Miracle-dz/UniFAR-FTUs huggingface dataset:
Silicon Nanowire Tunnel-FET Differential Amplifier Using Verilog-A Lookup Table Approach

Electrical characterization of a silicon nanowire Tunnel Field Effect Transistor (TFET) is used to construct a lookup table in order to model and simulate analog circuit through Verilog-A approach. The performance of a differential amplifier with current mirror load is evaluated using the TFET lookup table model and the TSMC 130 nm CMOS process design kit. Both circuits are evaluated in two different bias, with the TFET circuit presenting 20 dB higher voltage gain and power consumption of at least three orders of magnitude smaller than CMOS technology. All the simulations were realized with Cadence Spectre software.
An Anomaly Detector for CAN Bus Networks in Autonomous Cars based on Neural Networks

The domain of securing in-vehicle networks has attracted both academic and industrial researchers due to high danger of attacks on drivers and passengers. While securing wired and wireless interfaces is important to defend against these threats, detecting attacks is still the critical phase to construct a robust secure system. There are only a few results on securing communication inside vehicles using anomaly-detection techniques despite their efficiencies in systems that need real-time detection. Therefore, we propose an intrusion detection system (IDS) based on Multi-Layer Perceptron (MLP) neural network for Controller Area Networks (CAN) bus. This IDS divides data according to the ID field of CAN packets using K-means clustering algorithm, then it extracts suitable features and uses them to train and construct the neural network. The proposed IDS works for each ID separately and finally it combines their individual decisions to construct the final score and generates alert in the presence of attack. The strength of our intrusion detection method is that it works simultaneously for two types of attacks which will eliminate the use of several separate IDS and thus reduce the complexity and cost of implementation.
Machine Learning Based Automated Speech Dialog Analysis Of Autistic Children

Children with autism spectrum disorder (ASD) have altered behaviors in communication, social interaction, and activity, out of which communication has been the most prominent disorder among many. Despite the recent technological advances, limited attention has been given to screening and diagnosing ASD by identifying the speech deficiencies (SD) of autistic children at early stages. This research focuses on bridging the gap in ASD screening by developing an automated system to distinguish autistic traits through speech analysis. Data was collected from 40 participants for the initial analysis and recordings were obtained from 17 participants. We considered a three-stage processing system; first stage utilizes thresholding for silence detection and Vocal Activity Detection for vocal isolation, second stage adopts machine learning technique neural network with frequency domain representations in developing a reliant utterance classifier for the isolated vocals and stage three also adopts machine learning technique neural network in recognizing autistic traits in speech patterns of the classified utterances. The results are promising in identifying SD of autistic children with the utterance classifier having 78% accuracy and pattern recognition 72% accuracy.
Model Parameter Online Correction Method of Predictive Current Control for Permanent Magnet Synchronous Motor

Predictive current control (PCC) has been widely investigated in motor control field. However, the motor prediction model in PCC is quite sensitive to motor parameters which may change under different working conditions. The mismatch between model parameters and actual motor parameters will result in current prediction error, which will greatly reduce prediction accuracy. In this paper, a model parameter online correction method based on the relationship between inductance error and current prediction error is proposed for surface permanent magnet synchronous motor (SPMSM). The main idea of the method is to directly correct the inductance value in prediction model according to current prediction error. Simulation results verify that the proposed method can effectively reduce current prediction error and improve control accuracy.
Research on Trajectory Reconstruction Method Using Automatic Identification System Data for Unmanned Surface Vessel

The unmanned surface vessel (USV) trajectory with spatial and temporal information plays an important role in its positioning and navigation. Unlike traditional trajectory reconstruction methods, this paper proposes a novel method based on the automatic identification system (AIS) for USV. Aside from the AIS data applied for restoring the USV’s trajectory, the proposed method considers the constraints of the vessel’s navigation state, maneuvering factors and time stamps. This method consists of three steps: AIS data restoration, Empirical Mode Decomposition (EMD) denoising and Fermat’s spiral fitting. Firstly, AIS data restoration is applied to eliminate abnormal data. Next, the EMD noise reduction algorithm is used to reduce the jitters and interference of Speed over Ground (SOG) and Course over Ground (COG) components, and the denoised latitude and longitude positions are calculated using the kinematics model of the vessel. Finally, a curve fitting process based on Fermat’s spiral is employed to reconstruct a smoother trajectory. Several experiments illustrate that the low-cost AIS equipment is compatible with the navigation system of the USV. The reference trajectory is determined by Global Position System (GPS) and Inertial Measurement Unit (IMU) modules in USV. Compared to conventional methods, the residual errors of the proposed method is smaller. The results show that the novel trajectory reconstruction method is effective and can be applied to USV’s positioning and navigation.
A 6th-Order Complex Bandpass $\Delta\Sigma \text{AD}$ Modulator Using Dynamic Amplifier and Noise Coupling SAR Quantizer

This paper presents a 6th-order complex bandpass $\Delta\Sigma \text{AD}$ modulator (CBPDSM) with 2nd-order image rejection using dynamic amplifier and noise coupling (NC) SAR quantizer embedded by passive adder for the application of wireless communication system. A novel complex integrator using dynamic amplifier is proposed to reduce the power consumption of the CBPDSM. The NC SAR quantizer can realize an additional 2nd-order noise shaping and 2nd-order image rejection by the digital domain noise coupling technique. As a result, the 6th-order CBPDSM with 2nd-order image rejection is realized by two complex integrators using dynamic amplifier and the NC SAR quantizer. The feasibility of the proposed 6th-order CBPDSM is demonstrated by the SPICE simulation results in 90nm CMOS technology. Simulated SNDR of 76.30dB is achieved while a sinusoid −6.26dBFS input signal is sampled at 33.3MS/s and the bandwidth of 2.083MHz is achieved. The total power consumption of the CBPDSM is 6.74mW while the supply voltage is 1.2V.
Security Enhancement in NOMA Cooperative Network with a Proactive Attack Scheme

A cooperative non-orthogonal multiple access (NOMA) communication is a promising solution to improve both the reliability and the capacity of the NOMA system. However, a relay can be exploited by a cooperative attack, this leads to a degradation of performance and secure of the NOMA system. Therefore, we propose a proactive protection scheme to mitigate the effect of the cooperative attack on the NOMA system. We consider the secrecy outage probability (SOP) of the cooperative NOMA in two cases: (1) the NOMA system has no protected scheme; (2) the NOMA system implements the protected scheme using the proactive attack technique. The results demonstrate that using proposed proactive attack scheme improve the secrecy performance of the system. Monte-clalo simulations are provided to examine our proposed scheme.
Spatial Control Actions for a Physical Training with a Smart Walker

Research on smart walkers focuses mostly on low-level control methods using a variety of sensors to get inputs from a user or evaluate the user’s gait. Our work investigates a possibility to use such a walker for a targeted robot-based motor training. In this paper a concept of control actions used as building blocks for the motor training are presented. These actions enable to challenge or support the user by influencing the output velocity of our smart walker–Robotrainer. The concept and evaluation of four types of control actions are presented in detail and their use in the training scenarios is discussed.
Hardware Architecture Design For Vehicle Detection

Nowadays, road safety systems based on vehicles detection are researched and become one of the important issues in Vietnam. In these systems, there are many study using CNNs which require model and many samples to train and long time in processing. In this paper, we propose an efficient method for solving this problem. This algorithm is called Background Subtraction With Gaussian Mixture Model (GMM) and implemented on FPGA - a dedicated hardware architecture. In our algortihm, all vehicle detection functions, including vehicle corner detection, vehicle region expansion, and vehicle region validation, is implemented using parallel architecture. Our testing model using a stationary camera to capture vehicles on the road and the algorithm is applied in order to detect the moving objects.
A Scalability Approach Based on Multilevel Optimization for Link Prediction Methods

The link prediction task is defined as how to predict the appearance of future links in an existing network. The rise of large-scale and complex networks provoked a need for scalable link prediction approaches. Even simple link prediction algorithms perform too many operations, making them impractical for most real-world networks. To tackle this problem we propose an approach based on the multilevel optimization paradigm to improve the scalability of any link prediction algorithm. To the extent of our knowledge, this is the first use of the multilevel paradigm to the link prediction task. We show how to reduce the original network to a coarsened version. For this, we propose a new matching algorithm suited for the link prediction task. After applying the link prediction algorithm in the coarsened network, we show how to project the prediction results back to the original network. Results obtained in different sizes real-world networks show a significant reduction of the run-time of complex link prediction algorithms without significant losses of accuracy. In some cases, on networks with a higher average degree, our approach leads to better results than the obtained in the original networks.
Behaviour-aware Malware Classification: Dynamic Feature Selection

Despite the continued advancements in security research, malware persists as being a major threat in this digital age. Malware detection is a primary defence strategy for most networks but the identification of malware strains is becoming increasingly difficult. Reliable identification is based upon characteristic features being detectable within an object. However, the limitations and expense of current malware feature extraction methods is significantly hindering this process. In this paper, we present a new method for identifying malware based on behavioural feature extraction. Our proposed method has been evaluated using seven classification methods whilst analysing 2,068 malware samples from eight different families. The results achieved thus far have demonstrated promising improvements over existing approaches.
Modeling and Prediction of Time-Series-A Case Study with Forex Data

Time series data reveals dynamic behavior of systems. A few real life examples are traffic flow, amount of rainfall, usage of electricity, share values, Forex rate etc. Depending on the complexity of the system dynamics, algorithms differ to model the time series data accurately, so that the created model can be used for interpolation and more commonly extrapolation or prediction. For example, AR model performs well in stationary time series, but for non-stationary, it cannot capture the non-linear dynamics. In this research, we use Forex rate data, and experimented with various algorithms to capture the dynamics of the data. The success of the model is evaluated by accuracy in prediction.In our experiments, we applied two state-of-the-art models -Support Vector Regression (SVR) and Recurrent Neural Network (RNN). The target of the experiment is the prediction of longer future by recursion (feeding back predicted value to input for the next step prediction). The result shows that RNN with proper Long Short-Term Memory (LSTM) has better performance in predicting longer future.
Distributed Finite-Time Coordination Control system for Economical Operation of Islanded DC Microgrids

The operation of standalone DC microgrid (MG) with high penetration level of renewable energy sources (RESs) be more complicated because of MG’s low inertia and frequent operating conditions. This paper presents a fully distributed controller to ensure minimal total generation cost (TGC) for autonomous DC MG in a finite time manner. It is implemented by using a distributed finite-time consensus scheme which equalizes the incremental costs (ICs) of all sources to accomplish MG’s optimal operation facilitating the balance between generations and demands simultaneously by restoring MG’s average voltage within a finite settling time. The proposed controller guarantees an enhanced performance with faster convergence over the conventional distributed secondary controller with a reduced overshoots which is vital for critical loads. The developed control scheme is simulated to testify its effectiveness under different loadings and its resiliency against link-failure and the capability of plug and play is proved.
Analysis and Reduction of Electromagnetic Noise Induced by Dead Time Effect for an Axial-Flux Permanent Magnet Motor

This paper analyzes the influence of inverter dead time effect on the electromagnetic noise of axial flux permanent magnet (AFPM) motors. It is found that the 5th and 7th current harmonics induced by the dead time effect contribute the most to the increase of 0th spatial order electromagnetic force of the AFPM motor, thereby deteriorating its vibroacoustic performance. Then, by adding compensated switching time, a compensation method is used to reduce the output voltage errors of inverter. The simulation result validates the effectiveness of this dead time compensation method on the noise reduction of a 27-slot/30-pole AFPM motor. It is found that after compensation, the amplitude of the axial electromagnetic force drops by 70%, and its electromagnetic noise is reduced by over 6 dB(A). The noise prediction model used for simulation has been experimentally verified. This paper shows that dead time effect can increase the electromagnetic vibration and noise of AFPM motors and proper compensation methods will help mitigate this negative effect.
Design of a simple electrical characterization platform for semiconducting organic films

Printed organic electronic devices are considered a key technology for enabling the Internet of Things. The development of organic electronics is fueled by new materials, which allow the printing of thin films on flexible substrates at low cost. Green materials are preferable, which are recyclable or biodegradable and are based on environment-friendly, noncorrosive and abundant chemical precursors processed at low temperature without toxic waste. Especially for countries with large biodiversity, like Brazil, the screening of natural materials is a viable strategy to discover suitable prototype materials that may be further developed. We report here on the design of a simple electrical characterization platform able to distinguish between conducting, semiconducting and insulating behavior. In an exemplary case study we show that curcumin, a natural dye, resembles an insulator.
A Novel Approach for Privacy Preserving In Lane Surface Monitoring Using Fog Computing

Internet of things is an emerging technology that enables communication among various computing devices using sensors. In these days people are cautious about the road surface condition that they travel. Therefore the road surface should be monitored and maintained. Multiple solutions has been proposed to monitor the road surface condition based on mobile sensing, crowd sensing and vehicle based sensing applications. These has provided a method for the analysis of the quality of roads. Cloud is more efficient and reliable in transportation of the data. The various challenges in cloud computing are mobility support, location awareness, privacy and security, more downtime, low latency and geo-distribution. Fog computing has been proposed in order to overcome these challenges. The article here we put forward a privacy preserving protocol namely Identity Based Signcryption for lane surface monitoring system using fog computing to provide security to the data that is transmitted. This privacy preserving protocol should meet the challenging requirements on various aspects such as secrecy, validity, truth, privacy and ambiguity. In order to preserve the privacy of the user a highly efficient identity based signcryption is proposed in this paper. So, only the authorized data can hit the cloud. The aim of this project is to develop an intelligent Lane surface monitoring system that preserves the privacy of the user.
Computational Alleviation of Homonymous Visual Field Defect with OST-HMD: The Effect of Size and Position of Overlaid Overview Window

Visual field defect (VFD) refers to a symptom in which a patient loses part of his/her field of view (FoV). Medical therapy can halt the progression of VFD, but complete recovery is impossible. In this paper, we propose a computational method for alleviating the restricted FoV with an optical see-through head-mounted display (OST-HMD), where an overview scene captured by the installed camera is overlaid on the persisting FoV. Since the overview window occludes with the real world scene, there is a trade-off between the augmented contextual information and the local unscreened information. We hypothesized that such a trade-off can be resolved by taking into consideration the size of the overview window and its displacement from the center of the unimpaired FoV. We therefore conducted an empirical evaluation through a Whac-A-Mole type of task with ten VFD-imitative subjects, where three sizes of an overview window with a fixed aspect ratio and seven positions in terms of elevation and azimuth were used combinatorially on an OST-HMD to find the best size and position of the overview window. It was statistically proven that for left-sided homonymous VFD-imitative subjects, the performance of the task was better when the medium-sized overview window was placed in the lower right position. The obtained result can legitimate default settings for the proposed VFD alleviation method.
Transient Modeling and Analysis of VSC Based DC Microgrid During Short Circuit Fault

The application of distributed renewable energy (DER) drives the development of DC microgrid based on voltage source converter (VSC). And short circuit fault protection is a significant challenge for the development of VSC based DC microgrid. This paper proposes a transient modeling method for VSC based DC microgrid. Firstly, the transient characteristics of VSC under DC bus voltage control mode and active power control mode are analyzed. And then the faulty DC microgrid model, which consists of VSC model and DC line model, is modeled. And the calculation expressions of state variables in DC microgrid are presented. Next, the correctness of proposed modeling approach is verified by comparing with the electromagnetic transient (EMT) simulation by Matlab/Simulink. Moreover, the case study on the parameters configuration of DC microgrid protection devices shows that the transient modeling method can be effectively used in faulty DC microgrid analysis. The method proposed in this paper can not only ensure the calculation efficiency, but also improve the accuracy of DC microgrid analysis on fault.
Hardware-in-the-Loop Iterative Optimal Feedback Control Without Model-Based Future Prediction

Optimal control provides a systematic approach to control robots. However, computing optimal controllers for hardware-in-the-loop control is sensitively affected by modeling assumptions, computationally expensive in online implementation, and time-consuming in practical application. This makes the theoretical appeal of optimization challenging to exploit in real-world implementation. In this paper, we present a novel online optimal control formulation that aims to address the above-mentioned limitations. The formulation combines a model with measured state information to efficiently find near-optimal feedback controllers. The idea to combine a model with measurements from the actual motion is similar to what is used in model predictive control formulations, with the difference that here the model is not used for future prediction, the optimization is performed along the measured trajectory of the system, and the online computation is reduced to a minimum; it requires a small-scale, one time step, static optimization, instead of a large-scale, finite time horizon, dynamic optimization. The formulation can be used to solve optimal control problems defined with nonlinear cost, nonlinear dynamics, and box-constrained control inputs. Numerical simulations and hardware-in-the-loop experiments demonstrate the effectiveness of the proposed hardware-in-the-loop optimal control approach.
Single Layer, Differentially Driven, LHCP Antenna With Improved Isolation for Full Duplex Wireless Applications

This paper presents a unidirectional, left handed circularly polarized (LHCP), single layered antenna array with improved interport isolation for simultaneous transmit and receive (STAR) or in-band full duplex (IBFD) wireless applications. The proposed IBFD antenna is comprised of three identical and sequentially rotated LHCP radiating elements where two Rx patches are symmetrically placed with respect to a single Tx patch. The symmetry of proposed antenna structure results in same amount of coupling or self interference (SI) from Tx element and differentially driven Rx patches achieve effective suppression of resulting SI to obtain improved Tx-Rx interport isolation required for STAR applications. The deployed feed network for differential-driven Rx operation is composed of a simple 3dB/180° rat-race coupler (ring hybrid coupler) with nice in-band amplitude and out-of-phase balance characteristics. The implemented single layer, compact (antenna elements and feeding network etched on single-layered PCB) antenna array achieves 10dB return-loss bandwidth ≥ 75MHz for both Tx and Rx ports. The prototype achieves ≥ 47dB interport isolation in 75MHz bandwidth and 3 dB axial ratio beam-width is ~ 60° for implemented antenna.