Ensuring accurate traffic perception and road safety in complex urban environments remains a significant challenge. Advanced traffic monitoring increasingly relies on deep learning, which requires large data volumes. However, existing datasets are often limited to CCTV video footage or focus on dynamic scenarios captured by sensors mounted on ego vehicles. This narrow perspective reduces the effectiveness of comprehensive traffic monitoring, particularly for LiDAR sensors, which typically capture only the vehicle’s viewpoint and miss critical areas such as intersections and pedestrian crossings. To address these limitations, we propose a holistic strategy for rapid data collection in urban settings using simulated 3D intersections. Our approach introduces a point cloud collection framework using static LiDAR sensors to provide a global view of the entire traffic scene. By incorporating randomized traffic patterns observed from multiple angles, this method generates a diverse, comprehensive dataset for object detection and instance segmentation, showcasing its advantages for benchmarking smart mobility applications.

Spintronics-based devices for hardware security primitives have gained much interest due to their unique physical characteristics. In this work, the Cryptographic hash function (CHF) generation system uses spintronic devices, specifically current-induced nano-magnetic structures. The system models the device behavior with the Boltzmann equation, incorporating Gaussian noise to simulate process variations around the device’s inflection point (IP). Simulated outputs are binarized into 4-bit strings and used for generating SHA-1 and SHA-256 hash outputs. Post-processing via XOR-based whitening improves randomness, reducing inter-collision rates and enhancing security metrics. The effect of device-to-device (D2D) variations is considered to ensure robustness across multiple devices. Performance metrics like collision rate, avalanche effect, and entropy are evaluated, confirming the system’s robustness and scalability in hardware security applications.

In this paper we propose an automatic controller design methodology for DC-DC converters that comprehensively addresses both Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). This methodology leverages on Artificial Intelligence (AI) techniques. Specifically, we resort on the Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) methods. Both GA and PSO permit to optimally tune the component values employed in the compensation network, overcoming the limitations of traditional design methods. The latter focus indeed solely on CCM, leading to significant performance degradation in DCM operation. The proposed methodology can be seamlessly integrated into DC-DC converter design phase, and it is not restricted for specific DC-DC topologies or control architectures. As a case study, we apply the proposed approach to the design of a Type-Iii compensation network in a voltage-mode controlled Buck converter, aiming to improve the load-transient response. The optimization process is carried out in MATLAB. Then, a performance comparison with the conventionally designed controller is conducted via SIMPLIS simulations. An improvement in overall performance is demonstrated.

The automotive industry has seen rapid advancements with the integration of artificial intelligence (AI), particularly in autonomous driving. While significant progress has been made, navigating complex traffic scenarios, such as intersections, remains challenging due to their dynamic nature. Traditional rule-based systems often struggle to adapt, prompting the need for more advanced solutions. This study aims to enhance autonomous driving at intersections by integrating bird’s eye view (BEV) fusion and reinforcement learning (RL). Using the CARLA simulator, we fine-tune the UNetXST model to fuse multiple camera perspectives into a BEV representation, providing a holistic view of the vehicle’s surroundings. This comprehensive view serves as input for the RL agent, which is trained using the proximal policy optimization (PPO) algorithm to learn optimized driving strategies, avoid collisions, and ensure efficient navigation. Our results show that the proposed framework outperforms baseline techniques.

Motion Artifact (MA) noise is one of the most crucial components in an Electrocardiogram (ECG) signal, especially during the monitoring of normal daily activities. Because of this, they are widely investigated for optimized denoising applications, trying to maximize the physiological information while solving the noise-signal frequency overlapping. In this work, we propose a filtering approach that employs the Multilevel Discrete Wavelet Decomposition (MDWD) basis domain, in which the projections of the signal are easily separable from the noise components. Compared to other more complex denoising approaches, this method only requires the simple projection of the signal on the desired wavelet basis. We obtain the desired denoising effect through the elimination of part of the projected signal, i.e., we remove the projected coefficients with the largest scaling values. We show that these coefficients carry most of the noise introduced by MA. To validate the method and tune its parameters, we test ECG affected by MA from different datasets, proving that the reconstruction performance is on par with the state-of-the-art approaches, such as the Empirical Wavelet Transform method (EWT), while begin much simpler in practice. Moreover, while other approaches tend to destroy signal anomalies and non-idealities which are fundamental for diagnosis, our approach keeps them unaltered.

Video surveillance in remote areas poses significant challenges due to limited network coverage and the high data requirements of video transmission. In such environments, satellite communication offers a viable solution to provide coverage under limited bandwidth conditions. At the same time, emerging semantic communication technology addresses the issue of data volume by transmitting only the most relevant information. This work proposes a semantic communication framework for video surveillance data transmission in remote areas. The framework transmits compact semantic representations from cameras over LEO satellites using Narrowband communication technology. More importantly, we formulate and solve a resource allocation problem that maximizes semantic information transmission under bandwidth constraints by optimally selecting semantic representations and satellite resource units. Our approach demonstrates the feasibility of semantic video surveillance using limited-bandwidth IoT standards such as LTE eMTC and NB-IoT over LEO satellites.

This paper presents a novel decentralized navigation framework for urban environments, integrating an Unmanned Aerial Vehicle (UAV) with an Unmanned Ground Vehicle (UGV). The system is designed such that the UAV aims to reach a predefined destination to perform critical aerial tasks while the UGV maintains proximity to support dynamic charging and continuous operation. The navigation strategy employs a hybrid path planning approach, combining a Probabilistic Roadmap (PRM) with an$A^{\ast}$search algorithm, further optimized by path pruning for the UAV. In contrast, the UGV utilizes the Dynamic Window Approach (DWA) for adaptive navigation. Both unmanned vehicles operate while maintaining a safe distance to ensure uninterrupted communication and prevent connectivity loss. Validated through extensive simulations in ROS2 and Gazebo, this framework enhances mission efficiency and enables real-time coordination for effective decentralized navigation.

Collaborative perception is essential in autonomous driving, enabling connected autonomous vehicles (CAVs) to share sensor data and improve awareness of their surroundings. This is especially critical for detecting occluded objects at intersections, where limited visibility can compromise safety and hinder real-time decision making. However, early fusion of sensor data across multiple CAVs presents significant challenges in data management, as the sheer volume of 3D point cloud information demands substantial communication bandwidth. To address these challenges, this article proposes an optimized collaborative perception framework specifically designed for CAVs at intersections. Our approach begins with each CAV generating a Bird's Eye View (BEV) map from LiDAR data, which is then divided into sectors. The quality and size of the data for each sector are assessed and sent to a roadside unit (RSU) that acts as a data center. The RSU selectively coordinates the sharing of high-quality sectors only, reducing redundant data transmission by avoiding empty or low-density regions. Through this targeted data sharing, our framework minimizes communication loads and computational demands while preserving perception accuracy, thus supporting efficient and scalable collaborative perception in complex intersection environments.

Unmanned aerial vehicles (UAVs) can enhance wireless access by dynamically positioning themselves closer to users while maintaining a backhaul connection to cellular base stations. In scenarios where users are geographically dispersed, multiple UAVs can be orchestrated to establish multi-hop integrated access and backhaul (IAB) connections. Traditionally, finding optimal UAV placement has required computationally demanding methods, such as combinatorial optimization or reinforcement learning, which are often impractical for real-time applications due to their complexity and training requirements. Additionally, UAV operators may lack the capability to solve complex optimization problems during live operations. This paper presents a novel framework that leverages large language models (LLMs) for optimizing the placement of multiple UAVs through iterative structured prompting. The proposed method achieves near-optimal solutions in significantly fewer iterations compared to traditional methods, making it suitable for real-time deployment without extensive mathematical modeling. Simulation result demonstrate that the proposed method achieves scores over 82% of the theoretical optimal solution while reducing computational time from hours to minutes compared to the baseline deep Q network approach, ensuring robust network connectivity and service quality. The LLM-driven framework simplifies problem-solving for UAV network operators, paving the way for its application in more complex real-world scenarios.

The advent of multi-unmanned aerial vehicle (multi-UAV) networks in mobile edge computing (MEC) introduces dynamic computational topologies where UAVs, acting as mobile edge servers, are tasked with processing data from ground-based user equipment (UE). This paper addresses the dual challenges of optimizing both UAV deployment and task offloading within such networks to minimize communication latency and efficiently utilize UAV resources, which are limited by battery life and processing capabilities. We propose a bi-level optimization framework that simultaneously tackles the placement of UAVs and the distribution of computational tasks among them. At the higher level, UAV deployment is optimized to ensure minimal distance to the UEs, thereby reducing latency and energy consumption during data transmission. At the lower level, task offloading is optimized to balance the computational load across the UAV network, considering each UAV's capacity and battery constraints. We demonstrate through extensive simulations the significant improvements in system efficiency, latency, and resilience. This approach not only enhances the performance of UAV-assisted MEC networks but also provides scalable solutions adaptable to various operational scenarios.

As neural networks are trained to perform tasks of increasing complexity, their size increases, which presents several challenges in their deployment on devices with limited resources. To cope with this, a recently proposed approach hinges on substituting the classical Multiply-and-ACcumulate (MAC) neurons in the hidden layers with other neurons called Multiply-And-Max/min (MAM) whose selective behavior helps identify important interconnections, thus allowing aggressive pruning of the others. Hybrid MAM&MAC structures promise a 10x or even 100x reduction in their memory footprint compared to what can be obtained by pruning MAC-only structures. However, a cornerstone of maintaining this promise is the assumption that MAC&MAM architectures have the same expressive power as MAC-only ones. To concretize such a cornerstone, we take here a step in the theoretical characterization of the capabilities of mixed MAM&MAC networks. We prove, with two theorems, that two hidden MAM layers followed by a MAC neuron with possibly a normalization stage is a universal approximator.

Accelerated MRI acquisition is widely adopted and basically consists in undersampling the current slice at the cost of a quality degradation. What samples to skip is determined by an encoder, while the quality loss is partially compensated by the use of a decoder. The hypothesis behind accelerated MRI acquisition is that to higher acceleration factors always correspond lower reconstruction qualities with an undersampling pattern that is usually fixed at design time, neglecting adaptability on the slice acquired at inference time. This paper proposes a novel accelerated MRI acquisition method that enables single-slice adaptation by dividing the acquisition into incremental batches and estimating the reconstruction quality at the end of each batch. The acquisition terminates as soon as the target quality is reached. We demonstrate the efficacy of our novel method using a state-of-the-art neural model capable of jointly optimizing the encoder and decoder. To estimate the current quality of the slice we reconstruct and propose a neural quality predictor. We demonstrate the advantages of our novel acquisition method compared to classic acquisition for two different datasets and for both line-constrained and unconstrained Cartesian sampling strategies (theoretically implementable via 2D and 3D imaging respectively). • A novel incremental MRI acquisition method with adaptive undersampling. • Utilizes neural self-assessment to adjust MRI acquisition dynamically. • Introduction of target reconstruction quality, tunable before acquisition. • Reaching target quality more efficiently than classic acquisition. • Validated on FastMRI and IXI datasets: showcasing robust performance across settings.

In this paper, we present a novel and unified small-signal modeling technique for Pulse-Width Modulated (PWM) DC-DC Buck converters with Ripple-Based Constant On-Time (RBCOT) control. In fact, despite the spread of RBCOT-based converters in several applications requiring tight dynamic performances and a low architectural complexity, their description through small-signal models is not always as reliable as that of fixed-frequency PWM control architectures, and a general and exact modeling framework is not well established. The proposed methodology is grounded on the DC-DC converter state-space representation and thus, differently from other modeling techniques, it permits to fully characterize the dynamic behavior of generic RBCOT converter topologies with arbitrary complex power stage and ripple injection networks. As a case study, we derive the small-signal model for a Buck converter embedding a widely used ripple injection network in industrial applications. The validity of the theoretical results is confirmed through direct comparison with SIMetrix/SIMPLIS simulations and experimental measurements in practical application scenarios, confirming the accuracy of the model even well beyond the converter switching frequency.

We investigate here both from a theoretical and practical point of view the problem of optimizing EMI reduction by means of spread spectrum clocking when lower harmonics need to be analyzed with a smaller RBW, and higher harmonics with a larger one. This situation is indeed a trade-off, where a designer can trade performance in terms of EMI reduction for lower harmonics with that achieved for higher harmonics. Two approaches are considered and analyzed. The first trade-off, denoted as Single Triangular Modulation, consists in the standard and commonly adopted triangular based spreading, where the role of the parameters is investigated with the aim of optimizing EMI reduction both in the lower part and the upper part of the spectrum. The second one, denoted as Double Triangular Modulation, is inspired by a recent Application Note and it is much more complex from an implementation point of view, being based on two simultaneous triangular modulations with different parameters. The comparison shows very similar performance, so that the adoption of the more complex approach results questionable.

Multi-access edge computing (MEC) has emerged as a proven solution for reducing communication latency and enhancing user experience in delay-sensitive applications by offloading computation-intensive tasks to edge servers. In future networks, unmanned aerial vehicles (UAVs), with their flexible deployment and reliable communication capabilities, have the potential to be deployed as aerial MEC servers in areas lacking cellular infrastructure. However, the joint optimization of UAV placement and task offloading poses significant challenges due to the interdependence between communication latency, computational demands, and the resource limitations of UAVs. In this paper, we propose a novel joint optimization framework utilizing proximal policy optimization (PPO) to simultaneously address UAV placement and computation offloading in UAVenabled MEC networks. The framework dynamically adapts to changing network conditions, minimizing end-to-end latency while balancing computational loads and energy consumption. Extensive simulations demonstrate that the proposed PPO-based approach achieves superior performance compared to conventional optimization methods, with significant improvements in system latency, resource utilization, and network resilience. This work contributes scalable, adaptive solutions for UAV-assisted MEC networks in dynamic environments, enabling robust support for mission-critical and latency-sensitive applications.

The growing interest in the Internet of Things (IoT) and mobile artificial intelligence applications is pushing the investigation on deep neural networks (DNNs) that can operate at the edge using low-resources/energy devices. To obtain such a goal, several pruning techniques have been proposed in the literature. They aim to reduce the number of interconnections-and consequently the size, and the corresponding computing and storage requirements-of DNNs that traditionally rely on classic multiply-and-accumulate (MAC) neurons. In this work, we propose a novel neuron structure based on a multiply-and-max/min (MAM) map-reduce paradigm, and we show that by exploiting this new paradigm it is possible to build naturally and aggressively prunable DNN layers, with a negligible loss in performance. This novel structure allows a greater interconnection sparsity when compared to classic MAC-based DNN layers. Moreover, most of the already existing state-of-the-art pruning techniques can be used with MAM layers with little to no changes. To test the pruning performance of MAM, we employ different models-AlexNet, VGG-16 and the more recent ViT-B/16-and different computer vision datasets-CIFAR-10, CIFAR-100, and ImageNet-1K. Multiple pruning approaches are applied, ranging from single-shot methods to training-dependent and iterative techniques. As a notable example, we test MAM on the ViT-B/16 model fine-tuned on the ImageNet-1K task and apply one-shot gradient-based pruning. We remove interconnections until the model experiences a 6% decrease in accuracy. While the selected MAC-based layers need at least 38.2% remaining interconnections, MAM-based layers achieve the same accuracy with only 0.1%.

This paper presents a novel sequence-to-sequence LSTM-based routing algorithm, designed for delay-sensitive remote IoT communication via both terrestrial and Low Earth Orbit (LEO) satellite relays when no inter-satellite links are available. We introduce a matrix-based approach to compute the communication delay between pairs of IoT nodes and satellites while considering their orbital passes and temporary visibility. Then, we develop an encoder-decoder architecture based on LSTM networks augmented with a beam-search strategy that enables the efficient prediction of optimal routing paths. Through experimental evaluations, incorporating a K − 2 beam search optimization, the algorithm demonstrates superior performance in generating near-optimal and scalable routes. Comparative analyses indicate that this approach outperforms traditional routing methods, offering lower data delays with reduced computational overhead.

LiDAR sensor data is essential for autonomous vehicle navigation, traffic flow monitoring, obstacle detection, and passenger safety. However, the reliability of LiDAR data can be compromised by anomalies caused by sensor malfunctions, environmental conditions, or unexpected road events. To address this, detecting anomalies in spatial-temporal (ST) LiDAR data is critical for ensuring safety. This paper proposes a novel low-complexity unsupervised framework named CNN-BiLSTM VAE for anomaly detection (AD) in non-image LiDAR data. The framework combines variational auto-encoder (VAE) reconstruction, CNN for spatial learning, and bidirectional LSTM for time-series learning in a mirror-to-mirror (M2M) architecture. Experimental results show that this method effectively detects anomalies in multidimensional ST LiDAR data, thereby maintaining robustness under various environmental conditions.

Anomalies are rare events, and this affects the design flow of detectors that monitor systems that behave normally most of the time but whose failure may have serious consequences. This limitation is particularly evident in the detector performance evaluation: it requires an abundance of normal and anomalous data but realistically faces a scarcity of the latter. To address this, in this article, we develop a framework comprising a set of abstract anomalies modeling the effects real-world failures and disturbances have on sensor readings. In addition, we devise synthetic generation procedures for these anomalies. Given a dataset of normal tracks from the actual application, one may apply such procedures to produce anomalous-like time series for a comprehensive detector assessment. We show that this framework can anticipate the detector performing best with real-world anomalies in the context of human and structural health monitoring, also highlighting that, in these cases, the best detector is not the most complex.

Large monitoring systems produce data that is often compressed to be transmitted over the network. For latency or security reasons, compressed data may be processed at the edge, i.e., along the path from sensors to the cloud, for some purposes such as anomaly detection. However, the performance of a detector distinguishing between normal and anomalous behavior may be affected by the loss of information due to compression. We here analyze how lossy compression affects the performance of a generic anomaly detector. This relationship is formalized in terms of information-theoretic quantities. Within such a framework we leverage a Gaussian assumption to derive analytical results regarding the importance of white noise as a representative of both the average and asymptotic anomalies. Moreover, in an anomaly-agnostic scenario, we also show the existence of a level of compression for which an anomaly is undetectable though compression is not completely destructive. Numerical evidence confirms that the proposed information-theoretic quantities anticipate the performance of practical compressors and detectors in the case of Gaussian and non-Gaussian signals allowing an assessment of the tradeoff between compression and detection.

Anomaly Detection is a challenging task due to the limited knowledge about possible anomalies. This issue can be tackled by modeling anomalies through domain expertise or collecting sufficient anomalous data. However, some domains, such as monitoring systems, require detectors that are capable of detecting any potential alteration in the observed phenomenon. Hereby we propose a tool to generate anomalies as a statistical deviation from the characterization of the signal representing the normal behavior. Two families of deviation models are presented, and the effectiveness of the tool is proven using well-known unsupervised detectors. The effects of a possible intermediate data compression stage on the detection capabilities are also considered.

Due to its intrinsic sparsity both in time and space, event-based data is optimally suited for edge-computing applications that require low power and low latency. Time varying signals encoded with this data representation are best processed with Spiking Neural Networks (SNN). In particular, recurrent SNNs (RSNNs) can solve temporal tasks using a relatively low number of parameters, and therefore support their hardware implementation in resource-constrained computing architectures. These premises propel the need of exploring the properties of these kinds of structures on low-power processing systems to test their limits both in terms of computational accuracy and resource consumption, without having to resort to full-custom implementations. In this work, we implemented an RSNN model on a low-end, resource-constrained ARM-Cortex-M4-based Micro Controller Unit (MCU). We trained it on a down-sampled version of the N-MNIST event-based dataset for digit recognition as an example to assess its performance in the inference phase. With an accuracy of 97.2%, the implementation has an average energy consumption as low as$4.1\ \mu\mathrm{J}$and a worst-case computational time of$150.4\ \mu\mathrm{s}$per time-step with an operating frequency of 180 MHz, so the deployment of RSNNs on MCU devices is a feasible option for small image vision real-time tasks.

In this paper we provide an improved small-signal equivalent circuit model of a synchronous Buck converter which operates in Continuous Conduction Mode (CCM) and includes an alternative Zero Voltage Switching (ZVS) mechanism for the low-side power MOSFET that rely on the MOSFETs output capacitance. The addressed analysis improves the state of the art in DC/DC small-signal modeling as it is capable to predict unexpected effects on the dynamical system response such as the dependency on input voltage introduced by parasitics. Therefore, a complete design tool which permits to evaluate the impact of the MOSFETs output capacitance and the ZVS network on the converter dynamics is proposed. The derived equivalent circuit model which includes an additional feedforward path and a feedback loop is analyzed and the main open-loop transfer functions (control-to-output, line-to-output, output impedance) are analytically assessed. A verification has been carried out through SIMPLIS circuital simulations, corroborating the validity of the whole evaluation process.

Subspace analysis (SA) is a widely used technique for coping with high-dimensional data and is becoming a fundamental step in the early treatment of many signal-processing tasks. However, traditional SA often requires a large amount of memory and computational resources, as it is equivalent to eigenspace determination. To address this issue, specializedstreamingalgorithms have been developed, allowing SA to be run on low-power devices, such as sensors or edge devices. Here, we present a classification and a comparison of these methods by providing a consistent description and highlighting their features and similarities. We also evaluate their performance in the task of subspace identification with a focus on computational complexity and memory footprint for different signal dimensions. Additionally, we test the implementation of these algorithms on common hardware platforms typically employed for sensors andedgedevices.

Applying the chaos theory for secure digital communications is promising and it is well acknowledged that in such applications the underlying chaotic systems should be carefully chosen. However, the requirements imposed on the chaotic systems are usually heuristic, without theoretic guarantee for the resultant communication scheme. Among all the primitives for secure communications, it is well accepted that (pseudo) random numbers are most essential. Taking the well-studied 2-D coupled map lattice (2D CML) as an example, this article performs a theoretical study toward pseudorandom number generation with the 2D CML. In so doing, an analytical expression of the Lyapunov exponent (LE) spectrum of the 2D CML is first derived. Using the LEs, one can configure system parameters to ensure the 2D CML only exhibits complex dynamic behavior, and then collect pseudorandom numbers from the system orbits. Moreover, based on the observation that least significant bit distributes more evenly in the (pseudo) random distribution, an extraction algorithm$\mathbf {E}$is developed with the property that when applied to the orbits of the 2D CML, it can squeeze uniform bits. In implementation, if fixed-point arithmetic is used in binary format with a precision of$z$bits after the radix point,$\mathbf {E}$can ensure that the deviation of the squeezed bits is bounded by$2^{-z}$. Further simulation results demonstrate that the new method not only guides the 2D CML model to exhibit complex dynamic behavior but also generates uniformly distributed independent bits with good efficiency. In particular, the squeezed pseudorandom bits can pass both NIST 800-22 and TestU01 test suites in various settings. This study thereby provides a theoretical basis for effectively applying the 2D CML to secure communications.

The search for energy efficient circuital implementations of neural networks has led to the exploration of phase-change memory (PCM) devices as their synaptic element, with the advantage of compact size and compatibility with CMOS fabrication technologies. In this work, we describe a methodology that, starting from measurements performed on a set of real PCM devices, enables the training of a neural network. The core of the procedure is the creation of a computational model, sufficiently general to include the effect of unwanted non-idealities, such as the voltage dependence of the conductances and the presence of surrounding circuitry. Results show that, depending on the task at hand, a different level of accuracy is required in the PCM model applied at train-time to match the performance of a traditional, reference network. Moreover, the trained networks are robust to the perturbation of the weight values, up to 10% standard deviation, with performance losses within 3.5% for the accuracy in the classification task being considered and an increase of the regression RMS error by 0.014 in a second task. The considered perturbation is compatible with the performance of state-of-the-art PCM programming techniques.

The increasing need for small and low-power Deep Neural Networks (DNNs) for edge computing applications involves the investigation of new architectures that allow good performance on low-resources/mobile devices. To this aim, many different structures have been proposed in the literature, mainly targeting the reduction in the costs introduced by the Multiply and Accumulate (MAC) primitive. In this work, a DNN layer based on the novel Sum and Max (SAM) paradigm is proposed. It does not require either the use of multiplications or the insertion of complex non-linear operations. Furthermore, it is especially prone to aggressive pruning, thus needing a very low number of parameters to work. The layer is tested on a simple classification task and its cost is compared with a classic DNN layer with equivalent accuracy based on the MAC primitive, in order to assess the reduction of resources that the use of this new structure could introduce.

Several recent works have shown the advantages of using phase-change memory (PCM) in developing brain-inspired computing approaches. In particular, PCM cells have been applied to the direct computation of matrix-vector multiplications in the analog domain. However, the intrinsic nonlinearity of these cells with respect to the applied voltage is detrimental. In this paper we consider a PCM array as the encoder in a Compressed Sensing (CS) acquisition system, and investigate the effect of the non-linearity of the cells. We introduce a CS decoding strategy that is able to compensate for PCM nonlinearities by means of an iterative approach. At each step, the current signal estimate is used to approximate the average behaviour of the PCM cells used in the encoder. Monte Carlo simulations relying on a PCM model extracted from an STMicrolectronics 90 nm BCD chip validate the performance of the algorithm with various degrees of nonlinearities, showing up to 35 dB increase in median performance as compared to standard decoding procedures.

Leakage discharge currents represent one of the most detrimental factors for the maximum hold time in analog sample-and-hold circuits. Apart from the obvious passive solution of enlarging the sampling capacitor, alternatives based on active circuits have been proposed. We focus here on an existing solution which has proven to be effective in reducing the leakage discharge, hence extending the hold time, by a factor of 20. Being based on a feedback circuit built around the hold capacitor, it is paramount to understand its stability properties. This work tries to close the gap by analyzing the closed-loop stability of the nominal circuit. Classical control systems techniques are employed to thoroughly analyze the dynamic behaviour of the feedback circuit, highlighting the detrimental effect of device mismatches.

Performance in analog integrated circuits strongly depends on the mismatch between nominally identical devices. In this work we derive closed-form asymptotic expressions describing mismatch variance in multifinger structures, under the assumption of Gaussian autocorrelation for the mismatch-generating stochastic process. The analysis is performed on inter-digitated geometries, eventually modified to make them common-centroid. Comparison with the numerical results provided by an independent model validates the theoretical expressions presented here.

In a recent paper, a through-the-barrier communication technique suitable for isolated resonant converters has been proposed. The approach is capable of sending data bidirectionally at high speed (one bit for each converter clock period) without the need of any additional isolating device other than the transformer necessary for the power transfer, and has been demonstrated by means of a proof-of-concept low-frequency prototype. In this paper we review that work under the assumption of increasing the operating frequency by using a coreless transformer presenting low losses, but also a low coupling factor k. This allows to increase the efficiency of the converter to a very high value (92% in the proposed design working at 6.78 MHz), but the communication speed has to be reduced (one bit every four clock cycles).

Approaches for reconstructing signals encoded with Compressed Sensing (CS) techniques, and based on Deep Neural Networks (DNNs) are receiving increasing interest in the literature. In a recent work, a new DNN-based method named Trained CS with Support Oracle (TCSSO) is introduced, relying the signal reconstruction on the two separate tasks of support identification and measurements decoding. The aim of this paper is to improve the TCSSO framework by considering actual implementations using a finite-precision hardware. Solutions with low memory footprint and low computation requirements by employing fixed-point notation and by reducing the number of bits employed are considered. Results using synthetic electrocardiogram (ECG) signals as a case study show that this approach, even when used in a constrained-resources scenario, still outperform current state-of-art CS approaches.

The amount of data generated by distributed monitoring systems that can be exploited for anomaly detection, along with real time, bandwidth, and scalability requirements leads to the abandonment of centralized approaches in favor of processing closer to where data are generated. This increases the interest in algorithms coping with the limited computational resources of gateways or sensor nodes. We here propose two dual and lightweight methods for anomaly detection based on generalized spectral analysis. We monitor the signal energy laying along with the principal and anti-principal signal subspaces, and call for an anomaly when such energy changes significantly with respect to normal conditions. A streaming approach for the online estimation of the needed subspaces is also proposed. The methods are tested by applying them to synthetic data and real-world sensor readings. The synthetic setting is used for design space exploration and highlights the tradeoff between accuracy and computational cost. The real-world example deals with structural health monitoring and shows how, despite the extremely low computations costs, our methods are able to detect permanent and transient anomalies that would classically be detected by full spectral analysis.

Compressed sensing can be used to yield both compression and a limited form of security to the readings of sensors. This can be most useful when designing the low-resources sensor nodes that are the backbone of IoT applications. Here, we propose to use chaining of subsequent plaintexts to improve the robustness of CS-based encryption against ciphertext-only attacks, known-plaintext attacks and man-in-the-middle attacks.

This paper presents the design of an ultra-low energy, rakeness-based compressed sensing (CS) system that utilizes time-mode (TM) signal processing (TMSP). To realize TM CS operation, the presented implementation makes use of monostable multivibrator based analog-to-time converters, fixed-width pulse generators, basic digital gates and an asynchronous time-to-digital converter. The TM CS system was designed in a standard 0.18 μm IC process and operates from a supply voltage of 0.6V. The system is designed to accommodate data from 128 individual sensors and outputs 9-bit digital words with an average reconstruction SNR of 35.31 dB, a compression ratio of 3.2, with an energy dissipation per channel per measurement vector of 0.621 pJ at a rate of 2.23 k measurement vectors per second.

A recent paper improved the state of the art for resonant class-E dc/dc converters by relaying their design on the solution of an associated non-linear dimensionless mathematical system. We show in this paper that when the associated nonlinear system can be solved, the solution is not always unique. By considering a simple case study we are able to compute two different solutions leading to two different designs. In the first one the main spectral component of voltage and current waveforms is located around the first clock harmonic, and around the second harmonic in the other solution. Interestingly, the latter design leads to a reduction either in the size of inductors/transformers or in the oscillation frequency (a 2.46× factor), and also a non-negligible improvement in the converter efficiency (from 74.1% to 75.8%).

Atrial electrogram (AEG) acquired with a high spatio-temporal resolution is a promising approach for early detection of atrial fibrillation. Due to the high data rate, transmission of AEG signals requires considerable energy, making its adoption a challenge for low-power wireless devices. In this paper, we investigate the feasibility of using compressed sensing (CS) for the acquisition of AEGs while reducing redundant data without losing information. We apply two CS approaches, standard CS and rakeness-based CS (rak-CS) on real medical recordings. We find that the AEGs are compressible in time, and, more interestingly, in the spatial domain. The performance of rak-CS is better than standard CS, especially at higher compression ratios (CR), both during sinus rhythm (SR) and atrial fibrillation (AF). More specifically, the difference in the achieved average reconstruction signal-to-noise (ARSNR) in rak-CS and standard CS, for CR = 4.26, in the time domain is 7.7 dB and 2.6 dB for AF and SR, respectively. Multi-channel data is modeled as a multiple-measurement-vector problem and a suitable mixed norm is used to exploit the group structure of the signals in the spatial domain to obtain improved reconstruction performance over l1norm minimization. Using the mixed-norm recovery approach, for CR = 4.26, the difference in achieved ARSNR performance between rak-CS and standard CS is 5 dB and 2 dB for AF and SR, respectively.

Chaining, i.e., the mode of operation in which each message is encrypted considering a digital summary of previous ones, is here applied to block-cipher stages based on compressed sensing. We show that this simple and parsimonious technique may significantly harden the resulting system with respect to common threats such that ciphertext-only, known-plaintext, and man-in-the-middle attacks. Non-negligible robustness comes at the price of not more than a 2% of energy overhead with respect to the pure compression stage which represents a 24× reduction with respect to straightforward implementation of a traditional cryptography primitive like Advanced Encryption Standard.

Presents the introductory editorial for this issue of the publication.

Compressed Sensing (CS) has recently emerged as an effective way to simultaneously acquire, compress and possibly encrypt incoming signals in low-resource sensing devices. We here show that CS can be suitably exploited to add disturbance rejection properties, similar to those which are classically obtained by means of suitably designed and deployed signal conditioning stages. This may render such additional stages unnecessary and therefore substantial decrease both system complexity and energy requirements. An example dealing with electrocardiographic signals is developed in which the classical base-line and power-line disturbances are almost entirely rejected with no need of ad-hoc filters.

The incoming Internet of Things revolution requires the adoption of innovative paradigms for the design of low-power ubiquitous sensor nodes. This can be achieved by exploiting Compressed Sensing (CS), that is a recently introduced approach capable of simultaneously sampling and compressing an input signal with a limited amount of resources. While the underlying basic theory is well developed, in recent years we have seen a flourishing of CS techniques capable of exploiting some additional priors on the input signal to improve performance. In this paper, we propose a survey and a comparison of the most promising ones. We use a classification mechanism based on which prior is used and which processing block is modified with respect to the standard CS.

In common distributed sensing scenarios, a number of local wireless sensor networks perform sets of acquisitions that must be sent to a central collector which may be far from the measurement fields. Hence, readings from individual nodes may reach their destination by exploiting both local and long-range transmission capabilities. The compressed sensing (CS) paradigm may help finding a convenient mix of the two options, especially if it follows the rakeness-based design flow that has been recently introduced. CS is exploited by identifying local hubs that aggregate many sensor readings in a smaller number of quantities that are then transmitted to the central collector. We here show that, depending on the relative cost of local versus long-range transmission, carefully administering the choice of the hubs, the breadth of the neighborhood from which they collect readings, as well as the coefficients with which those readings a linearly aggregated, one may significantly reduce the energy needed to sample the field. Simulations indicate that savings may be over 50% for values of the parameters modeling nowadays local and long-range transmission technologies.

In the years since the launch of the World Wide Web in 1993, there have been profoundly transformative changes to the entire concept of publishing—exceeding all the previous combined technical advances of the centuries following the introduction of movable type in medieval Asia around the year 10001and the subsequent large-scale commercialization of printing several centuries later by J. Gutenberg (circa 1440). Periodicals in print—from daily newspapers to scholarly journals—are now quickly disappearing, never to return, and while no publishing sector has been unaffected, many scholarly journals are almost unrecognizable in comparison with their counterparts of two decades ago. To say that digital delivery of the written word is fundamentally different is a huge understatement. Online publishing permits inclusion of multimedia and interactive content that add new dimensions to what had been available in print-only renderings. As of this writing, the IEEE portfolio of journal titles comprises 59 online only2(31%) and 132 that are published in both print and online. The migration from print to online is more stark than these numbers indicate because of the 132 periodicals that are both print and online, the print runs are now quite small and continue to decline. In short, most readers prefer to have their subscriptions fulfilled by digital renderings only.

The need for fast and strong image cryptosystems motivates researchers to develop new techniques to apply traditional cryptographic primitives in order to exploit the intrinsic features of digital images. One of the most popular and mature technique is the use of complex dynamic phenomena, including chaotic orbits and quantum walks, to generate the required key stream. In this paper, under the assumption of plaintext attacks we investigate the security of a classic diffusion mechanism (and of its variants) used as the core cryptographic primitive in some image cryptosystems based on the aforementioned complex dynamic phenomena. We have theoretically found that regardless of the key schedule process, the data complexity for recovering each element of the equivalent secret key from these diffusion mechanisms is only (1). The proposed analysis is validated by means of numerical examples. Some additional cryptographic applications of this paper are also discussed.

Compressed sensing has been proposed to both yield low-cost compression and low-cost encryption. This can be very useful in the design of sensor nodes with a limited resource budget whose acquisition must be kept as private as possible. We here analyze the susceptibility of compressed sensing stages that are optimized to maximize compression performance by rakeness-based design to ciphertext-only and known-plaintext attacks. A tradeoff between compression and security is highlighted. Notwithstanding such a tradeoff, rakeness-based compressed sensing exhibits a noteworthy robustness to classical attacks.

Compressed Sensing (CS) is an effective way to sample a signal at a sub-Nyquist rate, i.e., by using a number of measurements smaller than the number of samples required when using the standard Nyquist approach. Measurements are obtained as linear projections of input signals along random sensing vectors. CS has been often regarded as a democratic method, in the sense that each measurement contributes to signal reconstruction with a similar amount of information. In this paper, by combining empirical observations with results from recent papers, we propose a different point of view, and show that CS is an oligarchic approach where performance is basically set by the measurements with the highest energy. This allows us to propose a new CS-based approach that bases the reconstruction on the maximum-energy measurements only and improves the compression performance with respect to classical approaches.

Wireless sensor nodes capable of acquiring and transmitting biosignals are increasingly important to address future needs in healthcare monitoring. One of the main issues in designing these systems is the unavoidable energy constraint due to the limited battery lifetime, which strictly limits the amount of data that may be transmitted. Compressed Sensing (CS) is an emerging technique for introducing low-power, real-time compression of the acquired signals before transmission. The recently developed rakeness approach is capable of further increasing CS performance. In this paper we apply the rakeness-CS technique to enhance compression capabilities for electroencephalographic (EEG) signals, and particularly for Evoked Potentials (EP), which are recordings of the neural activity evoked by the presentation of a stimulus. Simulation results demonstrate that EPs are correctly reconstructed using rakeness-CS with a compression factor of 16. Additionally, some interesting denoising capabilities are identified: the high-frequency noise components are rejected and the 60 Hz power line noise is decreased by more than 20dB with respect to the state-of-the-art filtering when rakeness-CS techniques are applied to the EEG data stream.

Compressed sensing, further to its ability of reducing resources spent in signal acquisition, may be seen as an implicit private-key encryption scheme. The level of achievable secrecy has been analyzed in the most classical settings, when the sensing matrix is made of independent and identically distributed entries. Yet, it is known that substantially improved acquisition can be achieved by tuning the statistics of such a matrix. The effect of such an optimization on the robustness with respect to classical cryptographic attacks is analyzed here.

Compressed Sensing (CS) has recently emerged as an interesting and effective way to sample an input signal and at the same time compress it (i.e., reduce the number of measurements for the correct signal reconstruction with respect to the standard Nyquist approach). We show here that CS can be used also to exploit some operations typically performed by the preceding signal conditioning stage (sometimes, by a post-processing stage). In detail, we show that CS can be used to filter environmental disturbances exactly like a notch filter. Furthermore, this solution presents advantages in terms of input signal distortion with respect to the classical notch filter approach. An example on electrocardiographic signal is presented as case study.

We report a low cost mobile EEG system for characterizing cortical auditory responses. The system is built using commercial off-the-shelf components and each unit costs less than $200. It measures seven EEG channels plus one audio channel (envelope only), and communicates the data to external devices via Bluetooth. A novel implementation was pursued in order to support local signal compression using compressed sensing. At the same time, it provides a low cost solution that is useful for recording cortical auditory responses and extracting clinically relevant features of the waveform. This system has been designed with the eventual goal of long term monitoring of the brain activity of schizophrenic patients outside a clinical setting, in order to better understand auditory hallucinations and manage their ongoing treatment. In this preliminary study we obtained simultaneous audio and cortical recordings of evoked auditory responses from normal healthy subjects wearing the EEG for several hours in duration. We report evoked auditory responses for 2 Hz and 40 Hz click trains. We also report alpha wave responses, demonstrating stable and high quality recordings over a five hour period.

The sensitivity of recovery algorithms with respect to a perfect knowledge of the encoding matrix is a general issue in many application scenarios in which compressed sensing is an option to acquire or encode natural signals. Quantifying this sensitivity in order to predict the result of signal recovery is therefore valuable when no a priori information can be exploited, e.g., when the encoding matrix is randomly perturbed without any exploitable structure. We tackle this aspect by means of a simplified model for the signal recovery problem, which enables the derivation of an average performance estimate that depends only on the interaction between the sensing and perturbation matrices. The effectiveness of the resulting heuristic is demonstrated by numerical exploration of signal recovery under three simple perturbation matrix models. Finally, we show how this estimate matches very well the degradation experienced by non-perfectly informed decoders in applications of compressed sensing to protecting the acquired information content in ECG tracks and sensitive images.

Power-Line Communication (PLC) systems represent a very interesting opportunity for introducing low-cost communication capabilities over already existing power-line wires. In this paper we introduce a PLC technique that can be applied to systems with a DC power bus that employ a switching power converter as main power supply unit. The proposed technique is extremely simple to be implemented, and requires only minor modifications on the main switching converter. As a proof of this, we are capable to implement the proposed PLC in a system composed by commercial DC-DC converter boards without any circuital modification to the boards themselves. Measurements on this test system show the capability to communicate up to about 80 kbit/s with a bit error rate so low as 10−5.

Resonant power converters represent a step further in the effort of increasing the operating frequency, and consequently the power density, with respect to conventional switching converter architectures. Nevertheless, resonant converters are used only in very specific applications. The main issue is their design that, being not based on a solid mathematical background, results in a non-trivial task. In this paper we present a prototype of a class-E resonant converter with a simplified architecture, allowing both a small size (and so a higher density) and a simple mathematical analysis. Conversely with respect to the state-of-the-art approach, the circuit design is obtained by means of a semi-analytic mathematical approach without any support from circuital simulation. Measurements confirm the performance expected according to the mathematical model, and prove that the design of circuits with the proposed architecture can be effectively achieved with the developed mathematical model.

This paper presents a new approach for the design of a class-E resonant dc-dc converter. The small number of passive components featured by the considered topology allows to exactly solve the differential equations regulating the circuit evolution, and to develop a semi-analytic design procedure based on the differential equations solution. This represents an important breakthrough with respect to the state-of-the-art, where class-E circuit analysis is always based on strong simplifying assumptions, and the exact circuit design is achieved by means of numerical simulations after many time-consuming parametric sweeps.

When transmission or storage costs are an issue, lossy data compression enters the processing chain of resource-constrained sensor nodes. However, their limited computational power imposes the use of encoding strategies based on a small number of digital computations. In this case study, we propose the use of an embodiment of compressed sensing as a lossy digital signal compression, whose encoding stage only requires a number of fixed-point accumulations that is linear in the dimension of the encoded signal. We support this design with some evidence that for the task of compressing ECG signals, the simplicity of this scheme is well-balanced by its achieved code rates when its performances are compared against those of conventional signal compression techniques.

Despite the linearity of its encoding, compressed sensing (CS) may be used to provide a limited form of data protection when random encoding matrices are used to produce sets of low-dimensional measurements (ciphertexts). In this paper, we quantify by theoretical means the resistance of the least complex form of this kind of encoding against known-plaintext attacks. For both standard CS with antipodal random matrices and recent multiclass encryption schemes based on it, we show how the number of candidate encoding matrices that match a typical plaintext-ciphertext pair is so large that the search for the true encoding matrix inconclusive. Such results on the practical ineffectiveness of known-plaintext attacks underlie the fact that even closely related signal recovery under encoding matrix uncertainty is doomed to fail. Practical attacks are then exemplified by applying CS with antipodal random matrices as a multiclass encryption scheme to signals such as images and electrocardiographic tracks, showing that the extracted information on the true encoding matrix from a plaintext-ciphertext pair leads to no significant signal recovery quality increase. This theoretical and empirical evidence clarifies that, although not perfectly secure, both standard CS and multiclass encryption schemes feature a noteworthy level of security against known-plaintext attacks, therefore increasing its appeal as a negligible-cost encryption method for resource-limited sensing applications.

The quest for optimal sensing matrices is crucial in the design of efficient Compressed Sensing architectures. In this paper we propose a maximum entropy criterion for the design of optimal Hadamard sensing matrices (and similar deterministic ensembles) when the signal being acquired is sparse and non-white. Since the resulting design strategy entails a combinatorial step, we devise a fast evolutionary algorithm to find sensing matrices that yield high-entropy measurements. Experimental results exploiting this strategy show quality gains when performing the recovery of optimally sensed small images and electrocardiographic signals.

In this work we combing two novelty in the area of Analog Information Converter based on Compressed Sensing. A new architecture, the Spread Spectrum Random Modulation PreIntegration and a new design flow, the rakeness based design of a Compressed Sensing system. We demonstrate that combining these approaches produces a strong reduction of the internal chipping frequency in the sensing coupled with a high compression ratio with respect to standard Analog to Digital Converter.

Radio communication is among the most energy consuming tasks in wireless sensor nodes. Reducing the amount of data to be transmitted holds a large power saving potential. The combination of compressed sensing (CS) and local signal parameter estimation can achieve a massive data rate reduction in applications where the primary interest is in the acquisition of a scalar feature of the signal rather than the reconstruction of the entire waveform. In this paper, We propose a compressed estimator, building upon an enhancement of the typical CS signal-modulation scheme via punctured sampling. Specifically, a subset of signal samples and associated weighting coefficients are chosen so as to minimize node power consumption while achieving a given estimation performance. We detail a corresponding puncturing algorithm and present the design of an integrated digital compressed estimation unit in 28nm FDSOI CMOS. In a concrete case study, local estimation combined with subsampling is shown to result in a power reduction of up to an order of magnitude with respect to the standard solution of sampling and transmitting samples for off-board processing.

We elaborate on the possibility of exploiting the (pseudo)random projection operator, which is at the heart of the most common architecture for compressed sensing, to prevent access to the acquired information by unauthorized receivers. In low-resource applications, this approach may make dedicated cryptographic layers unnecessary when the security requirement is not particularly high. Beyond proving that the proposed system is at least asymptotically immune to straightforward statistical attacks, we also exploit the sensitivity of compressed sensing recovery algorithms to the complete knowledge of the projection matrix to introduce two-class protection. The encoding is such that first-class decoders can retrieve the signal to its full resolution while second-class decoders are able to retrieve only a degraded version of the same signal. Examples are given with reference to ECG signal acquisition.

Classical design of Analog-to-Information converters based on Compressive Sensing uses random projection matrices made of independent and identically distributed entries. Leveraging on previous work, we define a complete and extremely simple design flow that quantifies the statistical dependencies in projection matrices allowing the exploitation of non-uniformities in the distribution of the energy of the input signal. The energy-driven reconstruction concept and the effect of this design technique are justified and demonstrated by simulations reporting conspicuous savings in the number of measurements needed for signal reconstruction that approach 50%.

In this work a novel approach for representing pulse width modulated (PWM) signals is introduced. PWM signals are usually represented according to the way they are generated, that is, by manipulating the sign of the comparison between the input signal and a reference wave. On the contrary, the new representation consists of a warped Fourier series, that is, a series of properly phase-modulated sinusoids, such that the zero-crossings of each warped harmonic comprehends the zero-crossing of the PWM signal. Yet, in contrast with the original signal, the spectrum of the resulting components decays exponentially, so they can be sampled at reasonable low rate while maintaining aliasing negligible. Being band-limited and keeping zero-crossings unaltered, this representation is suitable for computationally efficient PWM signal generation.

Though compressive sensing hinges on extracting linear measurements from the signals to acquire, actual implementations introduce nonlinearities whose effect can be far from negligible. We here address the problem of saturation in the circuit blocks needed by a Random Modulation Pre-Integration architecture. To allow a fair a comparison with previous analysis, we rely on a model capturing the essentials of saturations in actual implementations while being able to reproduce more abstract settings considered in the literature. Based on this, we analyze some methods already proposed to cope with simplified saturation mechanisms, briefly discussing their underlying principles. Finally, we introduce a novel approach that takes into account the more realistic model and, at the cost of an almost negligible hardware overhead, is extremely effective in countering saturation effects.

In this paper we review some statistical tests included in the NIST SP 800-22 suite, which is a collection of tests for the evaluation of both true-random (physical) and pseudorandom (algorithmic) number generators for cryptographic applications. The output of these tests is the so-called$p$-value which is a random variable whose distribution converges to the uniform distribution in the interval [0,1] when testing an increasing number of samples from an ideal generator. Here, we compute the exact non-asymptotic distribution of$p$-values produced by few of the tests in the suite, and propose some computation-friendly approximations. This allows us to explain why intensive testing produces false-positives with a probability much higher than the expected one when considering asymptotic distribution instead of the true one. We also propose a new approximation for the Spectral Test reference distribution, which is more coherent with experimental results.

We here address the problem of constructing sets of sequences with low integrated aperiodic auto- and cross-correlations when the constraint of antipodal symbols is enforced. Our method is based on Legendre Sequences and on the correlation properties of their rotations. Starting from this idea, an extremely lightweight procedure driven by asymptotic considerations yields sets of antipodal sequences that largely outperform known sequence families or algorithms, actually positioning extremely close to the performance upper bound.

Recently, proposals have been made for enhancing the Oscillation Based Test (OBT) methodology by using non-plain oscillation regimes, leading to so called Complex Oscillation Based Test (COBT). Here we focus on a recently illustrated strategy for the testing of analog 2ndorder filters, showing that the COBT dynamics is quite similar to that expressed by Resonate & Fire (R+F) neuron models. In this interpretation, the testing approach can be related to firing-rate measures. A brief description is given of the mathematical models necessary to achieve a precise characterization of firing times, showing how it can be used for testing purposes. A practical example with simulation data is also provided.

Analog to Information conversion is a new paradigm in signal digitalization. In this framework, compressed sensing theory allows to reconstruct sparse signal from a limited number of measures. In this work, we will assume that the signal is not only sparse but also localized in a given domain, so that its energy is concentrated in a subspace. We will present a formal and quantitative discussion to explain how localization of sparse signals can be exploited to improve the quality of the reconstructed signal.

Calibration or model parameter estimation from measured data is an ubiquitous problem in engineering. In systems biology this problem turns out to be particularly challenging due to very short data-records, low signal-to-noise ratio of data acquisition, large intrinsic process noise and limited measurement access to only a few, of sometimes several hundreds, state variables. We review state-of-the-art model calibration techniques and also discuss their relation to the general reverse-engineering problem in systems biology. For biomolecular circuits involving low-copy-number molecules we adopt a Markov process setup and discuss a calibration approach based on suitable metrics between probability measures and propose the metrics computation for the multivariate case. In particular, we use Kantorovich's distance and devise an algorithm, for the case when FACS (fluorescence-activated cell sorting) measurements are given. We discuss a case study involving FACS data for the high-osmolarity glycerol (HOG) pathway in budding yeast.

This paper presents a way to reduce the effect of narrowband interference (due to either an intentional jamming or the effect of traditional non-spread-spectrum transmission) in ultra-wideband (UWB) communication systems based on asynchronous direct-sequence code-division multiple access (DS-CDMA). To reach this goal, we derive a closed-form expression for the bit error probability in additive white Gaussian noise (AWGN) channel, where both multiple access and narrowband interference are the main cause of nonideality. By leveraging on this, we develop a new approach based on spectrum shaping of the spreading sequences waveforms which allows to significantly improve performance in many applicative scenarios.

Compressed sensing hinges on the sparsity of signals to allow their reconstruction starting from a limited number of measures. When reconstruction is possible, the SNR of the reconstructed signal depends on the energy collected in the acquisition. Hence, if the sparse signal to be acquired is known to concentrate its energy along a known subspace, an additional “rakeness” criterion arises for the design and optimization of the measurement basis. Formal and qualitative discussion of such a criterion is reported within the framework of a well-known Analog-to-Information conversion architecture and for signals localized in the frequency domain. Non-negligible improvements are shown by simulation.

Costs and difficulties associated to testing can be an Achilles' heel for modern large-scale mixed-mode systems. Here, a low-overhead technique is introduced to enable the design of analog filters capable of checking their own parameters. The approach can also be applied to add such functionality to existing filter designs. The case of a switched-capacitor 2ndorder band-pass stage is used for illustration. The approach is based on the recently introduced complex oscillation based test methodology and permits to appreciate not just deviations in characteristic frequencies, but also the merit factor (and thus bandwidth and roll-off).

Systems and synthetic biology are two emerging disciplines that hold promise to revolutionize our understanding of biological systems and to herald a new era of programmable hardware, respectively. Mathematical abstraction and today's abundance of quantitative biological data enables the up-scaling of analysis and design methodologies. In this tutorial paper we provide an engineering-centered introduction to those disciplines. Biological key concepts such as the central dogma of molecular biology are discussed, descriptions of bio-molecular reaction networks in terms of continuous-time Markov processes and ordinary differential equations are reviewed. Topological analysis of networks is introduced and the methods of metabolic flux balance analysis and elementary flux modes or extreme pathways are discussed.

In this paper we consider a side-channel attack on a chaos-based random number generator (RNG) based on power consumption analysis. The aim of this attack is to verify if it is possible to retrieve information regarding the internal state of the chaotic system used to generate the random bits. In fact, one of the most common arguments against this kind of RNGs is that, due to the deterministic nature of the chaotic circuit on which they rely, the system cannot be truly unpredictable. Here we analyze the power consumption profile of a chaos-based RNG prototype we designed in 0.35 mum CMOS technology, showing that for the proposed circuit the internal state (and therefore the future evolution) of the system cannot be determined with a side-channel attack based on a power analysis. This property makes the proposed RNG perfectly suitable for high-security cryptographic applications.

A low-noise amplifier for ultra wide band (UWB) applications is presented. The use of a dual-loop negative feedback topology is advantageous, since it allows to achieve both impedance matching and a very low noise figure, and saves a lot of chip area as no bulky inductors are needed. A nullor and a resistive feedback network are employed, and the values of the feedback elements involved are defined in order to fulfill the noise-figure, input impedance and power-gain requirements for an UWB receiver. To ensure circuit stability, frequency compensation is done by means of a phantom zero and the addition of a transistor connected between input and output, thus realizing a multipath structure. The design targets UMC 0.13mufrac14m CMOS IC technology and operation from a 1.2-volt supply. From circuit simulations, the power gain of the LNA amounts to 17dB, and the bandwidth spans up to 12 GHz. Su is below -lOdB up to 10 GHz and the noise figure is below 3dB up to 8 GHz, and below 4dB@10 GHz. The power consumption equals 14 mA. Compared to competitive solutions, using resonating load stages or LC ladder networks, this chip will be much smaller and cheaper; it will use standard CMOS technology, and achieve very low noise, high gain and wide band matching at reasonable power consumption.

The analysis of discrete-time two-valued processes is often addressed assuming they have at most the memory of one step in the past. We here relax this assumption and propose a generator of antipodal stochastic processes which relies on a linear probability feedback that implies a memory equal to that of the feedback Alter. For such a scheme an explicit spectrum formula is derived as well as a synthesis procedure going from a special type of spectrum specification to feedback filter design.

We show that any TRNG architecture based on the cascade of a noise source, an ADC, and a digital postprocessor can be simplified getting rid of the explicit noise source, by modifying the ADC to simultaneously act as both an entropy source and a data acquisition block. Though gains may vary, this is practicable with any ADC type and can be seen as the generalization of a recent proposal where a TRNG was obtained out of the1 + ½bit stages of a pipeline converter.

A chaos-based sequence generation method for reducing multiple access interference (MAI) in direct sequence UWB wireless-sensor-networks (WSNs) is presented, along with the schematic design in CMOS UMC130 1.2V technology of a modulator scheme, that takes into account both the transmitter and receiver antennas. Chaos-based spreading is numerically optimized to combine with the pulse profile (as obtained by extensive simulations) and maximizing the bit-rate (BR) at which each user may transmit given a certain link quality, measured as the signal-to-interference ratio (SIR); when compared with traditional random sequences, the BR increase is up to 30%.

Testing random number generators (RNGs) is as important as designing them. The paper considers the NIST test suite SP 800-22 and shows that, as suggested by NIST itself, to reveal non-perfect generators a more in-depth analysis should be performed using the outcomes of the suite over many generated sequences. Testing these second-level statistics is not trivial and, relying on a proper model that takes into account the errors due to the approximations in the first level tests, a tuning of the parameters in the simplest cases was propose. The validity of this consideration is widely supported by experimental results on several RNG currently employed by major IT players, as well as a chaos-based RNG designed by authors.

This paper aims at investigating the impact of non-white chopping on the offset compensation performance of time-interleaved analog-to-digital converters. The theoretical framework for selecting optimal chopping sequences is given allowing fast offset compensation and good spectral shaping. Both the signal-to-noise ratio and the spurious-free dynamic range are improved significantly.

The offset and gain error of ADCs represent two important limits for time-interleaved ADC architectures. The proposed method enables a precise measurement of the offset and the gain during system operation introducing a minimum disturb at the output. The method exploits the modulation of the input signal with a random sequence of +1 and -1. The random bit-stream is generated by using the wide-band output of a time-discrete non-linear circuit. The resulting spread spectrum signal can be easily distinguished by the DC offset and the use of a digital accumulator extracts the offset and the gain of each ADC. The input signal is finally reconstructed, in the digital domain, with a synchronous demodulation. The accumulation time can be as many clock periods (like 106) thus permitting an excellent accuracy in the offset and gain measurement. Simulations of the proposed approach at the behavioral level confirm the effectiveness of the method. It is shown that the offset of a 12-bit ADC can be measured with a 0.1 LSB accuracy

In this paper, we investigate the effect of spreading sequences on the peak-to-average power ratio (PAPR) in order to improve the power amplifier efficiency of multicarrier direct-sequence code-division multiple access systems. Baseband shaping has been identified to have a key role in reducing PAPR by spreading and we have found that chaos-based spreading sequences give good results as compared with Gold and i.i.d. sequences behaving differently depending on the number of subcarriers

We aim at comparing classical and chaos-based spreading sequences considering the Shannon capacity of the resulting system. Due to asynchronism, capacity turns out to be a random variable whose average can be computed once that the pdf of the eigen-values of a random matrix is known. We estimate such a pdf by Monte Carlo computation and try to fit it with a simple model. It turns out that the straightforward application of asymptotic results that hold for synchronous systems with random spreading is ineffective. Yet, a slight generalization of the profiles indicated by that theory allows a fitting of the empirical data with a relative error below 0.8% in all tested cases. Finally, by observing both the raw numerical evidence and the approximation based on pdf fitting, we are able to show that chaos-based spreading is able to produce a capacity increase with respect to classical codes designed to mimic purely random sequences.

We compare the limit performance of asynchronous DS-CDMA systems based on different sets of spreading sequences, namely chaos-based, ideal random, Gold codes and maximum-length codes. To do so, we consider the Shannon capacity associated to each of those systems that is itself a random variable depending on the relative delays and phases between different users. The statistical features of such a random capacity are evaluated by means of the Monte-Carlo simulation of a proper adjustment of the classical formula for vector AWGN channels. It is shown that chaos-based spreading results in a nonnegligible increase of the capacity.

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With the aim of explaining the formal development behind the chaos-based modeling of network traffic and other similar phenomena, we generalize the tools presented in the paper of Setti et al. (see ibid., vol.90, p.662-90, May 2002) to the case of piecewise-affine Markov maps with a possibly infinite, but countable number of Markov intervals. Since, in doing so, we keep the dimensionality of the space of the observables finite, we still obtain a finite tensor-based framework. Nevertheless, the increased complexity of the model forces the use of tensors of functions whose handling is greatly simplified by extensive z transformation. With this, a systematic procedure is devised to write analytical expressions for the tensors that take into account the joint probability assignments needed to compute any-order expectations. As an example of use, this machinery is finally applied to the study of self-similarity of quantized processes both in the analysis of higher order phenomena as well as in the analysis and design of second-order self-similar sources suitable for artificial network traffic generation.

The application of chaotic dynamics to signal processing tasks stems from the realization that its complex behavior becomes tractable when observed from a statistical perspective. Here we illustrate the validity of this statement by considering two noteworthy problems-namely, the synthesis of high-electromagnetic compatibility clock signals and the generation of spreading sequences for direct-sequence code-division communication systems, and by showing how the statistical approach to discrete-time chaotic systems can be applied to find their optimal solution. To this aim, we first review the basic mathematical tools both intuitively and formally; we consider the Perron-Frobenius operator its spectral decomposition and its tie to the correlation properties of chaotic sequences. Then, by leveraging on the modeling/approximation of chaotic systems through Markov chains, we introduce a matrix/tensor-based framework where statistical indicators such as high-order correlations can be quantified. We underline how, for many particular cases, the proposed analysis tools can be reversed into synthesis methodologies and we use them to tackle the two above mentioned problems. In both cases, experimental evidence shows that the availability of statistical tools enables the design of chaos-based systems which favorably compare with analogous nonchaos-based counterparts.

A BICMOS circuit is presented which can be electrically programmed to alter the probability density function of a random/chaotic PAM stream and to create a set of null probability. In an FM-DCSK communication system its adoption ahead of the FM modulator allows to place an interval of reduced power in the spectrum of the wideband transmitted signal in correspondence to the in-band multipath related nulls. The expected performance improvement over selective channels is confirmed by the numerical esteem of the bit error rate as a function of the bit-energy/noise-spectral-density ratio.

This paper deals with the optimization of the performance of DS-CDMA system by means of a proper choice of the spreading sequences among those offered by the chaos-based approach. Performance is here linked to the co-channel interference due to the presence of multiple asynchronous users. Optimization is performed considering random trials and looking at the performance achieved in each trial as an instance of a random variable whose average and variance can be combined to give hints on the most promising maps. The analytical results provided by a companion paper by the authors allows one to select the best candidates among a well characterized family of maps when small spreading factors are considered. The thorough optimization of the sequences produced by these best choices boosts the improvement in communication quality with respect to standard methods from the average 15% to a peak 68%.

This paper deals with the mathematical tools needed to optimize the performance of a chaos-based DS-CDMA system. The optimization exploits the degrees of freedom offered by the choice of a spreading sequence for each user. The impact of this choice depends on the map adopted and can be estimated by computing high-order autocorrelation functions of the spreading sequences, i.e. of the quantized version of chaotic trajectories. This computation can be effectively formulated and finalized within the tensor algebra framework proposed in this paper.

Chaos-based quasi-stationary frequency modulation has been shown to significantly reduce the electromagnetic interference due to possibly high frequency clock signals with respect to previously proposed and patented methods. Arguments are given highlighting the link between the spectrum of the modulated signal and the invariant probability density function of the chaotic system generating the modulating signal. Finally, experimental measurements are shown confirming the advantages of the proposed method and its applicability for different values of the victim apparatus bandwidth.