Miracle-dz/UniFAR-FTUs huggingface dataset:
A 2 to 18 GHz Compact High-Gain and High-Power GaN Amplifier

The design and implementation of a high-gain and high-power, efficient and broadband monolithic microwave integrated circuit (MMIC) power amplifier (PA) using a 0.1-μm GaN HEMT process is presented. For the first time, triple-stacked GaN transistors is used in a non-uniform distributed amplifier to achieve high power and high efficiency simultaneously. Further, a similar stacked transistors cell with feedback technique is adopted as a compact driver amplifier to enhance the broadband gain performance. Measured results of the MMIC PA across the 2 to 18 GHz band show at least 29 ± 1 dB small-signal gain, an output power of 9.1 to 15.8 W and an average power add efficiency greater than 25%. The chip occupies a die area of 2 × 3.5 mm2. To the best of the authors’ knowledge, this work reports the first PA which covers the frequency range of 2 to 18 GHz and achieves the combination of highest gain, about 10 W output power, together with the smallest die size among all published single chip GaN PAs to date.
Investigation of parameters affecting brain model validation and brain strains using the SIMon finite element head model

Finite element (FE) models of the human brain are often validated against cadaver brain deformation experiments and are then used to predict brain injury based on strain metrics. The current study evaluates three factors hypothesized to influence FE model brain displacements: the mass of tracking targets implanted in cadaver brains, variations in brain size/shape, and variations in Neutral Density Target (NDT) locations. The first two factors were also evaluated for their effect on brain strains. Baseline simulations of five experimental impacts were conducted with the Simulated Injury Monitor (SIMon) Finite Element Head Model (FEHM). Next, independent model variations were made and the impact kinematics were again applied. Point masses were added at NDT locations, the model was scaled to various sizes/shapes, and the NDT locations were offset in three directions. Modified model results were compared to the baseline simulations. Adding point masses had almost no effect on brain displacement metrics or brain strains, while model scaling influenced both. Changing the NDT locations had the largest effect on brain displacement histories. These results indicate that size, shape and NDT location differences between experimental specimens and FE models must be accounted for in brain model validation studies.
Extracting Conditions Specific Key Genes from Basal-Like Breast Cancer Gene Expression Data using Gene Co-expression Network

Drug discovery for specific disease is completely based on the drug target genes. The drug target gene plays a vital role in the different stages of the disease. Finding such key genes from the transcriptomic data is a complex task. In this paper, we have proposed a novel method for finding conditions specific key genes from the Basal-Like Breast Cancer (BLBC) data. The approach is divided into the two phases: Finding the significant biclusters and another is finding the key genes from the significant biclusters. For the first phase, ‘runibic’ biclustering algorithm has been used. The second phase is based on the concept of difference matrix, gene correlation matrix, gene co-expression network construction and key gene identification. Experimental results shows proposed approach has extracted the 95% and 85% significant biclusters at the p-value less than 0.05 and 0.01 respectively. For each significant bicluster, we have identified the key genes. Some of the important identified condition specific key genes are SHC4, PTOV1, PCNX4, STAG1, HMGB2, SHC3, MIB1, TMOD4, ASB8, PIK3CA etc. The identified key genes can be used as a biomarker for the BLBC disease.
Acoustic surveillance system for children’s emotion detection

This article presents a design of the system for acoustic event detection. The proposed pilot application is focused on child’s emotion/behaviour related sounds such as cry and laugh detection. Monitoring behaviour and safety of small children is particularly crucial in in-car environment and in households. The proposed application is based on Gaussian Mixture Model - Universal Background Model approach. The system is optimized by balancing false acceptance and false rejection rate. The classification accuracy of 71.6% was achieved although the system was trained only on small amount of data. The proposed approach has low computing and memory requirements thus is also suitable for implementation in embedded systems.
Design of a Conductive FSS based Ultrathin Absorber Using Impedance Analysis Method of Equivalent Circuit Model

In this paper, a conductive frequency selective surface (FSS) based ultrathin absorber is designed using impedance analysis of its equivalent circuit model. The proposed impedance analysis also helps to understand the absorption mechanism of conductive FSS based ultrathin absorbers. The unit cell of the designed absorber consists of a conductive square ring FSS on a low-loss grounded $\lambda_{0}/32$ thick substrate. The proposed absorber is fabricated and measurement results are provided to validate the circuit simulated result of the equivalent model and full-wave simulation result.
Sensor aiding of HSGPS pedestrian navigation

This thesis illustrates how a set of low-cost, self-contained MEMS sensors can be used to aid a High Sensitivity GPS (HSGPS) based pedestrian navigation system in signal-degraded environments. The HSGPS utilizes long dwell time technology and provides excellent signal availability in areas such as urban canyons, where conventional GPS receivers barely provide any position solutions. Large signal attenuation and degradation effects characteristic of downtown areas, along with a high probability of acquiring echo-only or cross-correlation signals through the HSGPS receiver, introduce significant errors in HSGPS measurements. These, in turn, cause very significant position and velocity solution errors. In this situation, it is very hard to estimate measurement errors based on common GPS signal quality characteristics, such as a carrier-to-noise ratio or satellite elevation. Inaccurate estimates of measured signals ultimately result in an unknown distribution of HSGPS solution errors, which present a challenge for the proper modeling of the covariance matrix of HSGPS updates for system integration. For pedestrian navigation applications, sensor data is mechanized in a pedestrian dead reckoning (PDR) mode, where the position is propagated through the detection of user steps. In this way, errors are proportional to the distance traveled, and not to the time. The analysis of the effects of errors introduced by the primary PDR system parameters, such as heading and step length on PDR position accuracy, is studied. A method for modeling PDR position error growth versus traveled distance for several stochastic step-length and heading drift error models is proposed. Based on the results of individual system performance analysis, a method to integrate the sensor and HSGPS data is proposed. This proposed method utilizes a
A Multiple-Layer Representation Learning Model for Network-Based Attack Detection

Accurate detection of network-based attacks is crucial to prevent security breaches of information systems. The recent application of deep learning approaches for network intrusion detection has shown promising. However, the challenges remain on how to deal with imbalance data and small samples as well as reducing false alarm rate (FAR). To address these issues, this work has proposed a multiple-layer representation learning model for accurate end-to-end network intrusion detection by combining deep convolutional neural networks (CNN) with gcForest. The contributions of this work lie in 1) a new data encoding scheme based on P-Zigzag to encode network traffic data into two-dimensional gray-scale images for representation learning without loss of original information; 2) The combination of gcForest and CNN allows accurate detection on imbalanced data and small scale data with fewer hyperparamters comparing to most existing deep learning models, which increase computational efficiency. The proposed approach is based on a multiple-layer approach consisting of a coarse layer and a fine layer, in which the coarse layer with the improved CNN model (GoogLeNetNP) focuses on identification of N abnormal classes and a normal class. While in the fine layer, an improved model based on gcForest (caXGBoost) further classifies the abnormal classes into N-1 subclasses. This ensures fine-grained detection of various attacks. The proposed framework has been compared with the existing deep learning models using three real datasets (a new dataset NBC, a combination of UNSW-NB15 and CICIDS2017 consisting of 101 classes). The experimental results show that our proposed method outperforms other single deep learning methods (i.e., AlexNet, VGG19, GoogleNet, InceptionV3, ResNet18) in terms of accuracy, detection rate, and FAR, which demonstrates its effectiveness in detecting fine-grained attacks and handling imbalanced datasets with high-precision and low FAR.
Time-Reversal Reconstructions of Clustered Sources and Diagnosis of Faulty Antenna Elements in Three Dimensions

The direct time-reversal (TR) method has been proposed for source reconstructions. However, it has limited capability in reconstructing clustered sources due to signal interferences generated by neighbouring sources. In this paper, we first develop the time-reversal theory in frequency domain. Then based on the analysis, we derive an important condition that formulates a time-reversal process with its solution being found by application of the regularized least squares (RLS) method. A numerical reconstruction of three-dimensional two-element linear array and a numerical diagnosis of faculty array elements in three dimensions are presented to verify the proposed method.
Research on Human Action Recognition Based on Convolutional Neural Network

This paper proposes a human action recognition (HAR) algorithm based on convolutional neural network, which is used for human semaphore motion recognition. First, collecting datas in three scenarios and Deep Convolution Generative Adversarial Networks(DCGAN) is used to implement data enhancement to generate the dataset (DataSR). Then, the 1*1 and 3*3 convolution kernels are used to design the full convolution network and the model is further compressed using the group convolution to obtain the new model HARNET. Experiments show that the mAP of HARNET on the DataSR dataset is 94.36%, and the model size is 76M, which is 30% of the size of the YOLOv3 model.
Accurate stereo matching based on weighted nonlocal aggregation for enhanced disparity refinement

Abstract. Disparity refinement is an important step to enhance the accuracy of stereo matching. This paper extends the scheme of a recent successful approach, namely the nonlocal disparity refinement algorithm, to exploit the initial disparity map in the aggregation phase of disparity refinement, in addition to the information of spatial distance and intensity difference. In addition, we propose a constraint function applied to the matching cost that constrains the scope of dissimilarity measures to further improve the accuracy of disparity refinement. Extensive experimental comparisons with several state-of-the-art methods using the Middlebury Stereo Evaluation version 3 datasets show that the proposed scheme has a great advantage in disparity refinement.
Accelerating CNN-RNN Based Machine Health Monitoring on FPGA

Emerging artificial intelligence brings new opportunities for embedded machine health monitoring systems. However, previous work mainly focus on algorithm improvement and ignore the software-hardware co-design. This paper proposes a CNN-RNN algorithm for remaining useful life (RUL) prediction, with hardware optimization for practical deployment. The CNN-RNN algorithm combines the feature extraction ability of CNN and the sequential processing ability of RNN, which shows 23%–53% improvement on the CMAPSS dataset. This algorithm also considers hardware implementation overhead and an FPGA based accelerator is developed. The accelerator adopts kernel-optimized design to utilize data reuse and reduce memory accesses. It enables real-time response and 5.89GOPs/W energy efficiency within small size and cost overhead. The FPGA implementation shows 15× CNN speedup and 9× overall speedup compared with the embedded processor Cortex-A9.
Systolic and Diastolic Blood Pressure Estimation from Pulse Transit Time Using Dual Split-Ring Resonators with Notch Structure

A novel cuffless blood pressure (BP) measurement technology is proposed in this paper. Systolic BP (SBP) and diastolic BP (DBP) can be estimated by measuring pulse transit time (PTT) between radial and carotid artery. The pulse signals of artery are measured by split-ring resonator microwave sensors with high sensitivity in a non-contact manner. Instead of measuring resonance frequency deviation, the measurements can be performed by detecting amplitude variation for not only simplified circuits but also increasing sampling rate and sensitivity. In addition, cascading a notch filter can steepen the transition response of sensor to further improves 135 times sensitivity compared to traditional frequency-based method. From experiment results, PTT and BP show the significant negative correlation, and the correlation coefficient is more than -0.79. Based on the relational function of PTT and BP, SBP and DBP can be estimated accurately, and the average error is lower than 8.4%.
45% RF-to-DC Conversion Efficiency Wireless Power Transfer System Through Biological Tissues Using Complex Conjugate Impedance Matching Taking Account of Tissue’s Properties

This paper presents a complex conjugate impedance matching technique taking account of the electric properties of biological tissues during power transfer. Output impedance of a wireless power transfer (WPT) system depends on the electrical property of tissues where the system is being used. So, impedance mismatch between Rx and rectifier occurs when the system optimized for air is used in biological tissues. First, the system is matched in the air and later, impedance mismatch is estimated when the same system is being used in chicken which is later compensated. The proposed matching technique will bring back the rf-dc power conversion efficiency (PCE) very close to that of the system being used in the air. Experiment was carried out in three different cases where PCE is 49.53% in the air, 30% in chicken with the conventional conjugate matching and 45.5% in the chicken using the proposed matching technique.
Investigation on the Impact of Various Intercalation Doping on the Signal Integrity in ML-GNR Interconnects

In this work, we perform a comparative study of signal integrity (delay, noise and noise delay product) for three different intercalation doped, side contact (SC), Multi-Layer Graphene Nano-Ribbon (ML-GNR) interconnects. For the first time, crosstalk delay of Lithium (Li) intercalated ML-GNR is evaluated and compared with Arsenic Penta-Fluoride (AsF5) doped, Ferric Chloride (FeCl3) doped ML-GNRs and copper interconnects for both intermediate and global level interconnects at 8 nm technology node. It is observed that Li intercalated ML-GNRs have least crosstalk delay as compared to other intercalation doping with specularity index of (P=0.8) for both intermediate and global level interconnects. As far as noise is considered, Li, FeCl3 and AsF5 doped ML-GNR interconnects are comparable but copper interconnects is not a good recommendation for low noise applications. Therefore, our analytical report paves the way for Li intercalated ML-GNR as the next generation interconnect material for low delay applications.
Large Power System Stability Analysis Using a FOSS-based tool: SciLab/Xcos

This paper describes the usage of an open-source tool namely Scilab-package for development of a multi-machine small-signal stability programme. It is shown that the package has enough computational capabilities to carry out large power system analysis. Analytical and time-domain simulation results obtained for a well-known 4-machine, 10-bus, 10-machine, 39-bus and 50-machine, l45-bus power systems demonstrate that Scilab/Xcos can be an alternate open-source tool to conventional proprietary software.
Analysis of E-shaped Patch Antenna for Wireless Applications

In this paper, design of E-shaped patch (ESP) antenna with coplanar waveguide (CPW) fed, operating on 6.5 GHz and 10.5 GHz frequency at wireless applications. The antenna designed at dual-band frequencies of reflection coefficients is −15.22 dB and −31.03 dB and gain of 5 dBi and 2.5 dBi respectively. The obtained simulated results are in good agreement with the measured results.
A Survey of Phylogenetic Databases

Phylogenetic study has found its way in various application domains like systematics, drug discovery, epidemiology etc. Accordingly, phylogenetic information is also being generated at an ever increasing pace, partly due to developments in algorithms and computational power. As the information increases, it must be managed and stored in proper way to avoid information overload. Phylogenetic databases are being developed for this purpose. However, due to being somewhat imperfect and still infant field of study, most efficient computational representation of evolutionary data is yet to be discovered. Many phylogenetic databases try to attack this problem from various directions, providing varying features, posing different limitations. In this paper we have tried to comparatively study some of the major phylogenetic databases. We have created a comparison chart for easy reference of features present and absent in various databases.
Intelligent Contactless Gesture Recognition Using WLAN Physical Layer Information

Contactless gesture recognition is an emerging interactive technique in ubiquitous and mobile computing. It combines the linguistics with the wireless signals to analyze, judge, and integrate human gestures by the usage of intelligent algorithms. The existing contactless gesture recognition studies can achieve gesture recognition with the machine learning technologies. But in practice, some objective factors, such as the user’s position, the non-line of sight condition, can seriously affect the performance of these gesture recognition systems. In this paper, we propose an intelligent and robust contactless gesture recognition using physical layer information. Instead of the usage of machine learning, we learn the gesture characteristics based on the Fresnel zone model of wireless signals. First, we denoise the collected channel state information (CSI) in a sliding window. Then, we extract the eigenvalues of channel phase information based on Fresnel zone model to depict four basic gestures. The features of gestures are independent of the user’s position and the signal amplitude. Finally, common-gesture recognition is achieved based on the decision tree classification. Moreover, we develop a hidden Markov model to achieve the complex-gesture recognition. The extensive experimental results show that our proposed method is position-independent and robust. The accuracy of basic-gesture recognition is as high as 91% on average. And, the accuracy of the complex-gesture recognition is also above 85% on average.
Crowd Detection Management System

Smart cities aim not only to make people's lives more enjoyable but also safer using advanced technology. Being in a crowded community spaces such as schools, colleges, stadiums, subway stations or holy spots on Pilgrimage impacts not only the level of human convenience but above all the threat of human security. An abnormal crowd conduct can lead to push, mass panic, stampede, crowd-crush, and causing an overall control loss. The current work introduced a mobile-based crowd abnormal behavior detection and management system. The system consists of two main parts; firstly, the server-side application connected to IP surveillance camera(s) to detect any abnormal crowd behavior and also crowd level in the entrance gates location(s), while the second main part is a mobile application with different users rights to receive an alarm from the server-side application in case of increasing crowd level or abnormal movement. The suggested framework provides an effective method to connect and alert all the system users immediately, preventing danger resulting from abnormal crowd behavior.
A 79-GHz Scalable FMCW MIMO Automotive Radar Transceiver Architecture with Injection-Locked Synchronization

This paper describes a 79 GHz four transmit, four receive channels automotive frequency modulated continuous wave multi-input-multi-output radar integrated circuit based on 20 GHz injection lock oscillator (ILO). The proposed architecture enables the cascading of multiple transceiver (TRX) chips in a daisy chained manner where each TRX chip is injection locked to the previous one. System architecture and circuit design of ILO has been described in detail with the measurement results.
A Review of Payment Card Fraud Detection Methods Using Artificial Intelligence

In the light of the twenty first century, payment cards like credit and debit cards are absolute basics in the global economic transactions. As these payment methods are popularising, the information associated with these are being accessed by a lot more people than required increasing the risk of a possible deceit. This paper presents a comprehensive and systematic review of artificial intelligence and machine learning algorithms and techniques for payment card fraud detection. This paper reviews that can be incorporated in the system according to specific needs.
Effect of PV and FACTS on Small Signal Stability

Renewable Energy sources like Photovoltaic (PV) systems are available in present grids. Under small signal disturbances, the presence of PV affects the stability. In this paper both PV and various Flexible AC Transmission system (FACTS) devices has been added in a standard two area systems for stability studies. This has been done by comparing the damping offered by different FACTS devices and PV. The detailed analysis is done using time domain simulations. The steady state stability as well as voltage profile is also thoroughly discussed. All the simulations and analysis are performed in PSAT toolbox of MATLAB.
Combining growth and level data: An estimation of the population of Belgian municipalities between 1880 and 1970

ABSTRACT Economic historians that study long-term changes during the nineteenth and twentieth century are fundamentally restricted by the availability of qualitative data. As a result, researchers are forced to either impute missing data, or otherwise combine datasets in some way. In this article, we demonstrate the versatility of state-space models in addressing these problems. Not only do they enable us to compose large data series of high quality, they also provide a clear estimate of how reliable this data is, allowing any subsequent analyses to take this reliability into account. We illustrate the advantages of a state-space model using the population of Belgian municipalities as a case study. By combining growth and level data, we are able to compute yearly population statistics of over 2600 municipalities from 1880 to 1970.