Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a Ka-Band Cascode Power Amplifier Linearized With Cold-FET Interstage Matching Network

A Ka-band CMOS cascode power amplifier (PA) linearized with a cold-FET-based interstage matching network is presented, which is designed in a 65-nm CMOS process. Since it is difficult to make a cascode PA matched to the optimum output and input impedances at high frequencies, a matching network has to be introduced at the node between the common-source (CS) and common-gate (CG) stages. The cold-FET-based matching network improves the linearity and the input and output impedance matchings, which is analyzed and optimized with its simple model. It makes the PA have gain expansion and phase lag with the power, which allows the PA to have less amplitude-to-amplitude (AM–AM) and amplitude-to-phase (AM–PM) distortions. In addition, it improves the return losses of the PA by making the impedances for power matching and conjugate matching located closely. The implemented PA achieves the peak power-added efficiency (PAE) of 38.2% and the saturated output power ( $P_{\mathrm {sat}}$ ) of 17.1 dBm at 31 GHz while occupying the chip area of 0.16 mm2. It is also shown that the OP1 dB is improved by 1.7 dB, and the AM–PM distortion is reduced to only 1.1° due to the linearization technique. It is tested with 64-quadrature amplitude modulation (QAM) signals, which has a 400-MHz channel bandwidth (BW) and a 9.7-dB peak-to-average power ratio (PAPR). It achieves average output powers of 9.6/7.7 dBm with error vector magnitudes (EVMs) of −25/−30 dB for 64-QAM OFDM signals, efficiencies of 17.7%/12%, and the adjacent channel leakage ratio (ACLR) of −28.2/−32.8 dBc, respectively.
On the use of the Relative Transfer Function for Source Separation using Two-channel Recordings

This paper investigates the use of the relative transfer function (ReTF) for source separation. ReTFs have been used to localize sound sources but have not been thoroughly investigated for the application of source separation especially when one of the sources is not speech. We propose two source separation algorithms using ReTFs. One of them is deterministic and enables the separation of two sources when one or both of their ReTFs are known. The other algorithm uses masking in the time-frequency domain and can be used for separating two or more sources. We also explore the limitations and assumptions of the ReTF and the proposed source separation algorithms.
Predictive Current Control of a Simplified Three-Level Neutral-Point Clamped Inverter

A three-level neutral point clamped (3L-NPC) inverter is built with a larger number of semiconductor devices. Therefore, predictive current control (PCC) of 3L-NPC faces two issues regarding the reliability and computational cost. To overcome the problem, this paper presents predictive current control of a three-level simplified neutral-point clamped (3L-SNPC) inverter. Due to having the simplicity of adding a newer constraint in the cost function, the capacitor voltages of NPC inverter are perfectly balanced. Simulation results indicate that the proposed PCC based SNPC inverter yields similar performance as obtained with the conventional 3L-NPC. However, the SNPC inverter requires eight less number of semiconductor devices, which enhances the reliability and reduces the computational burden of the proposed system. In comparison to conventional 3L-NPC, the computational burden is reduced by 27.78%.
ParsiPayesh: Persian Plagiarism Detection based on Semantic and Structural Analysis

In recent years, the rapid increase of Persian electronic resources and facility of access to them has seriously triggered the plagiarism problem of the Iranian scientific community. Despite the automatic systems of plagiarism detection, like Turnitin, Eve2, this problem has strongly remained due to lack of support from Persian. The main purpose of this article is to detect exact plagiarisms and re-writings in Persian science texts. In our proposed method, after the candidate retrieval based on the statistical characteristics, in the text alignment step, structural analysis and semantic analysis of expression has been performed to detect re-writing plagiarisms. Firstly, data-driven dependency parser has been improved with the help of a deep learning model for Persian language to analyze the structure of the expression, and then the degree of structural similarity of the expression is evaluated through the analysis of the dependency tree. In this paper, our suggestion to examine the semantic similarity of expression is to use the semantic role labeling obtained from the deep learning model presented. The experiments have been performed on the corpus prepared in the AAIC2015 and corpus of the PAN2015 competitions. The results indicate that structural and semantic information improves the performance of the proposed method. ParsiPayesh is available on http://www.parsipayesh. ir.
WiFi Fingerprinting based Floor Detection with Hierarchical Extreme Learning Machine

The indoor location-based services are high demand in the market, and precise location estimation in multi-floor buildings has received significant attention in recent years. In these environments, the absolute floor recognition is a precondition for accurate positioning. In this article, to floor determination based on the WiFi-fingerprinting technique, the hierarchical structure of extreme learning machine (H-ELM) is exploited. This deep architecture of ELM comprises of two sections: the multilayer feature encoding with unsupervised learning (ELM-sparse-autoencoder) and the supervised multiclass classification (original ELM). Floor identification using H-ELM can be more accurate than traditional ELM. For evaluating the proposed method, we utilize TI building data available in the public UJIIndoorLoc dataset. As indicated by our simulation results, using the proposed WiFi-fingerprint based floor detection system can achieve a more accurate hit rate than other state-of-the-art techniques.
Millimeter-Wave Waveguide-to-Microstrip Transition With a Built-In DC/IF Return Path

In this article, a novel waveguide-to-microstrip transition with a built-in dc/IF return path is proposed, in which a traditional E- plane probe is directly connected to the backshort by a thin metal wire along the axis of the waveguide. The proposed waveguide-to-microstrip transition implements an additional dc/IF return path while realizing mode conversion. Moreover, the presence of the dc/IF return path has little influence on the performance of the original E- plane probe. The operating principle is analyzed by theoretical analysis and full-wave simulation, and the design procedure of the transition is also presented. To implement the proposed dc/IF return path on different substrates, two approaches, including direct integration with planar circuits or with wire bonding technology, are developed. Furthermore, two back-to-back transition prototypes working in the millimeter and submillimeter bands were designed and characterized. The measured results are in excellent agreement with the simulations. Finally, as an application example, a 70-/140-GHz unbalanced frequency doubler adopting this structure for both dc biasing and dc grounding was designed and fabricated. Measured results show that the conversion loss of the fabricated doubler is less than 13 dB in the range from 125 to 145 GHz, and the minimum conversion loss is 11 dB at 140 GHz with 100-mW input power.
DASH: Dynamic Scheduling Algorithm for Single-ISA Heterogeneous Nano-scale Many-Cores

The difference in the performance of identical cores due to the process variation and the different distance of cores from the shared last level cache (LLC) besides power limitation should be considered in scheduling algorithms to exploit the maximum performance of the Nano-scale many-core processors. This paper presents a dynamic heuristic scheduling algorithm, called DASH, to maximize performance under the mentioned challenges. In this regard, we estimate the execution time of each task of a job on a core as a relation of frequency and the communication cost of the core, as well as the type of the job and its tasks. According to this estimation model DASH selects some cores to maximize performance while exploiting DVFS to reduce the effect of power limitation. The time overhead of our algorithm is compatible with dynamic systems. We evaluate DASH by running random sequences of jobs from SPLASH parallel benchmark suite in Sniper and MACPat simulator for performance and power estimation. The results show that the throughput of DASH is 7.1% and 30.4% more than two similar algorithms.
Research on Modeling and Security Defense Methods of Power CPS

Smart Grid is the deep interactive fusion of power information system and physical system under the background of modern energy interconnection. Compared with the traditional power grid, its performance has been greatly improved. The essence of smart grid is the application of cyber physical system (CPS) in power industry, which is called power CPS. In view of the cyber security risk caused by the integration of the two systems in power CPS, which may cause the system to suffer complex and severe cascading failures and blackouts, it is of great significance to study the power CPS modeling and security defense based on the integration of the two networks. Different things have different classification methods from different angles. This paper summarizes the main research and analysis methods of power CPS modeling and security defense protection at home and abroad, and puts forward the future development direction, hoping to provide reference for the relevant researchers.
An optimization of im2col, an important method of CNNs, based on continuous address access

Convolutional neural networks (CNNs) are now widely used in various common tasks such as image classification, semantic segmentation, and face recognition. Convolution layers are the core layers of CNNs, the computing speed of the convolution layer will directly affect the computing speed of the entire network, thereby affecting the real-time performance. The current general convolutional layer acceleration method is to use the image to column (im2col) algorithm to split the input image into a column matrix, then use the general matrix multiplication (GEMM) to perform matrix multiplication on the column vector and the convolution kernel. This operation can greatly improve the computing speed of the convolutional layer because most computing platforms have more mature optimizations for GEMM. However, DSP is very fast for vector multiplication and addition. In the inference of the convolutional layer, the memory access of the im2col algorithm consumes far more time than the GEMM. This has become a bottleneck for further optimization of computing speed. In this article, I will present an im2col algorithm acceleration method in the case of a single stride based on continuous memory address read. With this method, the speed of the im2col algorithm can be increased by more than 10 times when processing a single-step convolutional layer. This is a portable method. In this article, I’11 show the optimization effects on Xtensa BBE64ep DSP cores and stm32f4 processors.
Towards Energy Efficient Mobile Wireless Receivers Above 100 GHz

Wireless communication above 100 GHz offers the potential for massive data rates and has attracted considerable attention for Beyond 5G and 6G systems. A key challenge in the receiver design in these bands is power consumption, particularly for mobile and portable devices. This paper provides a general methodology for understanding the trade-offs of power consumption and end-to-end performance of a large class of potential receivers for these frequencies. The framework is applied to the design of a fully digital 140 GHz receiver with a 2 GHz sample rate, targeted for likely 6G cellular applications. Design options are developed for key RF components including the low noise amplifier (LNA), mixer, local oscillator (LO) and analog-digital converter (ADC) in 90 nm SiGe BiCMOS. The proposed framework, combined with detailed circuit and system simulations, is then used to select among the design options for the overall optimal end-to-end performance and power tradeoff. The analysis reveals critical design choices and bottlenecks. It is shown that optimizing these critical components can enable a dramatic 70 to 80% power reduction relative to a standard baseline design enabling fully-digital 140 GHz receivers with RF power consumption less than 2 W.
Electrochemical techniques used in MIC studies

This paper presents a review of the application of electrochemical techniques in studying the microbially influenced corrosion (MIC) process of metals and their alloys. Some electrochemical techniques used in microbiology and biofilm study are discussed as well as some classic electrochemical techniques, such as corrosion potential, redox potential, polarization curves, electrochemical impedance spectroscopy, electrochemical noise and microelectrode techniques. Electrochemical quartz crystal microbalance (EQCM) which is seldom used in MIC study is also discussed.
Wind-PV-Battery Based Microgrid with Discrete Three-Phase Frequency-Locked Loop Control

This paper presents a wind-photovoltaic (PV)-battery-based grid supported microgrid system. This system is developed to improve the power quality (PQ) and transfer the power to the loads and the grid. An improved discrete three-phase frequency-locked loop (DTP-FLL) GSC (Grid Side Converter) control is used to regulate the reactive power at the grid as well as to maintain the DC-link voltage of the GSC. Moreover, it improves power quality in different abnormal conditions. The rotor side converter (RSC) is used to provide the required amount of reactive power using the field-oriented control for the wind power generator (WPG). A doubly fed induction generator (DFIG) is used as a WPG. The tip speed ratio (TSR) algorithm is used to extract the maximum power from a wind turbine. A solar PV array with a battery is connected at point of common coupling (PCC), which is about a km far away from it. A battery management system (BMS) is used to charge and discharge the battery. According to the load demands at the microgrid, the BMS works in coordination with it. A PV feed-forward term is added with the total active component of the control, which damps out the dynamic changes into the system. The grid current THD (Total Harmonics Distortion) is also maintained within 5% according to the IEEE standard. Simulated results demonstrate performance of the microgrid in different conditions, such as variable speeds within the MPPT (Maximum Power Point Tracking) range of the wind turbine, performance of GSC and RSC, changes in PV array insolation, charging and discharging the battery are optimized using BMS at different circumstances. The grid current THD is maintained within 5% according to the IEEE-519 standard.
Magnetic Coupling Characteristics of Spiral Square - Circular Coupled Coils for Wireless EV Battery Charging System

Electric Vehicles (EVs) are gaining continuous interest due to higher efficiency than internal combustion engine vehicles, and environmentally friendly nature. Nowadays, most of the EV uses the conductive charging method to charge the battery. Besides that, wireless inductive charging technology for EV has recently received a great attention because of the advantages such as increased user convenience and safety. In this method of charging mutual inductance between the wireless inductive coils is the crucial factor. Hence, this paper describes the mutual inductance characteristics of the transmitter with spiral square - receiver with circular (i.e., interoperability spiral square and spiral circular of coils) at various vertical distances using FEM simulations.
Research and Application on the Improved SSD Chip Defect Inspection Algorithm

Defect inspection is an important part of the light-emitting diode (LED) production process. In the production environment, most of the defects to be inspected are small targets below 32×32 pixels, which cannot be detected by current SSD algorithm effectively. Based on the concept of DSSD (Deconvolutional Single Shot Detector) algorithm, an improved SSD (Single Shot Detector) chip defect inspection method is proposed to accurately inspect LED chips surface defects. First, the original algorithm’s basic network VGGNet is replaced by ResNet, and the first pooling layer is removed; then a prediction module and a deconvolution module are added to fuse the semantic information between the high-level and low-level; finally, model hidden layer is pruned, and the last convolution layer and deconvolution module are removed. The experimental results show that compared with the original SSD algorithm, which enhances the accuracy of chip defect inspection.
A New 3D Video Database for Stereoscopic Video Comfortable Prediction

Visual comfortable is certainly one of the most critical issue in the research field of stereoscopic video. This is because that some viewers feel visual fatigue and discomfort when they see a wonderful stereoscopic video for a moment. Therefore, the problem of how to use related technologies to solve the series of problems exist in 3D comfortable prediction becomes urgent that we should settle. In this paper, we present a new three-dimensional (3D) video database (3DVID) for developing a new visual comfortable prediction technique of stereoscopic video. Specifically, this database contains 68 stereoscopic video pairs, where all of them are 1920×1080 resolution. The single stimulus continuous quality evaluation (SSCQE) method is adopted in our subjective tests, and the Mean Opinion Score (MOS) can be computed accordingly. In addition, we also show that the relationship between disparity, texture, motion and subjective MOS for each stereopair. Experimental results show that the proposed 3D video database contains a wide variety of scene variations and comfort levels, which is a very good benchmarking tool for 3D visual comfortable prediction algorithm design.
Design and Analysis of Beam Forming Microstrip-Fed Antenna for 5G NR Applications

The main purpose of this research work is to present the design and analysis of beam forming microstripfed antenna for 5G New Radio (NR) applications. The proposed antenna is composed of circular radiator having 6mm radius along with feed line of 7. 5х1. 4mm2 to operate in NR n77/78/79 5G bands. The analysis shows that the proposed antenna exhibits fractional impedance bandwidth of 44.81% with wide band of (3.25-5.08 GHz), gain 2.6dBi and radiation efficiency of around 85% for 5G applications. To validate the suitability of proposed antenna for 5G applications, a beam forming capability is also analyzed via CST Microwave Studio (MWS) software. At resonating frequency of 4 GHz, a gain of 12.45 dBi and 12.27 dBi at the phase angle of 00 and 300, respectively are achieved for linear eight phased antenna array pattern. This demonstrates the applicability of proposed antenna for 5G applications.
Cardiovascular Disease (CVD) Prediction using Machine Learning Algorithms

This paper deals with the prediction of Cardiovascular Disease (CVD) by performing an analysis of six supervised machine learning algorithms such as K-Nearest Neighbors Classifier, Naïve Bayes Classifier, Decision Tree Classifier, Random Forest Classifier, Support Vector Machine Classifier and Linear Discriminant Analysis. Further, by introducing two health risk factors (feature extraction) – Blood Pressure and Body Mass Index – into the dataset, we have observed an increase in the accuracy. Feature selection was performed to find out the important risk factors. Our main goal is to find the most optimal results in terms of CVD prediction from the available dataset.
Fast Convergence by Machine Learning Optimizer for Adaptive MIMO Equalizer Used in SDM Transmission over Coupled-Core 4-Core Fiber and 4-Core EDFA

We applied several optimizers from the machine learning field to an adaptive MIMO equalizer for SDM transmission. We experimentally compared their convergence properties in a SDM transmission over a 52-km coupled-core 4-core fiber and 4-core EDFA and showed over 22% faster convergence with Adam.
Power Allocation Assisted New Control Jamming Scheme For NOMA Enabled AF Relay Network

This paper presents a new control jamming (CJ) scheme for AF relay aided NOMA network with optimal power allocation. The network contains a source, which forwards the multiplexed signal to the NOMA users by employment of AF relay. The jammer node forwards the interference signal to secure the confidential information of users against the eavesdropper attack. Secrecy rate enhancement at the NOMA user with the aid of CJ under total power constraint is taken as an optimization problem. The optimal power allocation factors of considered network are investigated using Invasive-Weed-Optimization (IWO) algorithm. The physical layer security of the network is analyzed in terms of secrecy rate under CJ condition. Furthermore, the secure performance gap between fixed and proposed power allocation schemes are examined with CJ and compared with other jamming conditions. Finally, the simulation results demonstrate that the secrecy performance is enhanced remarkably by CJ and proposed-power-allocation outperforms the fixed-power-allocation.
Architecture of Fog Computing, Issues and Challenges: A Review

In the recent past, majority of IoT services and applications are designed to make the human life easy. The number of IoT devices is increasing day by day. Smart cities, smart objects, smart phones, smart homes etc. are implemented to get automated environment. These devices use the capabilities of Cloud computing paradigm where processing, analysis and storage of data are performed over the cloud. When the data is sent from End devices to Clouds, the problems of latency and threat to privacy and security arises. A new layer called the fog layer is added between the traditional centralized data centre and terminal nodes. This layer plays vital role to solve these problems by providing cloud similar facilities close to the end devices. However, this additional layer brings new challenges in these areas. This paper discusses the fog computing paradigm, security issues and challenges. A summary of comparative study on issues and challenges in Fog Computing is also shown in this article.
Detection of COVID-19 using Chest X-Ray Scans

COVID-19 has proven to be the unseen and unforeseen pandemic nobody was prepared to face. Healthcare professionals and radiologists have been under a lot of pressure ever since the outbreak to treat and develop faster ways to detect the disease. The proposed research involves the classification of chest X-ray scans to identify whether a patient has been infected with COVID-19 or not using the concept of Transfer Learning. Two methods of classifying the images have been presented in the research. The first approach classifies a given image into two categories being COVID-19 and Non COVID-19. The second approach classifies a given image into three categories namely COVID-19, Healthy and Pneumonia. These two models have obtained unprecedented evaluation metrics and could prove to be extremely useful when it comes to fast and accurate detection of the disease.
Vertical GaN Split Gate Trench MOSFET with Improved High Frequency FOM

Using TCAD Simulation, we present a systematic analysis and comparison of a new vertical GaN split gate trench MOSFET (SGT-MOSFET) with a conventional trench gate MOS-FET for 600 V switching applications. We have calibrated our simulation models to match the experimental data as available in the literature. We show that the SGT-MOSFET exhibits 7 times lower HF-FOM (Crss ×Ron) and 3 times lower HF-FOM (QGD × Ron) without increase in the specific on-resistance, when compared to that of conventional MOSFET. We, also have presented the main process steps required for the fabrication of the proposed device. These improvements are important for reducing the conduction and switching losses, and making high frequency power conversion more efficient.