Stride width is vital for gait stability, postural balance control, and fall risk reduction. However, estimating stride width typically requires either grounded cameras or a full kinematic body suit of wearable inertial measurement units (IMUs), both of which are often too expensive and time-consuming for clinical application. We thus propose a novel data-augmented deep learning model for estimating stride width in individuals with and without neurodegenerative disease using a minimal set of wearable IMUs. Twelve patients with neurodegenerative, clinically diagnosed Spinocerebellar ataxia type 3 (SCA3) performed over ground walking trials, and seventeen healthy individuals performed treadmill walking trials at various speeds and gait modifications while wearing IMUs on each shank and the pelvis. Results demonstrated stride width mean absolute errors of 3.3±0.7cm and 2.9±0.5cm for the neurodegenerative and healthy groups, respectively, which were below the minimal clinically important difference of 6.0cm. Stride width variability mean absolute errors were 1.5cm and 0.8cm for neurodegenerative and healthy groups, respectively. Data augmentation significantly improved accuracy performance in the neurodegenerative group, likely because they exhibited larger variations in walking kinematics as compared with healthy subjects. These results could enable clinically meaningful and accurate portable stride width monitoring for individuals with and without neurodegenerative disease, potentially enhancing rehabilitative training, assessment, and dynamic balance control in clinical and real-life settings.

The emergence of low-latency wireless connectivity has opened significant new possibilities for human-machine interaction (HMI) systems, in which a human operator (HO) manipulates a remotely located robot or controls an object. However, data transmission, particularly over wireless links, suffers from impairments, such as random latency fluctuations and packet loss, affecting the overall control performance of an HMI system. In this study, we evaluate the impact of wireless impairments for a closed-loop joystick-controlled trajectory tracking task with two different types of feedback, visual and tactile. Wireless links are used both in the uplink (transmission of command signal) and the downlink (transmission of feedback signal). The effects of wireless impairments were incorporated by artificially introducing latency, jitter, and packet loss, both in the uplink and the downlink. As a baseline, we tested the system in ideal conditions with no latency and packet loss, comparing performance with configurations with packet loss combined with constant latency, as well as real-life measured latency profiles over wireless networks. The results obtained across 12 ablebodied participants showed that the tracking performance was better with visual feedback than with tactile feedback across all impairment conditions. However, the random fluctuations around the mean latency did not affect performance significantly.

The multichannel SAR system is an advanced imaging technique that enables achieving high resolution and wide swath simultaneously. However, the fundamental hindrance in the multichannel SAR system is the failure of reception by the multiantenna system due to being in hostile working conditions. As a result of this, the received multichannel SAR data often suffer from the presence of missing samples in the azimuth domain. Besides this problem, the multichannel SAR system also has to deal with the azimuth ambiguities to obtain high azimuth resolution and wide swath. This kind of received multichannel SAR data consisting of missing azimuth samples and azimuth ambiguities cannot be processed with the conventional multichannel SAR algorithms. Therefore, we propose a novel multichannel reconstruction method to address both challenges, enabling imaging of wide swath width with high resolution from the received incomplete multichannel SAR data. Our method uses the minimum variance distortionless response (MVDR) algorithm to reconstruct an unambiguous wide azimuth spectrum. In the end, we have provided simulation results that validate the efficiency of the proposed reconstruction method.

Cable-Driven Parallel Robots (CDPRs) utilize cables as actuators to maneuver rigid body links in a parallel mechanism setup. Typically, CDPR kinematic and dynamic models avoid cable-object (cable-link and cable-obstacle) interferences to prevent sudden changes in cable tension that could deviate the end-effector's trajectory. However, allowing these interferences can lead to cable wrapping, where cables wrap around complexshaped surfaces upon contact, enhancing the CDPR's workspace and reducing its footprint. Despite the potential benefits, there currently exists no kinematic and dynamic model that effectively incorporates cable wrapping around such complex-shaped surfaces. This paper introduces a novel numerical-based kinematic and dynamic modeling framework for CDPRs that detects and then manages cable wrapping around multiple links and obstacles with the assumption that the cables remain taut and maintain contact from the initial cable entry point to the final exit point on the surfaces. Through simulations and hardware tests on various shaped links and obstacles, it has been demonstrated that this framework can be efficiently and effectively used for modeling of cable wrapping, significantly enhancing CDPR functionality in complex environments.

In vehicular networks (VNets), ensuring both road safety and dependable network connectivity is of utmost importance. Achieving this necessitates the creation of resilient and efficient decision-making policies that prioritize multiple objectives. In this paper, we develop a Variational Quantum Circuit (VQC)-based multi-objective reinforcement learning (MORL) framework to characterize efficient network selection and autonomous driving policies in a vehicular network (VNet). Numerical results showcase notable enhancements in both convergence rates and rewards when compared to conventional deep-Q networks (DQNs), validating the efficacy of the VQC-MORL solution.

This paper comments on a recently published TPAMI paper presenting an iris recognition algorithm. While the approach is intriguing, we can identify several inconsistencies and errors in the exposition. Additionally, their comparison with the state-of-the-art methods lacks fairness. I take this opportunity to clarify and underline these errors, aiming to assist fellow researchers like me who are interested in advancing biometrics research.

This paper deals with the usage of a magnetic field oriented formulation that is derived from a mixed formulation using the penalty method, which reduces the problem to one equation. To achieve accurate results the ocurring penalty factor has to be chosen properly. For this reason, a rule to select this parameter is developed for linear and nonlinear material laws. It is shown that it yields good results when compared to measurements provided by the TEAM problem 13.

This project aims to develop a 3D printer specifically designed for producing prototypes with a volume of 3 cubic centimeters or more. Our primary focus is on leveraging the latest technology and innovative methods to facilitate quick modifications and customizations of designs while minimizing material waste. The project will incorporate a blend of cuttingedge manufacturing techniques, precise engineering, and ecofriendly practices to ensure that the printer performs optimally and sustainably. A key highlight of our project is the commitment to environmental sustainability, particularly through the incorporation of e-waste recycling. The printer's design will prioritize the use of recyclable and biodegradable materials, as well as energy-efficient components, to significantly reduce the environmental impact of the manufacturing process and contribute to the development of greener production practices.By combining these elements, we intend to create a 3D printer that is not only highly efficient and accurate but also easy to use and adaptable to various industries and applications. The printer will address common challenges in the field of rapid prototyping, such as speed, precision, and resource efficiency. Our approach will involve the integration of advanced software and hardware solutions to streamline the design-to-production process, making it more accessible for users to implement changes and produce high-quality prototypes swiftly.Ultimately, this project seeks to revolutionize the way prototypes are made, offering a solution that combines speed, accuracy, and sustainability. We believe that our advanced 3D printer, with its emphasis on e-waste recycling, will inspire new ideas and innovative manufacturing techniques, paving the way for more sustainable and efficient production methods across various sectors. Through this endeavor, we hope to set new standards in the field of 3D printing and contribute to a more environmentally conscious future.

The networking industry is offering new services leveraging recent technological advances in connectivity, storage, and computing such as mobile communications and edge computing. In this regard, extended reality, a term encompassing virtual reality, augmented reality, and mixed reality, can provide unprecedented user experience and pioneering service opportunities such as: live concerts, sports, and other events; interactive gaming and entertainment; immersive education, training, and demos. These services require high-bandwidth, low-latency, and reliable connections, and are supported by next-generation ultrareliable and low-latency communications in the vision of 6G mobile communication systems. In this work, we devise a novel scheme, called backup from different data centers with multicast and adaptive bandwidth provisioning, to admit reliable, lowlatency, and high-bandwidth extended reality live streams in next-generation networks. We consider network services where contents are non-cacheable and investigate how backup services can be offered by different data centers with multicast and adaptive bandwidth provisioning. Our proposed service-provisioning scheme provides protection not only against link failures in the physical network but also against computing and storage failures in data centers. We develop scalable algorithms for the service-provisioning scheme and evaluate their performance on various complex network instances in a dynamic environment. Numerical results show that, compared to conventional serviceprovisioning schemes such as those seeking backup services from the same data center, our proposed service-provisioning scheme efficiently utilizes network resources, ensures higher reliability, and guarantees low latency; hence, it is highly suitable for extended reality live streams.

Force myography (FMG) measures the movement of limbs or appendages by monitoring force at their surface to characterize the state of the underlying musculotendinous complex. Compared to electromyography methods, FMG offers a better alternative for monitoring the muscular activity due to direct measurement of force, less sensitivity to skin preparation and skin impedance, and high compatibility with wearable prosthetics and orthotics. Despite significant progress in developing FMG systems, existing devices are still bulky and restrictive to the user or to the placement of the exoskeleton systems. In this work, we develop a wristband integrating an array of ten skin-conformal and wearable strain sensors based on laser induced graphene optimized for continuous measurement of FMG signals. We characterize the device to identify several hand gestures and tasks while simultaneously using an optical camera-based hand tracking system to estimate the joint locations for ground truth generation. We develop machine learning models to predict the gestures as well as specific hand joint angles with a high accuracy (> 90% and > 95%, respectively). We find that sensors that are placed closer to actuation specific anatomical features contribute more towards the high accuracy. We also integrate the sensor array with a wearable readout system that wirelessly transmits the data in real-time, which is used to control a robotic arm as a proof of concept for human-robot interaction applications. The developed skin-conformal FMG device is expected to find wide applications in rehabilitation, sports sciences, and humancomputer interaction, paving the way for low profile prosthetic and orthotic control systems.

Integrated voltage regulator (IVR) based power delivery is becoming a necessity in high-performance computing systems. Embedded inductors are key components in such power delivery architectures. In this paper, novel package-embedded inductors using the dual-core spiral topology are designed and demonstrated for 48V-to-1V and 12V-to-1V power conversion applications. This topology consists of a spiral-shaped conductor winding layer sandwiched between dielectric and magnetic core layers. The conductor and dielectric layers separate the cores and act as an air gap for the magnetic flux of the spiral inductor. The air gap causes an increase in the saturation current performance of the inductor. In addition to the single spiral winding, two advanced spiral inductor configurations, namely two spiral windings in series and parallel, are explored. To enable design, monomial analytical expressions for inductance calculations are provided. To evaluate the performance of these designs, three inductors are fabricated with two different magnetic core materials and air gaps. For the large-signal characterization of the fabricated inductors, a current probe-based small-signal resistance scaling methodology is presented. Overall, packageembedded dual-core spiral inductors with a performance of 115 nH inductance with 5 A saturation current and 300 nH inductance with 3.5 A saturation current, occupying a 9 mm 2 area and having 75% to 80% efficiency, are demonstrated.

Generative Artificial Intelligence (AI) represents a significant advancement in AI, enabling the creation of synthetic data that closely mimics real data. This article provides a comprehensive overview of generative AI, including its definition and core concepts such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models. It explores applications across industries, including content creation, data augmentation, healthcare, gaming, fashion, finance, retail, cybersecurity, transportation, energy, education, entertainment, law, agriculture, real estate, and manufacturing. The article discusses future prospects of generative AI, highlighting enhanced creativity, improved human-AI collaboration, and personalized experiences. It also addresses dependencies and challenges, including data quality, computational resources, and ethical considerations. The benefits for businesses adopting generative AI are examined, emphasizing competitive advantage, enhanced customer experience, accelerated innovation, and improved decision-making. The article concludes by outlining the potential return on investment through increased efficiency, revenue growth, market expansion, and risk mitigation.

The aim of this study, therefore, is to reinvent the future of computing systems in terms of performance, efficiency, and adaptability by identifying the "frontier" of AI-driven innovations in CPU design. * This document surveys front-end optimizations driven by AI, new architectural ideas, and upcoming paradigms that might revolutionize CPU technology through a thorough literature review as well as empirical evaluations. * With the help of advanced artificial intelligence methods like as machine learning, deep learning, and reinforcement learning, the exploration of new CPU architectures and optimization techniques permits the unleashing of computational powers beyond anything previously imagined. * AI methods on CPU architecture and address significant concerns such as scalability, energy efficiency, security, and reliability have been systematically explored. Furthermore, the transformative ability of AI-driven CPUs is also illustrated through real-world-case applications in several fields, e.g. autonomous systems, healthcare informatics, edge computing. This paper helps clear the roadmap for the next system of creative revolution in AI-imbued computing, defining potential directions for additional research and cooperation.

In conventional remote sensing change detection (RS CD) procedures, extensive manual labeling for bi-temporal images is first required to maintain the performance of subsequent fully supervised training. However, pixel-level labeling for CD tasks is very complex and time-consuming. In this paper, we explore a novel self-supervised contrastive framework applicable to the RS CD task, which promotes the model to accurately capture spatial, structural, and semantic information through domain adapter and hierarchical contrastive head. The proposed SSLChange framework accomplishes self-learning only by taking a single-temporal sample and can be flexibly transferred to mainstream CD baselines. With self-supervised contrastive learning, feature representation pre-training can be performed directly based on the original data even without labeling. After a certain amount of labels are subsequently obtained, the pre-trained features will be aligned with the labels for fully supervised finetuning. Without introducing any additional data or labels, the performance of downstream baselines will experience a significant enhancement. Experimental results on 2 entire datasets and 6 diluted datasets show that our proposed SSLChange improves the performance and stability of CD baseline in data-limited situations. The code of SSLChange will be released at https: //github.com/MarsZhaoYT/SSLChange

Malaria is a serious and potentially deadly vector-borne disease that affects millions of people around the world. According to the latest report by the World Health Organization, there are an estimated 247 million cases of malaria worldwide. Malaria is caused by Plasmodium parasites and is transmitted by the bite of Anopheles mosquitoes. Early detection of Malaria is crucial to detect to treat the same early and prevent any potential spread that may cause serious fatalities. In the past, there are many approaches reported that uses machine learning approaches for early detection of Malaria from images. But still there are avenues where the detection accuracies are less and may need more advanced models for easy and accurate detection. This work proposes an approach for automated classification of Malaria from cell images using Convolutional Neural Networks (CNN). When compared with some state-of-the-art approaches, our proposed approach shows better classification accuracy and outperformed them.

Reversely computed dynamic temporary weights introduce a novel and significant enhancement to the adaptability and accuracy of large language models. By dynamically recalculating the weights of key hidden layers during inference, our methodology significantly improves the model's performance across various natural language processing tasks. Experimental results demonstrated substantial increases in accuracy, response time, and computational efficiency when compared to the baseline performance. The integration of dynamic weights enabled the model to adjust its internal parameters in real-time, resulting in more precise and context-aware predictions. Statistical analysis confirmed the significance of these improvements, providing robust validation for the proposed enhancements. This research not only advances the state-of-the-art in language model optimization but also paves the way for more intelligent and adaptable AI systems. Future work will address computational overhead and explore broader applicability to other neural network architectures.

Integrated electrical and heating systems(IEHS) are drawing huge attention because of their cost-effective and flexible operational capabilities. Optimal dispatch of renewable units in an IEHS is challenging because of their intermittent and stochastic nature. Uncertainties in the renewable energy source (RES) forecast, electrical, and thermal loads can cause voltage and temperature violations compromising the operational security of the system. In this paper, a rule-based dynamic dispatch is proposed to handle the increasing levels of uncertain RES and ensure the system's security. A chance-constrained (CC) co-optimization algorithm considering the uncertain loads and RES is developed. In the proposed method, electric boiler power and curtailment of RES are dynamically dispatched to reduce operational costs and network losses. The developed method is tested on the IEEE 15 bus distribution system and UK Generic Distribution System (UKGDS) 95 bus test system models. The results show that the proposed method significantly reduces the voltage deviations and temperature violations compared to the deterministic case.

A general-purpose instrumentation amplifier must be dc-coupled and have a differential input to handle both differential and single-ended input signals. It also must exhibit low input noise in both voltage and current, to accommodate a wide range of signal source impedances. Additionally, having a differential output is desirable to allow direct connection to current high-resolution analog-to-digital converters (ADCs) which have differential inputs. There are commercially available devices with \(e_n\) voltage noise spectral densities as low as 1 nV √Hz ⁄ but present high \(e_n\) current noise spectral densities \(i_n\) of a few pA/√Hz. On the other hand, there are also devices with \(i_n\) as low as a few fA/√Hz but presenting  \(e_n\)   around 10 nV/√Hz. To obtain low values of both \(e_n\) and \(i_n\) , a fully differential circuit topology combining discrete Junction Field Transistors (JFET) and Operational Amplifiers (OA) is proposed. Design equations, stability analysis, and experimental results are presented. As an example, a fully differential instrumentation amplifier has been designed, built, and tested showing \(e_n\) <1 nV/√Hz @1 kHz and \(i_n\) <10 fA/√Hz @1 kHz. The proposed topology finds applications such as front ends for measuring and testing instruments, industrial instrumentation, and audio circuits.