Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability Assessment of Feature Selection Algorithms on Homogeneous Datasets: A Study for Sensor Array Optimization Problem

A feature selection algorithm (FSA) is used to eliminate redundant and irrelevant features. Obviously, it can reduce dimensionality as well as the complexity of the original problem. Furthermore, the stability of FSA output becomes a major issue in real-world applications. Stability refers to the consistency of its feature preference related to the perturbation of data samples. In sensor array optimization, an FSA is used to find the best sensor combination in a sensor array. Typically, the main objectives of sensor array optimization are reducing data dimensions, electrical consumption, production cost, computational and traffic overhead, etc. Furthermore, the stable outputs of FSA in several observations are necessary to make a firm conclusion of selected sensors. The contribution of this research is to investigate the stability of FSAs in twelve homogeneous datasets in relation to the sensor array optimization problem. In this study, the stability of seventeen filter-based FSAs is compared across twelve homogeneous datasets. These datasets are generated from the electronic nose (e-nose) used to monitor twelve types of beef cuts. In this case, gas sensor array must have good generalization to differentiate all beef types. The experimental results show that a single FSA cannot guarantee stable sensors recommendation in sensor array optimization. Thus, it becomes a caution to researchers and practitioners to use a proper approach when performing sensor array optimization.
Deep Neural Networks based Invisible Steganography for Audio-into-Image Algorithm

In the last few years, steganography has attracted increasing attention from a large number of researchers since its applications are expanding further than just the field of information security. The most traditional method is based on digital signal processing (DSP), such as least significant bit (LSB) encoding. Recently, there have been some new approaches employing deep learning to address the problem of steganography. However, most of the existing approaches are designed for image-in-image steganography. In this paper, the use of deep learning techniques to hide secret audio into the digital images is proposed. We employ a joint deep neural network architecture consisting of two sub-models: the first network hides the secret audio into an image, and the second one is responsible for decoding the image to obtain the original audio. Extensive experiments are conducted with a set of 24$K$ images and the VIVOS Corpus audio dataset11https://ailab.hcmus. edu.vn/downloads, Through experimental results, it can be seen that our method is more effective than traditional approaches. The integrity of both image and audio is well preserved, while the maximum length of the hidden audio is significantly improved.
Cross-Domain Motion Transfer via Safety-Aware Shared Latent Space Modeling

This letter presents a data-driven motion retargeting method with safety considerations. In particular, we focus on handling self-collisions while transferring poses between different domains. To this end, we first propose leveraged Wasserstein auto-encoders (LWAE) which leverage both positive and negative data where negative data consist of self-collided poses. Then, we extend this idea to multiple domains to have a shared latent space to perform motion retargeting. We also present an effective self-collision handling method based on solving inverse kinematics with augmented targets that is used to collect collision-free poses. The proposed method is extensively evaluated in a diverse set of motions from human subjects and an animation character where we show that incorporating negative data dramatically reduces self-collisions while preserving the quality of the original motion.
Integrated Linear Regulator for GaN-based Gate Driver Applications

GaN transistors with low input and output capacitance and zero reverse recovery characteristics enable efficient operation in half-bridge and bridge-based power drivers. Integrating GaN power transistors and their drivers in a single chip solves a number of challenges using GaN devices, which monolithically integrate all power stages to design low cost and high power density power converters. An internal regulator is generally required in such a power converter application. The regulator has a variety of functions, including a voltage source that provides a regulated voltage before the power driver starts up, and a power supply for external devices such as digital isolators for the high side driver signals. This paper presents a GaN-based linear regulator applied to power converter applications. All linear components use GaN devices as building blocks. The regulator consists of two common-source differential stages in a cascaded fashion to provide a high voltage conversion gain, as well as sufficient phase margins to reduce instability caused by rapid load transitions. The overall regulator was implemented in a GaN-on-Si process with maximum input and output voltages of 20 V and 6 V, respectively, at cost of 250 μA quiescent current. The regulator can source a maximum load current of 150 mA with the line regulation of 17 mV/V and the load regulation of 0.67 mV/mA. The maximum output voltage variation for a load current step change of 150 mA is within 400 mV. More than 65 dB PSRR above 1 MHz is achieved with the design.
Low-Complexity Design of Massive Device Detection via Riemannian Pursuit

Active device detection is a precondition of realizing grant-free random access in beyond fifth-generation (B5G) cellular Internet-of-Things (IoT). However, due to the deployment of a large antennas array and the existence of a huge number of IoT devices, activity detection usually has high computational complexity and needs long pilot sequences. To overcome these challenges, we first propose a dimension deduction method by projecting the original device state matrix to a much lower dimension space. Then, we develop an optimized design framework with a logarithmic smoothing objective function and a coupled full column rank constraint. Under that framework, we transform the original interested matrix to a positive semidefinite matrix, followed by proposing a Riemannian trust-region algorithm to solve the problem in complex field. Simulation results show that the proposed algorithm outperforms the state-of-art algorithms in terms of device detection performance.
Broadband Circularly Polarized Patch Antenna Array for Vehicle Communication

A circularly polarized (CP) antenna which is a set of 2×2 patch array with parasitic rectangular elements is designed and measured. The parasitic patch can enhanced the gain of the antenna. Each antenna element is fed by two probe that is connected to the feeding network. The final design, with an overall size of 160mm×160mm×28mm $(\sim 1.1\lambda_{0}\times 1.1\lambda_{0}\times 0.2\lambda_{0}$ at 2.09 GHz), was fabricated and measured. The proposed antenna has been fabricated and measured. The antenna array has a measured $\vert \mathrm{S}_{11}\vert < -{10}$ dB bandwidth of 1.82-2.4 GHz(27.5%), the 3-dB axial-ratio(AR) bandwidth of 1.9-2.2 GHz is also obtained. Besides, the peak gain of the antenna array is 13.46 dBi at 2.01 GHz.
Secure Data Transmissions for Iraqi National Identification Card Using LoRaWAN Protocol

Presently, the Internet of Things (IoT) applications hold a considerable effect on the life of people whilst the IoT size has been growing swiftly. A massive amount of data, billions of devices, and various types of modern protocols are capable of bringing different security problems. In addition, the malicious activities have increased with more sophisticated attacks, that will result in higher risks. Therefore, the security aspect is becoming an increasingly important year-after year. LoRaWAN is a protocol for long-range communication with low-power devoted to IoT. This protocol can be utilized for transmitting messages among IoT end devices and gateways. Yet, because the LoRaWAN development remains at an early level, the security level of this protocol is not well developed. Therefore, there is an urgent and necessary requirement for developing and analyzing the security level of LoRaWAN. In order to be benefited from this modern protocol, it is practically implemented in the Ministry of Interior/Civil Status Directorate, Passports and Residence in the Iraqi Governorates/National Identification Card (ID). In each governorate, this section is connected with the subdistricts in the same geographical area for exchanging data as an alternative to the Internet service due to the problems and its interruptions. In this paper, the main objective is to utilize the usefulness of modern technologies and implement it, and connecting to a remote database for the purpose of dealing with the stored data, also, providing an additional layer of security to increase the integrity (reliability) of data sent within this network using elliptic curve cryptography without affecting the efficiency of the network and the speed of data transmissions.
Modeling and Band Offset of PE/ Fluorinated layer interface: A first Principle Study

In experiment, it was found that the electrons injected into fluorinated polyethylene (PE) were much less than pure PE. In order to clarify the microscopic mechanism of this phenomenon, the PE/fluorinated layer structure is constructed with the help of molecular dynamics. The constructed model’s density is in correspondence with the actual material. After that, the constructed structure is introduced to first principle to calculate the electronic structures. The calculation shows that a little amount of charge transfer happens from the PE side into the fluorinated layer, leading to the formation of a weak dipole moment at the PE/fluorinated lay interface. This effect suppresses electrons’ cross-interface movement.
Driver Fatigue State Detection Based on Facial Key Points

The proportion of drivers causing traffic accidents due to fatigue driving has been increasing year by year, which has become one of the main causes of traffic accidents. Therefore, accurate and effective detection of driver fatigue is a hot research topic at present. In this article, the driver's head posture estimation based on face key points is used to judge fatigue. Firstly, the face image is collected from the camera in real time. The model-based method is used to estimate the head posture of the person. The face key points method based on the Dlib library is used to judge the eye state, the mouth state, the head turning posture of the person. The experimental results show that the head pose estimation method based on face key points can accurately judge the fatigue state, and has good real-time and accuracy.
The Study of Cooperative Diversity Scheme for Indoor Visible Light Communication

In this paper, we proposed an indoor cooperative diversity scheme based on space-time labeling for visible light communication (VLC) to resist the channel fading and improve the system performance. This re-adjusted constellation at the relay node in order to magnify the distance product of the corresponding constellation points which are the source node and the relay node to improve diversity gain. The system model of this scheme and the detailed signals processing were presented. Numerical results showed that the proposed scheme had a significant performance improvement in bit error rate (BER) compared with conventional cooperative diversity scheme based on the repetition coding for indoor cooperative VLC.
On the Performance of Massive MIMO-NOMA Networks with Dual-Polarized Antenna Array

This paper investigates the performance of multi- cluster multi-user massive multiple-input multiple- output (MIMO) networks with non-orthogonal multiple access (NOMA) and a dual-polarized antenna array. Considering the downlink mode in which a single base station communicates with multiple users, a precoder design is proposed with the aim to maximize the number of user groups that are simultaneously served within a cluster. A closed- form expression for the outage probability is derived, based on which an asymptotic analysis is carried out and the diversity gain is determined. Moreover, the outage sum-rate is also examined. Representative numerical examples are presented along with insightful discussions. Our results show that the proposed dual-polarized MIMO-NOMA design outperforms conventional single-polarized systems, even for high cross-polar interference.
Kinect v2 Based Real-Time Motion Comparison with Re-targeting and Color Code Feedback

In this paper, we propose a system to evaluate a user's performance in a real-time manner. Kinect v2 is used to acquire a professional user's motion as a reference motion and a trainee user's motion as a query motion. By utilizing the open-end dynamic time warping algorithm, the system compares the pre-recorded reference motion with the query motion in real-time. The comparison result is shown to the user as a feedback by using a color-coded re-targeted 3D model.
High-Resolution Radar Imaging using Enhanced Sparse Bayesian learning

The synthetic aperture radar (SAR) image of moving targets usually has the sparse feature, which provides the sparse approach to improve the imaging performance. In this paper, we address the problem of SAR imaging and motion estimation of maritime targets using parametric and structured sparse Bayesian learning (SBL) approach. To model the motion of maritime targets, a parametric dictionary is used to represent the maneuverability. Meanwhile, a local-structure sparse Bayesian learning (LS-SBL) algorithm is presented by exploiting the structure of the targets. Benefiting from the use of local-structure sparse feature, the imaging performance can be effectively improved with preserving the target structure. Finally, the experimental analysis is performed to confirm the effectiveness of the proposed algorithm.
Soft Fingertips With Tactile Sensing and Active Deformation for Robust Grasping of Delicate Objects

Soft fingertips have shown significant adaptability for grasping a wide range of object shapes, thanks to elasticity. This ability can be enhanced to grasp soft, delicate objects by adding touch sensing. However, in these cases, the complete restraint and robustness of the grasps have proved to be challenging, as the exertion of additional forces on the fragile object can result in damage. This letter presents a novel soft fingertip design for delicate objects based on the concept of embedded air cavities, which allow the dual ability of tactile sensing and active shape-changing. The pressurized air cavities act as soft tactile sensors to control gripper position from internal pressure variation; and active fingertip deformation is achieved by applying positive pressure to these cavities, which then enable a delicate object to be kept securely in position, despite externally applied forces, by form closure. We demonstrate this improved grasping capability by comparing the displacement of grasped delicate objects exposed to high-speed motions. Results show that passive soft fingertips fail to restrain fragile objects at accelerations as low as 0.1 m/s$^{2}$, in contrast, with the proposed fingertips delicate objects are completely secure even at accelerations of more than 5 m/s$^{2}$.
Dynamic In-Network Classification for Service Function Chaining ready SDN networks

Service Function Chaining (SFC) paradigm consists in steering traffic flows through an ordered set of Service Functions (SFs) so that to realize complex end to end services. SFC architecture introduces all the logical functions that need to be developed in order to provide the required service. The SFC overlay infrastructure can be built on top of many different underlay network technologies. The high flexibility and centrally controlled feature of Software Defined Networking (SDN), make SDN networks to be a perfect underlay to build the SFC architecture. Due to Ternary Content Address Memory (TCAM) limited size, SDN switches have a limitation in the number of flow rules that can be hosted. This constraint is particularly penalizing in case of the SFC classifier function, since it requires to manage a high number of different flows. The limitation imposed by the TCAM size on the SFC classifier can be a bottleneck for the number of SFC requests that the SDN-based SFC architecture can handle. In this paper we define the Dynamic Chain Request Classification Offloading (D-CRCO) problem, as the one of maximizing the number of accepted SFC requests, having the possibility of: i) implement the SFC classifier also in a node that is internal to the SDN-based SFC domain, and ii) install classification rules in a reactive fashion. Furthermore, we propose the Dynamic Nearest Node (DNN) heuristic to solve the D-CRCO problem. Performance evaluation shows that by using DNN heuristic it is possible to triple the number of accepted requests, with respect to existing solutions.
A Multi-Objective Service Placement and Load Distribution in Edge Computing

Edge Computing emerges as a solution that overcomes some obstacles of traditional central data centers to support the performance-critical Internet of Things applications. However, a challenge therein is the resource allocation for heterogeneous applications at a network edge composed of distributed and resource-restricted nodes. In this paper, we investigate how to place replicas of applications, and distribute requests among these replicas to optimize multiple objectives. We propose a genetic algorithm based on Pareto fronts as a problem-solving meta-heuristic to prioritize latency-sensitive applications and optimize conflicted objectives. Evaluation results show that our proposal outperforms other benchmark algorithms in terms of response deadline violation, as well as terms of other important and sometimes conflicting objectives, such as cost and availability.
Convolutional Neural Network Accelerator on FPGA

This paper presents the design and FPGA implementation of a convolutional neural network accelerator (CNNA). Two kinds of sparsity, zero-valued weights and zero-valued input feature map, are exploited to save power. The design features hierarchical memory organization to reduce external memory access. Bandwidth compression and decompression are also proposed to reduce external memory bandwidth. The unified scratch memory can be configured dynamically layer-by-layer to maximize memory utilization. The proposed CNNA is designed in Xilinx high level synthesis (HLS) language and implemented on ZCU102 board. With totally 2048 multiply-and-accumulation (MAC) unit, the design is able to deliver 1TOPS computing power when running at 250MHz.
A 80∼101GHz Amplifier in 65nm CMOS process

This paper presents an 80∼101GHz low-noise amplifier (LNA) with 65-nm CMOS process. The first stage of the proposed low noise amplifier employs a novel amplifier structure to improve the noise figure and gain, which consists of a cascode amplifier and a feedback loop. The other stages are employed the cascode amplifier with gain improvement technique to improve the gain. The simulation results show that the LNA can provide a gain of 17dB with a 3 dB bandwidth of 21 GHz. With 1.2-V power supply, the LNA consumes 24-mW power consumption. Furthermore, the LNA achieves minimum noise figure (NF) of 6.5 dB at 90 GHz and NF of 6.5∼8.0 dB within a 3 dB gain bandwidth.
Adaptive alternating current transcranial electrical stimulation (tACS)

Traditional deep brain stimulation (DBS) has been proven effective in the treatment of neurological diseases, especially Parkinson's disease (PD), but the deep brain stimulation system can be further optimized to maximize therapeutic benefits. This paper proposes a programmable adaptive transcranial electrical stimulation, which consists of LFP acquisition circuit, TACS stimulation circuit and adaptive closed-loop control algorithm. Compared with the traditional deep brain electrical stimulation for Parkinson's disease, adaptive ac transcranial electrical stimulation for Parkinson's disease (PD) has a better effect, and the application of advanced technology and treatment program is expected to achieve better clinical effect, save the time of regulation and avoid side effects.
Implementation Of An Efficient Security Scheme Through Elliptic Curve Cryptography Based Radio-Frequency Identification(RFID) In Context Of Internet Of Things

IOT becomes a revolutionary transformation in our future digital world. Impact of rapid growth and real adaption of IOT touches day by day every ones life. IOT platform where every day a large no of physical objects are connected with others through internet and each objects have a unique identification to other devices. We are gifted from IOT generation as smart cities , smart homes, smart devices, parking sensors, real time medical data prediction in health care application and many mores. But the major concerns of IOT platform are privacy concerns, insecure update, week encryption technique. poor passwords and some UI based vulnerabilities. We have discussed about Radio Frequency Identification (RFID) under IOT environment where large amount of data are in open communication without proper authentication . Some widely used cryptographic algorithm already have used in IOT platform. For last few years , lots of attention of researcher to optimize size and power consumption of IOT devices which finds an efficient authentication scheme with minimum computational complexity. In this paper we have proposed a Elliptic curve Cryptography based RFID scheme for IOT platform.
OLPD Condition Monitoring of Complete Networks for Oil and Gas: Field Experiences and the Lessons Learned after Two Years of Continuous OLPD Monitoring

In this paper the authors are presenting the continuation of the previous work of on-line partial discharge (OLPD) condition monitoring challenges and solutions explained through case studies presented at the 2016 European Petroleum and Chemical Industry Conference (PCIC). This paper presents the field experiences and the lessons learned after two years of continuous OLPD monitoring carried out on a complete HV network comprising rotating machines, transformers, switchgears and HV feeders for an oil and gas facility in central Asia. The paper reports further findings, of OLPD condition monitoring, and the remedial actions implemented consequently to avoid failures and unwanted outages. This is an example of how site strategy can be shifted from a time-based maintenance regime toward a more condition-based maintenance regime.Furthermore, innovation of data analysis tools and a case study of an OLPD and Motor Current Signature Analysis (MCSA) test campaign are shown; this approach has now justified the deployment of a permanent continuous monitoring system.
Effects of Oxidants on the Bias-Stress Instabilities of In-Ga-Zn-O Thin Film Transistors Using HfO2 Gate Insulator Prepared by Atomic Layer Deposition

We fabricated the In-Ga-Zn-O thin-film transistors using HfO<sub>2</sub> gate insulator prepared by atomic-layer deposition with different oxidants of H<sub>2</sub>O and O<sub>3</sub> at 200 °C. While the device using H<sub>2</sub>O showed almost no change in threshold voltage (V<sub>th</sub>) even after <inline-formula> <tex-math notation="LaTeX">$10^{{4}}$ </tex-math></inline-formula> s with bias-stresses, the device using O<sub>3</sub> showed the V<sub>th</sub> shifts of −1.4 and +1.2 V under positive and negative bias stresses, respectively. Defect-related carrier generation model was suggested as a physical origin for the anomalous bias-stress instabilities and the related mechanisms, in which the incorporated hydrogens can interact with oxygen-related defects.