even for industrial applications. Today, the most promising long-range communication technologies are LoRaWAN and Narrow Band IoT (NB-IoT), which are driving a large IoT ecosystem. In this article, we evaluate the performance of LoRaWAN and NB-IoT with accurate in-field measurements using the same application context for a fair comparison in terms of energy efficiency, lifetime, quality of service, and coverage. The NB-IoT energy transmission is scarcely dependent on the payload length. Thus applications that can tolerate buffering and caching techniques on the node are favored. On the other hand, LoRaWAN consumes 10 × lower energy compared to NB-IoT for occasional and latency-sensitive communications, for which it enables much end-device lifetime. Finally, this paper provides design guidelines for future industrial applications with stringent requirements of long-range and low power wireless connectivity.

The human body can be used as a transmission medium for electric fields. By applying an electric field with a frequency of decades of megahertz to isolated electrodes on the human body, it is possible to send energy and data. Extra body and intra-body communication is an interesting alternative way to communicate wirelessly in the new era of wearable device and internet of things. This promising communication works without the need to design dedicate radio hardware and with lower power consumption. We designed and implemented a novel zero-power receiver targeting intra-body and extra-body wireless communication and touch sensing. To achieve zero-power and always-on working, we combined ultra-low power design and an energy-harvesting subsystem, which extracts energy directly from the received message. This energy is then employed to supply the whole receiver to demodulate the message and to perform data processing with digital logic. The main goal of the proposed design is ideal to wake up external logic only when a specific address is received. Moreover, due to the presence of the digital logic, the designed zero-power receiver can implement identification and security algorithms. The zero-power receiver can be used either as an always-on touch sensor to be deployed in the field or as a body communication wake up smart and secure devices. A working prototype demonstrates the zero-power working, the communication intra-body, and extra-body, and the possibility to achieve more than 1.75m in intra-body without the use of any external battery.

Low power and long-range communications are essential features of the Internet of Things (IoT) paradigm that is becoming widespread across a spectrum of industrial applications. In this paper, we present performance evaluation of the most promising long-range communication technologies, namely LoRaWAN and NB-IoT. We present accurate in-field measurements using a monitoring application as a testbench for a fair comparison in terms of energy efficiency and lifetime. Experimental results highlight that NB-IoT payload length does not impact on transmission energy. Thus, applications that implement buffering and caching techniques are favored. On the other hand, LoRaWAN consumes 10× less energy to transmit a payload equivalent to that of NB-IoT, thereby allowing longer end-device lifetime.

Electronic-skin aiming to mimic human skin is becoming a reality and systems able to process data close to the sensors are required to reduce latency and power consumption. This paper presents the design and implementation of an energy efficient smart system for tactile sensing based on a RISC-V parallel ultra-low power platform (PULP). The PULP processor, called Mr. Wolf, performs the on-board classification of different touch modalities. This demonstrates the promising use of on-board classification for emerging robot and prosthetic applications. Experimental results demonstrate the effectiveness of the platform on improving the energy efficiency of the online classification. In our experiments, Mr. Wolf runs 3.6 times faster than an ARM Cortex M4F (STM32F40), consuming only 28 mW. The proposed platform achieves 15× better energy efficiency, than the classification done on the STM32F40, consuming only 81mJ per classification.

Wireless sensor networks (WSNs) with energy harvesting capabilities have drawn increasing attention in the last few years, as they enable long-term monitoring applications. However, the level of power harvested is usually limited to few mW. To improve the energy efficiency of WSNs, many power management techniques have been proposed to adjust the quality of service according to the harvested energy fluctuations. As wireless communications consume a major fraction of the available energy, numerous medium access control (MAC) protocols have been proposed to minimize energy consumption, latency, and data collisions. In this paper, we present an innovative MAC protocol for energy-harvesting based WSNs exploiting ultralow-power wake-up radios. To overcome the limited range typical of wake-up radios, a multi-hop wake-up scheme based on a dual radio system is proposed enabling asynchronous communications between a base station and any node of the network while maintaining a low latency and a high energy efficiency. To reduce energy consumption, wake-up calls and data packets are transmitted using two distinct data rates. Combined with destination address decoding, using a higher data rate for data transmission also minimizes the risk of collisions. Our approach has been applied to monitoring applications composed of autonomous sensor nodes powered by indoor light energy. OMNeT++ simulation results demonstrate the benefits of our wake-up radio-based approach in terms of energy, latency, and collisions when compared with the state-of-the-art duty-cycled MAC protocols. Experiments performed with real WSN platforms equipped with a wake-up radio prototype confirm the efficiency of our approach.

One of the most ambitious goals of neuroscience and its neuroprosthetic applications is to interface intelligent electronic devices with the biological brain to cure neurological diseases. This emerging research field builds on our growing understanding of brain circuits and on recent technological advances in miniaturization of implantable multi-electrode-arrays (MEAs) to record brain signals at high spatiotemporal resolution. Data processing is needed to extract useful information from the recorded neural activity to better understand the function of underlying neural circuits and, in perspective, to operate neuroprosthetic devices. In this context, machine learning approaches are increasingly used in many application scenarios. This paper focuses on processing data of evoked local field potentials (LFPs) recorded from the rat barrel cortex using a miniaturized 16×16 MEA. We evaluated machine learning algorithms and trained an optimized classifier to detect at which cortical depth the neural activity is measured. We demonstrate with experimental results that machine learning can be applied successfully to noisy single-trial LFPs offering up to 99.11% of test accuracy in classifying signals acquired from different cortical layers. As such, the method is a very promising starting point toward real-time decoding of cerebral activities with low power consumption digital processors for brain-machine interfacing and neuroprosthetic applications.

High precision Global Navigation Satellite System (GNSS) is a crucial feature for geo-localization to enhance future applications such as self-driving of vehicles. Real Time Kinematic (RTK) is a promising technology to achieve centimeter precision in GNSS, however it requires radio communication and to reduce power consumption this is done at meters range, reducing the use in navigation systems. In this work, we present a high precision low power systems that can be energetically autonomous. The proposed approach exploits a GNSS module with RTK combined with a long-range communication radio (LoRa) to achieve a high-precision localization system with minimal wireless radio infrastructure requirements. Wireless sensor nodes, designed to be energy efficient, comprise the system and they include a solar energy harvesting for self-sustainability. Preliminary experimental results, with in-field measurements, show an average accuracy below 1 meter up to more than 1km distance of the end-node from the geostationary reference anchor; with a peak accuracy of only 10cm. Low power consumption is also presented with in-filed measurements.

Intra-body and extra-body communication is an attractive wireless communication method for wearable devices and their applications. In this work, we present the architecture, the design and the implementation of a novel zero-power receiver optimized for intra-body and extra-body communication and on-off keying modulation. The receiver includes an energy-harvesting subsystem that extracts energy from the received message to supply the low power circuits. The proposed zero-power receiver is able to receive and to parse data, as well as wake up external logic when a specific address is detected. The receiver can be used both as an always-on touch sensor to be deployed in the field and as a body communication wake up radio to other nodes on the human body. Experimental results with in-field measurements demonstrate the zero-power of the proposed device and that it is possible to achieve more than 1.70m in intra-body communication and hand-shake information exchange without the use of any external battery.

With the recent improvement of flexible electronics, wearable systems are becoming more and more unobtrusive and comfortable, pervading fitness and health-care applications. Wearable devices allow non-invasive monitoring of vital signs and physiological parameters, enabling advanced Human Machine Interaction (HMI) as well. On the other hand, battery lifetime remains a challenge especially when they are equipped with bio-medical sensors and not used as simple data logger. In this paper, we present a flexible wristband for EMG gesture recognition, designed on a flexible Printed Circuit Board (PCB) strip and powered by a small form-factor flexible solar energy panel. The proposed wristband executes a Support Vector Machine (SVM) algorithm reaching 94.02 % accuracy in recognition of 5 hand gestures. The system targets healthcare and HMI applications, and can be used to monitor patients during rehabilitation from stroke and neural traumas as well as to enable a simple gesture control interface (e.g. for smart-watches). Experimental results show the accuracy achieved by the algorithm and the lifetime of the device. By virtue of the low power consumption of the proposed solution and the on-board processing that limits the radio activity, the wristband achieves more than 500 hours with a single 200 mAh battery, and perpetual work with a small-form factor flexible solar panel.

Low-power and long-range communication technologies such as LoRa are becoming popular in IoT applications due to their ability to cover kilometers range with milliwatt of power consumption. One of the major drawbacks of LoRa is the data latency and the traffic congestion when the number of devices in the network increases. Especially, the latency arises due to the extreme duty cycling of LoRa end-nodes for reducing the overall energy consumption. To overcome this drawback, we propose a heterogeneous network architecture and an energy-efficient On-demand TDMA communication scheme improving both the device lifetime and the data latency of standard LoRa networks. We combine the capabilities of micro-watt wake-up receivers to achieve ultra-low power states and pure asynchronous communication together with the long-range connectivity of LoRa. Experimental results show a data reliability of 100 % and a round-trip latency on the order of milliseconds with end devices dissipating less than 46 mJ when active and 1.83 μW during periods of inactivity, lasting up to 3 years on a 1200 mA h Lithium battery.

Long-range (LoRa) radio technologies have recently gained momentum in the IoT landscape, allowing low-power communications over distances up to several kilometers. As a result, more and more LoRa networks are being deployed. However, commercially available LoRa devices are expensive and propriety, creating a barrier to entry and possibly slowing down developments and deployments of novel applications. Using open-source hardware and software platforms would allow more developers to test and build intelligent devices resulting in a better overall development ecosystem, lower barriers to entry, and rapid growth in the number of IoT applications. Toward this goal, this paper presents the design, implementation, and evaluation of KRATOS, a low-cost LoRa platform running ContikiOS. Both, our hardware and software designs are released as an open-source to the research community.

Intelligent connected objects, which build the IoT, are electronic devices usually supplied by batteries that significantly limit their life-time. These devices are expected to be deployed in very large numbers, and manual replacement of their batteries will severely restrict their large-scale or wide-area deployments. Therefore energy efficiency is of the utmost importance in the design of these devices. The wireless communication between the distributed sensor devices and the host stations can consume significant energy, even more when data needs to reach several kilometers of distance. In this paper, we present an energy-efficient multi-sensing platform that exploits energy harvesting, long-range communication and ultra-low-power short-range wake-up radio to achieve self sustainability in a kilometer range network. The proposed platform is designed with power efficiency in mind and exploits the always-on wake-up radio as both receiver and a power management unit to significantly reduce the quiescent current even continuously listening the wireless channel. Moreover the platform allows the building of an heterogeneous long-short range network architecture to reduce the latency and reduce the power consumption in listening phase at only 4.6 μW. Experimental results and simulations demonstrate the benefits of the proposed platform and heterogeneous network.

Recent advances in electrical sensing and flexible devices have demonstrated great potential in a wide range of applications including wearable devices for fitness and health care. Flexible plastic substrates, such as polyimide, or transparent conductive polyester are used to fabricate flexible devices that increase the comfort when they are worn by users or patients. However, one challenge that still limits the success of wearable devices is the limited lifetime especially in biomedical applications. Energy harvesting technology is one of the most promising approaches to address the short lifetime of wearable devices. However, harvesting energy for powering wearable devices is more challenging due to strict constraints in terms of size, weight and cost. In this work, we present the design of a wearable smart bracelet that uses thin-film small form factor flexible photovoltaic panels as energy source. The solar energy harvesting subsystem has been designed to maximize the energy conversion efficiency (up to 90%) to achieve a self-sustainable wireless wearable system. The full-system integration has been developed and assembled using polyamide film to realize a fully flexible smart bracelet for long term monitoring of patients or elderly people in healthcare applications. Preliminary in-field experiments show that a single flexible solar panel can harvest up to 16mW of power in outdoor and 0.21mW in indoor scenarios. We demonstrate that, the developed device, combining low power design and flexible energy harvesting, achieves perpetual work, acquiring one blood oxygenation measurement per minute and sending data via Bluetooth.

Augmented reality (AR) applications greatly benefit from direction estimation. In current AR devices, direction estimation is enabled by power hungry technologies such as GPS, management of inertial data and video processing. We present a low-complexity eyewear prototype capable of obtaining directional information from a set of environmental radio sources by using a multi-antenna system patented by Wagoo LLC. Eyewear devices are the natural match for this technology under the assumption that user line of sight represents the main reference for user interaction and orientation. In the demonstration, the prototype detects when a user aims at objects or people that are associated to standard Bluetooth Low Energy emitters (beacons) with a current 30° sensitivity and 5% error rate.

Data produced by wearable sensors is key in contexts such as performance enhancement and training help for sports and fitness, continuous monitoring for aging people and for chronic disease management, and in gaming and entertainment. Unfortunately, wearable devices currently in the market are either incapable of complex functionality or severely impaired by short battery lifetime. In this work, we present a smart watch platform based on an ultralow-power (ULP) heterogeneous system composed of a TI MSP430 microcontroller, the PULP programmable parallel accelerator, and a set of ULP sensors, including a camera. The embedded PULP accelerator enables state-of-the-art context classification based on convolutional neural networks to be applied within a sub-10-mW system power envelope. Our methodology enables to reach high accuracy in context classification over five classes (up to 84%, with three classes over five reaching more than 90% accuracy), while consuming 2.2 mJ per classification, or an ultralow energy consumption of less than 91 μJ per classification with an accuracy of 64%-3.2× better than chance. Our results suggest that the proposed heterogeneous platform can provide up to 500× speedup with respect to the MSP430 within a similar power envelope, which would enable complex computer vision algorithms to be executed in highly power-constrained scenarios.

Wireless sensor networks are employed in many applications, such as health care, environmental sensing, and industrial monitoring. An important research issue is the design of efficient medium access control (MAC) protocols, which have an essential role for the reliability, latency, throughput, and energy efficiency of communication, especially as communication is typically one of the most energy consuming tasks. Therefore, analytical models providing a clear understanding of the fundamental limitations of the different MAC schemes, as well as convenient way to investigate their performance and optimize their parameters, are required. In this paper, we propose a generic framework for modeling MAC protocols, which focuses on energy consumption, latency, and reliability. The framework is based on absorbing Markov chains, and can be used to compare different schemes and evaluate new approaches. The different steps required to model a specific MAC using the proposed framework are illustrated through a study case. Moreover, to exemplify how the proposed framework can be used to evaluate new MAC paradigms, evaluation of the novel pure-asynchronous approach, enabled by emerging ultra-low-power wake-up receivers, is done using the proposed framework. Experimental measurements on real hardware were performed to set framework parameters with accurate energy consumption and latency values, to validate the framework, and to support our results.

Low power communication has evolved towards multi-kilometer ranges and low bit-rate schemes in recent years. LoRa is an example of such a long-range technology that is triggering increasing interest. Using these technologies, a trade-off must be made between power consumption and latency for message transfer from the gateway to the nodes. However, domains such as industrial applications in which sensors and actuators are part of the control loop require predictable latency, as well as low power consumption. These requirements can be fulfilled using pure-asynchronous communication and idle listening elimination, allowed by emerging ultra-low-power wake-up receivers. On the other hand, state-of-the-art wake-up receivers present low sensitivity compared to traditional wireless node receivers and LoRa, which results in the fact that they can operate in short-range in the order of a few tens of meters. In this work, we propose an energy efficient architecture that combines long-range communication with ultra low-power short-range wake-up receivers to achieve both energy efficient and low latency communication in heterogeneous long-short range networks. The proposed hardware architecture uses a single radio transceiver that can communicate using both LoRa and state-of-the-art wake-up receivers while the proposed MAC protocol exploits the benefits of these two communication schemes. Experimental measurements and analytical comparisons show the benefits regarding both energy efficiency and latency enabled by the proposed approach. Analytical comparisons show that the proposed scheme allows up to 3000 times reduction of the power consumption compared to the standard LoRa approach.

Achieving energy efficient wireless communication is the most pursued goal in Wireless Sensor Networks (WSNs), as energy consumption is typically a major barrier to long term applications. In recent years, ultra-low power Wake-up Receivers (WuRx) have emerged, enabling pure asynchronous wireless communication that eliminates energy waste due to idle listening. However, to achieve a significant increase of energy efficiency compared to traditional duty-cycling approaches, Medium Access Control (MAC) protocols exploiting WuRx must be carefully designed. Therefore, we propose an analytical framework to model MAC protocols, leveraging WuRx or not, which gives an important evaluation of power consumption, latency and reliability. This framework was used to both model a WuRx-based MAC protocol, and to model two other state-of-the art MAC protocols for WSNs not using WuRx. Experimental power consumption and latency measurements were conducted to validate the proposed framework and the MAC protocol leveraging WuRx. Analytical results show the convenience of using WuRx and quantify the benefits of this emerging technology. These results demonstrate that using WuRx achieves up to 135 times lower power consumption and up to 23 times lower latency compared to traditional approaches in typical low throughput WSNs applications.

Long lifetime is the most pursued goal in Wireless Sensor Networks (WSNs). As communication is typically the most energy consuming task, a lot of effort has been devoted to design energy efficient communication protocols using duty-cycling in the last decades. However, in the recent years, a new kind of Ultra Low Power (ULP) receivers, called Wake-up Receivers (WuRx), is emerging. These devices allow the continuous monitoring of the wireless channel while having a power consumption orders of magnitude less than typical WSNs transceivers. WuRx can wake-up the rest of the system (microcontroller (MCU) and main radio) using interrupts only when needed, minimizing the idle listening. In this work, we present an experimental and an analytical study which ultimately serve as guidelines for the design of communication protocols leveraging WuRx.

The combination of low-power design, energy harvesting and ultra-low-power wake-up radios is paving the way for perpetual operation of Wireless Sensor Networks (WSNs). In this work we present the MagoNode++, a novel WSN platform supporting energy harvesting and radio-triggered wake ups for energy- neutral applications. The MagoNode++ features an energy- harvesting subsystem composed by a light or thermoelectric harvester, a battery manager and a power manager module. It further integrates a state-of-the-art RF Wake-Up Receiver (WUR) that enables low-latency asynchronous communication, virtually eliminating idle listening at the main transceiver. Experimental results show that the MagoNode++ consumes only 2.8uA with the WUR in idle listening and the rest of the platform in sleep state, making it suitable for energy-constrained WSN scenarios and for energy-neutral applications.

Wireless sensor networks (WSNs) are today widely employed in real world applications. However, their lifetime is still challenging and the most critical limitation for the success of this technology. In fact, wireless sensors nodes, which are the backbone of the network, are typically powered by limited energy storage devices (i.e. small batteries or supercaps) and their short lifetime is a critical issue. To overcome this limitation a major research effort focuses on reducing power consumption, especially of communication, as the radio transceiver is one of the highest power consumers. A critical energy-efficiency issue in WSN transceivers is idle listening. Wake-up radio receivers are very effective in minimizing idle listening. This fact has resulted in a significant number of wake-up radio receiver architectures proposed in last decade. In this work we present an advanced design and implementation of an advanced wake-up radio that is capable of both processing the received data (i.e. for addressing) and retransmitting data or wake up messages to the neighbours when necessary. With these features it can be possible to further enhance the energy efficiency of the communication and allowing ultra-low power multi-hop communication. Experimental results demonstrate the functionality as well as the power and range of the proposed design which is ready for future energy efficient and pure-asynchronous MAC protocols.

Wearable devices are massively entering in our life and they are more and more pushing the interest big electronic producer. Then, today many company are offering wearable "smart" objects to be worn which enable a wide range of application (form sport & fitness, to entrainment, from tracking to health care). A common issue of wearable device that is reducing the appeal of them is the limited lifetime due to limited energy that can be stored in them batteries. Self-sustainable devices which can avoid to recharge or replace the batteries, as it happens in automatic quartz watches, is still a dream for wearable devices. This paper presents a wearable device with an ultra low camera, which can achieve this dream. To achieve this goal, the Wearable camera has been carefully designed with low power consumption in mind and leveraging a kinetic energy harvester to scavenge energy from the human body movements. The experimental results shows the impressive amount of energy (up to 9.6mJ per minute) that can be acquired during different human activities (running, walking, etc) and the self- sustainability of the solution acquiring up to more than 2000 images for hour when is running.

Epidermal sensors, which form an intimate and robust contact with the skin, are capable of providing clinically relevant information about cardiovascular health, electrophysiology and dermatology with high accuracy and in an unobtrusive manner. To enable clinical applications, however, continuous and long-term monitoring is necessary. In addition, wireless and energetically autonomous systems are highly desirable to eliminate the needs of tethers and cables for powering and data transmission. Such requirements call for devices that combine accurate and precise sensing with high performance electronics for signal treatment, communication and power management in formats which conformal laminate on the body. In this work, we present a novel system whose design leverages on the recent developments in low power wearable devices and flexible sensors. It consists of an ultra-low power smartwatch connected to flexible solar modules assembled on a strap and an array of epidermal temperature sensors which are mounted on the wrist. Preliminary experiments show how this platform is well-suited for long-term, accurate and continuous mapping of the temperature of the skin.

Wireless sensor networks (WSNs) have received significant attention in recent years and have found a wide range of applications, including structural and environmental monitoring, mobile health, home automation, Internet of Things, and others. As these systems are generally battery operated, major research efforts focus on reducing power consumption, especially for communication, as the radio transceiver is one of the most power-hungry components of a WSN. Moreover, with the advent of energy-neutral systems, the emphasis has shifted toward research in microwatt (or even nanowatt) communication protocols or systems. A significant number of wake-up radio receiver (WUR) architectures have been proposed to reduce the communication power of WSN nodes. In this work, we present an optimized ultra-low power (nanowatt) wake-up receiver for use in WSNs, designed with low-cost off-the-shelf components. The wake-up receiver achieves power consumption of 152 nW (with -32 dBm sensitivity), sensitivity up to -55 dBm (with maximum power of 1,2 μW), latency from 8 μs, tunable frequency, and short commands communication. In addition, a low power solution, which includes addressing capability directly in the wake-up receiver, is proposed. Experimental results and simulations demonstrate low power consumption, functionality, and benefits of the design optimization compared with other solutions, as well as the benefits of addressing false positive (FP) outcomes reduction.

Today there is a clear trend towards deploying advanced computer vision (CV) systems in a growing number of application scenarios with strong real-time and power constraints. Brain-inspired algorithms capable of achieving record-breaking results combined with embedded vision systems are the best candidate for the future of CV and video systems due to their flexibility and high accuracy in the area of image understanding. In this paper, we present an optimized convolutional network implementation suitable for real-time scene labeling on embedded platforms. We show that our algorithm can achieve up to 96GOp/s, running on the Nvidia Tegra K1 embedded SoC. We present experimental results, compare them to the state-of-the-art, and demonstrate that for scene labeling our approach achieves a 1.5x improvement in throughput when compared to a modern desktop CPU at a power budget of only 11 W.

Wireless body area networks (WBANs) have the huge potential to enhance people's lives. They are already present in many application domains, for instance sport and fitness, but they are wide spreading in particular in health and rehabilitation. However, there are still challenging issues that limit their wide diffusion in real life: primarily, the limited lifetime due to the batteries that usually supply the devices. This limitation affects usability and force the data processing to be simple to match the power constraints. This work tries to address the energy limitation by enabling both efficient and complex signal-processing applications and extension of lifetime. We present a power management strategy combining an ultra-low power wake up radio with context awareness. The context aware power manager based on activity recognition decides which nodes must be activated exploiting a nano-power wake up radio and power management policies. Result shows that by using both approaches it is possible to extend battery life of sensor nodes from few hours to an entire week.

Emerging wake-up radio technologies have the potential to bring the performance of sensing systems and of the Internet of Things to the levels of low latency and very low energy consumption required to enable critical new applications. This paper provides a step towards this goal with a twofold contribution. We first describe the design and prototyping of a wake-up receiver (WRx) and its integration to a wireless sensor node. Our WRx features very low power consumption (<; 1.3μW), high sensitivity (up to -55dBm), fast reactivity (wake-up time of 130μs), and selective addressing, a key enabler of new high performance protocols. We then present ALBA-WUR, a cross-layer solution for data gathering in sensing systems that redesigns a previous leading protocol, ALBA-R, extending it to exploit the features of our WRx. We evaluate the performance of ALBA-WUR via simulations, showing that the use of the WRx produces remarkable energy savings (up to five orders of magnitude), and achieves lifetimes that are decades longer than those obtained by ALBA-R in sensing systems with duty cycling, while keeping latencies at bay.

Wake-up radio is an emerging technology with the ambitious goal of reducing the communication power consumption in smart sensor networks and Internet of Things. This reduction in power consumption will enable a new generation of applications which could achieve a longer lifetime than is achievable today. Wake up radios are required to work with a low power budget and should exhibit low latency coupled with high sensitivity and addressing capabilities. Typically they are combined with existing radio transceiver and power management techniques to reduce the overall communication power while maintaining the same communication performance. This paper presents a dual band (2.4GHz and 868MHz) wake up radio with the above mentioned characteristics. The dual band solution is exploited to increase the flexibility of the wake up radio, allowing interoperability with the two most common frequencies used in Wireless Sensors Networks and Internet of Things. Simulation results present a system able to exploit the two bands with sensitivity as low as -53dBm at 868MHz and -45dBm at 2450MHz. Experimental results on power consumption demonstrate the low power consumption of the proposed solution with only 1.276μW of power consumption in listening mode. The addressing is performed by an ultra low power on board PIC microcontroller with 40nW of power consumption when the wake up radio is in listening mode and only 70 μW when the data are received and parsed.

In power-limited wireless devices such as wireless sensor networks, wearable components, and Internet of Things devices energy efficiency is a critical concern. These devices are usually battery operated and have a radio transceiver that is typically their most power-hungry block. Wake-up radio schemes can be used to achieve a reasonable balance among energy consumption, range, data receiving capabilities and response time. In this paper, a high-sensitivity low power wake-up radio receiver (WUR) for wireless sensor networks is presented. The wake-up radio is comprised of a fully passive differential RF-to-DC converter that rectifies the incident RF signal, a low-power comparator and an ultra low power microcontroller to detects the envelope of the on-off keying (OOK) wake-up data used as address. We designed and implemented a novel low power tunable wake up radio with addressing capability, a minimal power consumption of only 196nW and a maximum sensitivity of -55dBm and minimal wake up time of 130μs without addressing and around 1,6ms with 2byte addressing at 10Kbit/s data rate. The flexibility of the solution makes the wake up radio suitable for both power constrained low range application (such as Body Area Network) or applications with long range needs. The wake up radio can work also at different frequencies and the addressing capability directly on board helps reduce false positives. Experimental on field results demonstrate the low power of the solution, the high sensitivity and the functionality.

Nodes in wireless sensor networks (WSNs) typically have limited power supply and networks are often expected to be functional for extended periods. Therefore, the minimization of energy consumption and the maximization of network lifetime are key objectives in WSN. This paper proposes an overlay, energy optimized, sensor network to extend the functional lifetime of an energy-intensive sensor network application. The overlay network consists of additional nodes that exploit recent advances in energy harvesting and wake-up radio technologies, coupled with an application specific, complementary, ultra-low power sensor. The experimental results and simulations demonstrate that this approach can ensure survivability of energy-inefficient sensor networks. Simulating applications using energy-intensive video cameras and air quality sensors, combined with the proposed overlayed ultra-low power sensor network, demonstrates that this approach can increase functional lifetime toward perpetual operation and is suitable for WSN applications in which complementarity exists between the required energy-intensive sensors and low-cost sensors that can be used as triggers.

Energy harvesting allows low-power embedded devices to be powered from naturally-ocurring or unwanted environmental energy (e.g. light, vibration, or temperature difference). While a number of systems incorporating energy harvesters are now available commercially, they are specific to certain types of energy source. Energy availability can be a temporal as well as spatial effect. To address this issue, ‘hybrid’ energy harvesting systems combine multiple harvesters on the same platform, but the design of these systems is not straight-forward. This paper surveys their design, including trade-offs affecting their efficiency, applicability, and ease of deployment. This survey, and the taxonomy of multi-source energy harvesting systems that it presents, will be of benefit to designers of future systems. Furthermore, we identify and comment upon the current and future research directions in this field.

The paper provides a framework to save energy and reduce the operative cost of some of today's industrial machinery. Low cost and low power wireless sensor networks is a novel approach to monitoring the tools in order to save energy and keep the tools monitored. Cutting tool wear degrades the product quality in manufacturing processes and also could have implications in health and safety of use. Monitoring tool wear value online is therefore needed to prevent degradation in machine quality. Unfortunately there is no direct way of measuring the tool wear online which is also very low cost. In this work is presented a low power and low cost accelerometer-based system for wear detection of bandsaw blade. The algorithm uses a simple data processing directly on board that can extract features and perform a classification on the state of the blade. Low power design of the node, on board processing and wake up radio capabilities reduce the wireless communication and the power consumption of the node significantly. Experimental results show the high accuracy, up to 100%, of the algorithm and the low power of the proposed approach.

Nowadays, the technology advancements of sensors, low power mixed-signal/RF circuits, wireless communication and Wireless Sensor Networks (WSNs) have enabled the design of compact, low power, high performance and low cost solutions for a wide range of applications, surveillance, building monitoring, sports/fitness, and of particular interesting are applications for health care. Novel sensors for human biomedical signal together with wireless connectivity and low power solutions are creating new opportunities for wearable devices which allow continuous monitoring together with freedom of movement of the users. This paper presents a low-power wearable sensor networks platform for on-body physiological measurements and wireless data communications. The platform hosts novel high sensitivity electric potential dry surface sensors that can be used in either contact or non-contact mode to measure ECG and EMG signals. Heart rate and respiration rate is performed runtime directly on the node. This approach reduces the amount of data than need to be transmitted, from raw measurement to analyzed data. In doing so the duty cycle of the radio has been reduced and the power consumption of the node optimized. Experimental measurements show the acquisition and processing of data from sensors and the low power consumption achieved with the node in different modalities.

This paper presents the design, implementation and characterization of an energy-efficient smart power unit for a wireless sensor network with a versatile nano-Watt wake up radio receiver. A novel Smart Power Unit has been developed featuring multi-source energy harvesting, multi-storage adaptive recharging, electrochemical fuel cell integration, radio wake-up capability and embedded intelligence. An ultra low power on board microcontroller performs maximum power point tracking (MPPT) and optimized charging of supercapacitor or Li-Ion battery at the maximum efficiency. The power unit can communicate with the supplied node via serial interface (I2C or SPI) to provide status of resources or dynamically adapt its operational parameters. The architecture is very flexible: it can host different types of harvesters (solar, wind, vibration, etc.). Also, it can be configured and controlled by using the wake-up radio to enable the design of very efficient power management techniques on the power unit or on the supplied node. Experimental results on the developed prototype demonstrate ultra-low power consumption of the power unit using the wake-up radio. In addition, the power transfer efficiency of the multi-harvester and fuel cell matches the state-of-the-art for Wireless Sensor Networks.

During recent years, there has been a growing interest on wireless sensor networks (WSNs) and on the opportunities opened by this technology. Since the energy consumption is a bottleneck in WSNs, reducing it has a significant impact on the applicability of this technology. Typically, the energy consumed by wireless communication and by power-hungry sensors as CMOS imagers or Gas sensors, is dominant over the power required for computation or other activities of the node. Hence, an efficient management of the resources leading to a reduction of unnecessary communication and minimizing the use of power-hungry sensor while keeping the same performance is desirable to extend the life-time of the network. In this paper we address the challenges of exploiting wake-up receivers and heterogeneous sensors in WSN applications to reduce the average power consumption of individual nodes. In particular, we show how to configure a WSN which includes Pyroelectric InfraRed (PIR) sensors, smart camera sensors and a nano-Watt wake up radio as secondary radio receiver to efficiently extend the autonomy of the system. The evaluation of the proposed approach shows a significant reduction of the activity of the primary radio and of the high power sensor while keeping the same accuracy. We prototyped and tested the nodes, and used their characterization to demonstrate through simulations the power consumption reduction and the life-time extension of the network in a typical surveillance application.

A major issue for video surveillance embedded systems is the need to continuously perform a number of highly demanding operations even when the analyzed scene does not show peculiar or interesting features, so that power consumption is a critical issue. In this paper we present a low-power multimodal embedded video surveillance system aimed at detecting objects abandoned/removed in/from a static monitored scene. Energy-awareness is achieved by means of an efficient and scalable objects abandon/removal detection algorithm, a Linux governor that controls CPU frequency and operating mode so as to establish an optimal trade-off between fulfilling the application efficiency-accuracy requirements and maximizing battery life and, finally, a pyroelectric infrared sensor that allows to wake up the CPU only when video processing is actually needed.

One of the major challenges in embedded system is reduction of power consumption. So far most of the microprocessors provide power saving mechanisms by changing the power operating mode as well as the frequency and core voltage at runtime. One of best methods is the management of available resources and operating systems like Linux define subsystems for power consumption management which require coordination and cooperation of hardware, kernel, and user-space applications, offering power savings options when the CPU is active as well as when it is inactive. In this paper we present a multimodal embedded visual surveillance system for the detection of abandoned/removed objects which exploits a Linux governor to control CPU frequency and operating mode to establish an optimal trade-off between fulfilling the application response time and accuracy requirements and maximizing battery life. To adopt an aggressive power management and to keep the whole system in sleep mode, a pyroelctric infrared sensor is also used to wake up the CPU only when video processing is actually needed.

Low-cost and low-power video surveillance systems based on networks of wireless video sensors will enter soon the marketplace with the promise of flexibility, quick deployment and providing accurate and real-time visual data. Energy autonomy and efficiency of the implemented algorithms are undoubtedly the primary design challenges to be addressed on systems subject to low computational capabilities and memory constraints. In this paper we present a low-power video sensor node designed for low-cost video surveillance which is able to detect abandoned and removed objects. The system exploits multi-modal sensor integration which saves on-board power consumption. In particular a pyroelectric infrared (PIR) sensor is exploited to optimize the use of the camera, grabbing images only when required in order to obtain the maximum efficiency from event recognition. Our fixed-point ARM-based approach is characterized in terms of runtime execution and power consumption, while efficiency is demonstrated by experimental results and compared with floating point implementations.

Building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. We present a multi-modal video sensor node designed for low-power and low-cost video surveillance, traffic control and people detection based on wireless sensor networks. It is equipped with a solar energy harvesting unit, which extends the autonomy ofthe nodes considerably using a solar cell of 70 cm2 and exploits CMOS video camera and Pyroelectric InfraRed (PIR) sensors to reduce remarkably the power consumption of the system in absence of events. The on-board microprocessor enables image classification using algorithms basedon support vector machines (SVM). We describe hardware-software architecture of the video sensor node and characterization in terms of power consumption and accuracy. Finally simulation results demonstrate the effectiveness of multimodal video sensors powered by harvesting circuits.

In contrast to video sensors which just "watch " the world, present-day research is aimed at developing intelligent devices able to interpret it locally. A number of such devices are available on the market, very powerful on the one hand, but requiring either connection to the power grid, or massive rechargeable batteries on the other. MicrelEye, the wireless video sensor node presented in this paper, targets a different design point: portability and a scanty power budget, while still providing a prominent level of intelligence, namely objects classification. To deal with such a challenging task, we propose and implement a new SVM-like hardware-oriented algorithm called ERSVM. The case study considered in this work is people detection. The obtained results suggest that the present technology allows for the design of simple intelligent video nodes capable of performing local classification tasks.