Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel FPGA based T/R Module Controller for Active Phased Array Radar

The current state-of-art technologies being researched upon globally for the various functionalities like Radar, Communication, Electronics Warfare, etc., have super components that performs similar functionalities. The Active Antenna Array Unit of modern Active Phased Array Radars consists of Radiating elements, Transmit/Receive (T/R) Modules and associated RF & Digital electronics. This paper explores the existing technologies and suggests improvements for the development of futuristic Active Phased Array Radars. FPGA based T/R Module Controller (TRMC) interfaces with the higher level controller over the Low Voltage Differential Signaling. The TRMC which controls $T$/R module is internally divided into three major sub blocks namely the Universal Asynchronous Receiver/Transmitter (UART) logic, Decoding logic and Control logic. The controller also provides real time status of critical components of the T/R Module, such as DC power supply temperature, forward power monitoring, reverse power monitoring etc. In proposed architecture we have used suitable Xilinx FPGA and achieved a switching time of <50ns. The realized unit has improved efficiency, reliability and compact without compromising the electrical parameters of T/R Module functionalities. Modern long range Active Array Radar systems are built with large number of solid state T/R Modules for realization of high power aperture product. These radars with multi-function capability require faster target update rates as well as low side lobe levels. For realization of large power aperture multiple T/R Modules are packaged in a single housing. The electronic scanning as well as low side lobe levels are achieved by controlling phase and amplitude of T/R Modules distributed in Active phased Array Radars. As a part of research, several TRMs have been realized, tested, qualified and integrated successfully with the Active Phased Array Radar.
Smart City Renovation using SDN Framework

Research and development in different technical dimensions of smart city has been a key challenge. It requires successful renovation with suitable technology and innovative design models while sharing knowledge and amenity to the society. Satellite communication plays an important role in providing accurate and timely information to the user nodes present in the community. Software-defined networking (SDN) has been evolved as an emerging platform in the last decade and solves various challenges faced by traditional network used in satellite communication. In this paper, we present an SDN deployed design framework for traffic management and weather forecasting in smart city environment. Our design framework is integrated into the ground stations and consists of four modules, namely; data retrieval, computation, verification, and information circulation. These modules coordinate with each other along with the satellites, and user nodes. The accuracy of the proposed framework is verified using a Z3 constraint solver. Also, the performance is compared with TCP/IP network and existing GPS systems. The results show 98% accurate message detection, 38% enhancement in throughput, and approximately 25ms delay in delivering the messages to the user devices.
Application of convolutional neural network for gender and age group recognition from speech

One of the major long-standing challenges in the task of sound recognition is the ability to recognize gender and age group from human speech. Although research projects in this subject achieve noticeable results but the accuracy is not as high as expectation. Therefore this paper aims at building one convolutional neural network-based model to classify gender and age group from human speech. We evaluate accuracy of prediction of our model on three audio sets and achieve impressive results compared with some other ones. In addition, we compare results of our study with several published articles.
Remote Sensing Image Scene Classification Based on SURF Feature and Deep Learning

Remote sensing image scene classification is one of the key points in remote sensing image interpretation. The traditional remote sensing image scene classification feature performance is not strong, and the deep learning extraction semantic feature process is complex. This paper proposes a fusion feature remote sensing image scene classification method which is based on artificial features and deep learning semantic features. Firstly, the SURF feature of the remote sensing image is extracted and encoded by the VLAD algorithm. The semantic feature of a remote sensing image is extracted by transfer learning. Then the feature reduction is performed by PCA algorithm and feature fusion is performed. Finally, the scene classifier is trained by using the random forest algorithm. The experimental results show that the classification accuracy and Kappa coefficient of this method are higher and the method is effective.
Sinhala-English Code-Mixed Data Analysis: A Review on Data Collection Process

This study presents a new Sinhala-English code-mixed data collected from Facebook comments, chat history and from public posts. Further, the data collection was annotated by three annotators in sentence level as well as in word level. Subsequently, the corpus was created for Sinhala-English automatic language detection study in code-mixed data which will be the first such study done on Sinhala-English code-mixed data in the industry by the time of the research is conducted. Accordingly, this data collection will be the first Sinhala-English code-mixed data published in this domain.
Warder: Online Insider Threat Detection System Using Multi-Feature Modeling and Graph-Based Correlation

Existing insider threat detection models and frameworks generally focus on characterizing and detecting malicious insiders, for example by fusing behavioral analysis, machine learning, psychological characters, management measures, etc. However, it remains challenging to design a practical insider threat detection scheme that can be efficiently implemented and deployed in a real-world system. For example, existing approaches focus on extracting features from user behavioral activities but they lack in-depth correlation and decision making for suspected alerts; thus, resulting in high false positives and low detection accuracy. In this work, we propose a novel online insider threat detection system, Warder, which leverages diverse feature dimensions (using neural language processing) and fuses content and behavior features to create a user's daily profile to facilitate threat detection. Besides, hypergraph-based threat scenario feature tree is designed to correlate suspicious users' activities with threat scenarios to further screen the users. In practice, Warder can also be constantly updated using newly discovered features and threat scenarios. We evaluate the performance of Warder using the public CMU CERT dataset, as well as that of approaches from the Oxford group and CMU group. Findings from the evaluation demonstrate that Warder outperforms the other two competing approaches.
Sentiment Analysis of Sindhi Tweets Dataset using Supervised Machine Learning Techniques

Social websites are major opinion sharing platform now a days with millions of people using them each day. Twitter is one of the top social media platforms on internet. With the introduction of multiple languages over social media, individuals are usually sharing their opinion in their native language. Individual and organizations are interested in people's opinion on internet for their future strategies for business and products. In this paper, we have performed sentiment analysis of Sindhi language using supervised machine learning techniques. The dataset we developed is tweets in Sindhi language. Automated approach using predefined lexicon is used to give positive and negative polarity to tweets so we have two classes. Pre-processing involves removal of non-Sindhi words, extra white spaces, removal of punctuations, after that tokenization of text is performed. After data cleaning and labelling, supervised machine learning techniques were applied on the Sindhi tweets dataset. We applied Support Vector Machine (SVM), Naïve Bayes, Decision Tree and K-nearest neighbour. Results showed that Decision Tree and K-nearest neighbour gave the best accuracy on Sindhi tweets dataset followed by SVM.
Hybrid Tree-Fuzzy-Rough Set Decision Support for Determining Plant Growth Using Vision-Based Descriptors

Correct identification of the growth stage of crops contributes largely to the proper allocation and control of environmental factors for optimized harvestable products. Machine vision approaches for lettuce growth stage prediction has issues such as feature extraction, feature selection and dimensionality reduction for optimum classification accuracy, and robust framework for the prediction system. This paper presented a methodology of classifying lettuce growth stage using a Hybrid Decision Tree-Fuzzy- Rough Set. Vision features are extracted and subjected to dimensionality reduction using Decision Tree. The reduced inputs are used to design the Mamdani Fuzzy Inference system. Rough Set Theory is then applied to the Fuzzy Logic model to simplify the rules. Experimental results show a high performance in determining the growth stage of test lettuce images.
Data Fusion-Based Network Anomaly Detection towards Evidence Theory

We propose a fusion model of deep neural networks and traditional algorithms for anomaly detection. The proposed model inherits the advantages of both these methods to create a robust anomaly detection algorithm. We employ the Dempster-Shafer theory (D-S) of Evidence, a very reliable and flexible data fusion technique, to form a fusion-based network anomaly detection (FuseNAD) by applying a basic probability assignment (BPA) function and modifying the D-S theory’s rule. FuseNAD fuses four anomaly detection methods consisting of a deep learning technique, namely Shrink Auto-Encoder, and three traditional ones such as One-class Support Vector Machine (OCSVM), Kernel Density Estimation (KDE) and Local Outlier Factor (LOF). The experimental results show increases in detection rate and overall accuracy in comparison to the individuals on several public network anomaly detection datasets.
Expediting Design Bug Discovery in Regressions of x86 Processors Using Machine Learning

As digital designs grow in size and complexity, design verification and debugging failures become increasingly more challenging. Verification today takes up to 70% of all design development cycles. Half of this time is spent on debug. Thus, automating any stage of the debug process would have significant effect on reducing the overall design time. In this paper, we present a tool that uses Machine Learning techniques to analyze and leverage data from failing simulations from regressions to detect which failures are related to actual bugs in the Register Transfer Level (RTL). Debuggers' time can be effectively utilized by prioritizing RTL-defects to be debugged first, which also helps in resolving these defects faster which means better RTL quality. The tool was tested using regression data from an x86 processor verification. A 90% capture rate was achieved for single-defect signatures, as well as a 95% capture rate for multi-defect signatures with reasonable debug overhead.
Area Efficient Multi-Threshold Null Convenction Logic

Multi-threshold null convention logic (MTNCL) is a commonly used asynchronous paradigm for designing low power NCL circuits. Traditionally, MTNCL circuits implemented using complementary metal oxide semiconductor (CMOS) technique that tends to occupy a large area. To address this limitation, a gate diffusion input (GDI) methodology is introduced for implementing MTNCL circuits. This GDI technique enables complex logic to be implemented using only two transistors that helps to reduce area utilization. In this paper, a novel approach to implement MTNCL designs based GDI methodology is proposed. The proposed approach has been verified by implementing TH23 MTNCL gate. Comparing to the conventional CMOS implementation, the proposed approach shows a 45% reduction in the area overhead.
Exploring Unknown States with Action Balance

Exploration is a key problem in reinforcement learning. Recently bonus-based methods have achieved considerable successes in environments where exploration is difficult such as Montezuma’s Revenge, which assign additional bonuses (e.g., intrinsic rewards) to guide the agent to rarely visited states. Since the bonus is calculated according to the novelty of the next state after performing an action, we call such methods as the next-state bonus methods. However, the next-state bonus methods force the agent to pay overmuch attention in exploring known states and ignore finding unknown states since the exploration is driven by the next state already visited, which may slow the pace of finding reward in some environments. In this paper, we focus on improving the effectiveness of finding unknown states and propose action balance exploration, which balances the frequency of selecting each action at a given state and can be treated as an extension of upper confidence bound (UCB) to deep reinforcement learning. Moreover, we propose action balance RND that combines the next-state bonus methods (e.g., random network distillation exploration, RND) and our action balance exploration to take advantage of both sides. The experiments on the grid world and Atari games demonstrate action balance exploration has a better capability in finding unknown states and can improve the performance of RND in some hard exploration environments respectively.
User Grouping and Power Allocation for Downlink NOMA-Based Quadrature Spatial Modulation

Conventional non-orthogonal multiple access (NOMA)-based spatial modulation (SM) systems utilize only the real part of amplitude-phase modulation (APM) symbols for transmission, but quadrature spatial modulation (QSM) can extend this to two orthogonal components to improve the spectral efficiency. In this way, we propose a NOMA-based QSM system in multiple-input multiple-output scenarios, where APM symbols are divided into real and imaginary components for transmission and then these two components are sent by real and imaginary transmit antennas, respectively. To further improve the performance of our proposed system, we also propose a user grouping and a power allocation with relatively low complexity. Different from previous schemes, the user grouping scheme is designed for considering channel conditions, user locations, and channel correlations jointly. Moreover, the power allocation scheme is performed in each group to meet users’ quality-of-service requirements, i.e., target-rate requirements. Simulation results demonstrate the effectiveness of proposed schemes by comparing with existing ones.
A Novel High Frequency-Link Bidirectional DC- DC Converter for Electric Vehicle Applications

In this study, a novel high frequency-link bidirectional DC-DC converter is proposed as part of electric vehicle (EV) to achieve bidirectional power flow: grid-to-vehicle (G2V) and vehicle-to-grid (V2G). The use of high frequency transformer (HFT) in the system not only increases the system efficiency, but also reduces the filter component size and dimensions. Furthermore, the transformer has been designed to operate in various conversion rates through the transformer selective pins. To verify the performance of the converter, simulation studies have been performed through Matlab/Simulink software.
Deep Learning-Aided Binary Visible Light Communication Systems

This paper investigates a deep learning (DL) method for on-off keying (OOK) based visible light communication (VLC) systems where a lighting emitting diode transmits binary optical pulses to a receiver. Universal dimming abilities are considered such that the VLC transceiver meets arbitrary dimming requirement of external users. This poses a combinatorial formulation optimizing binary codewords under multiple dimming constraints. To tackle this, DL techniques are employed to design an OOK encoder-decoder pair over noisy optical channels. For universal dimming support, the training of the DL-based VLC transceiver turns out to be a constrained training problem with multiple dimming constraints. This paper employs a dual formulation to develop a constrained training strategy. Numerical results show the effectiveness of the proposed transceiver design.
GenNet : Reading Comprehension with Multiple Choice Questions using Generation and Selection model

Multiple-choice machine reading comprehension is difficult task as its required machines to select the correct option from a set of candidate or possible options using the given passage and question.Reading Comprehension with Multiple Choice Questions task,required a human (or machine) to read a given passage, question pair and select the best one option from n given options. There are two different ways to select the correct answer from the given passage. Either by selecting the best match answer to by eliminating the worst match answer. Here we proposed GenNet model, a neural network-based model. In this model first we will generate the answer of the question from the passage and then will matched the generated answer with given answer, the best matched option will be our answer. For answer generation we used S-net (Tan et al., 2017) model trained on SQuAD and to evaluate our model we used Large-scale RACE (ReAding Comprehension Dataset From Examinations) (Lai et al.,2017).
A Chattering-Free Sliding Mode Filter Enhanced by First Order Derivative Feedforward

Noise reduction is one of the important issues for feedback control systems. Toward this problem, this paper proposes a new sliding mode filter, which is an improvement of Lv et al. ’s parabolic sliding mode filter. The proposed filter employs a first-order derivative feed-forward term for increasing both tracking and noise attenuating performances. Its discrete-time algorithm is derived by applying the implicit-Euler discretization, and it realizes exact convergence without producing chattering in discrete-time implementation. The effectiveness of the proposed filter is evaluated through an open-loop and a closed-loop numerical examples.
Passivity-Based Robust Compliance Control of Electro-Hydraulic Robot Manipulators With Joint Angle Limit

This paper presents a robust compliance control scheme for an electro-hydraulic robot manipulator with an electro-hydraulic torque actuators (EHTAs) and joint torque sensors. The EHTA, a torque-sourced hydraulic actuator, consists of electro-hydraulic backdrivable servovalve and a rotary hydraulic vane actuator and it allows us to design controllers similar to those for robot manipulators with electric motors. However, unlike in electric motors, the EHTA has a limited rotational angle and this may lead to instability when using a non-passive robust controller that generates the energy. As a solution to this problem, this paper proposes a robust two-loop control structure that has a passivity-based disturbance observer as an inner-loop controller and a nominal state feedback compliance controller as an outer-loop controller. The proposed control method was evaluated through single-degree-of-freedom experiments.
A Deep Reinforcement Learning Based D2D Relay Selection and Power Level Allocation in mmWave Vehicular Networks

5G millimeter wave (mmWave) communication is an efficient technique for low delay and high data rate transmission in vehicular networks. Due to the high path loss in 5G mmWave band, 5G base stations need to be densely deployed, which may result in great deployment expenditures. In this letter, we jointly consider a relay selection problem in multihop 5G mmWave device to device (D2D) transmissions and a power level allocation problem of mmWave D2D links. We propose a centralized hierarchical deep reinforcement learning based method to find an optimal solution for the problem. The proposed method does not rely on the information of links, and it tries to find an optimal solution based on the information of vehicles. Simulation results show that the convergence of the proposed method, and the transmission delay performance of proposed method is better than a link-quality-prediction based method, and close to a link-quality-known method.
Use of SAR/InSAR in Mining Deformation Monitoring, Parameter Inversion, and Forward Predictions: A Review

Ground surface deformation monitoring, parameter inversion, and forward prediction are essential to mining deformation mechanism interpretation and its related geohazard assessment. Interferometric synthetic aperture radar (InSAR) has the capability to measure surface deformation in a very wide area with high spatial resolution, low cost, and high efficiency, which traditional geodetic surveying techniques cannot accomplish. Consequently, InSAR offers great potential for promoting, or even revolutionizing, traditional mining deformation monitoring and forward prediction, with great efforts having been made in this field.
Segmentation of Defocus Blur using Local Triplicate Co-Occurrence Patterns (LTCoP)

Many digital images contain blurred regions which are caused by motion or defocus. The defocus blur reduces the contrast and sharpness detail of the image. Automatic blur detection and segmentation is an important and challenging task in the field of Computer vision “e.g. object recognition and scene interpretation” that requires the extraction and processing of large amounts of data from sharp areas of the image. Therefore, the sharp and blur areas must be segmented separately to assure that the information is extracted from the sharp regions. The existing techniques on blur detection and segmentation have taken a lot of effort and time to design metric maps of local clarity. Furthermore, these methods have various limitations “i.e. low accuracy rate in noisy images, detecting blurred smooth and sharp smooth regions, and high execution cost”. Therefore, there is a dire necessity to propose a method for the detection and segmentation of defocus blur robust to the aforementioned limitations. In this paper, we present a novel defocus blur detection and segmentation algorithm, “Local Triplicate Co-occurrence Patterns” (LTCoP) for the separation of in-focus and out-of-focus regions. It is observed that the fusion of extracted higher and lower patterns of LTCoP produces far better results than the others. To test the effectiveness of our algorithm, the proposed method is compared with several state-of-the-art techniques over a large number of sample images. The experimental results show that the proposed technique obtains comparative results with state-of-the-art methods and offers a significant high-speed advantage over them. Therefore, we argue that the proposed method can reliably be used for defocus blur detection and segmentation in high-density images.
Sample Size Calculation In Medical Research

For the reliability and general ability of the results of any epidemiological or medical research study, adequate number of study units is extremely important. Study of both less than needed and unnecessarily large sample size is ethically and economically unfair. Considering the importance of sample size, its determination and justification in all kinds of epidemiological or medical research studies, we propose simple and non-technical explanation to the available sample size calculation methods. In this review we covered most frequently encountered study designs in medical research. Present review provides statistical formulas for the sample size calculation of the research studies with varying objectives such as estimation of population proportion(s) and hypothesis testing, estimation of population mean, estimation of diagnostic accuracy, estimation of association and reliability, sample size calculation for case control studies, and sample size calculation for cohort studies. Limited technical details and explanations of underlying theoretical assumption for each method have been included to ensure the adaptability of the method with minimal theoretical understating and statistical knowledge.