There is growing interest in applying statistical and deep learning-based imputation techniques to address missing values in physiological time series data. However, traditional evaluation metrics like RMSE often fail to capture the accuracy of imputed values, particularly for heart rate (HR) signals. Additional metrics such as MAE, MAPE, Cohen's Distance Test (CDT), and Jensen-Shannon Distance (JSD) can yield inconsistent evaluations, complicating the choice of optimal imputation methods for downstream prediction tasks. To address this, we propose FRAM-SHAP, a novel framework that combines multiple predictive and statistical distance metrics into a weighted metric. Weights are derived using XGBoost optimized with Optuna and interpreted via L1-normalized SHAP values, based on each metric's ability to determine whether imputed values fall within the same distributional interval as the original data. Further, robustness is evaluated using 10 bpm and 20 bpm HR based distributional intervals across two datasets, 'D1NAMO' and 'BIG IDEAs Lab Glycemic Variability and Wearable Device Data', capturing different degrees of HR variability. Thus, FRAMSHAP provides a flexible, adaptable framework for evaluating imputation quality in application specific contexts.

Abstract Forecasting the behaviour of industrial robots, power grids or pandemics under changing external inputs requires accurate dynamical models that can adapt to varying signals and capture long-term effects such as delays or memory. While recent neural approaches address some of these challenges individually, their reliance on computationally intensive solvers and their black-box nature limit their practical utility. In this work, we propose Laplace-Net, a decoupled, solver-free neural framework for learning forced and delay-aware dynamical systems. It uses the Laplace transform to (i) bypass computationally intensive solvers, (ii) enable the learning of delays and memory effects and (iii) decompose each system into interpretable control-theoretic components. Laplace-Net also enhances transferability, as its modular structure allows for targeted re-training of individual components to new system setups or environments. Experimental results on eight benchmark datasets–including linear, nonlinear and delayed systems–demonstrate the method’s improved accuracy and robustness compared to state-of-the-art approaches, particularly in handling complex and previously unseen inputs.

Chronic diseases, especially diabetes, are burdens for the patient since lifelong management is required, and comorbidities can occur as a consequence of insufficient prevention. Hypoglycemia, a medical condition encountered by diabetic individuals, can result in severe symptoms if untreated, necessitating prompt preventive actions. Continuous health monitoring based on data collected with wearables can enable the early prediction of extreme blood glucose states. However, integrating and using data acquired from various sensors is challenging, especially when it comes to maintaining the quality and quantity of data due to inherent noise and missing values. To this end, the review discusses dataset constraints and highlights the temporal behavior of prominent features in predicting hypoglycemia. It outlines a framework of preprocessing techniques that could be adopted to mitigate dataset limitations. A thorough analysis of the imputation procedures employed in the reviewed studies is conducted. In addition, machine learning imputation techniques applied in other healthcare domains are studied to investigate if they could be adopted to close the longer gaps of missing values in the datasets involved in the prediction of hypoglycemia. Based on a comprehensive evaluation of imputation techniques, a paradigm, Impute-Paradigm, is proposed and validated through a case study, enabling imputation tailored to variable duration time gaps. After analysing the reviewed studies, we recommend studying the rate of change of individual features and conclude that different time gaps of separate features should be treated with multiple imputation techniques. • Analysis of preprocessing and imputation techniques for wearable sensor data. • Analysis of temporal patterns in blood glucose, heart rate, and accelerometer data. • Comprehensive quantitative evaluation of various imputation techniques. • Recommendation of imputation techniques based on time gap size using Impute-Paradigm.

Approximate hardware trades acceptable error for improved performance and previous literature focuses on optimizing this tradeoff in the hardware. We show in this article that the application and the hardware can be co-optimized to achieve the best-quality-performance tradeoff. We propose LAC: learned approximate computing to optimize the algorithm and approximate hardware at the same time to maximize quality of output. Our approach allows automatic selection of approximate computing hardware while achieving similar quality as dedicated training for a single hardware configuration. Our improved training algorithm allows simultaneous hardware selection and application optimization without additional runtime overhead. Multihardware setup chooses a separate approximate hardware for each part of an application which allows for more hardware configurations and further improves quality.

Approximate hardware trades acceptable error for improved performance and previous literature focuses on optimizing this trade-off in the hardware. We show in this paper that the application (i.e., the software) can be optimized for better accuracy without losing any performance benefits of the approximate hardware. We propose LAC: learned approximate computing as a method of tuning the application parameters to compensate for hardware errors. Our approach showed improvements across a variety of standard signal/image processing applications delivering an average improvement of 5.82db in PSNR and 0.23 in SSIM of the outputs. This translates to up to 87% power reduction and 83% area reduction for similar application quality. LAC allows the same approximate hardware to be used for multiple applications.

Large efforts have focused on ensuring that the controllers for mobile service robots follow proxemics and other social rules to ensure both safe and socially acceptable distance to pedestrians. Nonetheless, involuntary contact may be unavoidable when the robot travels in crowded areas or when encountering adversarial pedestrians. Freezing the robot in response to contact might be detrimental to bystanders' safety and prevents it from achieving its task. Unavoidable contacts must hence be controlled to ensure the safe and smooth travelling of robots in pedestrian alleys. We present a force-limited and obstacle avoidance controller integrated into a time-invariant dynamical system (DS) in a closed-loop force controller that let the robot react instantaneously to contact or to the sudden appearance of pedestrians. Mitigating the risk of collision is done by modulating the velocity commands upon detecting a contact and by absorbing part of the contact force through active compliant control when the robot bumps inad-vertently against a pedestrian. We evaluated our method with a personal mobility robot -Qolo- showing contact mitigation with passive and active compliance. We showed the robot able to overcome an adversarial pedestrian within 9 N of the set limit contact force for speeds under 1 m/s. Moreover, we evaluated integrated obstacle avoidance proving the ability to advance without Incurring any other collision.

The teacher-student framework aims to improve the sample efficiency of RL algorithms by deploying an advising mechanism in which a teacher helps a student by guiding its exploration. Prior work in this field has considered an advising mechanism where the teacher advises the student about the optimal action to take in a given state. However, real-world teachers can leverage domain expertise to provide more informative signals. Using this insight, we propose to extend the current advising framework wherein the teacher would provide not only the optimal action but also a qualitative assessment of the state. We introduce a novel architecture, namely Advice Replay Memory (ARM), to effectively reuse the advice provided by the teacher. We demonstrate the robustness of our approach by showcasing our experiments on multiple Atari 2600 games using a fixed set of hyper-parameters. Additionally, we show that a student taking help even from a sub-optimal teacher can achieve significant performance boosts and eventually outperform the teacher. Our approach outperforms the baselines even when provided with comparatively suboptimal teachers and an advising budget, which is smaller by orders of magnitude. The contributions of our paper are 4-fold (a) effectively leveraging a teacher's knowledge by richer advising (b) introduction of ARM to effectively reuse the advice throughout learning (c) ability to achieve significant performance boost even with a coarse state categorization (d) enabling the student to outperform the teacher.

Prescribed burning, a common fire management practice is routinely carried out by government departments and land management agencies to reduce wildfire hazard. To understand and measure the impacts of these fires on the landscape, spectral data from Eucalypt trees of an Australian dry sclerophyll forest was captured pre- and post-burn at two spatial scales. Results of this research indicate that NDVI was able to detect significant epicormic growth in response to the burn event at the object scale. The timing of NDVI decrease and returns were comparable at the two spatial scales considered in this paper.

Low power is an imperative requirement for portable multimedia devices employing various signal processing algorithms and architectures. In most multimedia applications, human beings can gather useful information from slightly erroneous outputs. Therefore, we do not need to produce exactly correct numerical outputs. Previous research in this context exploits error resiliency primarily through voltage overscaling, utilizing algorithmic and architectural techniques to mitigate the resulting errors. In this paper, we propose logic complexity reduction at the transistor level as an alternative approach to take advantage of the relaxation of numerical accuracy. We demonstrate this concept by proposing various imprecise or approximate full adder cells with reduced complexity at the transistor level, and utilize them to design approximate multi-bit adders. In addition to the inherent reduction in switched capacitance, our techniques result in significantly shorter critical paths, enabling voltage scaling. We design architectures for video and image compression algorithms using the proposed approximate arithmetic units and evaluate them to demonstrate the efficacy of our approach. We also derive simple mathematical models for error and power consumption of these approximate adders. Furthermore, we demonstrate the utility of these approximate adders in two digital signal processing architectures (discrete cosine transform and finite impulse response filter) with specific quality constraints. Simulation results indicate up to 69% power savings using the proposed approximate adders, when compared to existing implementations using accurate adders.

In this paper, we propose a design paradigm for energy efficient and variation-aware operation of next-generation multicore heterogeneous platforms. The main idea behind the proposed approach lies on the observation that not all operations are equally important in shaping the output quality of various applications and of the overall system. Based on such an observation, we suggest that all levels of the software design stack, including the programming model, compiler, operating system (OS) and runtime system should identify the critical tasks and ensure correct operation of such tasks by assigning them to dynamically adjusted reliable cores/units. Specifically, based on error rates and operating conditions identified by a sense-and-adapt (SeA) unit, the OS selects and sets the right mode of operation of the overall system. The run-time system identifies the critical/less-critical tasks based on special directives and schedules them to the appropriate units that are dynamically adjusted for highly-accurate/approximate operation by tuning their voltage/frequency. Units that execute less significant operations can operate at voltages less than what is required for correct operation and consume less power, if required, since such tasks do not need to be always exact as opposed to the critical ones. Such scheme can lead to energy efficient and reliable operation, while reducing the design cost and overheads of conventional circuit/micro-architecture level techniques.

In this paper, we present a unified approach to an energy-efficient variation-tolerant design of Discrete Wavelet Transform (DWT) in the context of image processing applications. It is to be noted that it is not necessary to produce exactly correct numerical outputs in most image processing applications. We exploit this important feature and propose a design methodology for DWT which shows energy quality tradeoffs at each level of design hierarchy starting from the algorithm level down to the architecture and circuit levels by taking advantage of the limited perceptual ability of the Human Visual System. A unique feature of this design methodology is that it guarantees robustness under process variability and facilitates aggressive voltage over-scaling. Simulation results show significant energy savings (74%-83%) with minor degradations in output image quality and avert catastrophic failures under process variations compared to a conventional design.

In this paper we characterize the proprietary active scanning algorithms of several wireless network interface cards (WNICs) and driver combinations. We believe our experiments are the first of their kind to observe the complete scanning process as the WNICs probe all the channels in the 2.4GHz spectrum. We discuss the 1) channel probe order; 2) correlation of channel popularity during active scanning and access point (AP) channel deployment popularity; 3) number of probe request frames (PRFs) sent on each channel across WNICs; 4) amount of time spent on each channel across WNICs (dwell time); and 5) variation in scanning algorithms. The knowledge gained from profiling WNICs is of significant importance to numerous disciplines. It enables us to understand different implementations (hardware and software) of active scanning. The same knowledge can help lay a foundation for implementing active scanning in network simulators. It is generically considered in only one of the popular simulators. Finally, the results from our work can also radically influence research in link-layer handovers, effective deployment of access points (APs), securing wireless networks, etc.