Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Weighted KNN Algorithm Based on RSS Similarity and Position Distance for Wi-Fi Fingerprint Positioning

In Wi-Fi fingerprint positioning, what we should most care about is the distance relationship between the user and the reference points (RP). However, most of the existing weighted k-nearest neighbor (WKNN) algorithms use the Euclidean distance of received signal strengths (RSS) as distance measure for fingerprint matching, and the RSS Euclidean distance is not consistent with the position distance. To address this issue, this paper analyzes the relationship between RSS similarity and position distance, propose a novel WKNN based on signal similarity and spatial position. Firstly, we obtain the weighted Euclidean distance (WED) by balancing the size between the RSS difference and the signal propagation distance difference according to the attenuation law of the spatial signal. Then, we obtain the approximate position distance (APD) by making full use of the position distances and WEDs between RPs. Finally, the nearest RPs can be selected more accurately based on the APDs between the user and different RPs, and the position of user can be estimated by the proposed WKNN based on the APD (APD-WKNN) algorithm. In order to fully evaluate the proposed algorithm, we use three fingerprint databases for comparison experiments with eight fingerprint positioning algorithms. The results show that the proposed algorithm can significantly improve the positioning accuracy of WKNN algorithm.
Interference in Large Scale wireless networks: A Case Study with LoRa (Long Range WiFi) and LTE (Long Term Evolution)

Advancement in communication systems has resulted in the increase of services of different wireless technologies such as WiFi (Wireless Fidefity). LoRa (Long Range WiFi), LTE (Long Term Evolution), WiMAX (World Wide Interoperabillty for Microwave Access), etc. 4G wireless technology gives all enhanced features such as cost-effectiveness high data transfer rate and provides direct communication. LoRa also provides mobility feature and portability feature. Installation and implementation of these networks are easy. LoRa and LTE wireless networks give high data transfer rates and better service when compared to other networks like 1G, 2G, 3G etc. and these networks are heavily used by the users for communication. LoRa caters to long-range communication. The other technologies such as WiMAX is not widespread across India. Bluetooth technology has disadvantages like short-range, slow data speed, poor data security when compared to LoRa and LTE. This paper provides related work on LTE and LoRa performance-based simulations. In our Simulation, we have shown how LTE and LoRa work with different node densities such as 25. 50. 100. 150 and 200. PLR (Packet Loss Ratio) is calculated as nodes increase with increased traffic. PLR is compared between LTE and LoRa. The experiment is done using OMNET++ tool. Performance of LTE and LoRa is tested separately. Simulation results have shown that performance of the LoRa is better when compared to LTE because PLR is high on LTE network with increased node density and increased traffic compared to LoRa.
Simulink Model of Controlling Fuel Cell Powered Direct Current Motor with Comparative Performance Analysis

The paper focuses on a Simulink based model and design of the different control mechanisms of a DC motor powered via fuel cell. At first, fuel cell is used as the prime power source of the DC motor and a differential controller is used to achieve a uniform output. Then, the differential controller is replaced with Proportional Integral (PI) controller, Proportional Integral Differential (PID) controller and Fuzzy Logic Controller (FLC) subsequently to compare and demonstrate the variations that occur in the different characteristic curves with the change in various parameters. If Photovoltaic cell is used instead of fuel cell, the output operation of the DC motor may vary. So, in addition, a comparative performance analysis of both methods of power with regards to DC motor control is also presented.
Scalability of Learning Tasks on 3D CAE Models Using Point Cloud Autoencoders

Geometric Deep Learning (GDL) methods have recently gained interest as powerful, high-dimensional models for approaching various geometry processing tasks. However, training deep neural network models on geometric input requires considerable computational effort. Even more so, if one considers typical problem sizes found in application domains such as engineering tasks, where geometric data are often orders of magnitude larger than the inputs currently considered in GDL literature. Hence, an assessment of the scalability of the training task is necessary, where model and data set parameters can be mapped to the computational demand during training. The present paper therefore studies the effects of data set size and the number of free model parameters on the computational effort of training a Point Cloud Autoencoder (PC-AE). We further review pre-processing techniques to obtain efficient representations of high-dimensional inputs to the PC-AE and investigate the effects of these techniques on the information abstracted by the trained model. We perform these experiments on synthetic geometric data inspired by engineering applications using computing hardware with particularly recent graphics processing units (GPUs) with high memory specifications. The present study thus provides a comprehensive evaluation of how to scale geometric deep learning architectures to high-dimensional inputs to allow for an application of state-of-the-art deep learning methods in real-world tasks.
A Clustering Algorithm via Kernel Function and Locality Preserving Projections

This paper combines kernel function and Locality Preserving Projections (LPP) in a framework to improve the accuracy of clustering model. Specifically, we first use the kernel function to project each feature into high-dimensional kernel space so as to mine the nonlinear relationship of data. At the same time, the l1-norm sparse regularization term is used for feature selection. Besides, LPP saves the local structure of data. Finally, the optimal clustering result is obtained by solving the objective function. Experimental results on six benchmark data sets show that our proposed method is superior to the compared methods in term of clustering tasks.
Road detection using lidar data based on plane assumption and graph model

Detection of traversable road area is one of the key tasks for unmanned platforms to achieve autonomous navigation. We propose a road detection method using lidar data based on plane assumption and graph model in this paper. Two detection algorithms for different speeds are provided to ensure the detection accuracy and to meet the real-time requirements, respectively. First, the point cloud is projected into a real image or an imaginary image for triangulation. The road points are then extended outward from the near platform according to the plane assumption. Then the dual Voronoi diagram of the triangulation is filled according to the attribute of the central element node, and the upper part of the horizon is removed. Finally, image post-processing yields the final detection results. This method perfectly combines the classification of points and the determination of road areas and uses the mature image algorithms in the processing of point cloud data. Experimental tests have been carried out on the public KITTI-Road benchmark, obtaining positive results.
Enhanced Secrecy Capacity under Eavesdropping Attack in Multi-Traffic Industrial WSN

Industrial networks are increasingly based on open protocols and platforms that are also employed in the IT industry and internet background. It is essential to secure the important information throughout transmission so highest secrecy capacity is chosen in Wireless Sensor Networks. The employment of sensing element to improve the physical layer security of industrial WSN can counter the eavesdropping attack and optimal sensor scheduling scheme targets to increase the secrecy capacity of wireless transmissions from the sensors to the sink. The system model with one eavesdropper and multiple sources with different data type is used. The scheduling theme satisfies the requirements in multiple traffics and achieves maximal secrecy capacity. Numerical results validate the proposed optimum scheduling theme in Nakagami fading environment. This system model performs better than the traditional Round Robin scheme in terms of the throughput, delay and packet delivery ratio and monitors the packet transmission according to the assigned priorities.
Dual Band Monopole Antenna with a Stepped Ground Plane

The paper presents a dual band monopole antenna with a defected ground plane for Wi-Fi and WLAN applications. The defect is introduced as a step in the ground plane. Initially, a single band monopole antenna is designed. This is antenna is modified with the step defect to provide dual band characteristics. The depth and width of the defect decides the second band of operation for a given monopole length. The optimized antenna in ANSYS HFSS is fabricated and tested and are in concurrence with each other.
Automatic detection of fake news on online platforms: A survey

In recent years, a lot of fake news are generated on the web in order to attract readership, influence opinions, and increase internet click revenue. The generation of this false information has become a worldwide phenomenon and its effects are notorious as it leads to confusion over facts and causes wrong decision-making.However, evaluating and detecting the veracity of news can be a complex and cumbersome task. This is simply because most of the studies carried out so far on the detection of such news, especially in real time, are not really performant. Our objective in this work is, therefore, to review the major works that have addressed this problem. Results of this study have revealed that two major approaches have been put forward, namely linguistic and network. In this article, we will try to quote a set of automatic detection of false news on the web and social networks.
Automatic Handgun and Knife Detection Algorithms: A Review

Nowadays, the surveillance of criminal activities requires constant human monitoring. Most of these activities are happening due to handheld weapons mainly pistol and gun. Object detection algorithms have been used in detecting weapons like knives and handguns. Handgun and knives detection are one of the most challenging tasks due to occlusion, variation in viewpoint and background cluttering that occurs frequently in a scene. This paper reviewed and categorized various algorithms that have been used in the detection of handgun and knives with their strengths and weaknesses.
An Information Theoretic Approach to Power-Substation Level Dynamic State Estimation With Non-Gaussian Noise

This letter presents an information-theoretic approach to implement a decentralized substation based dynamic state estimator. Field phasor measurement unit (PMU) data is generally corrupted with colored and non-Gaussian noise, owing to which the conventional techniques based on second-order statistics provide strongly biased state estimates. The breakdown point of the proposed correntropy based estimator is theoretically infinite, making it an ideal choice for filtering non-Gaussian errors and outliers of high magnitude.
Optically Sensorized Elastomer Air Chamber for Proprioceptive Sensing of Soft Pneumatic Actuators

Soft robotics has proven the capability of robots interacting with their environments including humans by taking advantage of the property of high compliance in recent years. Soft pneumatic actuators are one of the most commonly used actuation systems in soft robotics. However, control of a highly compliant actuation system remains as a challenging issue due to its nonlinearity and hysteresis. Addressing this problem requires integration of a soft sensing mechanism with the actuator for proprioceptive feedback. A soft optical waveguide with a reflective metal coating is a promising sensing mechanism with high compliance and low hysteresis. In this letter, we propose design and fabrication of a soft pneumatic actuator integrated with an optical waveguide that can provide the proprioceptive information of the actuator. We describe the design and fabrication, and present experimental characterization results of the proposed system. We also provide applications of the proposed system.
Analysis of Z-Source Based Single Stage Three-Port Converter for PV Renewable Energy System

Solar power unit is needed to increase the lesser voltage of solar panels to the voltage of the power grid; thus, converter should have an immense voltage gain with few switches otherwise it leads to system complexity. A three-port converter with enhanced topology for renewable power utilizations based on Z-Source network is suggested to fix these issues well. This Z-Source network symbolize the suggested converter's distinctive characteristics. One port of energy storage system for energy storage and supplying, and the last one is for loads. It is followed with new topology design and output analyses by MATLAB.
Investigation of Deep Learning Schemes in Medical Application

Deep learning models are equipped for thinking out how to concentrate on the correct features without anyone else's input, requiring a little direction from the software engineer. Essentially, deep learning mirrors how our brain is functioning to take decisions. Deep learning techniques are highly applied in medical imaging diagnosis. Deep learning techniques are used in medical applications in four different areas i. Detections ii. Classifications iii. Segmentations iv. Registrations. In this paper we have discussed deep learn scheme advantage, dataset, software and hardware used in medical applications. Further, we discussed the comparative analysis of medical application using deep learning techniques.
Intelligibility Improvement using Kalman Filtering & EM Approach in Formant Shifting Framework

A formant shifting (FS) based speech intelligibility improvement approach [1] performs modifications on clean vocal sound messages. However in real time conditions, clean speech signal is generally mixed with car noise environment and reverberations. The LP Coefficients (LPCs) of clean speech signal is generally unavailable. Hence in this work, we would like to estimate LPCs from the mixed noisy signal using a combination of Kalman Filtering and Expectation Maximisation (KF-EM) based recursive algorithm. Once LPCs of clean speech from mixed noisy signal are estimated, formants are then computed and shifted away from the region of competing noise. In comparison to traditional LP coding analysis (LPCA) [1] and repeated autocorrelation (RA) [2] based formant estimation methods, the proposed KF-EM algorithm accurately estimates the LPCs from the noisy signal and subsequently, accurate formants are estimated. This results in significant intelligibility improvement in car noise environment for proposed algorithm, when used in FS framework. The subjective and objective analysis on NOIZEUS database are used to evaluate the performance of the proposed method.
The Effect of Website Design Artifacts and Emotions on Behavioral Purchase Intention in M-Commerce

Due to the huge proliferation of m-commerce in online business, practitioners and researchers from all over the world have paid their attention towards it. Different website design artifacts have a different effect on consumers' emotions and behavioral intentions. Also, emotions and behavior vary from consumer to consumer. Therefore, it is important to highlight the website design artifacts that affect consumers' emotions and behavioral intentions in the context of m-commerce. So far, emotions have been widely neglected in the extant literature. To fill this gap and to provide useful insights, this study examined the effect of website design artifacts (i.e. visual design, content design, and navigation design) on positive and negative emotions leads to consumers' behavioral purchase intention. For the collection of data, a questionnaire was designed. Moreover, a partial-least square approach was employed for the analysis of the collected data. The results showed a substantial effect of visual design and content design on both positive and negative emotions and ultimately, on purchase intention. Navigation design significantly influences positive emotion but not on negative emotion. Lastly, positive emotion observed as a strong antecedent of purchase intention in m-commerce context.
An Ontology of Digital Video Forensics: Classification, Research Gaps & Datasets

The world lives in an epoch where people have an incredible variety of digital multimedia technologies. Although historically it was established that the computerized information is authentic information, but the technology of today has begun to erode this assuredness because an opponent uses several distortions to edit the original video for trademark infringement. Many investigators have introduced video forgery detection systems, due to the advent of omnipresent digital videos on the internet and to overcome the difficulties. This paper depicts a comprehensive overview of different video forgery detection techniques. It specifically discusses the importance of the techniques by classifying them according to functionality. This study delivers an authoritative and thorough review of existing digital video forgery detection techniques and also highlights various research gaps of the current techniques. Furthermore, the existing video forgery detection techniques are assessed by analyzing accuracy, precision and recall. Finally, the paper discusses different public and private datasets available for evaluating video forgery detection techniques.
Discerning Genuine and Acted Smiles Using Neural Networks

Deception recognition is a growing area of interest in studies involving electroencephalograms (EEGs), as it paves the way for further research in demystifying and understanding the human brain. Furthermore, it has other practical applications that can prove to be beneficial to several fields of research and industry. A part of deception recognition is identifying fake smiles and true smiles given in response to certain stimuli. Here, we develop a neural network to predict whether a smile is fake simply by going through the EEG signals, given an example dataset. The network used, obtained an average accuracy of 72.38 % before principle component analysis was applied, and 78.29 % after for all subjects. In addition, the average sensitivity for all subjects was 77.35 % and the average specificity was 88.29 %.
Bidirectional DC-DC Converter for Incorporating Regenerative Braking in E-bikes

Recently, electric bikes (e-bikes) have proved to be an affordable mode of short distance transportation in urban areas, and hence are attracting considerable attention from customers. However, most e-bikes do not offer regenerative braking facility, resulting in a total wastage of the kinetic energy during braking as well as a shorter electric riding range. The prime objective of this work was to design and fabricate a bidirectional dc-dc converter (BDC) of power rating less than 250 W that can aid the propulsion of the e-bike as well as support regenerative braking. A non-isolated half-bridge BDC topology containing two MOSFETs, two filter capacitors and an inductor was considered for the work to minimize the size, footprint and weight of the system. The two dc-side voltages of the BDC were fixed at 12 V and 24 V. The design was simulated using NI Multisim software. Further, a hardware prototype of the BDC was developed to demonstrate the feasibility of the proposed BDC. Results show that the cost-effective BDC achieves an efficiency greater than 92 % in either direction of power flow.
Evaluating the Impact of Feature Selection Methods on SNMP-MIB Interface Parameters to Accurately Detect Network Anomalies

Many approaches have evolved to enhance the process of detecting network anomalies using SNMP-MIBs. Most of these approaches focus on machine learning algorithms with a lot of SNMP-MIB database parameters, which may consume most of the hardware resources (CPU, memory, and bandwidth). In this paper, we introduce an efficient detection model to detect network anomalies using Lazy. IBk as a machine learning classifier, Correlation, and ReliefF as an approach for attribute evaluators only SNMP-MIB interface parameters. This model achieves a high accuracy of 99.94% with minimal hardware resources consumption. Thus, this model can be adopted in the intrusion detection system (IDS) to increase its performance and efficiency.
A Spark Based Big Data Analytics Framework for Competitive Intelligence

In the era of Big Data, an ever-growing stream of information is available online in different formats structured, semi-structured and unstructured, more and more companies and organizations are trying to take advantage of this phenomena and make data-based decisions through the use of automatic processes and software which gave birth to competitive intelligence systems. Though traditional techniques of data mining and statistics used in these systems do not respond to the main challenges of big data such as volume, variety, and velocity, which makes it a must for enterprises to harness the power of new technologies in big data analytics and create value out of its advantages. In this paper, we propose a framework based on Apache Spark for competitive intelligence strategy implementation in all its steps from data collection, data analysis, data visualization to results and findings communication in order to assist the decision-making process of an organization.
A Compact Four Port UWB-MIMO Antenna With Notch Band Characteristics And High Isolation

A simple four port antenna with Ultra-wide band (UWB) and multiple-input multiple-output(MIMO) behavior is proposed using two shared radiators. the two shared radiators are making 180° angle to each other. The problem of interference in broad-band of UWB from other sources is avoided by forming notch band at 5.5GHz center frequency. The proposed design is of size 48X48mm2 using FR4 Substrate (dielectric constant 4.4) and fed by micro strip line. The two shared radiator multiple-input multiple-output antenna provides a bandwidth of 2.9 to 11.4 GHz with reflection coefficients $\mathbf{S}_{\mathbf{11}},\mathbf{S}_{\mathbf{22}},\mathbf{S}_{\mathbf{33}},\mathbf{S}_{\mathbf{44}}$ very less than −10dB and all the mutual coupling coefficients for respective antenna elements are less than or equal to −22 dB. The design also provides triple frequency bands characteristics with wide band behavior. Also, In order to achieve low mutual coupling, the meandered line stub is utilized at the end of the ground plane. The stimulated outcomes of proposed design are shown.
Vulnerability Detection in Firmware Based on Clonal Selection Algorithm

With the security breaches in Internet of Things devices, the detection of firmware vulnerability is more crucial than ever. Presently, many methods for firmware vulnerability detection have been proposed, but there are still some room for improvement on the detection precision. In this paper, we propose to use clonal selection algorithm to detect vulnerability functions in firmware. Firstly, we use the Relief algorithm to select the features that are more suitable for clonal selection algorithm. Then, we utilize principal component analysis algorithm to calculate the weights of the features. In the process of detection, we establish a set of specific detectors for each vulnerability function. In the end, we detect the vulnerability functions through these specific detectors. The experimental results show that the precision of our approach on detecting real vulnerabilities is competitive to the typical algorithm VDNS which is based on the neural network.