Miracle-dz/UniFAR-FTUs huggingface dataset:
The Performance of the RSS-Based Lateration Algorithm for Indoor Localization

Localization methods have evolved over the years and led the invention of the Global Positioning System for outdoor localization. In time, the need for indoor localization has also arisen and research on indoor positioning systems has increased significantly. One of the most widely studied indoor localization algorithms is considered to be the Received Signal Strength-based lateration. The mentioned algorithm has a major advantage of being simple. In addition, it is low cost since it uses the existing infrastructure. In this paper, the performance of the RSS-based lateration is evaluated and compared for various scenarios through simulation studies. The accuracy of the algorithm is calculated when the path loss exponent value is known, when the path loss exponent value is estimated or when least-squares approximation is used. In addition, the impact of certain parameters such as the size of the area, the grid size or the noise level are investigated.
Improvements on Data Insertion Technique in Encrypted Image against Lossy Compression

This paper proposes two techniques in enhancing the robustness of a joint image encryption and data insertion method against lossy compression [1]. Safety zone is first introduced to ensure that pixel values representing ‘0’ are separated from those representing ‘1’ with a gap in between. This empty gap serves as a buffer so that pixels from both groups do not cross and eventually affect the extraction process. Next, majority vote is also adopted so that error can be further suppressed, although reducing number of bits that can be inserted. Results suggest that both strategies can suppress error caused by lossy compression.
Role of Social Media Marketing on Consumer Purchase Behaviour: A Critical Analysis

For sharing and expressing opinions, an individual can use the virtual space in the web and Social media is a platform for such things; where discuss on certain issues, comments on different facts and comparing things with others. On the other hand, consumer marketing and brand management are connected with it and create much more complexity in this global, quickly growing technology-world. Also it influences consumer purchasing behavior and purchasing attitude. Currently, social media like facebook, youtube, etc., has gained remarkable attention in the last decade. Social media has become so important in affecting purchasing behavior of prospective customers that the companies have their own social site to handle to provide customers information they require and found it easily. The major focus on this paper is to describe the potential benefits associated with the application of machine learning techniques. Adopting a Neuro-Fuzzy system that, under uncertainty, has the capacity to process information towards better choices linked to virtual retail system parameter. Focus is on the particular field like business, education, society and youth how it affects society in a broad way.
Softsign Function Hardware Implementation Using Piecewise Linear Approximation

Softsign is a widely used activation function in recurrent neural networks. However, no special attention has been paid to the hardware implementation of Softsign function. In this paper, we propose a hardware architecture, which is based on the piecewise linear (PWL) approximation, for the hardware implementation of Softsign function. The main advantage of the proposed hardware architecture is that we develop a low-power high-accuracy approximate multiplier for the PWL approximation. Implementation results show that the proposed hardware architecture can save 55.1 % power consumption with only 4.2% increase in mean absolute error.
Combine Facebook Prophet and LSTM with BPNN Forecasting financial markets: the Morgan Taiwan Index

The recurrent neural network (RNN) used by many people in machine learning often faces the situation where the gradient disappears, in order to solve this problem. Modern scholars often use Long Short-Term Memory (LSTM) proposed in 1997 to predict time series samples. However, Facebook believes that most of the past time series models have missing adjustment parameters. Therefore, it developed a set of predictive tools Prophet for periodic parameters and trend parameters in 2017. In this paper, LSTM and Prophet are used to predict the trend of time series data, and the prediction trend is combined with the inverse neural network model (BPNN) for prediction. The empirical results show that this method can indeed achieve accurate forecasting trends and reduce errors. This research promises to contribute to this research literature in the future, thereby enhancing the ability of investors to target the long-term layout.
Point Cloud Features-Based Kernel SVM for Human-Vehicle Classification in Millimeter Wave Radar

Human-vehicle classification plays an important role in advanced driver assistance systems (ADAS). The use of millimeter wave (mmWave) radar sensor in human-vehicle classification algorithms is of great significance since the sensor maintains to be robust in severe weather (e.g. fog, snow, etc.). To improve classification accuracy under complex scenes of autonomous driving, a new mmWave radar point cloud classification algorithm is proposed in this paper, which realizes human-vehicle classification employing a newly proposed point cloud feature vector with eleven dimensions and based on kernel support vector machine (SVM) classifier. To verify the validity and robustness of the proposed feature vector, a 77 GHz radar is used to collect two datasets for static and moving objects, respectively, with each dataset taken for pedestrians and vehicles at different distances and angles. Experimental results show that the proposed algorithm achieves higher classification accuracy than a conventional one based on signal features. For the comparison based on the same number of dimensions, the number of dimensions of the proposed feature vector is decreased by removing the features with low significance. Experimental results verify that the proposed algorithm maintains advantage over the conventional one.
Modeling of Heart Data using PSO and A-Priori Algorithm for Disease Prediction

Medical organizations such as hospitals include a lot of data and hidden Information. The medical field deals with a lot of data regularly. However, this data is not used correctly. Thus, this unused data can be converted into useful information by using various data extraction techniques. The main disease that causes sudden death of people is heart disease in medicine. It is imperative to predict this disease at an early stage. Large data processing by traditional means can affect results. Computer assisted systems help the doctor as a tool for prediction and analytic exploration of the heart disease. Advanced data mining algorithms and technique are used to discover the facts in the database and for medical research, especially in case of heart disease. This article has main purpose of understanding the datasets which are generally taken in medical field. The understanding of data within the attributes of disease is necessary for developing or modeling the proper algorithm for computing accuracy from the given data sets. The mining association is one of the most important data extraction techniques. Mining link rules can be used efficiently in any rule-building decisions based on decision-making wizard. In this article, we offer effective PSO optimization along with A-priori techniques to model data sets for prediction of heart diseases. Clustering provides the nearby occurrence and nature of datasets as considering the centroid of each cluster for disease. This paper presents the PSO, K-Mean clustering and the simple statistics which apply over the heart image data sets to provide the result for comparative analysis of proposed clustering technique.
Study of Issues and Challenges of Different Routing Protocols in Wireless Sensor Network

In present era of technology, the wireless communication has made it possible to use various applications widely. This is mainly due to the evolution and expansion of the boundaries of Wireless Sensor Networks (WSNs). Wireless Sensor Networks (WSNs) contains small nodes with power of sensing, computations, and wireless communications abilities. There are different issues and challenges in the WSN such as the energy efficiency, placement of sensor node, storage capacity, and operational complexity and so on. The main focus, however, has been given to the communication between sensor nodes and cluster heads which might differ depending on the application and network architecture and design. The responsibility of cluster head is to coordinate the process of information within the cluster and transmission of information between cluster nodes. The idea behind clustering has been used to reduce energy consumption and extend the life time of the network. In this paper we have discussed various routing mechanism and shows their results as a concluding remarks with comparison.
Security Mechanism to Protect the Privacy and Security of Patients Who have cardiovascular Diseases (ECG)

This paper presents a security mechanism to protect the privacy and security of patients who have cardiovascular diseases (ECG). New technologies must provide proactive management of well-being and focus on early detection and disease prevention. Wearable and Implantable Medical Devices (WIMD) have been widely used in the health sector for purposes of monitoring, diagnosis and treatment of diseases, such as the electrocardiogram (ECG), pacemaker to treat arrhythmias and neurostimulators for epilepsy. Several research works in the field of computer security have identified serious security and privacy risks in the WIMD that could compromise the implant and even the health of the patient transporting it. This article analyzes the most relevant protection mechanisms proposed up to now and examines the main mechanisms of patient safety. On the one hand, security proposals must take into account the inherent limitations of these small and deployed devices. On the other hand, the proposed solutions must achieve an adequate balance between patient safety and the level of safety offered.
Influence of material volume conductivity on electric field and surface charge of RIP valve-side bushing core under DC electro-thermal coupling stress

Modifying the volume conductivity of resin impregnated paper (RIP) material is a promising way to eliminate the electric field and the surface charge along the RIP valve-side bushing core surface. In this paper, an electro-thermal coupling model is established based on the geometry of a ±200 kV RIP valve-side bushing and the conductivity of RIP material taken from cuts of an actual bushing core. The influence of volume conductivity of RIP material on the surface charge, the duration of capacitive-resistive field transition, and the tangential electric field along the core surface are studied. Results indicate that decreasing the volume conductivity of RIP material can help reduce the positive surface charge and the tangential electric field along the core surface. On the contrary, it increases the duration of capacitive-resistive field transition. To meet the requirements of polarity reversal test, the volume conductivity of RIP material should be decreased by two orders of existing material's volume conductivity to reduce surface charge and tangential electric field. The results are valuable for the design of RIP converter valve-side bushings.
Flushing Pressure Automation for Efficient Machining in EDM Process

Electrical discharge machining (EDM) is important unconventional machining process in which utilizes electrical spark energy to remove the material from the workpiece. In such process, the workpiece and tool electrode has to be separated with the constant air gap throughput the machining process for producing the discharge energy. In the present study, an endeavor has been made to automate the flushing mechanism for enhance the machinability by monitoring the gap voltage in EDM process. It has been observed that the proposed approach can reduce the unwanted arcing effect in a considerable way.
Delay Analysis and Optimization of Beam Scanning-Based User Discovery in Millimeter Wave Systems

In order to satisfy higher requirements for data transmission rate and system capacity, millimeter wave (mmWave) with abundant spectrum resources has be exploited and become a research hotspot of the fifth generation (5G) mobile communication systems. However, introducing mmWave technologies into cellular networks also faces many severe challenges, especially the initial user equipment (UE) discovery. In the UE discovery, the narrow-beam directional scanning is often used to search the UE, but it increases the control overhead of the system and brings about a non-negligible search delay. In this paper, we will analyze the average search delay of mmWave systems under two different UE access methods, i.e., the random scanning access method in independent networks and the search method based on UE location in heterogeneous networks. Taking the average search times discovering UEs as a measure and the number of search sectors in beam-space as an optimization parameter, the optimal number of sectors with the maximum capacity is obtained so that the search delay and the capacity can be balanced. The simulation results verify the correctness of the analysis and show that the performance of the user discovery method in heterogeneous networks is better than that of the random scan access method in mmWave independent networks.
A Filtering Method for Classification of Motor-Imagery EEG Signals for Brain-Computer Interface

A brain-computer interface (BCI) utilizes brain signals such as electroencephalogram (EEG) and provides a path way for people to interact with external assistive devices. The objective of this work is to classify the tasks so that we can assist the disabled person in doing things on own way with the aid of BCI. The raw EEG signals have a chance of being affected with interference and hence have low signal to noise ratio (SNR) which may lead to erroneous results. These EEG signals are decomposed into intrinsic mode functions (IMFs) using different standard algorithms like empirical mode decomposition (EMD), multi variare empirical mode decomposition (MEMD). Different features like skewness, K-Nearest Neighbour (K-NN) entropy, sample entropy and permutation entropy are extracted from these IMFs which will significantly contribute to the classification of tasks. This work is carried out on the well established BCI motor imagery dataset, BCI competition IVa dataset-1 which will support the analysis. These extracted features are subjected to classifiers like random forest, Naive Bayes and J48 classifiers. The classification accuracies have been recorded and improved results are achieved using MEMD.
Fractional Bilinear Transform Based Design of Wideband Digital Differentiator Using Multirate Technique

In this paper, a design of first-order wideband digital differentiator based upon the fractional bilinear transform is presented. The Proposed design manifests the optimization of fractional bilinear transform up to $0.5\pi$, then the bandwidth of proposed differentiator is extended by using the concept of multirate technique to cover the entire Nyquist range. It matches the ideal magnitude response perfectly with mean relative magnitude error (MRME) as low as −46.5580 (dB). The proposed first-order differentiator is compared with recently proposed differentiator designs and found to be the most accurate in terms of absolute relative magnitude error (ARME) for the entire Nyquist range.
Extension Topologies for Modular Multilevel Series/Parallel Converter With Switched-Inductor Energy Transfer Between Modules

This paper presents three extension topologies for the modular multilevel series/parallel converter (MMSPC) with intermodule switched-inductor power transfer. The switched-inductor voltage conversion feature allows controllable and efficient transfer of energy between modules with nonnegligible voltage difference, providing both step-down and step-up functionalities. Thus, this converter can accurately control and rapidly adjust the voltage of each module to generate an ac output voltage waveform with a controllable number of levels, increasing the quality of the output. In this paper, is presented three novel configurations, in order to increase the flexibility and number of output voltage levels to the standard MMSPC with intermodule switched-inductor power transfer topology.
Power Converter Maintenance Optimization Using a Model-Based Digital Reliability Twin Paradigm

Optimization of operations and maintenance activities in factories was estimated to have a global economic potential of 1.2 to 3.7 trillion USD by recent studies. Digital twins offer a framework to achieve such optimization by studying potential improvements in the virtual space before applying them to the real world. We studied the use of a digital twin based on a general model of system failure behaviour for maintenance optimization by combining existing methodologies into a general framework. Applying it to a real-world power converter use case, we identified either reactive or preventive maintenance to be more cost-effective depending on the operating conditions. This allowed to predict optimal maintenance for existing and future systems.
Hardware Architecture of Emotion Recognition from Speech Features using Recurrent Neural Network and Backpropagation Through Time

Emotion recognition from speech feature is one of the application where the system needs temporal information in order to produce a correct prediction. On the other hand, recurrent neural network has the advantage of retaining temporal information. This paper proposed a hardware architecture design for emotion recognition system using LSTM (Long Short Term Memory) and BPTT (Backpropagation Through Time). For this application, the proposed architecture consists of a two-layer stacked LSTM with 53 cells on the first layer and 8 cells on the second layer. The architecture is implemented and verified using Verilog language and FPGA.
Human Gait Analysis Using OpenPose

Gait analysis refers to the scientific study of body movements that are responsible for locomotion in human beings. Gait parameters are known to be reliable indicators of neuromuscular and skeletal health. Gait analysis is used in the design of therapeutic paradigms for stroke and spinal cord injury rehabilitation. In this study, we introduce a marker-less, cost-effective, and user-friendly approach to human gait analysis. We use a simple mobile phone camera and a 2D pose estimation system to obtain important anatomical landmarks. From these landmarks, we calculate the knee flexion/extension angle. Further, we analyze the effects of ambient lighting and the subject's attire on the measured knee angle. We also test the efficacy of our approach by comparing the measured knee angles with a normative gait database. Finally, we compare our results with the knee angle readings obtained using MS Kinect (our previous study).
Network Intrusion Detection and Monitoring in Cloud Based Systems

In recent times there has been a shift towards Cloud based systems for varieties of applications. But, security concerns have always been a challenge for its widespread acceptance. Some of the major challenges relate to data security, deployment of applications, and security of infrastructural elements. In this paper, we have dealt with one of the important security issues, namely, network intrusion detection, in order to protect cloud infrastructure from malicious users. Our work involves continuous tracking of communication among virtual machines and analyse the packets transmitted over the network for possible intrusive attempts. We have developed appropriate mechanism to monitor the network and measure the sensitivity of certain network ports used for data transmission.
Image Classification by Multilayer Feature Extraction Based on Nuclear Norm Minimization

This paper describes a novel multilayer image classification method based on a nuclear norm of dictionary atoms minimization; in each layer, the method designs dictionary atoms for training dataset by solving a nuclear norm regularized convex optimization problem. The problem promotes non-orthogonality of dictionary atoms and makes coefficient vectors non-negative; these contribute to the improvement of classification accuracies. Experiments result shows that our method is superior to the conventional KLT (Karhunen-Loéve Transform) based multilayer classification method in classification accuracy.
Proposal of Extracting Pulse Wave During Driving a Car Based on Frequency Domain BSS

Recently, monitor for the health condition of a driver during driving a car is actively studied [1]. Especially, pulse wave (PW) is studied to monitor the health condition of a driver during driving a car. Conventionally, the observed signals are assumed instantaneous mixture of PW and noise, and the separation precision with conventional method is lower [3]. Therefore, the observed signal may be convolutive mixtures. In this paper, in order to improve separation precision, we propose the method based on frequency domain Blind Source Separation (BSS) for convolutive mixture. We evaluate the advantage of the proposed method by computer simulation.
Modeling and Stability Analysis of Salmon Mortality due to Microalgae Bloom using Linear Parameter-Varying Structure

This paper investigates a non-autonomous system that describes a biomathematical model in a salmon farming context. More precisely, we consider two coupled nonlinear differential equations that model the dynamic behavior among aquatic pollution, salmon and microalgae populations. In this maritime habitat, we represent a harmful algal bloom (HAB) due to the climate event called El Niño. This phenomenon produces an ecological problem increasing the water temperature that starts a HAB, increasing the salmon mortality due to microalgae toxins and, consequently, incorporating additional aquatic pollution. In this work, we provide some stability and error analysis techniques based on a linear parameter-varying system modeling. Finally, we present numerical results and computational simulations in order to illustrate the applicability for our approach to model real ecological problems.
A longitudinal study of pre-pregnancy antioxidant levels and subsequent perinatal outcomes in black and white women: The CARDIA Study

Background Although protective associations between dietary antioxidants and pregnancy outcomes have been reported, randomized controlled trials of supplementation have been almost uniformly negative. A possible explanation is that supplementation during pregnancy may be too late to have a beneficial effect. Therefore, we examined the relationship between antioxidant levels prior to pregnancy and birth outcomes. Methods and findings Serum carotenoids and tocopherols were assayed in fasting specimens at 1985–86 (baseline) and 1992–1993 (year 7) from 1,215 participants in Coronary Artery Risk Development in Young Adults (CARDIA) study. An interviewer-administered quantitative food-frequency questionnaire assessed dietary intake of antioxidants. Pregnancy outcome was self-reported at exams every 2 to 5 years. Linear and logistic regression modeling was used to assess relationships of low birthweight (LBW; <2,500 g), continuous infant birthweight, preterm birth (PTB; <37 weeks) and length of gestation with antioxidant levels adjusted for confounders, as well as interactions with age and race. Results In adjusted models, lycopene was associated with higher odds of LBW (adjusted odds ratio for top quartile, 2.15, 95% confidence interval 1.14, 3.92) and shorter gestational age (adjusted beta coefficient -0.50 weeks). Dietary intake of antioxidants was associated with lower birthweight, while supplement use of vitamin C was associated with higher gestational age (0.41 weeks, 0.01, 0.81). Conclusions Higher preconception antioxidant levels are not associated with better birth outcomes.