Miracle-dz/UniFAR-FTUs huggingface dataset:
Entropy-Based Ultra-Wide Band Radar Signals Segmentation for Multi Obstacle Detection

The development of safe intelligent transportation systems (ITS) has driven extensive research to come up with efficient environment perception techniques with a variety of sensors. In short range settings, Ultra Wide-Band (UWB) radars represent a promising technology for building reliable obstacle detection systems as they are robust to environmental conditions. However, UWB radars suffer from a segmentation challenge: localizing relevant regions of interest (ROIs) within its signals. This article proposes a segmentation approach to detect ROIs in an environment perception-dedicated UWB radar. Specifically, we implement a differential entropy analysis to detect ROIs. We evaluate our technique on a benchmark of more than 47 thousands samples. The obtained results show higher performance in terms of obstacle detection compared to state-of-the-art techniques, and a stable robustness even with low amplitude signals.
Accelerating End-to-End Deep Learning Workflow With Codesign of Data Preprocessing and Scheduling

In this article, we investigate the performance bottleneck of existing deep learning (DL) systems and propose DLBooster to improve the running efficiency of deploying DL applications on GPU clusters. At its core, DLBooster leverages two-level optimizations to boost the end-to-end DL workflow. On the one hand, DLBooster selectively offloads some key decoding workloads to FPGAs to provide high-performance online data preprocessing services to the computing engine. On the other hand, DLBooster reorganizes the computational workloads of training neural networks with the backpropagation algorithm and schedules them according to their dependencies to improve the utilization of GPUs at runtime. Based on our experiments, we demonstrate that compared with baselines, DLBooster can improve the image processing throughput by 1.4× – 2.5× and reduce the processing latency by 1/3 in several real-world DL applications and datasets. Moreover, DLBooster consumes less than 1 CPU core to manage FPGA devices at runtime, which is at least 90 percent less than the baselines in some cases. DLBooster shows its potential to accelerate DL workflows in the cloud.
On the Secrecy for Relay-Aided SWIPT Internet of Things System With Cooperative Eavesdroppers

In this paper, the secrecy performance in internet of things (IoTs) networks with cooperative eavesdroppers has been investigated, where simultaneous wireless information and power transfer (SWIPT) nodes perform as relay nodes. The secrecy energy efficiency (SEE) maximization problem for single relay node has been introduced to extend the life-time for IoT nodes without power supply. The optimization problem is firstly transformed into two sub-problems, and then each sub-problem is solved by bisection method. Furthermore, the model has been extended into multiple relay nodes and a novel relay selection algorithm for SEE maximization has been proposed. Finally, the advantage of the proposed algorithm has been verified from simulations.
Highly Sensitive Metamaterial Biosensor for Cancer Early Detection

A novel design of metamaterial biosensor for cancer early detection is proposed and analyzed. The suggested structure is based on two loops of hexagonal gold layers on a polyamide substrate. High field confinement is obtained in the sensing region with high absorption of 0.999 and high quality factor of 11.33 at f = 3.15 THz. The resonance frequency is sensitive to the sample refractive index variation. Therefore, high average sensitivity of 1649.8 GHz/RIU is achieved for cancer early detection through basal cell, breast cell, bervical cell, jurkat, MCF-7, and PC12. Further, high linear performance is obtained with good robustness against fabrication imperfection. The analysis is carried out using full vectorial finite element method.
Dual-inverter control synchronization strategy to minimize the DC-link capacitor current

This paper proposes the possible switching synchronization strategies of an automotive dual-inverter against the background of minimizing the RMS DC-link capacitor current. The publication mainly focuses on the straight-ahead motion of a rear axle with one electric drive per wheel. In addition to it, a dual-inverter consisting of two subinverters with a common DC-link capacitor is taken into consideration. These sub-inverters each have three phases and are based on a 2-level voltage-source-topology. To control the electrical machines, the continuous Space-Vector-Modulation strategy is used. For this application, different control signal synchronization strategies of the two sub-inverters of the dualinverter are presented. Apart from the existing strategies, this paper proposes a new method, which inherits a compromise between low complexity and high effectiveness. In contrast to previous publications, the resulting capacitor currents are quantified and subsequentlyevaluated. This novel quantification, which is dependent on the dual-inverter’s operating point, provides a base for targeted dimensioning of the capacitor. Moreover, this forms the foundation for further investigations of vehicle’s cornering, as well as for the possible synchronization of stand-alone inverters. In the context of this publication, the presented results are verified by experimentally determined data of a motor-inverter system.
EV Charging Station Integrated with Electric Railway System Powering by Train Regenerative Braking Energy

During the last years, investment in developing electric vehicles (EVs) has been increased as an important strategy by nations to reduce carbon emission and consumption of fossil fuels. On the other side, the power supplying of multitudinous EV charging stations (EVChSs) may overload the utility grid and cause indirect emissions. However, a substantial content of electric energy is generated by applying regenerative braking in electric railway systems (ERSs). Integration of ERSs and EVChSs in the strategic points, like parking areas close to ERS stations or rail freight intermodal terminals with targeted exploitation of trains regenerative braking energy (RBE) as an ancillary supply, can improve the energy efficiency in EVChSs and reduce the cost. In this paper, a new method is proposed to charge EVs by transferring RBE from ERS to EVChS taking advantage of interface power electronics converters. In the proposed strategy, a railway power flow controller (RPFC) integrated with DC/DC converter has been adopted to restore RBE. The simulation results are provided to confirm the effectiveness of the proposed strategy.
Research on Energy Management of Integrated Energy Systems Considering Multi-agent

With the rapid development of China's economy, energy shortage and environmental pollution gradually become serious. The integrated energy system provides a way of thinking for the transformation of China's energy structure. And with the development of the electricity market, different stakeholders will emerge in the integrated energy system. In view of the multi-energy coupling characteristics of the integrated energy system and considering the coexistence of multiple stakeholders, this paper first classifies the system into different entities based on the operating characteristics of each device in the system and the” Source-Grid-Load-Storage” as a clue. Then, according to the profit mode of each subject, the corresponding benefit model is established, and two energy management strategies, centralized and coordinated, are proposed to realize the goal of maximizing the overall income of the integrated energy system or equalizing the income of each subject through the dispatching of the energy management center. While ensuring the safe, stable and efficient operation of the integrated energy system, the interests of all subjects are protected.
Morphology-controlled synthesis of PbMoO4 crystals by a simple sonochemical method

PbMoO 4 nanoparticles and pagodalike microcrystals have been controllably prepared by a simple sonochemical method without any surfactants. The as-prepared powders were characterized by X-ray powder diffraction (XRD) and transmission electron microscopy (TEM). The results showed that the pH played a crucial role on the controllable synthesis of PbMoO 4 . Pagodalike microcrystals could be formed on acid condition, and nanocrystals were obtained in the case of pH 7 and pH 14. The synthesis mechanism was also discussed.
VIMA: Modeling and Visualization of High Dimensional Machine Sensor Data Leveraging Multiple Sources of Domain Knowledge

The highly integrated design of the electrified powertrain creates new challenges in the holistic testing of high-quality standards. Particularly test technicians face the challenge, that lots of machinesensor data is recorded during these tests that needs to be analyzed. We present VIMA, a VA system that processes high dimensional machine-sensor data to support test technicians with these analyses. VIMA makes use of the concept of interactive labeling to train machine learning models and the process model of knowledge creation in visual analytics to create new knowledge through the interaction with the system. Its usefulness is demonstrated in a qualitative user study with four test technicians. Results indicate that through VIMA, previously undetected abnormal parts, could be identified. Additionally, a model trained with labels generated through VIMA, was deployed on a test station, that outperforms the current testing procedure, in detecting increased backlashes and improved the test benches output by 15%.
Deep Residual Network-Based Fusion Framework for Hyperspectral and LiDAR Data

This article presents a deep residual network-based fusion framework for hyperspectral and LiDAR data. In this framework, three new fusion methods are proposed, which are the residual network-based deep feature fusion (RNDFF), the residual network-based probability reconstruction fusion (RNPRF) and the residual network-based probability multiplication fusion (RNPMF). The three methods use extinction profile (EP), local binary pattern (LBP), and deep residual network. Specifically, EP and LBP features are extracted from two sources and stacked as spatial features. For RNDFF, the deep features of each source are extracted by a deep residual network, and then the deep features are stacked to create the fusion features which are classified by softmax classifier. For RNPRF, the deep features of each source are input to the softmax classifier to obtain the probability matrices, and then the probability matrices are fused by weighted addition to producing the final label assignment. For RNPMF, the probability matrices are fused by array multiplication. Experimental results demonstrate that the classification performance of the proposed methods significantly outperform existing methods in hyperspectral and LiDAR data fusion.
A Variable Neighborhood Search Algorithm for Two-Dimensional Bin Packing Problem with Compatible Categories

Adding additional constraints in Bin Packing with Compatible Categories using Variable Neighborhood Search metaheuristic algorithm will solve more real-life size instances problem. This research study determined the best assignment of packing a distinct product in order to minimize the required number of bins. This method was performed by testing extensive computational experiments in different algorithms with a very large set of 2000 instances. The tested instances differ in terms of number of items, bin capacities, compatibility matrices and proportion of items that belong to the different categories, length, weight and width constraints. The researchers had chosen First Fit Category Decreasing for Bin Packing with Compatible Category, and Variable Neighborhood Search for Bin Packing with Compatible Category algorithms for comparative experiment of the result and an average of one (1) bin reduced was achieved. Therefore this indicates that a Variable Neighborhood Search algorithm for two-dimensional can effectively solve the problem of Bin Packing with Compatible Categories.
Strain-Insensitive Simultaneous Measurement of Bending and Temperature Based on Cascaded Long-Period Fiber Gratings Inscribed on Double-Clad Fiber

We demonstrated an optical fiber sensor capable of performing strain-insensitive simultaneous measurement of bending and temperature using cascaded long-period fiber gratings (LPFG’s) inscribed on double-clad fiber with a CO<sub>2</sub> laser at <inline-formula> <tex-math notation="LaTeX">$10.6~\mu \text{m}$ </tex-math></inline-formula>. In the interference fringe spectrum of the fabricated cascaded LPFG’s, we selected three transmission dips as sensor indicators for the simultaneous measurement of bending and temperature. The measured bending sensitivities of the three indicator dips were −427.63, −911.29, and 233.33 pm/<inline-formula> <tex-math notation="LaTeX">$\text{m}^{-1}$ </tex-math></inline-formula>, and their measured temperature sensitivities were 60.49, 56.44, and 74.61 pm/°C. Owing to their linear and uncorrelated responses to bending and temperature, we could simultaneously estimate applied bending and temperature with two of the three indicators by converting the measured wavelength shifts of the two selected dips with their pre-determined bending and temperature sensitivities. In particular, our sensor showed a very low strain sensitivity less than 0.05 pm/<inline-formula> <tex-math notation="LaTeX">$\mu \varepsilon $ </tex-math></inline-formula>, 25 times less than that of a conventional LPFG, which can significantly reduce strain-induced measurement uncertainty. With two optimal indicator dips chosen considering the measured perturbation responses of the three indicators, we carried out the simultaneous measurements of bending and temperature for arbitrary sixteen points of applied bending and temperature. The standard deviations of the measured bending and temperature values were <inline-formula> <tex-math notation="LaTeX">$\sim 0.059\,\,\text{m}^{-1}$ </tex-math></inline-formula> and ~0.85 °C, respectively.
Malware Detection by Eating a Whole APK

As Android is one of the most popular and widely used open-source mobile platforms, the security and privacy of Android apps are very critical, especially that over 6000 apps are added to the Google Play Store every day. This makes Android a prime target for malware. This paper proposes a modeling technique with experiments conducted using a dataset with about 10,000 benign and 10,000 malicious Android Application Packages (APK), in addition to other experiments that were conducted on the same dataset with a reduction in the number of benign files to be equal to 578 files. These files are analyzed using image classification techniques, where the whole APK file is converted into a grayscale image, and Convolutional Neural Networks (CNNs) with transfer-learning models are applied; to efficiently construct classification models for malware detection. Experiments have shown that the proposed technique has achieved favorable accuracy in the CNN model.
Analysis of Irreversible Thermodynamic Losses in Emissive-Energy Harvesters Based on Photon Beams

An analysis of irreversible energy losses that happen upon emissive-energy conversion is presented in this article. For this, we show that in these converters, the total amount of entropy produced in the conversion is proportional to the number of emitted photons, which justifies their description as counterparts of thermophotovoltaic engines. In the last part of this article, using a model of low-intensity photon beams and the detailed balance principle, we show the effects of irreversibilities on the output power and efficiency of emissive-energy harvesters.
A Group Decision Optimization Analogy-Based Deep Learning Architecture for Multiclass Pathology Classification in a Voice Signal

Non-invasive identification of abnormal voice using feature descriptors and machine learning classifiers has been the preference of many literatures. Using feature descriptors and time-frequency image with deep learning is a better alternative. Most voice pathology deep learning frameworks are based on binary classification model. Implementing a hardware system requires a network which can recognise the exact pathology. A thorough investigation of time-frequency analysis using a sophisticated deep learning system is required. Current work explores a non-invasive, robust and computationally non-intensive architecture for detecting multiclass laryngeal pathology. For applying in a realistic environment, the capability of a fully-connected network and a fully convolutional deep-learning voice denoiser network is initially investigated. Denoised training samples are used to create three different time-frequency image corpuses. These multivariate image datasets will train three improved variants of some state-of-the-art convolutional neural network models which use 3D convolution kernel. A “group decision analogy” technique is employed for training and attaining the global maxima of the current classification problem. The concept of group decision analogy stems from well-known clustering and swarm optimization algorithms. It uses three stages to optimise the predicted score. In the first level, these enhanced deep-learning models are trained using the three datasets to recognise healthy, hyperkinetic dysphonia, hypokinetic dysphonia, and reflux laryngitis classes. The prediction then undergoes a second and third stage. A score of 80.59% is obtained before using “group decision analogy” which is raised to 97.7% finally. The hypokinetic dysphonia and reflux laryngitis achieves 100% classification.
Network Flow Analytics: Multi-Class Classification of DDoS Attacks Based on OKNN

in the past decade DDoS attacks detection solutions have been one hot area in the research of cyberspace. Different techniques including machine learning and recent complex deep learning models were used and improved. However, only a few have used real network data and multiclass classification. In this paper, we challenged an Optimized K-Nearest Neighbor (OKNN) on a recent public dataset of the real network containing labeled classes of normal network flow and DDoS attacks. While performing minimum preprocessing to keep data original, OKNN with a tune of hyper-parameters such as; n_neighbors, metric, weights, n_jobs, has identified a normal traffic flow and DDoS attacks with high accuracy. The results of the experiment show that our model would perform better than its counterparts if only they are trained in the same conditions.
Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing With Genetic Simulated-Annealing-Based Particle Swarm Optimization

Smart mobile devices (SMDs) can meet users’ high expectations by executing computational intensive applications but they only have limited resources, including CPU, memory, battery power, and wireless medium. To tackle this limitation, partial computation offloading can be used as a promising method to schedule some tasks of applications from resource-limited SMDs to high-performance edge servers. However, it brings communication overhead issues caused by limited bandwidth and inevitably increases the latency of tasks offloaded to edge servers. Therefore, it is highly challenging to achieve a balance between high-resource consumption in SMDs and high communication cost for providing energy-efficient and latency-low services to users. This work proposes a partial computation offloading method to minimize the total energy consumed by SMDs and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of SMDs, allocated bandwidth of available channels, and transmission power of each SMD in each time slot. It jointly considers the execution time of tasks performed in SMDs and edge servers, and transmission time of data. It also jointly considers latency limits, CPU speeds, transmission power limits, available energy of SMDs, and the maximum number of CPU cycles and memories in edge servers. Considering these factors, a nonlinear constrained optimization problem is formulated and solved by a novel hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSP) to produce a close-to-optimal solution. GSP achieves joint optimization of computation offloading between a cloud data center and the edge, and resource allocation in the data center. Real-life data-based experimental results prove that it achieves lower energy consumption in less convergence time than its three typical peers.
Improve IoT Streaming QoS by Determining the Message Propotion in CoAP

Recently the Internet of Things (IoT) have gain much attention and, therefore, the demands for various streaming applications based on the IoT have increased. In this regard, the Constrained Application Protocol (CoAP) is a lightweight transmission protocol widely used in the IoT environment for providing streaming services. Generally, the CoAP protocol defines two message types i.e. confirmable message and non-confirmable message. However, since CoAP is not originally a transmission protocol design for streaming applications. Therefore, it cannot guarantee the reliability and real-time performance when streaming media is transmitted over it. In this paper, we propose a method to improve the QoS of streaming media transmission by dynamically changing the proportion of confirmable messages and non-confirmable messages. As the network conditions gradually gets better, the proposed method appropriately reduces the proportion of confirmable messages to meet the real-time requirements of streaming media. Finally, the simulation results show that the proposed scheme significantly enhances the QoS of the CoAP protocol for streaming service.
Degradation Analysis of Utility-Scale PV Plants in Different Climate Zones

Accurate performance modeling of utility-scale PV plants is crucial for reducing the risk of investment, as well as improving operations and maintenance. In spite of much research, the precise rate at which PV plants degrade is still not well known. This article uses a well-known methodology for calculating degradation rates to assess the impact of climate on long-term performance. The RdTools analytic approach is used to perform a comparative case study of two utility-scale PV plants with the same setup and almost identical technology yet located in distinct climates. A significant difference is observed between the degradation rates of each park. The median of the nominal system degradation rate measured at the dc-side of the inverters in the hotter and more humid climate is <inline-formula><tex-math notation="LaTeX">$ - 1.4{{\%}}/{\rm{yr}}$</tex-math></inline-formula>, with 95% of the inverters between <inline-formula><tex-math notation="LaTeX">$ - 1.5{{\%}}/{\rm{yr}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$ - 1.3{{\%}}/{\rm{yr}}$</tex-math></inline-formula>, whereas in the temperate climate the median rate is <inline-formula><tex-math notation="LaTeX">$ - 0.94{{\%}}/{\rm{yr}}$</tex-math></inline-formula>, with 95% of the inverters between <inline-formula><tex-math notation="LaTeX">$ - 1.0{{\%}}/{\rm{yr}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$ - 0.9{{\%}}/{\rm{yr}}$</tex-math></inline-formula>. These results suggest that climate zones must be included when evaluating the long-term performance of PV plants.