Miracle-dz/UniFAR-FTUs huggingface dataset:
Postural activity monitoring for increasing safety in bomb disposal missions

In enclosed suits, such as those worn by explosive ordnance disposal (EOD) experts, evaporative cooling through perspiration is less effective and, particularly in hot environments, uncompensable heat stress (UHS) may occur. Although some suits have cooling systems, their effectiveness during missions is dependent on the operative's posture. In order to properly assess thermal state, temperature-based assessment systems need to take posture into account. This paper builds on previous work for instrumenting EOD suits with regard to temperature monitoring and proposes to also monitor operative posture with MEMS accelerometers. Posture is a key factor in predicting how body temperature will change and is therefore important in providing local or remote warning of the onset of UHS. In this work, the C4.5 decision tree algorithm is used to produce an on-line classifier that can differentiate between nine key postures from current acceleration readings. Additional features that summarize how acceleration is changing over time are used to improve average classification accuracy to around 97.2%. Without such temporal feature extraction, dynamic postures are difficult to classify accurately. Experimental results show that training over a variety of subjects, and in particular, mixing gender, improves results on unseen subjects. The main advantages of the on-line posture classification system described here are that it is accurate, does not require integration of acceleration over time, and is computationally lightweight, allowing it to be easily supported on wearable microprocessors.
A Multidimensional Fingerprint Positioning Method using Support Vector Machine

Fingerprint positioning is an effective positioning method for a rich scattering indoor environment. Most fingerprint positioning methods are based on the received signal strength (RSS). However, as a coarse-grained parameter, RSS may fluctuate at a fixed position, making the positioning results unstable. The recent advanced positioning systems use the amplitude information of channel state information (CSI) instead of RSS. Yet the non-line-of-sight (NLOS) environment limits their applicable scene. This paper proposes a dispersion maximization least squares support vector machine (DM-LSSVM)-based multi-dimensional fingerprint positioning model that can work in an NLOS environment. We first use a Multiple Signal Classification (MUSIC) algorithm to estimate the angle-of-arrival (AoA) and multipath number, and then a multi-dimensional fingerprint library is built by combining the RSS value with the estimated AoA and multipath number. Finally, we introduce the LSSVM as the positioning algorithm to realize positioning and calculate the weight of each fingerprint through the dispersion maximization method, implement a method which can enhance the positioning accuracy of AoA in NLOS scenarios through RSS and multipath numbers. The result shows that in a high NLOS deployment environment, the median accuracy is 0.38m.
Remote Sensing Image Denoising Based on Multi-Scale Feature Fusion and Regional Contextual Information

The various types of noise in Remote Sensing (RS) images resulting from environmental factors and the imaging system, often significantly degrade the imaging quality and impair high-level visual tasks. Therefore, denoising plays an essential role in the applications of RS images. Traditional methods mainly focus on dealing with a single type of noise, while the denoising performance is rather limited with the complex noise in practice. Given the advanced representation learning ability of deep neural networks, investigations have been made to apply them to the RS image denoising. However, existing methods tend to pay more attention to global features, the detailed local information are often overlooked. Therefore, in this paper, we propose a hyperspectral RS image denoising model by leveraging both multi-scale feature fusion and regional contextual information. More specifically, the proposed model includes two branches, i.e., the global branch based on Multi-scale Feature Fusion Module (MFFM) to aggregate global features from multiple scales and the local branch based on Transformer Attention Module (TAM) to explore the regional context. The denoised RS image is then obtained by combining feature maps from two branches. Extensive experimental results demonstrate that our model performs favorably on both simulated and real noisy RS images. The proposed model is also evaluated on the high-level visual tasks including object detection and clustering, which further illustrates the potential of our model for facilitating downstream tasks.
Experiment Result of High Frequency Switching SiC Mosfet Gate Driver

DC-DC converter battery charger application commonly applied a high-frequency switching method. The high-frequency technique aims for a smaller size transformer size and weight. Overall, the chosen strategy leads to more economical end-product. The SiC-MOSFET becomes an additional solution to achieve high power and high frequency application applications. This paper focused on the design of the gate driver for SiC-MOSFET that can be applied in many applications including battery charger and inverter applications. The result showed that the gate driver designed had successfully switched SiC-MOSFET up to 35 kHz.
Active Gate Driving of Cascoded SiC JFETs

In this paper, the gate driving characteristics of a SiC cascoded Junction Field Effect Transistor (JFET) is explored. Firstly, a Conventional Gate Drive (CGD) circuit is analyzed and implemented, followed by the proposal and implementation of two Active Gate Driving (AGD) methods for the cascoded JFET. Two approaches of gate control are explored: i) using the low-voltage -MOSFET gate & (ii) using the JFET gate. Switching characteristics during turn-on and turn-off for the CGD and both AGD approaches are compared, and it is observed that the proposed AGD circuits provide controllability of both switching edges and thus allowing an application optimized switching operation. Analytical, simulation and experimental results are shown to verify the proposed methods.
Analysis and Design of Online State Grid Electric Power Big Data Application Value and Sharing Platform based on BP Algorithm

Analysis and design of online state grid electric power big data application value and sharing platform based on BP algorithm is the focus of this paper. The security of power big data is the premise of the digital security transformation of power grid, and also an important guarantee for the core implementation of the new smart grid in China. Although cloud computing power can improve the utilization of social resources, there is a possibility of privacy leakage when power big data is directly applied to third-party computing platforms. Hence, for the better analysis, the optimized BP NN is defined. The genetic optimization BP algorithm has a variety of optional training strategies, in order to meet the requirements of containerized deployment commonly used in the development of the current information systems. These ideas are then combined to construct an efficient big data application value and sharing platform. The performance of the model is then evaluated by testing under different aspects.
Robustness Analysis and Modeling of UAV Cluster System Based on Complex Network

For the robustness problem of UAV cluster after being attacked and reconstructed, Firstly, the hive UAV network model is established by complex network method, Then random attacks and targeted attacks are carried out on the model, The method of link reconstruction using the motif degree centrality and other indexes is proposed. Thus, the attack-reconstruction model is constructed. In order to evaluate the effectiveness of the model, a comprehensive evaluation method considering the robustness of complex network indicators and UAV cluster mission performance is proposed. Then, the robustness of UAV cluster network under two attack modes is analyzed. Finally, the simulation results show that this method can quickly judge whether UAV cluster network has robustness.
External Liquid Cooling Method for Lithium-Ion Battery Modules Under Ultra-Fast Charging

Electric vehicles (EVs) are booming all over the world for a low carbon emission and greener environment. The fast charging is an urgent demand for consumers. However, the dramatic temperature rising during high-power charging has a high risk of trigging thermal runaway and other safety issues. This article proposes an external liquid cooling method for lithium-ion battery module with cooling plates and circulating cool equipment. A comprehensive experiment study is carried out on a battery module with up to 4C fast charging, the results show that the three-side cooling plates layout with low coolant temperature provides better temperature control effect, while the coolant velocity and type have very weak impact. Then the simulation model with high precision is built for ultra-fast charging, and a safe charging range under 5C charging is proposed. The safe charging range under 5C can be increased by 50.1%. The environment close to the room temperature demonstrates the best effects of external cooling. For actual applications in the EVs, the circulating cooling equipment outside EVs can be integrated with high-power charging infrastructure and provide a 117 km improvement in mileage under 5C safe charging which indicates a tremendous application prospect.
Speech Enhancement Regularized by a Speaker Verification Model

In the last years, end-to-end neural networks were employed to enhance single-channel noisy speech. The Conv-Tasnet architecture is such a neural network and it has been successfully trained on the scale invariant signal-to-distortion ratio (SI-SDR) loss. However, we find that in a speech enhancement task Conv-Tasnet trained on the SI-SDR loss introduces distortions to the enhanced signal as the mismatch between training and testing data increases. To mitigate these effects, we investigate on a new loss function, that combines SI-SDR with a pretrained speaker verification (SV) model as a regularizer. As the SV model captures information about important characteristics of clean speech signals, we argue that the proposed regularization ensures that the estimated speech signal is speech-like even if there is an increasing mismatch between training and testing data. Accordingly, we observe considerable improvements in POLQA in mismatched training and testing conditions, e.g. when training on anechoic speech but testing on reverberant data.
Non-homogeneous rapid distortion theory on transversely sheared mean flows

Abstract This paper is concerned with the small-amplitude unsteady motion of an inviscid non-heat-conducting compressible fluid on a transversely sheared mean flow. It extends previous analyses (Goldstein, J. Fluid Mech., vol. 84, 1978b, pp. 305–329; Goldstein, J. Fluid Mech., vol. 91, 1979a, pp. 601–632), which show that the hydrodynamic component of the motion is determined by two arbitrary convected quantities in the absence of solid surfaces and hydrodynamic instabilities. These results can be used to specify appropriate upstream boundary conditions for unsteady surface interaction problems on transversely sheared mean flows in the same way that the vortical component of the Kovasznay (J. Aero. Sci., vol. 20, 1953, pp. 657–674) decomposition is used to specify these conditions for surface interaction problems on uniform mean flows. But unlike Kovasznay’s result, the arbitrary convected quantities no longer bear a simple relation to the physical variables. A major purpose of this paper is to complete the formalism developed in Goldstein’s earlier two papers by obtaining the necessary relations between these quantities and the measurable flow variables. The results are important because they enable the complete extension of non-homogeneous rapid distortion theory to transversely sheared mean flows. Another purpose of the paper is to derive a generalization of the famous Ffowcs Williams and Hall (J. Fluid Mech., vol. 40, 1970, pp. 657–670) formula for the sound produced by the interaction of turbulence with an edge, which is frequently used as a starting point for predicting sound generation by turbulence–solid surface interactions. We illustrate the utility of this result by using it to calculate the sound radiation produced by the interaction of a two-dimensional jet with the downstream edge of a flat plate.
The large-scale distribution of molecular gas in the first galactic quadrant

The emission parameters for the molecular component of the interstellar medium are determined throughout the first Galactic quadrant. Face-on maps of the peak midplane molecular density, scale height, and offset from b = 0 deg with roughly 300 pc resolution are presented. These maps are compared with the distribution of cataloged H II regions to test whether both species trace the same Galactic locations. The properties of the spiral (or ringlike) arms are deduced from the derived images and pitch-angle upper limits are estimated. The H II regions are strongly correlated with the dense molecular rings. 33 references.
Transfer Learning in ML-based Radar Systems for Automotive Applications

Frequency modulated continuous wave radar systems are a vital and widespread component in modern vehicles. Their ability to sense the surrounding environment quickly and reliably is heavily used in today’s driving assistance systems. Latest applied research introduced machine-learning based algorithms for real time detection and classification of radar targets without the need of traditional radar signal processing. The reliable and fast characteristics of these systems are a huge improvement of protecting vulnerable road users like pedestrians and bicyclists, overcoming the traditional methods of radar signal processing. In exchange, machine learning based systems need to be trained with a suitable dataset representing as many as possible real-world case scenarios. Refining adequate datasets from raw sensor data is a time- and cost-consuming effort. In order to achieve the best results, the sensor data must be manually processed and labeled after measuring the use-case in a real-world environment. Hence the application of transfer learning approaches derived from computer-based vision algorithms in automobile radar processing is investigated. A synthetical dataset including labeled radar readings is proposed to eliminate the necessity for manual dataset construction. Transfer learning approaches for machine-learning based radar processing algorithms are explored using a synthetic dataset to enhance detection and classification performance in automotive-related applications. The use of transfer learning already improves the effectiveness of activities based on machine learning for detection and classification in related applications like ship detection with synthetic aperture radar.
Short-Circuit Ruggedness and Partial Discharge Evaluation of a 3.3 kV SiC MOSFET Power Module

This paper presents the initial evaluation of a 3.3-kV, SiC MOSFET power module (not fully populated) from Wolfspeed, including static, short circuit (SC), and partial discharge (PD) test results. Static characterization results are presented first. The module performance change after repetitive pulse SC tests is provided by comparing the original static characterization test results to the one performed after the SC stress tests. More electric aircrafts (MEA) require an increased electric system voltage level (e.g., 230 VAC constant or variable frequency, ± 270 VDC or 540 VDC) and make PD a very relevant and urgent issue. Thus, PD evaluation tests for the module package are included for different air pressures from Sea Level (i.e., 760 Torr) to 50,000 feet (i.e., 87 Torr) altitude. The PD inception voltage (PDIV) and extinction voltage (PDEV) obtained via experimentation are presented in this paper.
Spectral Unmixing And Mapping Of Coral Reef Benthic Cover With Deep Learning

Coral reefs are important to the biological ecosystem and the communities and wildlife that rely on the habitat they create. However, coral reefs are in critical and rapid decline: reefs have degraded over recent decades and what remains is at increasing risk of loss. Despite this, only a small fraction of the world’s reefs have been studied quantitatively, when mapping these reefs would allow us to monitor and understand their health. We seek methods to better model reefs at global scales. In this paper, we train with high spatial resolution satellite imagery and use low-spatial, high-spectral resolution priors from labeled imagery to demonstrate better accuracy in generalizing to unseen examples than prior work. We also develop on a novel method using a Deep Conditional Dirichlet Model to perform unmixing of spectral data across different resolutions. Accounting for mixing yields improvements on the accuracy of the predicted class probabilities. We map coral benthic cover with this method and intend to generalize and apply it at global scales.
An 84-dB-SNDR Low-OSR Fourth-Order Noise-Shaping SAR With an FIA-Assisted EF-CRFF Structure and Noise-Mitigated Push-Pull Buffer-in-Loop Technique

To design a low-oversampled high-resolution noise-shaping successive approximation register (NS-SAR) analog-to-digital converters (ADCs), two main bottlenecks need to be addressed. One is to implement high-order optimized NS with simple and low-power hardware that maximally preserves a SAR’s efficient nature, and the other is to alleviate the sampling noise and input driving burden. This article presents a fourth-order NS-SAR ADC that synergistically addresses both challenges. It proposes an innovative error feedback-cascaded resonator feed-forward (EF-CRFF) structure and a noise-mitigated buffer-in-loop (BIL) technique. The former balances the robustness and energy efficiency by combining the merits of EF and CRFF structures, while the latter ensures the ADC to be low noise and easy-to-drive simultaneously. Prototyped in 65-nm CMOS technology, this work achieves 84.1-dB signal-to-noise-distortion ratio (SNDR) with 500-kHz bandwidth (BW) under a small OSR of 5. The prototype consumes 133.8- $\mu \text{W}$ power under 1.2-V supply (including the power of in-loop buffer under 2-V supply), leading to a 180-dB Schreier figure of merit (FoM).
Optimized Hazard Free Pipelined Architecture Block for RV32I RISC-V Processor

When analysing the two different types of processors available in market, RISC-based processors are more effective than CISC-based processors, for applications that demand higher speed and reduced power. RISC-based processors might be the ideal solution for pipelined instruction execution because of the reduced instruction complexity and reduced interdependence among the instructions. Pipelining improves any processor initial design by enhancing the processor’s speed, throughput, and thus performance. In any CPU, there are millions of instructions that must be implemented in a specific order, which takes a long time. Whenever an instruction is getting executed in a pipeline, it allows the following instruction to be executed without influencing the current instruction’s implementation, speeding up the process. The biggest downside of pipelining is the risk of data and control hazard, which can cause pipeline stalling. This paper presents a 32-bit RISC-V pipelined processor design that incorporates hazards elimination techniques such as data forwarding and branch prediction. To implement all of the instructions in the RISC-V processor’s basic integer instruction set, the processor is designed with five different stages of pipelining. In the proposed core the power consumption is reduced by 4% and speed is improved by 7.82% than the RISC-V processor developed for low power application reported in literature.
Touching The Droid: Understanding and Improving Touch Precision With Mobile Devices in Virtual Reality

Touch interaction with physical smartphones and tablets in Virtual Reality offers interesting opportunities for cross-device input. Unfortunately, any imprecision in the alignment of the visual representation of either the hand or device can impact the precision of touch and the realism of the experience. We first study a user’s ability to rely solely on preoperative feedback to perform touch interaction in VR, where no rendering of the hand is provided. Results indicate that touch in VR is possible without a visual representation of the hand, but accuracy is influenced by how the device is held and the distance traveled to the target. We then introduce a dynamic calibration algorithm to minimize the offset between the physical hand and its virtual representation. In a second study, we show that this algorithm can increase touch accuracy by 43%, and minimize depth-based “screen penetration” or “floating touch” errors.
Distributed Multiantenna Frequency-Selective Energy Beamforming With Joint Total and Individual Power Constraints

We analyze distributed multi-antenna energy beamforming over frequency selective fading channels in wireless power transfer (WPT) systems with joint total and individual transmit power constraints. The constraints allow the WPT system to limit energy consumption for cost or environmental factors, and prevent individual coordinated energy transmitters (CETs) from overdriving their high-powered amplifiers due to hardware limitations. The harvested power at the energy receiver is modeled as an arbitrary non-linear, continuous, and non-decreasing function of the received radio frequency (RF) power. We show that the optimal frequency selective energy beamforming solution for <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> CETs with <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> antennas each is equivalent to maximizing the total RF power coherently combined at the energy receiver over <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> shared subchannels. We obtained the optimal frequency selective energy beamforming strategy with the following insightful properties: First, for any subchannel, either all antennas of all <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> CETs allocate zero power to this subchannel (i.e., inactive), or all antennas of all <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> CETs allocate non-zero power to this subchannel (i.e., active). Second, for the active subchannels, we show that the optimal power allocation obeys a proportionality principle for the powers from all antennas of all <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> CETs, which reveals that higher powers are not necessarily allocated to subchannels with higher channel gains. Third, we prove that optimally no more than <inline-formula> <tex-math notation="LaTeX">${T}\,\,+$ </tex-math></inline-formula> 1 subchannels are selected for energy beamforming, where <inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> (less than <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula>) is the number of CETs transmitting at their maximum individual powers due to the total power constraint. Based on the above properties, we show that the energy beamforming solution can be efficiently achieved via solving a low-complexity dual problem in simpler form with only <inline-formula> <tex-math notation="LaTeX">${K} +1$ </tex-math></inline-formula> variables compared to the original primal problem with NMK variables. Numerical examples verify our theoretical findings and show the impact of the joint total and individual power constraints on the average harvested power.