Target detection using cameras or automotive radar to identify traffic or prevent collisions is an important issue in Autonomous Vehicles (AV) research. Traditional Constant False Alarm Rate (CFAR) methods are commonly employed. Although these methods are suitable for lightweight hardware, improving the target detection process often leads to losing real-time performance. The method proposed in this letter improves detection accuracy. It reduces response time by modifying the position of guard cells in the first stage and employing harmonic averaging (inverse of the sum of the inverse of data) while eliminating data sorting in the second stage. Moreover, this approach exhibits better performance in the presence of interfering targets. Since the proposed method is more applicable to the Range-Doppler map, it has been named RD-CFAR. The proposed method also enhances target detection in Synthetic Aperture Radar (SAR) images.

This paper analyzes the performance of Alamouti coded short-packet non-orthogonal multiple access (NOMA) systems with hybrid multicast and unicast transmission over Nakagami-m fading, where only the statistical channel state information is available at the transmitter, and the multicast and unicast signals are intended for all users and a particular user, respectively. Due to some practical limitations, channel estimation errors (CEE) caused by a linear minimum mean-square error estimator and residual hardware impairments (RHI) caused by imperfect hardware implementation are taken into account. We first derive the approximate closed-form expressions for the average block error rate (BLER) and the corresponding asymptotic expressions at all users. Using such expressions, we analyze the diversity performance including conventional diversity order and finite-SNR diversity order. After this, we quantify the relationship among the common blocklenth, power allocation, and pilot sequence length under users' reliability constraints. Finally, numerical and simulation results verify our theoretical analysis and show that i) CEE and RHI greatly affects the transmission blocklength, ii) RHI leads to the error floor phenomenon at high SNRs and the finite-SNR diversity order is the effective performance metric in the presence of RHI, iii) there exist the optimal power allocation coefficients and pilot sequence length to minimize the transmission blocklength in the considered hybrid multicast-unicast system, and iv) the NOMA scheme is superior to the OMA counterpart by achieving low-latency transmission.

Movable antenna (MA) has attracted increasing attention in wireless communications recently. As compared to conventional fixed-position antennas (FPAs), the geometry of MAs can be dynamically reconfigured, such that more flexible beamforming can be achieved for different purposes. In this paper, we investigate the application of MAs to wide-beam coverage, aiming to jointly optimize the MAs' beamforming weights and positions within a line segment to maximize the minimum beam gain among all possible directions in a target region. However, the resulting optimization problem is non-convex and difficult to be optimally solved. To tackle this difficulty, we first derive a closed-form optimal solution to this problem in the special case with two MAs. While for the case with more than two MAs, an alternating optimization (AO) algorithm is proposed to obtain a high-quality suboptimal solution, where the MAs' beamforming weights and positions are alternately optimized by applying the successive convex approximation (SCA) technique. To reduce computational complexity, we further propose a more efficient MA position optimization method by leveraging the frequency modulation continuous wave (FMCW) design. Specifically, we construct a spatial FMCW-based continuous phase profile for the entire line segment and then select an optimal set of MA positions to optimize the wide-beam coverage performance with their FMCW-based phase profiles, thus greatly simplifying the wide-beam design. Furthermore, we extend the proposed AO and FMCW-based algorithms for the linear MA array to the planar MA array. Numerical results show that both our proposed algorithms can significantly outperform conventional FPAs even with optimized beamforming weights.

Functional near-infrared spectroscopy (fNIRS) is a preferred neuroimaging technique for studies requiring high ecological validity, allowing participants greater freedom of movement. Despite its relative robustness against motion artifacts (MAs) compared to traditional neuroimaging methods, fNIRS still faces challenges in managing and correcting these artifacts. Many existing MA correction algorithms notably lack validation on real data with ground-truth movement information. In this work, we combine computer vision, ground-truth movement data, and fNIRS signals to preliminarily characterize the association between specific head movements and MAs. Fifteen participants (age = 22.27 ± 2.62 years) took part in a whole-head fNIRS study, performing controlled head movements along three main rotational axes. Movements were categorized by axis (vertical, frontal, sagittal), speed (fast, slow), and type (half, full, repeated rotation). Experimental sessions were video recorded and analyzed frame-by-frame using the SynergyNet deep neural network to compute head orientation angles. Maximal movement amplitude and speed were extracted from head orientation data, while spikes and baseline shifts were identified in the fNIRS signals. Results showed that head orientation and movement metrics extracted via computer vision closely aligned with participant instructions. Additionally, repeated and Up/Down movements tended to compromise fNIRS signal quality. The occipital and pre-occipital regions were particularly susceptible to MAs following Up/Down movements, while temporal regions were most affected by bendLeft/bendRight and Left/Right movements. These findings underscore the importance of cap adherence and fit in the relationship between movements and MAs. Overall, the work lays the foundation for an automated approach to developing and validating fNIRS MA correction algorithms.

The deployment of IoT platforms for SmartCity applications is demanding solutions to assure security and integrity levels. In this context, physical unclonable functions (PUF) may overcome the security drawbacks of storing the security key in a non-volatile memory. The existence of SRAM in embedded systems has driven the implementation of PUF solutions using memory circuit as entropy sources. The use of a non-specific memory design requires the need of identify the adequate cells useful for PUF applications. This work proposes a new methodology to distinguish the adequate cells based on mismatch factor calculation. The mismatch analysis using physical and performance parameters shows the viability of the selection methodology and computes the robustness of the responses in terms of probability of error in the start-up value.

The rapid evolution of software development practices, coupled with the increasing prominence of open-source software (OSS) projects, has positioned versioning systems as invaluable repositories of information for analysis. This paper delves into the factors that shape software development activities by examining documented methodologies and tools used to mine and analyze OSS repositories. Through a detailed review of approaches addressing developer contributions, code modifications, and collaborative workflows, we identify key influences on productivity, code quality, and team dynamics. Our exploration includes an analysis of analytics techniques and tools, such as CVSSearch and CVSAnalY, that facilitate a deeper understanding of developer behaviour and project evolution. Furthermore, we discuss the implications of these insights for enhancing software engineering practices across both OSS and proprietary environments. This paper provides a conceptual framework that connects theoretical insights with practical approaches, offering actionable strategies to improve processes, foster collaboration, and support learning in software development. Ultimately, the findings hold potential value not only for practitioners aiming to optimize workflows but also for educators leveraging OSS environments to teach and evaluate software engineering concepts.

The increasing integration of artificial intelligence (AI), learning analytics, and process mining has paved the way for enhancing the interpretability of educational data. This paper explores how these emerging technologies can synergize to improve the transparency and effectiveness of learning analytics, particularly in Massive Open Online Courses (MOOCs) and Free/Libre Open-Source Software (FLOSS) communities. A major challenge lies in the "blackbox" nature of AI models, which can hinder trust and adoption, especially in educational contexts. To address this, we delve into the role of explainable AI (XAI) in providing insights into learner behaviors while retaining the predictive power of complex machine learning algorithms. By reviewing state-of-the-art studies and synthesizing methodologies from process mining, AI, and learning analytics, this paper seeks to present a comprehensive framework for leveraging these technologies to make educational data more accessible and interpretable. The findings point toward future directions for combining process discovery techniques, explainability approaches, and educational data mining to create adaptive and transparent learning environments.

The magnetic model of electric motors characterizes the magnetic flux as a non-linear function of the stator current as well as the rotor angle. An accurate magnetic model for electric motors is essential for high performance control strategies. The magnetic model is typically acquired by high-precision simulations or massive experiments of measuring magnetic flux data throughout the operating current range, and then applied in the control process via a look-up table of measurements. Both the acquisition and the application processes are time-consuming and may not be suitable for low-latency controls. To address this issue, we propose a novel compressed sensing-based method to acquire the magnetic flux map from limited randomly sampled data-points and further infer an analytical magnetic model. This analytical model can be used to efficiently compute the magnetic flux given any stator current instead of looking up measurement data in the control loop. The proposed approach is first validated on data simulated by finite element analysis (FEA) and then verified on data acquired on a permanent magnetic machine through experiments.

In this paper, several typical temperature sensitive electrical parameters are investigated regarding their applicability in Schottky-contact and Gate-Injection GaN HEMTs. In addition, the effect of charge carrier trapping on the temperature behavior of the threshold voltage and gate current is analyzed. It is found that the drain-source on-resistance, the drain-source capacitance, and the transconductance can all be exploited for temperature estimation. On the other hand, shifting of the threshold voltage due to gate voltage stresses obfuscates any shifting caused by temperature changes in Schottky-contact device and the ohmic-contact device to a smaller degree. The gate current of the Schottky-contact device is also stress-dependent around the threshold voltage, but stabilizes at higher gate-source voltage values. In contrast, the gate current of the ohmic-contact device is unaffected by gate stress in the transistor on-state.

The present work explores the geomorphotectonics of the Kathiawar (Saurashtra)-Kutchch Outlier in Gujarat, India north of the SONATA lineament. Using spatial statistics, the author explores intricate links between spatially resolved data, reveal patterns and correlations that drive the region's geological history. The work addresses an underlying issue, i.e., that of finding the spatial position of entities under study, which is a central issue in spatial analysis. The methodology combines several spatial analytic methods in order to derive a detailed representation of the geomorphotectonics of the area. Results of this study would make an important contribution to the ongoing task of understanding the geological history of the Kathiawar (Saurashtra)-Kutchch Outlier, providing insight to the complex processes that have created this peculiar geological unit.

The escalating complexity and adaptability of ransomware attacks have exposed critical gaps in traditional detection models, which struggle to effectively identify evolving threat behaviors without a significant margin of error. The Adaptive Pattern Scoring Mechanism (APSM) offers a novel approach to this challenge, leveraging a dynamic scoring framework that recalibrates based on real-time behavioral anomalies, enabling enhanced precision in distinguishing ransomware activities from benign events. APSM addresses inherent limitations in signaturebased and heuristic methods by integrating an adaptive scoring algorithm, which continuously adjusts detection thresholds and assigns differential weights across key behavioral dimensions, achieving a robust balance between detection accuracy and resource efficiency. Empirical evaluation reveals APSM's high accuracy and precision rates, with lower false-positive rates than conventional approaches, indicating its significant capability to detect ransomware with minimal operational disruption. The integration of dynamic scoring not only optimizes APSM's performance in real-time environments but also enables scalability across high-load conditions, marking a substantial advancement in automated cybersecurity infrastructures. Ultimately, APSM provides a sophisticated, resource-efficient solution that aligns closely with the requirements of modern ransomware detection, showing its value as a responsive tool in proactive threat management.

This paper addresses stability challenges in Dual Active Bridge (DAB) converters with input LC filter, increasingly adopted in emerging DC grids. The targeted system is studied in response to wide supply voltage variations in both steady-state and transient conditions. Although numerous Modulation Schemes (MSs) are developed to optimize efficiency, their impact on stability has not been fully addressed in literature. In response, a novel method for informed MS design based on small-signal stability analysis is proposed. To that end, the closed-loop input impedance of the converter is modeled with full consideration of the MS and controller parameters. The impact of an Active Damping (AD) strategy is even considered for stability margin enhancement, thus allowing for the design and experimental validation of an informed MS that strikes a balance between stability and efficiency.

The United States housing market has historically exhibited regional imbalances in housing supply and demand, which have contributed to reduced housing affordability and market volatility. The application of big data technology enables the utilisation of data to enhance comprehension of these imbalances, thereby informing the formulation of policy. We put forth a model for analyzing the housing supply and demand based on big data, which employs a comprehensive approach to examine both the demand and supply sides. With regard to the demand side, the model incorporates a multitude of data sources to ascertain and delineate the pivotal elements influencing housing demand. These include population growth rate, household income level, employment opportunity distribution, migration and flow trends, and cost of living. By constructing cubes, the model is capable of capturing the characteristics of dynamic demand changes in different regions. With regard to the supply side, the model assesses land use, building materials and labor costs, the timeliness of building permitting and approval processes, and the impact of regional policies and regulations. By means of a quantitative analysis of the aforementioned factors, the model is able to identify housing supply bottlenecks in different regions. The model's efficacy in identifying significant imbalances between supply and demand in the United States housing market was validated through experimental analysis of historical data.

The Impedance Boundary Condition (IBC) is a homogenization approximation, of great importance, especially in the design of metasurfaces. However, the standard Electric-Field Integral-Equation formulation of the IBC boundary-value problem (EFIE-IBC) has been shown to lead to numerical instabilities for some impedance ranges of practical interest, in particular inductive reactances. This contribution shows that the numerical instabilities are due to an intrinsic ill-conditioning of the EFIE-IBC operator for the concerned surface impedance values, that can degenerate into an ill-posedness that does not allow for definite solution. Hence, the stable discretization of the EFIE-IBC operator requires a regularization. The analysis leads to proposing a regularization by systematically limiting the wavenumber spectrum of the basis functions, which amounts to a spatial filtering. This is implemented using entire domain basis functions. Given the possible ill-posedness, we devise two "ground truth" test examples starting from a physical metasurface, then approximated via IBC. Comparison to ground truth results shows that the standard EFIE-IBC may lead to significant errors, and that these may be hard to detect. Conversely, the regularized system yields stable results that well match the ground truth of the physical structure of which the IBC is an approximation.

Interface defect response time is a key parameter in MOS devices interface defect density measurements such as the hi-lo CV measurement as well as the conductance measurement. It is widely accepted that interface defects at energies close to the band edge have too short a response time to be measured properly by these techniques, unless very high measurement frequencies are employed, which is challenging experimentally. Consequently, near band edge interface defects are difficult to measure. Here we show that the time constant of band edge defects are not as short as commonly believed. Specifically, we show that as the capacitor is biased into accumulation to measure defect levels near the band edge, the defect response time levels off instead of continue to decrease. As a result, the commonly available 1MHz measurement frequency is more than adequate even for defects near the band edge.

In recent years, deep learning-based joint source-channel coding (DJSCC) has gained significant attention for its impressive performance in image transmission. Unlike traditional separate source-channel coding (SSCC) methods, DJSCC performs particularly well in low signal-to-noise ratio (SNR) and limited bandwidth environments. However, ensuring the security of private information during transmission remains a critical concern. A notable limitation of DJSCC is its incompatibility with traditional encryption methods used for secure communications, making it vulnerable to eavesdropping attacks. To address this issue, we propose integrating a chaotic map encryption method into the DJSCC framework for secure wireless image transmission. This approach leverages chaotic sequence to shuffle the position of the elements in latent space without altering the values of the latent tensor. This allows the encryption process to be designed independently of DJSCC, eliminating the need for retraining the end-to-end model. Our proposed method preserves DJSCC's superior transmission characteristics, ensuring high-quality image reconstruction at the receiver, while effectively ensuring the security against deep learning-based known plaintext attacks (Deep KPA).

This paper introduces DynamicRenderNet, a novel neural network architecture designed to optimize graphics rendering in real-time Augmented Reality (AR) and Virtual Reality (VR) games. By adaptively adjusting rendering parameters based on scene complexity and user interaction, DynamicRenderNet significantly improves frame rates and reduces latency. Our model utilizes a deep learning approach to analyze real-time input data, such as camera feed, user head movements, and scene geometry, to predict optimal rendering settings. This enables dynamic resource allocation and prioritization, leading to a more efficient and responsive rendering pipeline. Experimental results demonstrate substantial improvements in frame rates and reduced latency, resulting in a more immersive and visually appealing AR/VR experience.

Concentric tube robots (CTRs) have garnered significant attention in various minimally invasive procedures due to their small size and dexterity, enabling access to target clinical sites through minimal incisions. Despite extensive technical advancements in the development of CTRs, there is a lack of design approaches specific to their function as surgical instruments. In this study, we propose a compact CTR specifically designed for percutaneous nephrolithotomy (PCNL), adaptable for both hand-held operation and mounting on a passive arm. The proposed robot measures 46 mm in diameter, 322 mm in length, and weighs 570 g. An ergonomic evaluation of the robot through muscle activation analysis during the handling of the robot by expert urologists and non-clinicians showed better ergonomics than standard hand-held PCNL devices. Additionally, closed-loop position control of the distal end of the CTR was implemented based on resolved-motion rate inverse kinematics. The performance of the robot was empirically validated through a life-size abdominal phantom-based experiment in three phases of the procedure that included skin puncturing, autonomous deployment, and navigation to all parts of a renal stone. The results showed mean closed-loop position errors of 1.2±0.8 mm for autonomous navigation to 100 target points on the stone, indicating a performance level in line with the specific requirements of PCNL.