Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Framework for Biometric Identification from Wrist-Worn PPG With Acceleration Signals

The photoplethysmography (PPG) signal has advantages in terms of accessibility and portability, which makes its usage in many applications such as user access control very attractive. In this paper, we propose a novel deep learning-based biometric identification framework (BIDNET), which uses photoplethysmography (PPG) and 3-axis acceleration signal data collected from wrist-worn sensors in an ambulatory environment. We developed a completely personalized data-driven method using eight layers of deep neural networks, which uses five convolutional neural network (CNN) layers and two bidirectional long short-term memory (Bi-LSTM) layers, followed by one dense output layer. It is used to model the time series inherent in the beating signal representing the heart activity. The proposed network structure was evaluated on the ISPC dataset, which was collected from 20 subjects (including the test set) participating in sports activities. The best performance achieved in terms of average accuracy, F1 score, recall, and precision are 0.98, 0.99, 1.00 and 0.99 respectively over 20 subjects for five-fold cross-validation. The proposed model outperforms the state-of-the-art methods for PPG-based biometric identification.
SV-Loop:Semantic-Visual Loop Closure Detection with Panoptic Segmentation

Loop closure detection plays an important role in simultaneous localization and mapping (SLAM) system. Traditional visual-based methods of loop closure detection are hard to relieve the issue of dynamic objects and change of light in complex environments. To improve robustness of loop closure detection, a loop closure detection algorithm named SV-Loop combines semantic and visual information is proposed in this paper. This work leverages the latest panoptic segmentation network to extract semantic information. Firstly, similarity of details is calculated through visual module in micro perspective. Then similarity of objects is computed by semantic module from a macro perspective. Finally, visual and semantic loop candidates are integrated to determine loop closure. Experiments have been conducted on open source datasets and practical scene. Experimental results have shown higher accuracy and better robustness of proposed method in complex environmental changes.
An in-depth study and improvement of Isolation Forest

Historically, anomalies detection was an important issue for industrial applications such as the detection of a manufacturing failure or defect. It is still a current topic that tries to meet the ever increasing demand in different fields such as intrusion detection, fraud detection, ecosystem change detection or event detection in sensor networks. That’s why anomalies detection remains a research topic of great interest for various research communities. In this paper, we focused on Isolation Forest (IForest), a well known, efficient anomalies detection algorithm. We provided a deep and complete view on IForest. We evaluated the impact of its input parameters (number of trees, sample size and decision threshold) on the efficiency of the detection and on the execution time. We discussed the benefit of including some anomalies into the training phase. To address the limits of IForest, we performed different experiments on commonly used real datasets and also on synthetic datasets with non trivial distributions. We designed multidimensional datasets where anomalies are carried by several dimensions simultaneously. Moreover, we used a varying density and distance between anomalies and normal data, for a variable similarity between these two data classes. We compared the performance of IForest against its improved version called Extended IForest. Finally, we designed and validated a new extension of IForest, based on the different individual trees decisions instead of a global forest decision that we call Majority Voting IForest (MVIForest). The experiments show that MVIForest has a shorter execution time than IForest, with almost the same accuracy.
A Review of Field Text Analysis

Field text analysis is the process of using natural language processing technology to understand the Text and extract information from unstructured text data. In recent years, due to the increasing maturity of machine learning technology, higher requirements have been put forward for field text analysis. This review first introduces the process of Text preprocessing and the development of text representation. Secondly, it summarizes the four typical techniques of text analysis, including named entity recognition, relation extraction, extractive text summarization, and text classification. Finally, for each typical technology, it introduces the commonly used methods and part of the researcher’s exploration of this technology.
A Baseline Modeling Algorithm for Internet Port Scanning Radiation Flows

Aiming at the port scanning traffic obtained from the background radiation flow on a specific Internet location, this paper proposes a baseline model construction method named Scanning_Baseline. The proposed algorithm maps the scanning numbers of each port to a set of coordinates and tracks the centroid to form the baseline model. With the Scanning_baseline model proposed in this paper, we can compare the morphological differences of scanning background traffic in different time windows or different network locations. The algorithm is applied on 420 groups of measured data obtained from 60 campus network boundaries in seven consecutive days according to two schemes respectively. The results show that Scanning_Baseline algorithm can construct stable baselines for different scenarios and find anomalies by comparing the shape and distance between baselines. This method can also be used to model Internet traffic in other contexts.
Real Aperture and Synthetic Aperture Joint Imaging Approach for Vehicle-mounted Forward Look Radar

The vehicle-mounted millimeter-wave forward-looking imaging is a research hotspot in the radar field recently. If imaging is done only through the real aperture, the azimuth resolution of the radar will be still greatly restricted for the limitation of the aperture size. Considering the movement of the car itself, the synthetic aperture principle can be used to improve the azimuth resolution. However, synthetic aperture radar cannot completely solve the problem due to the blind area and left-right blurring of forward-looking area imaging (a small angular range directly in front). To this end, a new real aperture synthetic aperture joint imaging method is proposed, in which the real aperture can solve the problem of radar left and right blur, and can image at the forward-looking area. For the squint area, the length of the synthetic aperture is often longer than the real aperture, so higher resolution can be obtained by using synthetic aperture processing. Finally, the simulation results demonstrate the correctness of the back projection algorithm used in this vehicle-mounted forward look radar.
Defect Detection of Printed Circuit Boards Using EfficientDet

Printed circuit board is an important part of electronic industry. The detection of PCB defects could improve the reliability of PCB and electronic devices. Comparing to the traditional inspection methods, the methods based on Convolutional Neural Network have higher accuracy, faster speed, and more robustness. In this paper, a PCB defects inspection method based on EfficientDet-D1 are proposed, which uses EfficientNet-B1 as the feature extraction backbone. Besides, we use K-means clustering to predict more accurate anchor ratios. The results of our work show that the method achieves a similar mAP with other PCB defect detection methods while costing less time in detection.
An Improved D-Linknet Method for Road Extraction from High Resolution Remote Sensing Images

To solve the problem that the road information in high-resolution remote sensing image is confused with the surrounding environment and interfered by non road information such as trees, buildings and shadows, this paper proposes an improved road extraction model by adding Non-local block in D-Linknet encoder to capture remote dependency, which can improve the shadow-shielding-road recognition ability. The experimental results show that this method can effectively reduce the interference of non road factors and significantly improve the effect of road extraction. This method decreases the fragmentation of road patches during extracting, and keeps the integrity of the roads,so it can be used for smart manufacturing and mapping in high-resolution remote sensing image.
Device Identification for IoT Security

IoT (Internet of things) devices have become integral parts of our everyday life to function especially in smart homes/offices/cities. However, their huge varieties and scales make it hard to manage the security and the quality of services of IoT networks with generic policies. Furthermore, IoT devices owned by employees can unknowingly endanger the security and integrity of the network from other IoT entities they are connected to. Fortunately, IoT devices of the same type tend to have similar vulnerabilities making attack prevention more manageable. Thus, identifying the device types is crucial for IoT security. This paper presents an approach to building a classifier to identify different types of IoT devices using various machine learning techniques. While most existing empirical studies use network traffic data, we use communication protocol data. The paper describes the data collection/treatment, the experiments using four machine learning techniques and compares our proposed approach with an existing work based on the Jaccard similarity measure. The results show that both approaches have competitive accuracy of up to 99% but the Jaccard approach does not scale well (e.g., about 1000 times more than the training time of our approach).
Moving Target Shadow Detection and Tracking in ViSAR Based on DNN and Multi-Object Tracking Algorithm

Video synthetic aperture radar (ViSAR) system presents significant potential for moving target detection and surveillance of dynamic region of interests. In this paper, a novel moving target shadow detection and tracking approach for ViSAR is proposed based on deep neural network (DNN) and multi-object tracking (MOT) algorithm. The faster region-based convolutional neural network (FrRCNN) with feature pyramid networks (FPN) is exploited for shadow detection in a single frame. Then, an online MOT approach is employed for tracking the shadows and improving the detection performance. Finally, experiments on measured data have demonstrated that the proposed approach processes an accepted tracking performance.
Enhanced System Robustness of Asynchronous BCI in Augmented Reality Using Steady-State Motion Visual Evoked Potential

This study evaluated the effect of change in background on steady state visually evoked potentials (SSVEP) and steady state motion visually evoked potentials (SSMVEP) based brain computer interfaces (BCI) in a small-profile augmented reality (AR) headset. A four target SSVEP and SSMVEP BCI was implemented using the Cognixion AR headset prototype. An active (AB) and a non-active background (NB) were evaluated. The signal characteristics and classification performance of the two BCI paradigms were studied. Offline analysis was performed using canonical correlation analysis (CCA) and complex-spectrum based convolutional neural network (C-CNN). Finally, the asynchronous pseudo-online performance of the SSMVEP BCI was evaluated. Signal analysis revealed that the SSMVEP stimulus was more robust to change in background compared to SSVEP stimulus in AR. The decoding performance revealed that the C-CNN method outperformed CCA for both stimulus types and NB background, in agreement with results in the literature. The average offline accuracies for W = 1 s of C-CNN were (NB vs. AB): SSVEP: 82% ±15% vs. 60% ±21% and SSMVEP: 71.4% ± 22% vs. 63.5% ± 18%. Additionally, for W = 2 s, the AR-SSMVEP BCI with the C-CNN method was 83.3% ± 27% (NB) and 74.1% ±22% (AB). The results suggest that with the C-CNN method, the AR-SSMVEP BCI is both robust to change in background conditions and provides high decoding accuracy compared to the AR-SSVEP BCI. This study presents novel results that highlight the robustness and practical application of SSMVEP BCIs developed with a low-cost AR headset.
In-Vehicle Intrusion Detection Based on Deep Learning Attention Technique

Vehicles currently are computerized, connected devices on the roads. The In-vehicle attacks are safety critical and life threatening, as intrusion does not affect data only like the other connected devices, it may affect people's lives. Currently In Vehicle Intrusion detection systems (IDS) goes beyond just a research, now it is a part of automotive Software[1], Traditional and current implemented solutions doesn't fit for all attacks as it doesn't cope with the new attacks, IDS that based on deep learning can adapt with the new attacks as it's not based on preset rules like in traditional techniques, We proposed an attention based[2] technique for detecting intrusion for CAN attacks dataset[3], The attention technique mainly used for natural language processing (NLP), we adapted this algorithm to detect in vehicle attacks, this technique outperforms long short term memory (LSTM) architecture and other machine learning algorithm in terms of precision, recall and F1 Score.
Research on Intelligent Supply Chain Mode of Business Big Data Technology Based on Machine Learning

In the information age, great changes have taken place in all fields of the world, and intelligence and automation have become important signs of development. Supply chain finance is trusted by commercial banks and small and medium-sized enterprises because of its personalized financing services. Commercial banks will focus on small and medium-sized enterprises with large financing needs in order to seek new profit growth points and break the traditional credit mode. Compared with the traditional loan business of banks, the financial risk of supply chain has its own characteristics and complexity. Since the rise of Big Data, the position of commercial banks in supply chain finance has changed, especially the emergence of a large number of supply chain financial service companies in the market. One of the keys for commercial banks to occupy the supply chain financial market is risk control. This paper starts with the financial risk management of commercial banks’ supply chain, analyzes the intelligent supply chain with machine learning algorithm, discusses the influence of commercial big data technology on the supply chain, and puts forward some suggestions on the construction and development of intelligent supply chain with commercial big data technology.
E-DARTS: Enhanced Differentiable Architecture Search for Acoustic Scene Classification

Neural architecture search (NAS) has attracted a lot of attention in recent years due to the growing interest in automating the neural architecture design. Recently, Differentiable Architecture Search (DARTS) has achieved promising results on the image classification task with a significantly reduced search cost to only a few GPU days. Specifically, DARTS relaxed the search space to be continuous and thus allowing the architecture to be optimized with gradient descent, where a set of simple candidate operations were used to construct the searched network. However, in our experiments, original DARTS achieved a limited classification accuracy of 50.45 ± 2.34% on the acoustic scene classification (ASC) dataset of DCASE2020 Task lA. In this work, we propose an enhanced version of DARTS, namely Enhanced-DARTS (E-DARTS), which uses residual and squeeze-excitation blocks to build more complex networks with DARTS. The aforementioned blocks were used as candidate operations for the super-network in addition to the convolutional and pooling layers used in original DARTS. The proposed approach has achieved a significantly improved classification accuracy of 64.57 ± 0.87% on the same dataset.
A weighted ℓ2 minimization using a Capon-like weighting scheme for DOA estimation

Sparse Iterative Covariance-based Estimation (SPICE) is a typically hyperparameter free algorithm for DOA (Direction Of Arrival) estimation. In this paper, we modify the SPICE criterion to form a weighted ℓ2 minimization. Instead of the weighted SPICE with the single weighting value, the proposed algorithm exploits multiple and different weighting values to further enhance the sparsity of the solution. Numerical examples demonstrate that the proposed algorithm produces better accuracy of DOA estimation and higher spatial resolution compared to several typical methods of sparse signal recovery.
An Extendable Auto-Position for UWB-based Indoor Positioning Systems

Indoor positioning systems with centimeter accuracy have been developed to support services in indoor locations where the Global Navigation Satellite System (GNSS) is unable to operate. The most ubiquitous method involves a tag measuring ranges with at least three static anchors by applying ultra-wideband (UWB) technology that provides centimeter ranging accuracy. However, most existing systems require intensive survey and calculation on anchor placements before deployment of the systems. As a result, installing such a system is complicated and time-consuming as well as restricting the expansion in a dynamic and ad-hoc indoor positioning system. In this paper, we implemented an auto-positioning system where the system will automatically obtain the positions of the anchors once added into the system without human intervention. Therefore, the time needed for the installation process will be reduced. In our system, there is no restriction on the number of anchors involved in the system. In other words, our system was intended to be ad-hoc and dynamic. We employed the Double-sided Two-way ranging (DS-TWR) mechanism as well as a simple trilateration calculation for position computation. In addition, to increase the position accuracy we put more effort on reducing ranging errors by applying the ADS-TWR formula and performing antenna delay calibration of the devices in the system. Even though the calibration process may be considered time consuming, the process is done once and for all. Our experiments showed that antenna delay calibration is crucial to achieve high position accuracy of the auto-positioning RTLS.
Earth re-entry capsule CFD simulations taking into account surface roughness and mass injection at the wall

Future space exploration missions will likely use capsule type ballistic re-entry vehicles that will re-enter the earth atmosphere at very high speed (velocities in the order of 11.7 km/s or higher, this compared to 7.5 km/s for the US Space Shuttle). Passive concepts will be employed for the deceleration, and the thermal protection system (TPS) needs to be dimensioned to protect the vehicle and samples from the effects of the aerodynamic heating. The technology readiness level of these passive concepts is today still rather low, leading to large design margins. This paper is concerned with the design of a possible earth re-entry capsule, it discusses the re-entry trajectory and heat loads encountered during descent. Detailed CFD simulations were made for three selected points on the re-entry trajectory. The ablation of the TPS material was taken into account through a mass injection boundary condition. The turbulence models employed in the study were modified to account for surface roughness due to ablation. Calculated heat fluxes and shear stresses for a smooth wall are compared with results from empirical correlations and showed a fairly good agreement. For rough walls, the CFD results show a substantial increase in convective heat flux, while the injection of mass at the wall reduces the convective heat flux.
A Novel SCMA Codebook Design Method Based on Low Error Probability Criteria

A method to construct codebooks for downlink sparse code multiple access (SCMA) systems is proposed in this paper. Codebooks for each sub-channel are based on the most commonly used QAM modulation. Then, low error probability criteria to measure the SCMA codebook performance in AWGN channel and Rayleigh fading channel are proposed. We propose to use simulated annealing algorithm to optimize the proposed codebooks based on the criteria. Simulation results show that the proposed codebooks exhibit excellent performance in both Rayleigh fading channel and AWGN channel, especially for high-order SCMA systems.
Performance of Cooperative NOMA Communications in Mixed Fading Channels

User cooperation in non-orthogonal multiple access (NOMA) is recognized to substantially enhance spectrum efficiency in dynamic resource-constrained systems. This paper examines the end-to-end performance of a two-stage cooperative NOMA network with decode-and-forward relaying over mixed fading channels. The source to the cell-center user and the cell-center user to edge-user links undergo either Rician or Rayleigh fading. Based on the signal-to-noise ratio (SNR) obtained, we derive the closed-form expression for the system's outage probability and the average error probability (BEP) of M-ary quadrature amplitude modulations for the mixed fading conditions. The simulation results demonstrate the Rician Kfactor's significance and enhanced system performance in a Rician/Rayleigh compared to a Rayleigh/Rician fading channel.
ACCIONAMENTOS DE RELUCTANCIA AUTOCONMUTADOS CON CONTROL DE PAR PARA DIRECCIÓN ASISTIDA ELECTRICA

Nowadays, the power steering is a standard device in commercial vehicles. The electric power steering is becoming an alternative to the traditional hydraulic power steering. Right now, the electric drive commonly used in this application is the DC brush motor drive. Today, different electric drives are seen as a future option but the best candidate seems to be the permanent magnet synchronous motor drive. However due to its constructive simplicity, absence of permanent magnets and fault tolerance the switched reluctance motor drive is an alternative that should not be ignored. In this article a switched reluctance motor drive is presented for a rack-type electric power steering for midsize cars with a rack force 10 kN. The drive consists of a switched reluctance motor of four-phase with 8 stator poles and 6 rotor poles fed through an electronic power converter with Power MOSFETs switches, an incremental encoder and digital torque control to minimize torque ripple and to mitigate disturbances that appear when clearing a fault and the drive works with one or two open phases. The drive has been built and tested in the laboratory and experimental results have confirmed its suitability as a drive for power steering.
A 0.75-5GHz Wide-Range 8-Phases Delay-Locked Loop With Low Noise Charge Pump

A 0.75-5GHz wide-range delay-locked loop (DLL) with a low noise charge pump is presented. A start-controlled circuit is used to avoid false locking. The DLL has 8-phases output clocks with a 45° phase difference, using a modified charge pump to reduce static phase error. This DLL has been designed in a 22nm CMOS process. The measured root-mean-square and peak-to-peak jitters are 0.5ps and 1.83ps at 5GHz, respectively. The power dissipation is 14.8mW for a supply voltage of 0.9V.