This study investigates an innovative method to combat ransomware, leveraging the New Technology File System (NTFS) spare space in Windows operating systems. The focus is on RansomCillin, a tool designed for effective data recovery after ransomware attacks. The methodology entailed a simulated environment using prevalent ransomware strains to assess RansomCillin’s file recovery success, time efficiency, data integrity, and system performance impact. Results showed RansomCillin’s high effectiveness in restoring encrypted files with minimal system disruption. Despite its promising performance, the study is limited to NTFS file systems and specific ransomware families, indicating the need for broader application in future research. This work suggests a paradigm shift in ransomware mitigation strategies, emphasizing proactive recovery and continuous adaptation to evolving cyber threats. RansomCillin’s development and testing highlight its potential as a practical solution in the ongoing battle against ransomware.

Molecular communication via diffusion (MCvD) expects Brownian motions of the information molecules to transmit information. However, the signal propagation largely depends on the geometric characteristics of the assumed flow model, i.e., the characteristics of the environment, design, and position of the transmitter and receiver, respectively. These characteristics are assumed to be lucid in many ways by either consideration of one-dimensional diffusion, unbounded environment, or constant drift. In reality, diffusion often occurs in blood-vessel-like channels. To this aim, we try to study the effect of the biological environment on channel performance. The Red-Blood Cells (RBCs) found in blood vessels enforces a higher concentration of molecules towards the vessel walls, leading to better reception. Therefore, in this paper we derive an analytical expression of Channel Impulse Response (CIR) for a dispersion-advection-based regime, contemplating the influence of RBCs in the model and considering a point source transmitter and a realistic design of the receiver.

A document by Aniruddha Das . Click on the document to view its contents.

Radar-based human activity recognition (HAR) is a popular research field. Despite claims of high accuracy on self-collected datasets, the ability of these models to handle unexpected scenarios has been largely overlooked. This work introduces a framework for analyzing corruption robustness of radar micro-Doppler spectrogram classification. A set of corruptions are categorized, applied, and systematically tested on common model architectures. Diverse training methods, including adversarial training, cadence velocity diagram (CVD) transformation and data augmentation, are explored. The performance is evaluated on two tasks: indoor HAR and continuous aquatic HAR. Our study unveils several insights. Firstly, relying solely on accuracy may not adequately assess model performance due to dataset limitations. All well-trained models exhibit sensitivity to corruptions. Secondly, deeper convolutional neural network (CNN) models excel in both accuracy and robustness, but confront the problem of overfitting to background. Thirdly, adversarial training enhances robustness against corruptions, albeit at the cost of a slight decrease in accuracy. Lastly, combining data augmentation and adversarial training achieves a balance between accuracy and robustness. In essence, our study contributes to a more profound understanding of the complex interplay between model architecture, classification accuracy, and corruption robustness in radar HAR tasks.

This paper introduces a low-cost, easy-tomanufacture, dual polarization reconfigurable reflectarray antenna based on Liquid Crystal (LC) that operates at W-band. The antenna is electrically large and is capable of independently steering the beam of two orthogonal polarizations. Two different implementations of single-layer unit cells (single resonant and multi-resonant) capable of providing suitable phase range to independently control the two RF polarizations with enough isolation have been investigated. The single resonant cell was finally used to design, manufacture and test a complete reflectarray antenna made of 55×55 elements, for which an accurate and efficient modeling of the cells was implemented. The effect of the LC bias lines is minimized by following a thorough impact study. At a cell level, the measurements validate both the modeling and the capability of the LC to be locally biased at the same cell provided that the biasing network and the resonators of each polarization are properly distributed. At an antenna level, the  measurements are predicted by simulations with excellent accuracy, which validates the design and modeling process. The measured antenna shows 35º of 1D scanning range with 25dBi gain and a maximum SLL of -9dB in the entire range for both polarizations at 98 GHz

Walking on uneven terrain is a common task throughout daily living and can cause changes in lower-limb kinematics and mechanics. Joint loading, while difficult to measure experimentally, may also increase on uneven terrain, which could contribute to the development of joint pain and debilitating conditions. This study investigated how lower-limb joint loading is affected during a step on coronally-uneven terrain and during the subsequent recovery step using data from individuals without mobility impairments (n=5) and musculoskeletal modeling and simulation, which can be used to reliably estimate joint contact forces. The simulations indicated changes in joint loading during both the uneven and recovery steps on uneven terrain compared to level-ground walking, and these changes varied throughout stance. During both the uneven and recovery steps, the largest increases in peak joint contact forces and impulses were at the ankle joint during early stance. These results suggest that individuals may rely more on early compensations at the ankle than compensations at the hip or knee throughout stance. Future work should include populations with mobility impairments and investigate if different compensatory strategies or interventions can influence the outcomes.

In the rapidly evolving landscape of software development, Artificial Intelligence (AI) emerges as a pivotal force, reshaping traditional methodologies and introducing groundbreaking efficiencies. This research delves into the integration of AI in software development, aiming to elucidate its transformative impact on coding practices, project management, and quality assurance. We commence with a comprehensive literature review, highlighting the historical progression and current state of AI applications in software development. Our methodology encompasses a blend of qualitative and quantitative analyses, including case studies of AI-driven projects and comparative evaluations of AI tools in real-world scenarios. The core of our research focuses on key AI technologies such as machine learning algorithms and natural language processing, examining their roles in automated code generation, testing, and debugging. The results reveal significant enhancements in efficiency, accuracy, and complexity management, attributable to AI integration. However, our discussion also addresses the challenges and ethical considerations that accompany AI adoption, emphasizing the need for balanced and responsible approaches. Looking forward, we explore emerging trends and potential future advancements in AI, proposing avenues for further research. This study not only underscores the transformative potential of AI in software development but also serves as a guiding framework for practitioners and researchers in navigating this dynamic field. Â

A document by Tianyi Wang . Click on the document to view its contents.

A document by Federica Bellizio . Click on the document to view its contents.

Effective model compression plays a pivotal role in mitigating the computational and interpretational challenges inherent in the domain of time series forecasting. In this study, we introduce an innovative data-centric methodology tailored to identify a representative data subset from the entirety of the dataset. This chosen representative segment forms the cornerstone for the training of proficient time series forecasting models. Furthermore, our investigation unveils a compelling outcome of this approachâ\euro”a substantial reduction in the size of time series forecasting models when trained with this selected representative data segment. This model compression strategy results in a remarkable 56.31% decrease in storage consumption, a discovery of considerable significance for optimizing resources and enhancing scalability in time series forecasting. By distilling the dataset to its fundamental components through our data-centric approach, we aim to enhance both computational efficiency and the interpretability of the resultant models. This paper introduces a pioneering technique to tackle the challenges associated with data volume and model complexity in the field of time series forecasting, offering potential pathways for more efficient and insightful modelling in this domain.

We present a novel high-accuracy rotary magnetic sensor system composed of a two-track coded multi-pole magnet and a dual-spot multi-axes magnetic sensor. The novelty is the measurement of two orthogonal magnetic field components in each of the two sensing spots of the sensor, one associated with each magnet track. As the two orthogonal signals in each spot are naturally in quadrature, i.e. they represent a sine and a cosine signal, the measurement principle is virtually independent of the magnet pole size and pitch. We can therefore design a magnet with much larger poles which in turn generate stronger magnetic flux, allowing for an increased air gap between sensor and magnet. The calibrated encoder system was characterized to deliver 13 bits of absolute accuracy and 17 bits of resolution over the full 360° range. Article ACCEPTED for publication in 2023 IEEE Sensors.

In order for tokenized asset networks to be accountable as Web3 marketplaces for token-related transactions, identity verification must be conducted by gateways into those token networks. This includes the identity validation and legal status verification of the originators and beneficiaries, the gateway owners/operators, and other relevant service providers. The classic identity provider model could be enhanced to support anti-money laundering regulations, notably the Travel Rule. A privacy-preserving IdP model in combination with a legal service provider is explored where the IdP becomes the issuer of a blinded attestation regarding the user attribute, and where the legal representative with attorney-client privilege becomes the first point of contact for requests for the disclosure of the blinded attestations.

The kVA limit of a power electronic converter (PEC) is often limited to keep the junction temperature Tj within a safe operating area (SOA). Therefore, controlling Tj with advanced algorithms such as Active Thermal Control (ATC) methods is a promising way to increase the kVA limit of PEC without sacrificing their reliability, which has not been explored enough in the literature. Therefore, this paper presents a novel approach combining the discontinuous pulse width modulation (DPWM) strategy with a system-level current controller. The proposed method enables the grid-connected inverter-based resources (IBR) to increase its kVA limit without increasing the Tj. This method also allows the converters to operate at higher ambient temperature Ta for more efficient power transfer without sacrificing its power handling capacity. Additionally, this paper systematically explains the effect of DPWM on switching losses Psw by establishing an analytical relationship between the bus clamp angle α with respect to Psw. Furthermore, for controlling the Tj, a simple Foster thermal model-based temperature estimation method is described, and its accuracy has been shown. Additionally, the impact of the proposed ATC method on maintaining the Tj of a 3Ph 2L-VSC with respect to the conventional sinusoidal PWM method is also compared.

Radar-based human activity recognition (HAR) is a popular area of research. In this paper, we investigate methods to improve the generalization of micro-Doppler-based swimming activity recognition. We identify three main challenges to this task: a small dataset lacking motion diversity, inaccurate period estimation, and inefficient network design that does not take into account the unique characteristics of spectrograms. To address the limited motion diversity, we propose a spectral data augmentation tailored for micro-Doppler spectrograms, including positive augmentations that account for physical fidelity and negative augmentations that penalize the unrealistic examples. We also investigate self-supervised pre-training to effectively use these negative augmentations. To address inaccurate period estimation, we introduce a segmentation approach based on energy distribution to handle temporal period variation. To exploit the spreading pattern of limb motion in the Doppler dimension and the continuous properties of torso motion in the temporal dimension, we design a module consisting of both 2D convolution and 1D temporal dynamic convolution to serve as a feature extractor. Our evaluation on a self-collected swimming activity recognition dataset shows that our model achieves the best classification accuracy and robustness to corruptions, even compared to much larger models and multi-domain fusion models.

Active power decoupling (APD) is an emerging technique for second-harmonic ripple filtering in single-phase power conversion systems. It reduces the dc bus capacitance requirement while adding extra active components to the converter. Though capacitance reduction is favourable for improving converter power density and reliability, added active components often make the circuit complex apart from increasing the power losses. Therefore, APD with minimum active component count and reduced loss have gained significant research interest. This article discusses the synthesis of minimum switch count APD topologies that use low-power rated devices. Low-power APD circuits help to reduce the filter loss, making their efficiency comparable to that of the passive filters. The topology derivations are elaborated, and the significant attributes of the derived filters are compared with the state-of-the-art active and hybrid filters to comprehend their relative merits and limitations. The operations of the synthesized unique topologies are experimentally validated using laboratory hardware.

Preprint of submitted article to TDEI. Compare partial discharge activity at different voltage shapes on aluminium nitride substrates embedded in silicone liquid and gel.Â

The landscape of privacy protection within the domain of video analytics is in a constant state of evolution, driven by advances in computer vision and machine learning techniques. These advancements provide increasingly sophisticated video analysis capabilities, but they also raise the stakes in terms of personal privacy. In this review, we have conducted a thorough exploration into the intersection of video analytics and privacy preservation, focusing on two core techniques: face de-identification and background blurring. We rigorously analyze the latest advancements in face de-identification, including the nuanced approaches involving facial feature perturbation, deep learning methodologies, and the intriguing domain of generative adversarial networks, meticulously highlighting both their efficacy and inherent limitations. Simultaneously, we research numerous background blurring techniques, showcasing their remarkable ability to obscure contextual information while preserving the essential elements of the visual scene. This paper underscores the practical significance of these techniques through real-world applications in surveillance and the dynamic landscape of social platforms. In essence, our comprehensive review combines the multifaceted aspects of face de-identification and background blurring within the sphere of video analytics, thereby offering a nuanced and thorough understanding of this pivotal domain.

Quantum approximate optimization algorithm (QAOA) is used for NP-hard problems on noisy intermediate-scale quantum (NISQ) devices. We demonstrate QAOA’s near-optimum maximum likelihood (ML) decoding for short block-lengths in Gaussian channels using random linear codes and channel coded modulation. Simulations with a p-layer QAOA decoder for  p ∈ [1, 4], coding rates R = k/n ∈ [0.3, 1], signal-to-noise ratio (SNR)  ∈  [0, 10] dB and k ∈ [10, 26] show near-optimum bit and block error rates. We conjecture near-optimum performance for p ∈ [1, 10], R = 0.5, SNR = 10 dB and k ≦ 250 indicating QAOA’s potential in short block-length decoding.