Miracle-dz/UniFAR-FTUs huggingface dataset:
A Forecasting Method of Dual Traffic Condition Indicators Based on Ensemble Learning

By the prediction of traffic conditions, the occurrence of traffic congestion can be warned in advance, so that the traffic managers can intervene in time, which can help to reduce the risk of traffic congestion. Therefore, aiming at the problem of traffic congestion, a prediction method for dual traffic condition indicators is proposed. The method for capturing spatial dependence based on the topology of roads and road driving direction is proposed to provide more flexible and targeted spatial features for predicting traffic conditions. In addition, according to the real-time and accuracy requirements of traffic conditions prediction, a novel model named dual-channel convolution block is designed to capture the temporal dependence of traffic conditions. Learning from the idea of ensemble learning, $K$ independent base models are trained to predict traffic condition at the same time, and a model fusion mechanism based on real-time traffic conditions is proposed to fuse the predictions of the base models so that the model can have stronger generalization ability to adapt to various noise data in real traffic conditions. The proposed method is validated on the traffic data sets and compares with the optimal model of all the existing models, the proposed method reduces MAPE of speed prediction by 12.1% and TTI prediction by 10.4%.
Acceleration Of International Tourism Improves Digital Payments Usage: The Case Of Thailand

This research examines the state revenue from international tourism on the rising usage of digital payment in Thailand from 2011 to 2020. The data are analyzed through multiple linear regression using the annual data of the World Bank and Thailand government. Surprisingly, the international tourism variable can explain 91 percent of the variation in digital payment usage in Thailand with the model is proven to be fit. Jarque Bera test shows the residuals are normal. Breusch-Godfrey Serial Correlation Lagrange Multiplier test proves that the residuals are free from serial correlation. Breusch-Pagan Godfrey depicts that the residuals are free from heteroskedasticity. In conclusion, there is a significant influence of the international tourism receipts on accelerated usage of digital payment in Thailand.
Process for Automatic Target Detection Using Small Marine RADAR

There are many collisions between vessels in the United States every year. Most small marine RADAR that are commercially available and affordable to most boaters do not have Automatic Radar Plotting Aids (ARPA) that can detect and provide warning of collisions. This paper discusses how a system could be implemented using small marine RADAR and the OpenCV computer vision library to automatically detect targets and how those targets could be displayed to the operator and warn them if necessary about an impending collision.
Loaded Reciprocal Filter for OFDM-based Passive Radar Signal Processing

The Range-Doppler maps of Passive Bistatic Radar (PBR) are often evaluated by means of the suboptimal batches algorithm. For OFDM waveforms of opportunity, a typical choice is to fragment the signal in batches length equal to the OFDM symbol length. This is shown to provide Signal-to-Noise Ratio (SNR) losses for both Matched Filter (MF) and Reciprocal Filter (RF), especially for long target ranges. We investigate the case of “non-OFDM fragmentation”, with special attention to the case of batch length much longer than a single OFDM symbol that can potentially reduce the losses at long ranges. While this is effective for the MF, we show that the RF filter in this case still provides high Peak-to-Sidelobes Ratio (PSLR) values but at the expense of significant SNR losses. Therefore, we propose the Loaded Reciprocal Filter (LRF) as a modified version of the RF for the non-OFDM fragmentation case, which is shown to provide a trade-off between SNR losses and PSLR. Finally, a simulated case study is presented for a DVB-T-based PBR, which clearly shows the effectiveness of the proposed LRF scheme when using the non-OFDM fragmentation.
Drone Control Using Electroencephalogram (EEG) Signals

In this project, we present the development of a system that can control a drone using a headset sensor that detects electroencephalographic (EEG) waves from the drone’s pilot when he/she performs facial gestures. The drone is controlled using specific facial expressions which are recorded using a commercial EEG headband, the OpenBCI EEG headband. The EEG headband uses electrodes to read the electric potentials from the brain. The EEG signals were recorded and analyzed using the OpenBCI GUI software. The data files recorded from the EEG headband were exported to Matlab to perform the signal conditioning, feature extraction, and design and training of the Artificial Neural Network (ANN) that was used to classify the facial gestures. For each data recording, three statistical values were computed: the standard deviation, root mean squared and mode. These values were used as the features for each facial gesture. The feature extraction data were used as the inputs to the ANN. The input to train an ANN consisted of a 9x45 array generated from the pilot performing fifteen recordings of each facial gesture. The target matrix was a 3x45 size, this is 3 classes and 45 recordings. The Neural Net Pattern Recognition tool from Matlab was used for the implementation of the ANN. After the ANN was trained to classify the 3 facial gestures, the output of the ANN was used to control the drone. The drone used in this project was a palm sized DJI Tello drone. Three facial gestures were selected to control the motion of the drone as follows: raising eyebrows, hard blinking and looking right. Results of the ANN training yielded a 97% accuracy in the classification of the facial gestures.
Applying Universal Chip Telemetry to Detect Latent Defects and Aging in Advanced Electronics

Mission critical applications and zero downtime tolerant systems are dominating semiconductor consumption, with AI at the Datacenter, 5G and Automotive being implemented on leading edge FinFET silicon technologies and advanced 2.5D and 3D packaging, required to perform at increasing functionality and performance profiles, for extended lifetime durations. With these advanced technologies, comes a plethora of new concerns to the product health and reliability. Out of these applications, Automotive deserves special consideration being subject to additional regulatory and safety concerns.This paper will discuss how deep data analytics based on Universal Chip Telemetry™ (UCT) offers a new approach to lifecycle health and performance monitoring in advanced electronics. By combining on-chip monitoring with machine learning inference in the cloud and edge, production quality is improved through advanced latent defect detection, while lifetime reliability is increased through degradation monitoring and predictive fault detection.
Analysis of Fixed-Point Quantization Noise in Multicarrier Transmitter Schemes

In this paper we investigate the effect of quantization in MultiCarrier (MC) transmitters which use an IFFT block. Three MC modulations are considered: Orthogonal Frequency Division Multiplexing (OFDM), Filter Bank MultiCarrier (FBMC) with PolyPhase Network (PPN) implementation (FBMC-PPN), and FBMC with Frequency Spreading (FS) implementation (FBMC-FS). The Quantization Noise Power (QNP) in the case of fixed-point implementation at the output of the three transmitters are expressed analytically and verified through simulation. The results show that the OFDM transmitter has the lowest QNP, while the FBMC-FS transmitter has the highest QNP in the case of fixed-point arithmetic.
Comparative Analysis of Controllers for Power System Dynamic Stability Improvement

A modern power system network is an interconnection of generating stations, long transmission lines, and distribution network loads. Interconnected power system networks face various challenges of instability. The presence of a dynamic disturbance in the power system network causes the problem of power system dynamic instability. Changes in the load, the use of high gain exciters, the use of high voltage direct current converters with negative damping, and the connection of poorly tuned generator excitations are examples of small-signal disturbances that disrupt a power system. The presence of a small-signal perturbation causes a constant increase or decrease in the alternator rotor speed, as well as an increase in the amplitude of the rotor oscillation. In this paper, these challenges were addressed by using suitable controllers to improve the synchronizing and damping torque of the system. The dynamic instability of the power system using a fuzzy logic controller (FLC) and STATCOM was investigated through simulation, individually and in comparison. The required data for simulation purposes was obtained from the IEEE 9 bus test system. The application of FLC is studied through simulation using the systems model with its linear simplified equations using MATLAB Simulink. The input parameters of the FLC used to enhance the power system dynamic instability are speed deviation and rate of change of speed of the synchronous alternator. The change of speed, rotor angle, and electrical torque deviation response versus time of full and 25% input mechanical power change was obtained. The simulation results show that the oscillation of the rotor angle and amplitude response of the system with the FLC is damped at 0.2s with an amplitude of 0.5 p.u, but the oscillation of the rotor angle and amplitude response of the proposed system with STATCOM is damped at 3.5s with an amplitude of 0.8 p.u. As a result, the proposed power system with FLC provides good damping of local mode oscillation, low overshoot and settling time response as compared to STATCOM.
Short-Circuit Capability with GaN HEMTs : Invited

Short-circuit capability with GaN HEMTs is demonstrated thanks to an integrated Short-Circuit Current Limiter (SCCL) and a commercial gate-driver with DESAT protection. The SCCL is applied to GaN HEMTs to achieve a sufficiently long short-circuit withstanding time (SCWT) while retaining competitive on-state resistance. The SCWT is tuned from 0.3 μs to 3 μs (a remarkable 10x increase) with a relatively small penalty in on-resistance, no leakage increase, no threshold voltage degradation and no reliability degradation. The gate-driver has desaturation detection (DESAT) and soft shutdown circuitry to achieve a fast protection response of 800 ns with high noise immunity greater than 100 V/ns. The combination of GaN power devices with SCCL and a commercial gate driver with fast DESAT and high noise immunity allows short-circuit protection and fail-safe operation of GaN power electronics for additional robustness in motor drive applications.
A STUDY OF VARIOUS VIEWPOINTS AND ASPECTS : SOFTWARE QUALITY PERSPECTIVE

The software quality is very important research of software engineering grown from the last two decades. The software engineering paradigm adopted by many organizations to develop the high quality software at affordable cost. The high quality software is considered as one of the key factor in the rapid growth of Global Software Development. The software metrics computes and evaluates the quality characteristics and used to take qu antitative and qualitative decisions for risk assessment and reduction. The multiple stakeholders can view the software quality in multiple angles with various aspects. In this paper we present multiple views of the software quality with respect to various quality aspects.
Exploring Fault Injection Attack Resilience of Secure IC Chips : Invited Paper

Fault injections and attacks on an integrated circuit (IC) chip using a variety of physical measures are reviewed. Laser fault injection (LFI) is highlighted for the essential vulnerability on the backside of an IC chip in flip-chip packaging. On-chip voltage waveform monitoring characterizes the voltage peaks induced by LFI and establishes the detection scheme against LFI attacks. The backside buried metal (BBM) technique is deployed by preventive measures for shielding from laser exposure, for avoidance and also waring of invasive laser attacks. The alleviation of backside LFI is proven by Si experiments with on-chip waveform monitoring.
Time-frequency-sparsity-based interference mitigation using order statistics for automotive radar

Recently, radar has been massively applied in driver assistance and autonomous driving system. However, radars in traffic may interfere each other, leading to decreased detection sensitivity or ghost targets. Over the past decades, there are multiple approaches proposed to solve this problem. In this work, we propose a sparsity-based method to mitigate the interference in the so-called time-frequency-slow-time (TFS) domain using order statistics for the first time. We evaluate and compare the performance of the proposed method with existing signal processing-based methods.
Reciprocity of the Turbulent Cascade Model

This work is focused on modeling the turbulent atmosphere and pointing out its important property, non-reciprocity. This feature plays a very important role in wireless optical channel communication, where optical beam routing in the forward and backward channels is important. Therefore, this work offers an introduction to the modeling of a cascade of turbulent cells by optical elements and proposes a model based on the equivalent temperature gradient (ETG) method. By combining individual mathematical procedures, it is possible to calculate the resulting effect of the turbulent cascade on the deflection of the optical beam.
A Canadian Perspective on Arctic and Polar Over-the-Horizon Radar

Two classes of Canada-specific Over-the-Horizon Radar (OTHR) systems are defined. Arctic OTHR (A-OTHR) systems are situated outside the auroral oval but perform some of their azimuthal scan in the direction of the oval. Clutter from ionospheric irregularities associated with the aurora must be suppressed over a finite azimuth extent, which can be done with a planar receive array that is either regularly sparse or oversampled sparse. Polar OTHR (P-OTHR) systems are situated inside the polar cap, and must suppress clutter from all azimuth angles. Auroral clutter control in P-OTHR systems requires a planar array that is either oversampled sparse or fully sampled.
Automatic Detection of Brain Tumor from CT and MRI Images using Wireframe model and 3D Alex-Net

Automatic detection of brain tumors from CT and MRI images is always an effortful task because of the complexity and heterogeneous images. Many neural networks architecture (NN) have recently been developed for segmentation and classification tasks and have proved quite successful. Studies that have taken into account the sizes of items have been rare; as a result, the majority of them show poor detection performance for tiny objects. This has the potential to have a significant influence on illness identification. Recently, the 3D neural network became popular because it can work with a large labeled dataset. We proposed a 3D Alex-Net-based architecture that can classify the different types of a brain tumors at an early stage. First, the image contour is identified and given to the classifier for class-wise identification. We tested our proposed approach on RSNA- MICCAI brain tumors and found that the proposed method delivers the highest accuracy, and the results provide a clear advantage for the classification of a brain tumor in medical images.
Performance Analysis of Open-Source Hypervisors for Automotive Systems

Nowadays, automotive products are intelligence intensive and thus inevitably handle multiple functionalities under the current high-speed networking environment. The embedded virtualization has high potentials in the automotive industry, thanks to its advantages in function integration, resource utilization, and security. The invention of ARM virtualization extensions has made it possible to run open-source hypervisors, such as Xen and KVM, for embedded applications. Nevertheless, there is little work to investigate the performance of these hypervisors on automotive platforms. This paper presents a detailed analysis of different types of open-source hypervisors that can be applied in the ARM platform. We carry out the virtualization performance experiment from the perspectives of CPU, memory, file I/O, and some OS operation performance on Xen and Jailhouse. A series of microbenchmark programs have been designed, specifically to evaluate the real-time performance of various hypervisors and the relevant overhead. Compared with Xen, Jailhouse has better latency performance, stable latency, and little interference jitter. The performance experiment results help us summarize the advantages and disadvantages of these hypervisors in automotive applications.
Efficient DBSCAN Implementation in a Multi-core DSP for FMCW Radars

The new trend of Multiple-Input Multiple-Output (MIMO) imaging radars in the automotive industry increases significantly the number of reflections in the point cloud, which demands low memory footprint solutions able to perform robust clustering with a low-latency. In this paper, we design, implement, and verify a tailored Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, dubbed Leaf-DBSCAN, whose heap memory is significantly reduced in comparison to the state of the art. The clustering strategy is modified to reduce the frequent distance queries between the target locations for cluster formation, and the clustering parameters adapt according to the spatial distribution of points. Leaf-DBSCAN is implemented in a RISCV-based processing element with constrained resources, and it is extensively validated using clustering standard datasets, and real data collected with a commercial 77-GHz Frequency Modulated Continuous Wave (FMCW) radar. The results display power and heap memory reduction in comparison to the traditional DBSCAN algorithm, while being able to process a high number of reflections in a small fraction of the frame time.
Printed Circuit Board Implementation of N-way Power Combiner with Isolated Input Ports

A planar implementation of N-way radial power combiners with isolated input ports is presented. While keeping the design simple, the presented combiner can be manufactured solely using printed circuit board technology, and as a result, has a low weight and manufacturing cost. As proof of concept, two power combiners are designed and built. First, a 6-way combiner working at the center frequency of 3.1 GHz with a fractional bandwidth of around 30% was designed. Our measurements of the fabricated combiner showed an input and output return loss of better than 10 dB in a frequency band of 2.65GHz-3.55 GHz where an isolation of higher than 23 dB, amplitude balance of ±0.1 dB, and phase balance of ±1 degree between the input ports were obtained. Then, a simple approach was implemented to widen the frequency band, achieving a fractional bandwidth of 80%. According to our measurements, the wideband combiner has an operating frequency band of 1.35-3.15 GHz, in which an input and output return loss of better than 10 dB with an isolation of higher than 21 dB, amplitude balance of ±0.15 dB, and phase balance of ±1 degree were achieved.
mmSCALE: Self-Calibration of mmWave Radar Networks from Human Movement Trajectories

We present mmSCALE, a practical self-calibration method that automatically estimates the relative position and orientation of a network of millimeter wave (mmWave) radars by post-processing the trajectories of detected targets that move within the radars’ fields of view (FoVs). This is a key component of multi-device mmWave radar deployments for indoor human sensing. As commercial mmWave radars have limited range (up to 6–8 m) and are subject to occlusion, covering large indoor spaces requires multiple radars. A fully self-contained system should estimate the location and orientation of each radar with no intervention by a human operator. To solve this problem, mmSCALE fuses target detections from multiple radars, yielding median errors of 0.18 m and 2.86° for radar location and orientation estimates, respectively. For this, mmSCALE requires no specific target trajectories or controlled conditions, it autonomously assesses the calibration quality over time, and is robust to occlusion and to the presence of multiple subjects.
An Empirical Study of Facial Expression Recognition Methods

Facial Expression Recognition (FER) is a field that is being well researched due to its great impact on decision-making application in the domain of medical, security, corporate and other systems. In this paper, a high-level overview of the FER techniques, databases, and analysis of significant research is performed in four-folds. First, the importance of FER applications in the industries is discussed. Second, the performance of the latest frameworks is analyzed. Third, the challenges faced in adaptability are described. Fourth, the gaps in the literature are addressed. The study reveals that FER techniques are useful in the development of AI applications for pattern analysis that work by data pre-processing, feature extraction, feature selection, and expression recognition. The main challenge faced is concerned with description-level data. This study provides a better understanding and updated information for future researchers who wish to explore this domain.
Subdomain Adaptive Learning Network for Cross-Domain Human Activities Recognition Using WiFi with CSI

WiFi-based human activity recognition has been widely used in many fields such as health diagnosis, intrusion detection and smart home. Most existing recognition methods can achieve a satisfying accuracy only in one domain, but low accuracy occurs when models are trained in source domain but are used in target domain. Meanwhile, considering finetuning network directly is impossible or easy to overfit with limited labeled target data, transfer learning based methods with domain adaptive layers are proposed to solve above problems but just aligning marginal distribution, which may lose massive fine-grained features. Based on this, we present an end-to-end deep subdomain adaptive network based activities recognition (DSANAR) using Channel State Information (CSI) that aligns marginal and matches conditional distribution simultaneously for more fine-grained features in each category of relevant subdomains based on a local maximum mean discrepancy (LMMD). Besides, by using a joint cross-entropy and an adaptive loss as training loss, DSANAR outperforms other state-of-art methods on an autonomous dataset with average 95.6% cross-domain accuracy.
Enterprise Patent Intelligence Analysis Based on Social Network Analysis

This article first summarizes the application of the social netw ork analysis ( SNA ) in the patent intelligence analysis. Then a practical application approach for enterprise patent intelligence analysis based on SNA is presented , w ith an empirical analysis using patent data about the solar air - conditioner to test the proposed approach. The emphasis of the analysis is placed on the technology development situation and the competitive - cooperative relationships of enterprises. The empirical example proves the great value of adopting SNA in enterprise patent intelligence analysis.