Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Target Classification And Identification Algorithm For 77G LFMCW Automotive Collision Avoidance Radar

Study on Electromagnetic scattering and micro-Doppler signatures of pedestrians are fundamentally important in pedestrian recognition for developing robust pedestrian collision avoidance systems. To develop algorithms for person, obstacles and cars detection, classification, identification and tracking, it is necessary to acquire the knowledge of the radar characteristics of these targets. Firstly, this paper describes the principle of proposed 77G LFMCW radar and analytical formulas were derived to measure the distance, velocity and angle information of targets. Secondly, based on actual data, we focused on investigation of the echo characteristics of pedestrians, obstacles and cars using a radar system operating at 77 GHz. Further research based on their difference of spectral scattering characteristics presented in this paper will contributes to the classification and identification algorithm, ultimately forming to their respective track points.
Interval Partitioning for Packet Classification in OpenFlow vSwitch

Packet classification is a key factor for choosing proper action for incoming packet and has to be done fast and effectively, especially in OpenFlow. But OpenFlow vSwitch technology doesn’t always allow to use some fast hardware technology for packet classification like TCAM (Ternary Content Addressable Memory). Instead of TCAM, decision tree methods are preferred solutions for fast classification in OpenFlow vSwitch in the literature. But most of these methods can cause the rule replication problem. As a result, while the duration of packet classification decreases, rule update duration increases. There are also rule partitioning methods in the literature to solve this problem, but the running time of these methods mostly depends on the number of rule fields. Also, some of these solutions don’t overcome the rule replication problem. At that point, the main question of this letter is that how can we make the rule partitioning fast by both preventing the rule replication and allowing fast packet classification and rule update in OpenFlow vSwitch? To solve the rule partitioning problem, we propose a greedy based interval partitioning strategy. Here, the running time of the partitioning algorithm only depends on the rule number. After partitioning, we propose to use HyperCuts to construct decision trees for fast packet classification and rule update. According to performance evaluation results, we do the rule partitioning and rule updates faster than the PartitonSort method with the percentage of 88, 15, respectively. Also, we classify packet faster than the TupleMerge method with the percentage of 40 for online and 50 for offline scenarios.
Studying the Aging Evolution of Oil-Paper Insulation Comprising of a Gas Cavity Under the Stress of AC and DC Voltage Depending on Partial Discharge and Dissolved Gases Measurements

The oil-paper insulation system is widely used for the insulation purposes of power transformers as a result of its ability to withstand high thermal and electrical stresses. The presence of a gas cavity between impregnated papers layer is a serious problem. When localized breakdown takes place within this cavity, partial discharge (PD) occurs and ages the rest of the insulation system. This aging phenomenon declines the expected life time of the insulation system and subsequently the life time of the power transformers. Dissolved gas analysis is a recommended technique that is used in order to assess the state of the insulation system inside the transformers. In this paper, PD is recorded in the case of gas cavity presence between the layers of the impregnated papers under the effect of AC and DC voltage stress. This record is extended from the instance of PD inception until the breakdown of the whole insulation system. Furthermore, the concentration of the gases that dissolve in the insulating oil due to the aging of the impregnated paper is measured eight times during the record of the PD. Finally, the degradation of the insulating papers is studied optically through the use of a microscope at different stages of the aging test. Depending on PD measurements, dissolved gas analysis (DGA) results, and the optical study of the paper aging, the evolution of the aging of oil-paper insulation system as a result of internal PD is declared when AC and DC voltage stresses are applied. It is obtained that the aging evolutionary process totally differs with the alteration of the applied voltage.
High Throughput WMN for the Communication in Disaster Scenario

The Wireless mesh network (WMN) is a popular network architecture used to support disaster recovery operations. However, few research works have addressed the capacity problem of such a network. This is due to the assumption that the communication network in disaster scenario is built to support services with a low rate requirement like delay tolerant messages. At the same time, the demand for higher data rates has increased in recent years due to the digitalisation of rescue operations and the use of new services (e.g. VoIP, drones and robots). Therefore, the capacity of the WMN is becoming a central issue in the design of future WMNs. This paper proposes a Layer 1 cluster-based network to solve the throughput bottleneck in the WMN. The proposed architecture is evaluated by several real world measurements. The obtained results are compared with the theory. The proposed solution shows a throughput improvement compared to a single-radio WMN and a multi-radio WMN using the CoMTaC channel allocation strategy.
Genotyping Single Nucleotide Polymorphisms in Mandarin Cultivars Using High Resolution Melting Analysis

High resolution melting analysis(HRM)is capable of detecting changes in melting temperature(Tm)of double strand DNA sequences caused by single nucleotide changes,and is suitable to genotype genetic samples using known single nucleotide polymorphic site(sSNPs). Citrus reticulata ESTs from NCBI EST database were searched for SNPs,and 5 SNPs were selected to genotype 11 mandarin and 1 sweet orange cultivars using HRM genotyping of small amplicons. The results showed that,at a given SNP site,a heterozygous genotype could be easily and clearly distinguished from a homozygous genotype by HRM,and 2 different homozygous genotypes could also be easily identified when temperature was calibrated. All 5 SNPs were found polymorphic in analyzed citrus samples,with an average polymorphic information content(PIC)value of 0.3190,indicating that these samples are highly heterozygous at these SNP sites.
A detailed survey of Artificial Intelligence and Spftware Engineering: Emergent Issues

Recently, Artificial Intelligence (AI) has become a trend and part of our typical mainstream albeit its existence ages ago. AI is at the top of the list of trending technologies and one of the hottest topics. In the coming decades, the world is going in the direction of increasing automation and cognitive technologies. This study attempts to explore and discuss the tipping points and evolution of AI. Further, it envisions the future impact that the advances of AI would have on occupations in various sectors such as technological unemployment. The study also tackles on the ethical and policy concerns involved in this upcoming AI evolution. Organizations should be able to manage both humans and robots at the same time. Software engineers’ roles will be completely changed. Thus, we need to redesign education in a way that can make the future generations ready for the future that has a totally different requirement than ours. Policies should also be in place. If the AI applications are controlled by particular parties, AI will only represent that narrow and biased specific segment. The researchers should play an important role to make AI a beneficial invention rather than a risk to humanity.
Beamforming and Deep Models Integrated Multi-talker Speech Separation

Recently, although the traditionally proposed Permutation Invariant Training (PIT) has attracted much attention, it performs poorly on datasets of unknown number of speakers. In this paper, we propose an approach based on beamforming and deep models (BDM) to solve the problem mentioned above. BDM firstly estimates the number of speakers by sound source localization algorithm and then enhances the target speech with beamforming in spatial domain. Subsequently the supervised deep models are used to extract the clean speech of the target speaker in time and frequency domain. Experimental results show that the BDM can improve the separation performance as well as speech intelligibility compared with the single channel and multi-channel PIT when tested on two-speaker, three-speaker and four-speaker mixtures, respectively.
Research on Modeling of Air Target Motion Characteristics and Track Identification Method

The track is an important basis for the classification and identification of radar air targets. In this paper, the equations of the aircraft motion model, including the horizontal straight, the horizontal arc and the vertical arc, are firstly derived. Then, by taking the Earth-fixed coordinate system as a bridge, the coordinate transformations are used to obtain the radar target parameters, i.e., the range, azimuth and pitch angle. The influence of the Earth curvature in long-distance conditions is compensated. The track recognition method based on radial basis function (RBF) neural network is further designed. Finally, this paper established four flight situations of the target track on the basis of three models above, aiming to obtain radar measured data. The necessity of coordinate transformation is verified by quantitative analysis of long-distance effects. A large number of track recognition tests are performed to analyze the influence of track fluctuations on the recognition rate. Experiments show that, when the track initial position and initial velocity fluctuations are in a certain area, this paper can achieve a high target recognition rate, which verifies the correctness and effectiveness of the proposed method.
Power Cycling Capability Comparison of Si and SiC MOSFETs under Different Conduction Modes

Different from the single conduction mode of IGBT and diode, the MOSFET has three conduction modes and corresponding to three different power cycling test (PCT) methods respectively. In order to fully understand the difference between these conduction modes, the Si and SiC MOSFETs are selected due to their different body diode characteristics to compare the power cycling capability under the same thermal stress but in different modes. The experimental results show that PCT in different conduction modes have an impact on the failure mechanism and lifetime, and the corresponding PCT method must be selected according to the actual working conditions of MOSFETs.
Paleoclimate Shaped Bluefish Structure in the Northern Hemisphere

ABSTRACTBluefish (Pomatomus saltatrix), a highly migratory cosmopolitan predator, is the only extant representative of the family Pomatomidae. It has been the subject of many studies due to its commercial and recreational value, but much less research has been conducted on its global population structure. Here we investigate the population structure of this species and the effects of present and past oceanographic barriers to dispersal in its North Atlantic, Mediterranean, Marmara, and Black sea populations. We employed mitochondrial (cytochrome b and cytochrome oxidase subunit I genes) and nuclear (eight microsatellite loci) DNA as molecular markers. Three main genetic units of Bluefish were identified: American (West Atlantic waters), Spanish (East Atlantic–Western Mediterranean regions), and Turkish (Eastern Mediterranean, Marmara, and Black seas). Our results suggested that Bluefish is panmictic in the northwest Atlantic Ocean but not in the Mediterranean Sea. The common ancestor of the studied popula.
SigFox-based Internet of Things Network Planning for Advanced Metering Infrastructure Services in Urban Scenario

SigFox is a Low Power Wide Area Network (LPWAN) technology using unlicensed frequency bands with Ultra Narrow Band technology. It has advantages in terms of very low power consumption, high receiver sensitivity, and the cheap cost of end devices. SigFox technology is based on Link Quality Control (LQI). One parameter is the division of zones is based on radio configuration, where Indonesia included in zone 2 with radio configuration RC4. SigFox is very suitable as a solution in terms of radio connectivity. In this study, an analysis of the Internet of Thing (IoT) network design was carried out in the East Java province of Indonesia, particularly in the cities of Surabaya, Sidoarjo, and Gresik as Urban Scenario. The Advanced Metering Infrastructure services include electricity, water, gas, and fuel. From the simulations that have been carried out, the optimal number of gateways obtained respectively 34 sites for the Surabaya area with the average signal level received was −78 dBm, and SNR value was 17.27 dB. While five gateways need for the Sidoarjo area with the average signal level received was −89.68 dBm, and SNR value was −1.06 dB, and eight sites for the Gresik area with the average signal level received was −89.14 dBm, and SNR value was −1.12 dB.
Optimal Capacity Design and Operation of Energy Hub Systems

This article takes an integrated view of optimized capacity design and operation of islanded energy hubs. We consider energy hubs that incorporate emerging distributed energy resources as well as energy storage devices and fully support electricity and heat demand of an islanded installation. Both battery and hydrogen storage are incorporated. To explicitly account for the stochasticity in renewable energy generation and load, the energy hub capacity design problem is first expressed as a chance-constrained optimization problem and then reformulated as a robust counterpart problem, where battery charging/discharging responds to stochastic renewable energy generation and load realizations through a control policy. In particular, an affine policy is considered, enabling a linear program formulation of the problem. To reduce conservativeness of the design, an iterative algorithm is proposed, where the interaction between a chance-constrained design problem and a validation problem is achieved through a scalar auxiliary variable. The design result demonstrates a balanced tradeoff between robustness and cost efficiency. After the energy hub has been designed, we propose a bi-level operating strategy, where a day-ahead schedule is optimized at the higher level and model predictive control is used for tracking the schedule in real time at the lower level. Finally, we discuss the potential for increasing the reliability of energy hub systems while decreasing operational cost by sharing energy between multiple energy hubs through networking.
CaGAN: A Cycle-Consistent Generative Adversarial Network With Attention for Low-Dose CT Imaging

Although lowering X-ray radiation helps reduce the risk of cancer in patients, low-dose computed tomography (LDCT) technology usually leads to poor image quality, such as amplified mottle noise and streak artifacts, which severely impact the diagnostic results. To improve diagnostic performance, we propose an algorithm based on a cycle-consistent generative adversarial network (CycleGAN) to suppress noise and reduce artifacts. In addition, we include attention mechanisms in the proposed network to expand the receptive field and capture richer contextual dependencies. Unlike traditional methods that manually match similar local blocks, our proposed method can autonomously learn the relationship between local features and their global dependencies. Specifically, two different types of attention modules (criss-cross self-attention (CCSA) and channel attention (CA)) are adopted to enhance feature interdependencies in the spatial and channel dimensions separately. Because of the CCSA mechanism, noise and artifacts can be restored using cues from all local feature locations; the CA mechanism adaptively reassigns the weights of each feature map. Furthermore, we also performed a diagnostic quality assessment of the results and ablation studies of the loss functions and the structural modules, which showed the validity of our proposed method. Extensive experiments show that our proposed method achieves better metrics and visual effects than state-of-the-art methods.
Network Protocol Automatic Vulnerability Mining Technology Based on Fuzzing

With the increasing complexity and importance of network applications, the security requirements for network protocols are getting higher and higher. Fuzzing, as one of the important Testing techniques to discover undisclosed vulnerabilities, tests the security of network protocols by producing and sending large amounts of data and injecting them into software, many important vulnerabilities such as denial of service, buffer overflows, and formatting strings can be found. Manual generation of test cases can be more appropriate to the target under test, but manual Fuzzing requires accurate understanding of network protocol details and tedious work to construct a large number of test data sets, resulting in limited coverage and poor effect. In order to solve this problem, this paper first investigates the types of vulnerabilities, summarizes the fuzzy strategies, and then constructs a fuzzer based on the existing framework, adopts mutation strategy to construct malformed network packets, which are sent to the tested target for testing. The results show that this method is more efficient than manual analysis in vulnerability mining, which provides a good foundation for improving the security of network protocols.
Research on Delamination Damage of YBCO Tape Under DC Impact Based on Hilbert–Huang Transform

The short-circuit fault current on the dc side affects the operation safety of the multiterminal flexible HVdc transmission system seriously. The resistance-type superconducting fault current limiter (RSFCL) has received extensive attention in limiting the dc impact current because it has fast response speed and zero impedance at steady state. YBCO tapes are susceptible to delamination damage under large dc impact and it will affect the operation of RSFCLs. However, the existing methods of distinguishing the delamination damage of YBCO tape need an extra observation or testing after dc impact. These methods cannot meet the requirements for monitoring and protection when RSFCLs coming into use in the power system. In this article, the Hilbert–Huang transform (HHT) is used to analyze the delamination damage of YBCO tapes under dc impact to avoid extra observation and testing. The dc impact test on the YBCO tape sample is carried out and the test data are obtained. HHT is applied on the joule thermal power waves (TPWs) of the YBCO tape samples obtained from the dc impact test as the input signal. Hilbert spectrums and instantaneous energy spectrums (IEs) of the TPW signals are calculated and the difference between IEs of TPW signals of intact samples and delaminated samples is shown and explained. The feasibility of IEs of TPW signals can be as a monitoring target to avoid the stratification damage of YBCO tapes. The proposed method may aid in the on-load monitoring on RSFCLs and provided a new tack for related research.
Robust Self-Sparse Fuzzy Clustering for Image Segmentation

Traditional fuzzy clustering algorithms suffer from two problems in image segmentations. One is that these algorithms are sensitive to outliers due to the non-sparsity of fuzzy memberships. The other is that these algorithms often cause image over-segmentation due to the loss of image local spatial information. To address these issues, we propose a robust self-sparse fuzzy clustering algorithm (RSSFCA) for image segmentation. The proposed RSSFCA makes two contributions. The first concerns a regularization under Gaussian metric that is integrated into the objective function of fuzzy clustering algorithms to obtain fuzzy membership with sparsity, which reduces a proportion of noisy features and improves clustering results. The second concerns a connected-component filtering based on area density balance strategy (CCF-ADB) that is proposed to address the problem of image over-segmentation. Compared to the integration of local spatial information into the objective functions, the presented CCF-ADB is simpler and faster for the removal of small areas. Experimental results show that the proposed RSSFCA addresses two problems in current fuzzy clustering algorithms, i.e., the outlier sensitivity and the over-segmentation, and it provides better image segmentation results than state-of-the-art algorithms.
Performance Comparison of M-ary and CSK Spread Spectrum Communication over Deep-sea Vertical Channel

This paper is concerned with two classical modulation schemes of M-ary and CSK in spread spectrum technology, our treatment is focused on the communication performance of two schemes in the deep-sea vertical channel. We process long-term experimental data with sea-depth from 1207m to 3870m for three times, and present the deep-sea vertical channel properties such as the time-delay spread, the amplitude of the successive paths and the time-varying multipath structure. The influence of the deep-sea vertical channel on the communication performance of the two schemes is analyzed, their performance are compared based on the experimental data.
A Novel Extension for e-Safety Initiative Based on Developed Fusion of Biometric Traits

Safety is one of the central elements of society functioning. The eSafety aims at increasing road safety through the use of intelligent integrated safety systems. This article presents the concept and PCB implementation of an on-board compact eCall device that can be installed at the owners’ request in a used vehicle. The prototype includes accident and rollover detection, passenger detection, and GSM communication with the eCall PSAP service center. Two accident detection algorithms are implemented: the first relates to the collision detection based on exceeding the threshold of acceleration using an accelerometer to measure the dynamic force caused by movement and the gravity force, the second – the rollover detection based on exceeding the threshold of the vehicle angle of inclination using data coming from the accelerometer and a gyroscope. The prototype was tested in eight different test scenarios using laboratory accident simulators as well as during driving. The proposed solution of passenger detection is based on images coming from a built-in 360° view camera. In the authors’ approach the obtained images were transformed into linear ones and subject to subsequent operations – initial processing, object detection and redundant detection filtering. The authors utilize biometric methods in object detection such as Viola-Jones and Single Shot MultiBox Detector for face localization, and You Look Only Once for whole body detection. An additional testing mechanism for passenger detection are seat pressure pads that are independent of camera detection. All necessary sensors and communication components verified by practical experiments have been integrated on the custom designed motherboard with the NVIDIA Jetson Nano processing module installed. The tests confirmed the performance is sufficient for rapid accident detection, real-time image analysis and all mandatory eCall communication functions. Results of experiments were promising, providing high accident and passenger detection rates.