Miracle-dz/UniFAR-FTUs huggingface dataset:
On Secure UAV Communication Systems with Randomly Located Eavesdroppers

Unmanned aerial vehicles (UAVs) have been extensively utilized in various scenarios due to their flexible mobility and rapid on-demand deployment. It becomes necessary and urgent to examine the secrecy performance of the UAV communication systems due to the open characteristics of the transmission technologies employed in UAV systems. In this paper, we investigate the physical layer security of the UAV communication system, in which the ground base station sends secret messages to the UAV hovering in the air, while a random number of eavesdroppers are randomly located around the ground base station. Using the Poisson point process theory, we obtain the exact and approximate expressions of the cumulative distribution function for the signal-to-noise ratio of the ground-to-air link, and then the analytical expressions of the secrecy outage probability and the average secrecy rate are derived. Finally, the accuracy of the analytical results is validated by Monte Carlo simulation.
A New Approach for Scheduling Job with the Heterogeneity-Aware Resource in HPC Systems

Scheduling in High Performance Computing (HPC) platforms is often a challenge with a huge range of researches and a noticeable phenomenon is a divergence between theory and practice. Especially, the heterogeneity of jobs today is becoming more popular, while a static scheduler is often prohibited by the runtime behavior as well as the multi-demand of programs running on a specific system. The hitherto conventional solution of scheduling jobs is only partially satisfying, because the program's behavior may differ significantly when comparing to the original scale. In this paper, we develop a dynamic scheduler based on characteristics and runtime behaviors of submitted jobs on a CPU/coprocessor-based cluster. Fundamentally, the behaviors of large-scale programs on an HPC system can be extracted from the history or log files, then draw a relational model between its characteristic and an objective function. This problem can be fitful in applying machine learning to find a scheduling function for job submission. We use a two-stage approach: first using simulation to simulate all situations of run jobs, in order to generate a dataset which then is used for training models with machine learning. This model enables to improve the scheduling performance and fit well the relationship between the job's characteristics and criteria in practice. More importantly, our experiments highlight the influence of the heterogeneity-aware resource over schedulers.
An Energy-aware Anchor Point Selection Strategy for Wireless Rechargeable Sensor Networks

With the development of wireless power transfer (WPT) technology, it has become a good method to solve energy problem in wireless sensor networks (WSNs). In this paper, a mobile wireless charging vehicle (WCV) is employed in wireless rechargeable sensor networks (WRSNs) to periodically visit every anchor point and charge multiple nodes within its charging radius simultaneously. Traditional anchor point selection strategies for WRSNs almost focus on the location relationship, which take little consideration of energy consumption of sensor nodes. We propose an energy-aware anchor point selection (EAPS) strategy to minimize overall charging time, which consists of stopping time at anchor points and traveling time. We first group nodes into clusters based on the charging radius, and then apply genetic algorithm (GA) to determine the anchor point in every cluster. The simulation results show that EAPS strategy outperforms several state-of-the-art methods in terms of total traveling length and charging time.
ACWB: Access-Characteristic Write Buffer Management Scheme for Flash Memory

Compared with traditional hard disk drives (HDD), solid state drives (SDD) have distinct characteristics, such as efficient continuous access and random reading capability. Using of SSD is greatly limited due to the out-of-place update and endurance problem. Many flash-aware buffer schemes have been proposed to reduce write and erase operations. However, a good buffer management scheme should reduce the effects of random writes and suit access patterns. In this paper, we propose an access characteristic write buffer management scheme (ACWB) on flash memory that integrates write buffer and cost regulation based on access characteristic. Experimental results show that ACWB increases the response time by 35%, reduces erase times by 25%, and improves the hit ratio by 27% on the average compared with current flash-aware write buffer schemes.
New Bio-Marker Gene Discovery Algorithms for Cancer Gene Expression Profile

Several hybrid gene selection algorithms for cancer classification that employ bio-inspired evolutionary wrapper algorithm have been proposed in the literature and show good classification accuracy. In our recent previous work, we proposed a new wrapper gene selection method based-on firefly algorithm named FF-SVM. In this work, we will improve the classification performance of FF-SVM algorithm by proposed a new hybrid gene selection algorithm. Our new biomarker gene discovery algorithm for microarray cancer gene expression analysis that integrates f-score filter method with Firefly feature selection method alongside with SVM classifier named FFF-SVM is proposed. The classification accuracy for the selected gene subset is measured by support vector machine SVM classifier with leave-one-out cross validation LOOCV. The evaluation of the FFF-SVM algorithm done by using five benchmark microarray datasets of binary and multi class. To show result validation of the proposed we compare it with other related state-of-the-art algorithms. The experiment proves that the FFF-SVM outperform other hybrid algorithm in terms of high classification accuracy and low number of selected genes. In addition, we compare the proposed algorithm with previously proposed wrapper-based gene selection algorithm FF-SVM. The result show that the hybrid-based algorithm shoe higher performance than wrapper based. The proposed algorithm is an improvement of our previous proposed algorithm.
Wideband, Flat-Top, SOI Filter using an Apodized Sub-Wavelength-Grating Contra-Directional Coupler

We present a wideband, flat-top, optical add-drop filter using an apodized sub-wavelength-grating contra-directional coupler on a silicon-on-insulator platform. Our device achieves a >30 nm wide, flat-top drop-port response, centered at 1545 nm, with an extinction ratio >20 dB.
Low-Cost Analysis of Size-Bandwidth Trade-Offs for Compact Rat-Race Couplers Using Surrogate-Assisted Multi-Objective Optimization

Design of rat-race couplers (RRCs) with respect to conflicting objectives such as size reduction and bandwidth enhancement is a challenging task. Generally, the trade-offs between design requirements can be found using multi-objective optimization (MO). However, constrained MO is a considerable challenge for multi-objective algorithms, especially those involving population-based metaheuristics. Yet, this aspect is an important one in the design of RRCs. In this work, a deterministic method for a low-cost multi-objective optimization of couplers is proposed. The algorithm combines a local-search routine with a heuristic technique for generating candidate designs between pairs of points optimized in the previous iterations. Owing to an appropriate constraint definition, the local algorithm generates compromise solutions while maintaining the acceptable RRC performance. To ensure a low design cost, a local search method is embedded in a surrogate-based optimization loop. The proposed framework is used to analyze size-bandwidth trade-offs for a compact broadband coupler. The structure performance is compared to other RRCs optimized in the multi-objective setup.
Equivalent Force Analysis of a Schottky Depletion Layer MEMS Actuator

Depletion layer MEMS actuators have been demonstrated in the past in p-n junctions as well as Schottky barrier interfaces to be effective transducers for internal transduction of high Q MEMS resonators without the need for piezoelectric materials or small resonator-electrode gaps. However they remain under-utilized compared to conventional capacitive electrostatic and piezoelectric transducers, and consequently have scant analytical studies in literature. In this paper we present an analytical model based on equivalent force analysis to derive expressions for the displacement amplitude and transduction efficiency of a Schottky barrier embedded in a micro-cantilever resonator. The methodology presented here is widely applicable to any resonators conducive to excitation through internal transducers.
Studying Aging and Soft Error Mitigation Jointly under Constrained Scenarios in Multi-Cores

Soft errors and aging are two substantial reliability-related problems in today’s computing systems. While soft errors are transient in nature, they can lead to wrong application outputs and even application failures. However, aging has permanent effects and results in slower performance and timing errors in processors. In this paper, we will discuss different approaches how these reliability threats can be mitigated in multi-core systems for performance/power-constrained scenarios. Different research challenges and corresponding solutions will be described which jointly target the above-mentioned reliability issues while also considering additional problems like process variation and dark silicon. These solutions are implemented in different system layers and make use of an analysis of and adaptation to the application properties. Moreover, they leverage the opportunities offered by multi-core systems, for instance, to efficiently enable redundant multithreading.
Scale Voting With Pyramidal Feature Fusion Network for Person Search

Person search aims to find the target person among a large gallery set of real scene images. Candidates should be detected and cropped before recognizing their identifications. The detection process results in a great variance of object image scales. To address this we propose Pyramidal Feature Fusion Network that integrates the top-down mid-level features to provide multi-level feature outputs. Furthermore, we propose to apply each of the mid-level features independently in ranking and we design a ranking algorithm named Scale Voting that votes among these ranking results from different feature levels to get the final ranking order. In this approach, we can make better use of the diversity and consistency information that is hidden in different mid-levels of features. The proposed algorithm achieves the state-of-the-art performance on prevalent person search datasets.
Joint PHY/MAC Layer AN-Assisted Security Scheme in SVD-Based MIMO HARQ system

With the explosive data growth arise from internet of things, how to ensure information security is facing unprecedented challenges. In this paper, a joint PHY/MAC layer security scheme with artificial noise design in singular value decomposition (SVD) based multiple input multiple output hybrid automatic retransmission request (MIMO HARQ) system is proposed to resolve the problem of low data rates in existing cross-layer security design and further adapt to the high data rate requirement of 5G. First, the SVD was applied to simplify MIMO systems into several parallel sub-channels employing HARQ protocol. Then, different from traditional null space based artificial noise design, the artificial noise design, which is dependent on the characteristics of channel states and transmission rounds, is detailed presented. Finally, the analytical and simulation results proved that with the help of the proposed artificial noise, both the information security and data rate performance can be significantly improved compared with that in single input single output (SISO) system.
Joint-Sparse-Blocks Regression for Total Variation Regularized Hyperspectral Unmixing

Sparse unmixing has attracted much attention in recent years. It aims at estimating the fractional abundances of pure spectral signatures in mixed pixels in hyperspectral images. To exploit spatial-contextual information present in the scene, the total variation (TV) regularization is incorporated into the sparse unmixing formulation, promoting adjacent pixels having similar not only endmembers but also fractional abundances, and thus having similar structural sparsity. It is therefore hoped to impose joint sparsity, instead of classic single sparsity, on these adjacent pixels to further improve the unmixing performance. To this end, we include the joint-sparse-blocks regression into the TV spatial regularization framework and present a new unmixing algorithm, termed joint-sparse-blocks unmixing via variable splitting augmented Lagrangian and total variation (JSBUnSAL-TV). In particular, a reweighting strategy is utilized to enhance sparsity along lines within each block. Simulated and real-data experiments show the advantages of the proposed algorithm.
Design and Training of Deep CNN-Based Fast Detector in Infrared SUAV Surveillance System

Real-time detection of small unmanned aerial vehicle (SUAV) targets in SUAV surveillance systems has become a challenge due to their high mobility, sudden bursts, and small sizes. In this study, we used infrared sensors and Convolutional Neural Networks (CNN)-based detectors to achieve the real-time detection of SUAV targets. Existing object detectors generally suffer from a computational burden or low detection accuracy on small targets, which limits their practicality and further application in SUAV surveillance systems. To solve these problems, we developed a real-time SUAV target detection algorithm based on deep residual networks. In order to improve the sensitivity to small targets, a laterally connected multi-scale feature fusion approach was proposed to fully combine the context features and semantic features. A densely paved pre-defined box with geometric analysis was used for single-stage prediction. Compared with the state-of-the-art object detectors, the proposed method achieved superior performance with respect to average-precision and frames-per-second. As the training set was limited, to improve generalization, we investigate the benefits introduced by data augmentation and data balance, and proposed a weighted augmentation approach. The proposed approach improved the robustness of the detector and the overall accuracy.
Non-linear Multiphysic Numerical Study of Bone Tumor Hyperthermia Using Magnetic Biomaterials

This work deals with the non-linear and multiphysic modeling of the hyperthermia treatment of bone tumors using novel biomaterials loaded with magnetic nanoparticles. The possible treatment of Osteosarcoma tumor with magnetic hydroxyapatite and poly-caprolactone doped with magnetite nanoparticles are analyzed. During the procedure, the materials and tissue physical properties modifies due to the heating, implying a coupled solution of thermal and electromagnetic equations. In the present study a numerical model, implemented using a commercial finite element software, is developed to include the intrinsic non-linearities of the hyperthermia multiphysic treatment. Therefore, an accurate in silico analysis is performed to identify the strength and frequency of the external magnetic field which would ensure an effective therapy, evaluated using the thermal dose damage analysis.
Automotive Radar Signal Classification Using Bypass Recurrent Convolutional Networks

In this paper, we propose recurrent convolutional neural networks for V2X communication, which classify moving target in automotive radar system. Moving target classification is of importance in protecting the vulnerable road users. The proposed neural network is computationally efficient and has improved performance. We introduce bypass connection used in recurrent convolutional neural networks. For the systems like radar systems, in which memory is restricted and high reliability is required, we show that the proposed neural network outperforms the conventional approaches by comparing classification accuracy using radar data measured in realistic scenario.
An Empirical Study of Bug Isolation on the Effectiveness of Multiple Fault Localization

Bug isolation is the main approach to multi-fault localization, where failed test cases are divided into groups, and each group failed test cases are used to localize a single fault combined with all passed test cases. Ideally, all failed test cases within a single group execute the same faulty statements. However, misgrouping usually occurs due to the clustering algorithms may not able to divide failed test cases accurately. This paper focuses on the impact of fault localization by the accuracy of the clustering algorithm. A large quantitative empirical study is conducted on 12786 version programs with multiple faults, in which the misgrouping are simulated with different accuracy by a controlled experiment. The results indicate that the effect of fault localization will become worse as the accuracy of clustering decreases.
Measurement of Temperature-Dependent Bulk Viscosities of Nitrogen, Oxygen and Air From Spontaneous Rayleigh-Brillouin Scattering

In this paper, the spontaneous Rayleigh-Brillouin scattering spectra of air are simulated to study the effect of uncertainties of pressure, temperature, scattering angle and the characteristic parameter uncertainty of the Fabry-Perot interferometer on the accurate measurement of the bulk viscosity. It is found that those uncertainties have an obvious impact on the bulk viscosity measurement deviation and the bulk viscosity can be measured accurately under higher pressures (≥3.0 bar). In order to obtain the accurate bulk viscosity of nitrogen, oxygen and air, the spontaneous Rayleigh-Brillouin scattering spectra are measured with the wavelength of 532 nm under pressure of 4.0–7.0 bar and at temperature from 289.0 K to 400.0 K. The linear relation between the measured bulk viscosity and temperature is established with R2 being above 0.99 for nitrogen, oxygen and air respectively. By comparison, it is found that our measured bulk viscosities mostly agree with the reported values obtained by spontaneous Rayleigh-Brillouin scattering, coherent Rayleigh-Brillouin scattering, ultrasonic determination or theoretical calculation for nitrogen, oxygen and air within $3\sigma $ results at the same temperature. The factors arousing the differences between them are attributed to the obvious measurement error and the measured uncertainty of the bulk viscosity under low pressures and the defects in the theoretical model itself. The empirical formula for calculating the bulk viscosity for air from pure components is proposed and it can match our measured results well.
Leading-Following Consensus for Multi-Agent Systems With Event-Triggered Delayed Impulsive Control

In this paper, the exponential leader-following consensus problem is investigated for networked multi-agent systems. In order to further reduce the usage of the communication resources, event-triggered delayed impulsive control strategy is put forward, which successfully combines the delayed impulsive control and event-triggered mechanism, and the delayed impulsive controller is implemented only at event-triggered instants. By constructing the novel Lyapunov based event-triggered strategy and impulsive control theory, some easy-to-check conditions are derived such that the error system is exponentially stable. Furthermore, it is shown that the Zeno behavior can be eliminated from the event-triggering rules. Numerical simulation is proposed to illustrate the effectiveness of the theoretical results.
Performance Analysis of High Throughput MAP Decoder for Turbo Codes and Self Concatenated Convolutional Codes

The effect of parallelism on Bit Error Rate (BER) performance of Turbo Code (TC) and Self Concatenated Convolutional Code (SECCC) with different levels of parallelism and frame sizes is investigated. Next Iteration Initialization (NII) method is employed for mitigating the BER degradation resulting from increased parallelism. In order to analyze and compare the architectural performance of both schemes, this paper presents the Very High Speed Integrated Circuit Hardware Description Language (VHDL) design of Maximum Aposteriori Probability (MAP) decoder for TC and SECCC, both employing the same constituent code. The simulation results show that for BER of 10−4, without parallelism, TC is 0.4 dB superior to SECCC, whereas, with parallelism of 64, the difference in performance between both schemes reduces to 0.25 dB. It is found that SECCC outperforms TC for frame sizes less than or equal to 2048 bits, when invoking a parallelism of 16, 32 and 64. The BER performance of both schemes shows that SECCC outperforms TC at parallelism of 256 by 0.3 dB at BER of 10−4. Hence, for high throughput architectures employing higher parallelism (beyond 64 and 128) without significant degradation in BER performance, SECCC performs better than TC. The synthesis results of VHDL design of the MAP decoder obtained using Xilinx ISE verify that both schemes have equal clock frequency and resource consumption. It is demonstrated that the MAP decoder achieves the clock frequency of 86.3 MHz which is capable of producing a throughput of 691 Mbps using parallelism of 64.
A Double-CNN BP Decoder on Fast Fading Channels Using Correlation Information

This paper develops a novel double convolutional neural network (CNN) based belief propagation (BP) decoder to improve the error correcting performance on fast fading channels with correlated channel gain and correlated noise. The proposed double-CNN BP decoder consists of two CNNs for predicting channel gain and noise samples, respectively, concatenated with a BP decoder. The input of the BP decoder is the log-likelihood-ratio (LLR) values obtained according to the predicted channel gain along with denoised signals based on predicted noises. We note that the residual noises no longer obey a Gaussian distribution when denoising the signals. Thus, we further derive a new method to obtain the LLR values accurately. Simulation results show that the proposed double-CNN BP decoder achieves a maximum of 7dB gain compared to the conventional BP decoder and the proposed algorithm compensates the performance loss of BP decoder caused by correlation information.
Homogeneity of the RF Field in MRI TEM Coils

The transverse-plane B1+homogeneity in TEM RF coils is shown to be frequency invariant and solely dependent on the excitation purity of the fundamental m=1 mode. It is demonstrated that the capacitive coupling introduced by the interconnecting lumped capacitors should maximize the separation between the resonance frequency of the fundamental mode and that of all other higher order modes in order to maximize this purity. A full-orthogonal excitation of the coil, if possible, would also mitigate this design challenge, especially if high-Q operation cannot be guaranteed. The presented optimization of the capacitive coupling is based on a field theoretical technique of TEM coils, which offers simple analytic design equations. Simulation results are presented to validate the presented approach.
Control of a Cluster created by real and virtual machines using HPC software in the CAPDS

This work deals with the handling of a cluster created with available machines. In order to improve the resources of the machines, virtual machines have been created, which increases the number of machines used in the cluster. Also, to reduce the execution times of the processes, parallel programming has been used with the Message Passing Interface (MPI). Additionally, for the classification of the images, a complete procedure using the Satellite Image Classification Of Satellite Image (CIS) software has been developed.