Miracle-dz/UniFAR-FTUs huggingface dataset:
Geo-spatial Clustering of Sentiments on Social Media

Social networking sites have tremendously captured online communication over the social web. With the growth in number of users on social networks, the social data has also grown exponentially. One of the predominantly used social networking sites includes Twitter. It is one of the most authenticate social platform that allows users to express their views on current trends and topics. Sentimental analysis of such dynamically changing user behavior upholds huge amount of contextual information. The behavioral data could be further evaluated to find the associated sentiments. Our research is focused on pre-processed analysis and classification of real-time tweets, based on the emotional content. Our novel approach applies density-based clustering with longitudinal locations from the tweets to reveal social communities for sentimental analysis.
Improved Single Particle Model Based State of Charge and Capacity Monitoring of Lithium-Ion Batteries

State of charge and state of health monitoring of lithium-ion batteries is a hot topic in the area of battery management. Although much work has been done for state estimation based on equivalent circuit model, more research is needed to monitor battery state using electrochemical model which can reflect chemical reactions inside the battery. In this paper, an online state of charge and capacity estimation strategy is proposed based on improved single particle model using extended Kalman filter. Firstly, an improved single particle model which incorporates Li-ion concentration distribution in electrolyte phase is established. Then two extended Kalman filters with different time scales based on the model are used to estimate state of charge and capacity. Finally, the ability of the method to against erroneous initial values is evaluated, and the experimental results show the feasibility of the proposed approach.
Determination of Characteristics and Performance Appraisal of GaN MESFET

Several decades in the past have seen the rapid development of modern electronics and recent trends have led to the proliferation of studies that incorporate gallium nitride as a key aspect of fabricating different types of field effect transistors. Moreover, it is fast becoming an essential instrument in other aspects of electronics as well because of its phenomenal inherent properties such as high power, high temperature, and high frequency. A considerable amount of literature has been published on the mathematical formulation of gallium nitride based field effect transistors. However, there has been little quantitative analysis of various electronic properties like intrinsic delay faced by the carrier. This research article examines several properties such as intrinsic delay, gate to source capacitance, gate to drain capacitance, transconductance, cutoff frequency, and channel current for gallium nitride metal-semiconductor field-effect transistor as well as investigates the impact of variation in gate to source voltage on the channel current with proper evaluation of respective characteristics curves.
Dual-Band Inverted F-Shaped Antenna Array for Sub-6 GHz Smartphones

In order to meet the requirement of dual-band MIMO operation in 5G smartphones, a 12-port dual-band inverted F-shaped antenna array for 3.5 GHz (3400- 3600 MHz) and 5.8 GHz (5725-5785 MHz) bands is designed. The proposed dual-band antenna array elements are located symmetrically on the inner surface of side frames instead of on the circuit board. Thus, the structure without ground clearance along the long edges is suitable for wide-screen smartphones. To embed 12 antenna elements into the limited space of a smartphone without additional decoupling structures, we propose a structure that bend the branches of inverted-F antenna to a smaller size (8mm×6mm) reducing the occupied space of antennas. A prototype of the proposed 12-antenna array is manufactured and measured. Both simulation and experimentally measured results show that our designed antenna array achieves desirable antenna impedance matching (better than 6/10 dB return loss), acceptable isolation (better than 10 dB) and antenna efficiency (better than 40%). Envelope correlation coefficients and channel capacities are also calculated to validate that the proposed dual- band antenna arrays have good radiation and MIMO capacity performance.
Experimental Evaluation of the Long-Range MIMO Outdoor Channel at 2.4 GHz

Low Power Wide Area Networks (LPWAN) technologies in the 2.4 GHz band offer the opportunity to employ multiple antennas at the sensor, which can be used to create antenna diversity. In this paper, we present our software-defined, interference-robust, and highly mobile channel sounder architecture and experimentally evaluate the long-range outdoor-to- outdoor MIMO channel in a typical Smart City environment. Analyzing the measurements we have obtained over a wide area, we estimate path loss, coherence bandwidth, and antenna correlations. Link budget calculations show that, despite the higher frequency compared to popular LPWAN technologies such as LoRa or SigFox, multi-kilometer links are feasible. Furthermore, the comparison of an IEEE 802.15. 4 PHY designed for LPWANs with a recently proposed multi-antenna scheme indicates that considerable performance gains are possible through antenna diversity schemes, even with rather high antenna correlation.
Change Detection for Hyperspectral Images Using Extended Mutual Information and Oversegmentation

We propose a change detection algorithm for hyperspectral images by properly extending the description of commonly used mutual information metric in monochrome images to hyperspectral images. The newly extended metric for the additional spectral dimension in hyperspectral images accumulates the effects of all spectral bands to the statistical relation between the pixels of the two images at the same location. In order to avoid blurring kind of distortions in the change maps resulting from the usage of fixed size kernels during the calculation of mutual information in previous literature, the proposed method first applies an oversegmentation to the hyperspectral images and then, computes the extended metric over the produced superpixels. The proposed approach based on joint superpixels and extended mutual information is compared with two basic approaches, whereas the first one uses conventional mutual information of two images and the second method utilizes the extended mutual information over rectangular kernels. The experimental results indicate that the change masks obtained by the proposed method are more accurate compared to the baseline approaches.
Voltage Sensor utilizing Inverse piezoelectric effect and Fiber Grating

A voltage sensor based on inverse piezoelectric effect and fiber gratings is presented in this paper. Besides basic sensor validation test of working frequency signals, two sensors with similar parameters were tested and analyzed with working frequency signals and operation voltage signals. Test results indicate that the sensor can work well in working frequency but can only respond to part of the operation voltage signal properly. This is a potential passive sensor consist of the piezoelectric element and fiber element, more work should be conducted to enlarge its working frequency range.
Evaluation Platform of Platoon Control Algorithms in Complex Communication Scenarios

Cooperative Adaptive Cruise Control (CACC) extends the Adaptive Cruise Control technology with additional information exchange between vehicles over vehicle-to-everything (V2X) communications in an ad-hoc network at 5.9 GHz band (ITS-G5) in Europe. Using beyond line-of-sight information provided by V2X, the platoon control algorithms realize a shorter safe inter-vehicle distance. Nevertheless, the platoon performance (e.g., the allowable inter-vehicle distance) may be impacted by the imperfectness of wireless communications. Specifically, in congested traffic scenarios, a Decentralized Congestion Control method that regulates message rate based on congestion level (Transmit Rate Control (TRC)), may significantly reduce the platoon performance. In this work, we propose an evaluation platform for platoon control algorithms based on industrial V2X nodes operating in the ITS-G5 channels. The real car is simulated by a longitudinal vehicle dynamic model. The model-in-the-loop test results demonstrate that the performance of CACC goes down significantly when the message rate is restricted and reduced by TRC. Our evaluation results further conclude that the effect of such complex communication scenarios imposed by the existing standards should be explicitly modelled in the future platoon control algorithms.
Research on Effective Coupling Range of Medium Distance Wireless Power Transfer via Coupled Magnetic Resonances

As a branch of the wireless power transmission technology, Wireless Power Transfer via Coupled Magnetic Resonances system can achieve high-power and high-efficiency medium-distance power transmission. However, in the system space, not all spatial points can achieve effective energy transmission, and the transmission efficiency of the system is deeply affected by the change of coil position. This problem has became one of the bottlenecks restricting the development of this technology. Aiming at the above problems, we can build a model of Wireless Power Transfer system by using circuit theory. It’s easy to know the expression of system receiving power and transmission efficiency with the model. At the same time, the effective coupling range of the system can be defined by simulation analysis. Finally, the finite element model and experimental system are built to verify the theoretical analysis. The experimental results are in good agreement with the theoretical analysis. The system transmission power can reach more than 80% of the critical coupling power within the effective coupling range. The results not only prove the correctness of the proposed method, but also provide a beneficial reference for Wireless Power Transfer via Coupled Magnetic Resonances system optimization.
Matlab Based GUI for ECG Arrhythmia Detection Using Pan-Tompkin Algorithm

The purpose of paper is to detect heart arrhythmia which is generated due to irregular heart rhythm. The heart problem of a person can be noticed by examining the ECG (Electrocardiogram) signal and withdrawing the various features of the ECG signal like RR interval, width of QRS complex, P wave, R wave and heart rate. Data for electrocardiogram signal occupied from MIT-BIH arrhythmia database. Graphical user interface (GUI) has been developed for the detection of arrhythmia using Pan-Tompkins algorithm. Modification has been done in the Pan-Tompkins algorithm for the detection of abnormalities related to heart by calculating various parameters. This toolbox is developed in MATLAB software. Developed GUI is an efficient and effective way to diagnose heart diseases
Characterization of the Fluorescence Peak on Remote Sensing Reflectance For Different Conditions of Lakegarda

The presence of phytoplankton in the oceans and freshwaters basins provides relevant ecological indications about water quality. Its abundance influences the interaction between the solar light and the water body, which in turn can be detected by remote sensing techniques. The water leaving light field contains information about all the processes that occur in the water body itself, including the complex interactions with phytoplankton. While elastic scattering is mostly ascribed to the phytoplankton surface, the absorption and fluorescence processes occur at the chlorophyll-a molecules level. Thus, measurements of the fluorescence signal emitted represents a suitable proxy to identify phytoplankton abundance in inland waters. In this context, the aim of this work is to establish and quantify how the fluorescence signal affects the Remote Sensing Reflectance (Rrs). For this purpose, radiative transfer simulations computed by Hydrolight code were generated for various levels of chlorophyll content, fluorescence quantum yield and optical properties, typical of a clear blue lake (Lake Garda, Italy). This work would form the basis for better exploiting satellite observations from upcoming hyperspectral missions (FLEX, PRISMA, Sentinel-10, HyspIRI).
Iterative Localization Method Using AoA for IoT Sensor Networks

Internet of Things (IoT) integrates numerous technologies to obtain the location information of the sensors in various ways. The most common localization methods are based on measuring a location dependent parameter such as Received Signal Strength (RSS) or Angle of Arrival (AoA). Then the measured parameters are used to estimate the sensor location by using a multilateration algorithm. AoA estimation can provide good localization accuracy for narrowband array- based systems. The research has drawn considerable attention in the literature and numerous methods have been proposed to improve the localization accuracy. The two-step method suffers from information loss from the received signals when the estimation of the parameters is communicated to the multilateration step. In this paper, we propose a new localization algorithm which iterates between AoA estimation and multilateration steps to refine the position estimate. We develop a localization scenario for the assessment and validation of the algorithm in Bluetooth Low Energy (BLE) sensor networks. Simulation results show that the proposed solution achieves a better localization accuracy when compared to the common two-step and achieving this after only a few iterations. Additionally, our method comes very close to the performance of the position estimation relying directly on the received signal, known as Direct Position Estimation (DPE), while reducing its complexity.
Efficient Linear Feature Extraction Based on Large Margin Nearest Neighbor

Linear feature extraction methods have become indispensable tools in pattern recognition. The linear dimensionality reduction optimizes some objective to produce a linear transformation and derives the discriminative low-dimensional transformed data wherein the similarly labeled samples cluster tightly and the differently labeled samples keep away from one another. In the past, most of the methods achieve between-class distance by maximizing between-class-center-mean to make the differently labeled samples separated from each other. However, the samples in this study located in the class margin or within the other classes called hard samples often move slowly, which results in a small between-class distance and deteriorates the classification performance. Hence, we utilize the large margin nearest neighbor (LMNN) to quickly push only the hard samples toward the center of the class to get the large class margin instead of between-class, further improving the performance of classification. Combined with the linear discriminant analysis (LDA), a novel linear feature extraction method called LDA–LMNN is proposed. The method can address the limitations of LDA. Furthermore, the dropout is employed to the learning of linear transform matrix to improve the generalization ability and overcome the overfitting. The comparative experiments on various real-world datasets by using the state-of-the-art feature extraction methods demonstrate the effectiveness of the proposed method.
ULBP-RF: A Hybrid Approach for Malware Image Classification

The growing dependence on internet for performing critical activities in every domain has raised serious concerns about the security of the computer systems. Malwares have become a significant threat to computer systems and recently, a massive growth has been observed by experts in the number and sophistication of new malwares. Therefore, the task of malware detection and classification is of utmost importance. However, the task of classifying malwares has become more challenging since the introduction of code obfuscation and metamorphism techniques. These techniques easily alter the malwares' code signatures and make the static techniques of malware detection ineffective. Dynamic analysis is effective but time-consuming. Recently, image processing techniques along with machine learning techniques have been explored by researchers for visualization and classification of malwares. In this paper, we introduce a new approach ULBP-RF that uses Uniform local binary pattern with circular neighborhood strategy to extract features from the malware images dataset. The resulting datasets are classified using Random Forest. To assess the performance of the proposed approach, experiments are performed and a comparative analysis of the performance of various combinations of feature extraction techniques and classification algorithms is done. It has been found that ULBP-RF has the highest classification accuracy.
Performance Analysis of Multi-Modal Overlay/Underlay Switching Service Levels in Cognitive Radio Networks

Cognitive radio networks have become a popular platform for many systems and applications of the future, and especially for Smart City applications and the Internet of Things. The wireless transceivers used for communicating between the different devices in the cognitive radio networks can operate in either of the two modes, namely overlay and underlay, or a hybrid of these two modes. While operating in the underlay mode, secondary users are likely to experience varying transmission rates due to the fluctuating power levels from the primary users. This has the effect of the channel capacity being dynamic, which forces the secondary user to switch between different transmission rates, or “service modes”, during a single networking session. In our previous work, we developed a discrete time queueing model for analyzing the performance of secondary users in such networks with multi-modal and hybrid overlay/underlay switching service levels. In this paper, we extend our previous work to present our novel result for computing the waiting-time distribution of the secondary users. Such results are essential for investigating the sensitivity of the secondary user’s performance due to the queueing delays, and especially for real-time applications.
Channel Estimation Based Equalizer for Underwater Acoustic Multiple-Input-Multiple-Output Communication

Multiple-input-multiple-output (MIMO) underwater acoustic communication offers a promising way to improve the data rate under very limited bandwidth underwater channels. However, MIMO underwater acoustic suffers from not only inter-symbol interference (ISI) but also co-channel interference (CoI), which pose significant difficulties to the conventional channel equalizer. Designed to overcome ISI, the traditional channel estimation based decision feedback equalizers (CE-DFE) will experience substantial performance loss, because the impact of CoI is not taken into account. In this paper, first, we modify the calculation of filter coefficients for the traditional CE-DFE to accommodate the presence of CoI. Second, we propose CE-DFE with interference cancellation (IC) for MIMO underwater acoustic communication by adding IC filters. The filter coefficients for proposed CE-DFE with IC are derived using channel estimates. Furthermore, the performance of the traditional CE-DFE, our modified CE-DFE, and our proposed CE-DFE with IC in terms of maximum access error, excess error, and output signal-to-noise ratio is analyzed. Finally, both simulation results and sea trial results demonstrate the effectiveness of the proposed methods.
Scale-Free Estimation of the Average State in Large-Scale Systems

This letter provides a computationally tractable necessary and sufficient condition for the existence of an average state observer for large-scale linear time-invariant (LTI) systems. Two design procedures, each with its own significance, are proposed. When the necessary and sufficient condition is not satisfied, a methodology is devised to obtain an optimal asymptotic estimate of the average state. In particular, the estimation problem is addressed by aggregating the unmeasured states of the original system and obtaining a projected system of reduced dimension. This approach reduces the complexity of the estimation task and yields an observer of dimension one. Moreover, it turns out that the dimension of the system also does not affect the upper bound on the estimation error.
A Survey on Secure Cloud: Security and Privacy in Cloud Computing

Cloud computing is an emerging technology that is still unclear to many security problems. The security problem becomes amplified under the cloud model as new dimensions enter into the problem scope related to the architecture, multi-tenancy, layer dependency, and elasticity. This survey paper introduces a detailed analysis of the cloud security problem. In this paper various existing approaches related to data encryption and message authentications are discussed. After study the existing approaches, issues and challenges are point out during data processing over the cloud. Instead of only encryption or authentication, this paper suggests attribute based encryption and attribute based authentication together, during communication over the cloud for achieving better security.
Using Psychophysics to Measure Negative Side Effects in Immersive Virtual Environments

A study was done to determine the effectiveness of using free modulus magnitude estimation, a psychophysical measurement technique, to assess the experience of negative side effects in users of immersive virtual environments (VEs). Two different task environments, a maze and an office, were factorially combined with head-tracking on or off to provide multiple levels of side effect inducing conditions. Sixteen subjects participated in a four day experiment. Both head-tracking and task environment showed significant main effects. The experiment showed that magnitude estimation is a sensitive, efficient, and effective measure of negative side effects experienced in users of immersive VEs.
Performance Enhancement of OAM-MIMO Using Successive Interference Cancellation

This paper proposes a method that employs successive interference cancellation (SIC) to further enhance the capacity of uniform circular array (UCA)-based orbital angular momentum (OAM)- MIMO systems. The central feature of the proposed approach is to exploit the additional space diversity benefit in the remaining streams by means of SIC owing to the large difference in the received power among different UCAs in each OAM mode. Therefore, it is expected that the proposed method can further stabilize the performance of UCA-based OAM-MIMO along with the communication distance. Our numerical results show that the proposed method outperforms the traditional method without SIC in terms of the system capacity owing to the large received power difference among different UCAs.
Development of Writer Independent Offline Signature Verification System through Multiple Classifier and Geometric Features

The signature is a very significant trait of an individual which serves not only for the identification of an individual but also for establishing the genuineness of official documents. The aim of this work is to investigate the scope of geometric features to develop a proficient offline signature verification system through multiple classifiers using writer-independent approach. The key focus of this work is to minimize the false acceptance rate for the simulated forgery. The classification task is accomplished through Support vector machine with Gaussian radial basis function and polynomial kernel. The k-fold cross validation method is utilized to construct the multiple classifier system of diverse classifiers. The genuine and random forgery signatures are considered to train the classifiers of multiple classifier system whereas genuine, random forgery and simulated forgery signatures are utilized to perform the testing process. A public signature database named GPDS is utilized to assess the performance of the proposed system. The simulation study reveals FRR of 7.00%, FARR of 0.00%, FARS of 0.00% thereby AER of 2.33% as the best result.