Miracle-dz/UniFAR-FTUs huggingface dataset:
Fixed-Time Consensus Tracking with External Disturbance

In this paper, we deal with consensus tracking problem for nonholonomic chained-form dynamics with unknown external disturbance. Unlike many existing fixed-time consensus tracking methods, this paper considers the unknown external disturbance. Moreover, the initial state is not a factor to affect the time required to achieve consensus tracking. First, we design a distributed observer for the follower robot to estimate the target robot's state, control input and external disturbances within a fixed time. Next, a tracking protocol is designed to track the estimated state of the target robot. Finally, simulation is provided to prove that the tracking protocol is practical and correct.
Determination of Cytoplasmic Tyrosine Residues of Tim-3 Involved in Regulation of TCR signaling (35.30)

T-cell immunoglobulin and mucin 3 protein (Tim-3) is a type-I transmembrane protein expressed on murine T-helper type 1 (Th1) cells which regulates Th1 cell proliferation and development of tolerance. Tim-3 has six tyrosine residues in the cytosplasmic tail, two and three tyrosines residues are clustered in proximal and distal region of the cytoplasmic tail respectively. Using a motif scan program, we have predicted that two tyrosine residues from each cluster may be phosphorylated by Src family kinases, and three of the five residues may interact with SH2 domains. We generated serial deletion and point mutation constructs of Tim-3 and demonstrated that deletion of the cytoplasmic tail region containing the distal tyrosine residues (Y250, Y251, Y253) slightly increased NFAT-Luciferase reporter activity, while deletion of the region containing distal and proximal (Y235, Y242) tyrosine residues abolished the reporter activity to basal level in Jurkat cells. Point mutation of both Y235 and Y242 decreased NFAT-Luc reporter activity close to basal level while single point mutation of Y235 or Y242 did not result in significant changes in reporter activity. Therefore we conclude that phosphorylation of Y235 and Y242 residues of Tim-3 are responsible for potentiation of signal transduction initiated by activation of TCR, upstream of NFAT/AP1 pathways, in a redundant manner.
Local Binary Pattern with Hyperparameter Tuned Support Vector Machine for Fingerprint Classification

In recent times fingerprint authentication has become essential part of many organisation. With increase in use the risk of spoofing the fingerprint is also increased.Previously many software approaches has been used to solve this problem. Hyperparameter tuning shows improvement by Increase in Accuracy Score(IAS%) upto 27.43%. The proposed model of Local Binary Pattern with Hyperparameter tuned Support Vector Machine achieves Average Accuracy Score(AAS) of around 98.69% on LivDet 2009, 96.39% on LivDet 2011, 97.57% on LivDet 2013, 94.84% on LivDet 2015 dataset.
Group Activity Recognition by Using Effective Multiple Modality Relation Representation With Temporal-Spatial Attention

Group activity recognition has received a great deal of interest because of its broader applications in sports analysis, autonomous vehicles, CCTV surveillance systems and video summarization systems. Most existing methods typically use appearance features and they seldom consider underlying interaction information. In this work, a technology of novel group activity recognition is proposed based on multi-modal relation representation with temporal-spatial attention. First, we introduce an object relation module, which processes all objects in a scene simultaneously through an interaction between their appearance feature and geometry, thus allowing the modeling of their relations. Second, to extract effective motion features, an optical flow network is fine-tuned by using the action loss as the supervised signal. Then, we propose two types of inference models, opt-GRU and relation-GRU, which are used to encode the object relationship and motion representation effectively, and form the discriminative frame-level feature representation. Finally, an attention-based temporal aggregation layer is proposed to integrate frame-level features with different weights and form effective video-level representations. We have performed extensive experiments on two popular datasets, and both have achieved state-of-the-art performance. The datasets are the Volleyball dataset and the Collective Activity dataset, respectively.
Interference-Robust Processing of OFDM Radar Signals Using Compressed Sensing

An essential prerequisite for radars in automotive applications, safety systems, future autonomous driving, and industry applications is the reliable operation even under difficult conditions such as interference from other radars. Recently, digital radar principles such as orthogonal frequency division multiplexing (OFDM) have gained increasing attention. In this letter, an interference-robust processing for OFDM radar signals using compressed sensing is presented, which is particularly suitable for random OFDM but also applicable to stepped and standard OFDM. It allows robust radar operation in the presence of interfering signals that have a comparable low instantaneous bandwidth and high narrowband power. The method is validated experimentally using measurements of an OFDM radar setup at 76 GHz and a state-of-the-art FMCW radar as an interferer.
Design and Modeling of Bionic Robot Arm Actuated by Pneumatic Artificial Muscles

In this paper, a 2-DOF bionic robot arm actuated by pneumatic artificial muscles (PAMs) is developed. As a new type of actuator, PAM has been widely utilized due to its superiorities of lightweight, strong output power, and inherent safety. This bionic arm, designed for human rehabilitation training, is a planar two-bar mechanism equipped with pretension springs and PAMs. The kinematics and dynamics of the bionic robot are obtained. Consequently, the workspace of the bionic robot is determined and experimentally tested. The motion accuracy of PAM is significantly affected by its hysteresis. As a result, PID control is used to compensate for the hysteresis in the swing angle control of the joints. The experimental results show that the swing angles of the two arms can follow the desired trajectory very well. The tracking errors of the upper and lower arms are within 0.25° and 0.3°, respectively. The experimental results demonstrate that PAM can be utilized to provide actuation for rehabilitation applications.
A Low-Complexity Algorithm and Its Low-Power and High-Throughput Architecture for 3D-HEVC DMM-1 Encoding Tool

This paper presents a low-power and high-performance hardware design for the Depth Modeling Mode 1 (DMM-1) of the 3D-High Efficiency Video Coding (3D-HEVC). The designed architecture is based on a low-complexity algorithm developed to reduce the DMM-1 computational effort and to avoid the use of memory. The architecture was described in VHDL, and the ASIC synthesis was performed for the TSMC 40nm technology. The synthesis results showed a gate count of 1,283k gates and a power dissipation of 51.36 mW, when running at 82 MHz. The designed architecture is capable of processing 3D 1080p videos with up to 11 views at 30 frames per second. The reached results surpass the published works in terms of throughput and power dissipation.
Role of Dielectrics in the Capacitive Wireless Power Transfer System

Wireless power transfer through capacitive coupling interface is being investigated widely as a dual of inductive coupling interface for low to medium air-gap (~1 mm – 100 mm) applications. The value of coupling capacitance is generally dependent on the dielectric material used, area of the plates in the interface and distance between the plates. The paper evaluates the influence of dielectrics on capacitive coupler interface for various air-gaps. The coupling interface can be arranged in different configurations to achieve sufficient power transfer and is validated through the electric field analysis in JMAG.
Detection of DDOS Attacks in Cloud Computing Environment

In today’s world everyone uses cloud technology, which provides various features like data-storage, service-oriented sophisticated models which are hosted remotely to provide software services, infrastructure services, platform services, for users to deploy and develop applications, anywhere accessible data, QoS, cost effective solutions for business setups, no hardware and software maintenance problem for the user etc. While the cloud services are mainly carried over the internet, it is prone to various attacks which may lead the exposure of sensitive data to adversaries. Many attacks pose an active threat to the cloud environment. Amongst these attacks, DDOS is one of the cheap online attacks which disrupts the services that are available for the legitimate users. Separating legitimate and malicious traffic is not an easy task. The main idea of the DDOS attack is to affect numerous user machines, infecting them with a certain malware. The infected machines also known as zombies or botnets are then controlled by the attacker to launch the flooding attacks on a particular network or a server. The infected machines carry out the main attack by increasing the traffic flow to a victim machine. Due to which the victim machine loses all its computing and communication resources. The purpose of this paper is to analyze the various detection techniques used and implemented in various cloud environments to counter these types of attacks.
Moving Discretised Control Set - Model Predictive Control for Dual-Active-Bridge

In many DC/DC converters applications, classical proportional-integral control is often considered. However, when complex converters structures, such as Dual Active Bridge converters, are considered, several challenges may be faced in the control design. In fact, achieving an accurate Dual Active Bridge model may be complex, considering that the small signal model of the circuit varies according with its operating mode. Moreover, Dual Active Bridge converters usually requires a robust control design, due to their high input/output voltage ratio and power level. In order to address these issues, a Moving Discretized Control Set - Model Predictive Control is introduced. The proposed control can achieve faster dynamics and stable operation throughout the power and terminal voltage ranges, considering global control parameters. It also presents a fixed switching frequency with low computational burden, due to the use of only two prediction horizons. Experiments on a 20kHz 1kW Dual-Active-Bridge converter are carried out to verify the effectiveness of the proposed control technique.
Impact of Contact Material on the Resistive Switching in Nanocrystalline ZnO Films for Forming-Free Neuromorphic Elements Manufacturing

The work represents the results of contact material impact on the resistive switching in nanocrystalline ZnO film. It was shown, W/ZnO/ZnO:In structure demonstrated forming free resistive switching with HRS/LRS ratio 10. The results of experimental studies of W/ZnO/ZnO:In structure showed, that at sweep voltage 3V, HRS and LRS were equalled to 155.2±7.5 kΩ and 15.6±4.1 kΩ for endurance test, also 148.7±28.1 kΩ and 18.3±10.2 kΩ for homogeneity test, respectively. The results can be used to manufacture neuromorphic systems based on forming-free nanocrystalline ZnO films.
Field Oriented Predictive Current Control on NPC Driving an Induction Motor

This paper proposes a predictive current control (PCC) strategy with a field orientation applied on a three-level neutral point clamped (NPC) inverter to drive a three-phase induction motor. It was used the optimal switching vector (OSV) strategy, which is most suitable for power electronics. The field orientation, along with a rotor flux estimation, made it possible to implement a control loop measuring only stator voltage, stator current and rotor speed. The simulation results showed that the average estimated rotor flux error was 0.03%, the total harmonic distortion (THD) for the stator current was 9.29%, and the average stator current error was 5.82%. The proposed model proved to be a viable option to industrial plants, especially when compared with classical controllers.
Auto-tuning Method for decentralized PID controller of TITO systems using firefly algorithm

The present work uses the Firefly Algorithm (FA) to design a decentralized PID controller for the TITO system. Generally, the difficulty arises in TITO systems is that if any one of the input changes then it’s an adverse effect on both the output responses. therefore; it causes a peak overshoot and takes more time to settle. To overcome this drawback, a new objective function is introduced in this paper. Here Both the PID controller parameters are tuned at the same time to avoid the multidimensional non-linear problem that might be simply resolved by the firefly algorithm with greater convergence fastness and low calculus time. The proposed method is compared with direct synthesis methods in simulation, the study shows the convincingness of the proposed method performance. Measured performance indices are optimal, and the effect of one input to another output is also small with minimal peak overshoot.
A Multi-Agent Formation Control Method Based on Bearing Measurement

A method of designing and controlling formation configuration based on bearing measurement information of adjacent agents is proposed, in order to solve the problem of difficulty on improving the multi-agent formation control accuracy under the condition of inaccurate positioning. According to the relationship between geometric configuration and the angle between vertices, a method is proposed to define the expected configuration of formation by using the angles between agents. On this basis, a control method is designed to converge the multi-agent formation to the desired configuration only using the bearing measurement information of the adjacent agents. A Lyapunov function is designed to prove the asymptotic stability of the formation control law. The simulation results show that the multi-agent formation can be controlled only by bearing measurement information, which verifies the effectiveness of the proposed method.
Modified Blowfish Algorithm for Internet of Things Devices using new 4-Dimensional Chaotic System

Blowfish algorithm (BA) is a popular security algorithm. Blowfish algorithm (BA) is a symmetric mass cipher per a 64 bits mass size besides variable key lengths after 32 bits up to a full of 448 bits. The focus of this paper stays on the proposal of modify new four-dimension system to use in modified blowfish algorithm. The proposed chaotic system is 4 chaotic system equations chosen with a good qualities of dynamic chaos field with positive Lyapunov extensions (1.015454, 0.946243, 0.036685, and 0.183673) in interval (0, 1) for all dimensional, as well as its ease and speed. Modification to Blowfish by used chaos keys in each F-function and internal round keys generation, in order to increasing randomness of the encrypted text. The working summary is to generate random keys from the new chaotic system and use these random keys with Blowfish algorithm to encryption the data. It has been observed through the work of the new system with blowfish algorithm increasing the security of data encryption as well as the speed of data encryption time.
EFLOPS: Algorithm and System Co-Design for a High Performance Distributed Training Platform

Deep neural networks (DNNs) have gained tremendous attractions as compelling solutions for applications such as image classification, object detection, speech recognition, and so forth. Its great success comes with excessive trainings to make sure the model accuracy is good enough for those applications. Nowadays, it becomes challenging to train a DNN model because of 1) the model size and data size keep increasing, which usually needs more iterations to train; 2) DNN algorithms evolve rapidly, which requires the training phase to be short for a quick deployment. To address those challenges, distributed training platforms have been proposed to leverage massive server nodes for training, with the hope of significant training time reduction. Therefore, scalability is a critical performance metric to evaluate a distributed training platform. Nevertheless, our analysis reveals that traditional server clusters have poor scalability for training due to the traffic congestions within the server and beyond. The intra-server traffic on the I/O fabric can result in severe congestions and skewed quality of service as high performance devices are competing with each other. Moreover, the traffic congestions on the Ethernet for inter-server communication could also incur significant performance degradation. In this work, we devise a novel distributed training platform, EFLOPS, that adopts an algorithm and system co-design methodology to achieve good scalability. A new server architecture is proposed to alleviate the intra-server congestions. Moreover, a new network topology, BiGraph, is proposed to divide the network into two separate parts, so that there is always a direct connection between any nodes from different parts. Finally, accompany with BiGraph, a topology-aware allreduce algorithm is proposed to eliminate the traffic congestion on the direct connection. The experimental results show that eliminating the congestions on network interface can gain up to 11.3xcommunication speedup. The proposed algorithm and topology can provide further improvement up to 6.08x. The overall performance of ResNet-50 training achieves near-linear scalability, and is competitive to the top-rankings of MLPerf results.
Comparison of Multi Chopper Amplifiers for Capacitive MEMS Transducer

This paper reports the analysis and comparison of various multi chopper amplifier architectures that are used in capacitive MEMS transducer. The power consumption, Noise density and signal to noise ratio of the chopper amplifiers are the primary focus of this work. These architectures have been implemented in the 130nm technology and the power consumption of these architectures are less than 20μW and input referred noise are less than 20nv/✓***check pdf***Hz. The reported bandwidths of these architectures are between DC to few KHz. The present work also reports the simulated value of CMRR and PSRR for the Dual chopper amplifier that has been implemented in 180nm technology and use the dummy based chopper for reducing the filtering requirement.
Loop Closure Detection for Monocular Visual Odometry: Deep-Learning Approaches Comparison

In order to decrease monocular visual odometry drift by detecting loop closure, this paper presents a comparison between state of the art, 2-channel and Siamese, Convolutional Neural Networks. The work consists of training these networks in order to make them able to robustly identify loop closures. As we are in the case of having two input images, we perform our trainings and tests on both 2-channel and Siamese architecture for each network.
Structural and Performance Comparison Between Harmonic Selective Repetitive Controllers for Shunt Active Power Filter

The repetitive controller (RC) represents a very effective solution for the current control of shunt active power filters. Using this structure as basis, several authors have proposed harmonic selective schemes that are more advantageous than the conventional RC when the control system references only contain a well-known family of harmonic components, such as 6k ± 1 or 4k ± 1 (all odd). In this sense, this paper presents a structural and performance comparison between real and complex harmonic selective RC-based solutions. Experimental results were used to validated the comparative study.
Control Strategy for Multifunctional PV Converter

This paper proposes a multifunctional converter applied to distributed generation with an energy storage system. The main functionalities include the possibility to have programmable power dispatch to the utility grid and trade of energy between the Battery Energy System (BESS) and utility grid, accordingly to the specific interest of an Energy Management System (EMS). In order to allow these functionalities, modifications are proposed in the bidirectional buck-boost converter that is responsible for the BESS power processing. With that, it is also proposed the sharing of the DC bus voltage controller between the bidirectional DC-DC converter and the DC-AC converter. To validate the proposed system, real-time hardware-in-the-loop (HIL) simulations are presented. In the simulations, real data of solar irradiance is considered. Moreover, a model of a lithium- ion battery bank is also incorporated. The proposed system guarantees programmable dispatch with low power errors. In addition, the system is able to operate with and without BESS at the same time it ensures smooth transitions between the operation modes.
Online State and Parameter Estimation of Ultracapacitor Using Marginalized Kalman Filter

Recent adoption to the usage of renewable resources has emerged as a consequence of the threats posed by the decreasing amount of fossil fuels and the increase in the level of greenhouse gases (GHG) in the atmosphere. This harnessed renewable energy needs to be stored in an energy storage device. One amongst the most prominently used energy storage devices is the ultracapacitor (UC). To ensure proper deployment and safer long-term operation, the dynamic behavior of the UC has to be observed carefully which is done by estimating the parameters of the UC online, which can be further used in the deduction of the state of health (SOH) and the state of charge (SOC) of the UC. The equivalent circuit model used for the purpose of estimation is demonstrated. Then the parameter estimation is done using the marginalized Kalman filter technique. This technique is implemented in MATLAB and the results are attached. The effectiveness of the method is validated by comparing the acquired results with the results that are obtained by employing other online parameter estimation techniques.
Topological Deep Network Embedding

Graph mining methods have been used to analyze complex network systems. However, with the increase in the complexity and dimensions of these networks, it is often required to have access to high computational processing to extract insights from networks. As such, it becomes vital to map complex graph into low dimensional vector space. Network embedding has been used to generate representation of graph in feature vector form. This brings many challenges as to what the dimension of the vector space should be, and which structure of the network has to be preserved. In this paper, we propose a semi-supervised deep learning model to generate network representation by preserving local and global structural properties of the network. We test the deep learning model on three datasets and compare it with state-of-the-art embedding methods. The results reveal effectiveness of the proposed method.
Massive MIMO System Capacity Analysis in Case of Banded Correlation Matrix Model Application

The research performs an analysis of the massive multiantenna system channel capacity for the case of with different system correlation matrix models: exponential-type and quadratic exponential model. For both of them the channel capacity was obtained under various considerations about the availability of the channel state information at the transmitting and/or receiving sides with different power allocation strategies deployed (including equal scaling and water filling). The performed simulation demonstrated that in the case when the full correlation matrix is being bordered the sign of the capacity distortion depends upon the average SNR. The regimes of capacity gain and capacity loss, implied by the bordering are identified and studied.