Miracle-dz/UniFAR-FTUs huggingface dataset:
The Consumer Trust Influencing Intention to Use Electronic Wallet in Thailand

The intense competition of globalization and the advancement of technology cause the business sector to adjust and expand into digital business. In addition, the consumer behavior has also changed such as using a smartphone as a part of life and financial transactions. Using of electronic wallets is a new technology that has been popular for consumers for conducting financial transactions via smartphones. The objective of the research is to develop structural model of consumer trust to intention to use electronic wallet. It is also studied the factors that influence consumer trust and intention to use electronic wallet. It was found that consumer trust in electronic wallet is caused by 3 aspects; 1) Information quality which has four attributes (Accuracy, Completeness, Relevance, and Up-to-date) 2) System quality which has four attributes (Ease of use, Availability, Reliability, and User-friendly) and 3) Service quality which has three attributes (Assurance, Responsiveness, and Empathy). It was also found that consumer trust will affect the satisfaction and intention to use electronic wallet of consumers.
Bidirectional Wireless Power Transfer Using Single Phase Matrix Converter for Electric Vehicle Application

In recent years, the demand for bidirectional wireless power transfer (WPT) for the grid connection is increasing. A single phase AC source is connected to matrix converter (MC) in primary side which is isolated to H-bridge converter in secondary side which is further connected to battery. A high frequency (HF) transformer is isolating primary converter to secondary converter which is also used to transfer power from grid to vehicle (G2V) and vehicle to grid (V2G). A series-series (SS) compensation is used to achieve WPT application. This paper also presents control strategy to transfer power from single phase AC source to DC and vice-versa. The bidirectional power is controlled by a phase shift angle ($\boldsymbol{\theta}_{\boldsymbol{D}}$) between the primary and secondary voltages of high frequency transformer. The system is simulated in MATLAB / Simulink at 1.05 kW of power and analysis was being done.
A GEVD Based Interference Alignment Method for Uplink Heterogeneous Network Under imperfect CSIT

The heterogeneous network (HetNet) is a promising technology for deploying the next generation wireless networks. The major issue concerning multi-tier architecture in HetNets is the interference between the users in femtocells and macrocells i.e. cross-tier interference. In this paper, we propose a generalized eigenvalue decomposition (GEVD) based interference alignment (IA) technique for the uplink of a HetNet under imperfect channel state information at the transmitter (CSIT). The proposed method exploits the stochastic knowledge of error in CSI mismatch to model an approximated channel. By using the approximated channel, we design a robust precoder and decoder to harvest the benefit promised by multi-tier architecture under imperfect CSIT. We perform exhaustive simulation studies to evaluate the performance of the proposed technique in terms of the achieved bit error rate (BER) and sum rate. We compared the proposed method with a prevailing method in the literature known as Max-SINR algorithm and found that it achieves better performance. Further, the proposed method can be applied to realistic scenarios where the channel state information is imperfect.
Towards Fog-Based Mobile Crowdsensing Systems: State of the Art and Opportunities

— MCS is an emerging paradigm that leverages the pervasiveness of mobile, wearable, and vehicle-mounted devices to collect data from urban environments for ubiquitous service provisioning. In order to manage MCS application data streams efficiently, a scalable computing infrastructure hosting heterogeneous and distributed resources is critical. FC, as a geo-distributed computing paradigm, is a key enabler for this requirement as it bridges cloud servers and smart mobile devices. Research on the integration of MCS with FC has recently started to be explored recognizing the requirements of MCS and their coexistence with the cyber-physical systems. In this article, we analyze the state of the art of FC solutions in MCS systems. After brief overview of MCS we emphasize the link between MCS and the FC. We then investigate the existing fog-based MCS architectures in detail by focusing on their building blocks, as well as the challenges that yet remain un-addressed. Our detailed review on the subject results in a taxonomy of FC solutions in MCS systems. In particular, we highlight the node structures, the information exchanged, the resource and service management, and the type of solutions adopted concerning privacy and security. Moreover, we provide a thorough discussion on the open issues and challenges by reporting useful insights for the researchers in MCS and FC.
Feynman Machine: A Cortical Machine Intelligence for Path Detection in Advanced Driver-Assistance Systems

Mobility being a prime desire of mankind, the effort and resources plunged into the development of safe, efficient, low-cost and easily controllable vehicles has always remained at its peak. Several advancements have been brought about in the field, yet several challenges still exist while one major challenge is achieving robustness at a low computational footprint so as to reduce the power requirements and negate the need for heavy GPUs and other system pre-requisites. This research puts forth an application of Feynman Machine, a hierarchical temporal memory based cortical learning intelligence model analogous to the mammalian neocortex to learn the images and detect a suitable traversable path for navigation. The online learning ability of the Feynman Machine improves performance with time while negating the need for GPUs during training and prediction. The computation can be carried out in computers as small as a Raspberry Pi having low processor specifications, thereby reducing the power requirements of the model. A Unity based simulation and a Raspberry Pi based prototype were also built to substantiate the proposed idea in this research.
Weighted Block Compressed Sensing for Multichannel Fetal ECG Reconstruction

Wireless body area networks (WBAN) have become very popular for telehealth applications in recent times where signals from the body are continuously acquired and transmitted to a remote server or cloud placed in a Hospital for their storage and subsequent analysis. Multichannel fetal electrocardiogram (MFECG) signals are preferred over single-channel FECG as they provide more features from diagnostic point of view. One of the main concern of WBAN is the energy requirement as they are mostly battery driven and requires continuous monitoring of signals from the body. WBAN-enabled MFECG monitoring results in the huge data and therefore demand efficient data compression. Compressive sensing (CS) is a relatively new signal processing technique for simultaneous acquisition and reconstruction of signal from a few available samples of the actual data. FECG signals are inherently very weak due to the superimposition of mother's abdominal ECG besides instrumentation noise. In this paper, we propose a novel block sparsity based CS algorithm which successfully reconstruct MFECG signals from just a few measurements by exploiting simultaneous spatial and temporal information of MFECG in the wavelet domain. The proposed algorithm further incorporates in it the multiscale wavelet structure by adaptive weighting of the wavelet coefficient of MFECG signals. Experimental results show that the proposed method outperforms other recently developed methods both qualitatively and quantitatively.
A 4.9-GHz GaN MMIC Doherty Power Amplifier for 5G Application

-This paper presents the simulation result of a 4.9-GHz GaN MMIC Doherty Power Amplifier for 5G Application using a 0.25-μm gallium nitride (GaN)-HEMT process. According to the simulation results, a saturated power of 40 dBm-40.3 dBm, a saturated drain efficiency (DE) of 60%-63%, and a 6-dB back-off DE of 51%-53% are achieved from 4.8 GHz to 5 GHz. When driven by a 100MHz orthogonal frequency division multiplexing (OFDM) signal with 6.1 dB peak-to-average power ratio (PAPR), an adjacent channel leakage ratio (ACLR) of -29 dBc with a DE of 50% was achieved at an average power of 34 dBm. After a digital predistortion process, the ACLR was improved to -46 dBc.
Forecasting Changes in States in Social Networks and Sentiment Security Using the Principles of Percolation Theory and Stochastic Dynamics

This paper proposes a novel method of forecasting changes in social network systems based on applying percolation theory and a stochastic dynamics approach. New results from computer modeling the influence of a social network density on its percolation threshold are discussed. Percolation thresholds are calculated for various network densities and can be used in models describing the stochastic dynamics of a system's transition from one state to another. For example, with the help of the developed models, it is possible to forecast the change in time of the share of people with certain opinions and views depending on the network density, percolation threshold, etc. The stochastic model represented in this paper presents the possibility of a jump-like transition of moods (states) of people in a social network during a very short period of time without any external influences, which is defined by the system's self-organisation features and the memory of its nodes about the previous states.
Photovoltaic-Based Water Pumping System using Brushless DC motor

In this paper, control of photovoltaic (PV)-based water pumping system using brushless DC (BLDC) motor is presented. Novel control schemes are developed for power balance in the system operating in stand-alone and grid-connected mode. In stand-alone operation, motor-side inverter regulates the power balance, while in grid connected operation grid-side inverter regulates the power balance among PV, load and grid. In grid connected mode, flexible operation of grid-side inverter is proposed so that power drawn from the grid can be reduced by reducing the speed of the motor within limit. Hence, depends on consumer's choice, by changing the reference power setting of the grid side controller, electricity consumption can be reduced. The proposed control scheme are verified through detailed simulation results.
Model and training method of moving object classification system for a compact drone

The classification model which consist of motion detector, object tracker, convolutional sparse coded feature extractor and stacked information-extreme classifier is developed. Proposed model is characterized by low computational complexity and it can be used as labeling dataset gathering tool for deep moveable object detector. Furthermore, training method for moving object detector is developed. The method consisting in unsupervised pre-training feature extractor based on sparse coding neural gas, supervised pre-training and following fine-tuning of stacked information-extreme classifier. Labeling new emerging data through self-labeling for high prediction score cases and manual labeling for low prediction score cases, and following labeled object tracking are also offered. In this case, class balancing using undersampling within dichotomous strategy “one-against-all”. Simulation results on open datasets confirm the suitability of proposed model and training method for practical usage.
TLBO based Droop Optimization Technique for DC Microgrid

Droop control technique is one of the most commonly used techniques in DC microgrid (MG) to attain proportional load sharing and voltage regulation. Due to the presence of impedance in the transmission line, there are some current sharing errors and voltage deviations across the DC bus. Therefore, loading conditions (LCs) and location of sources in MG have significant effect on current-sharing and voltage regulation. In the present work, droop parameters such as rated voltage and virtual resistance of ith distributed generation (DG) in a MG network are optimized by using optimization procedure by considering all necessary information in order to minimize the impact of line impedances. During the optimization, constraints such as voltage deviations and current sharing errors should be less than five percent of rated voltage and two percent of rated current respectively. By taking combination of voltage regulation and current sharing error, a cost function is formulated. By using load stochastic model, impact of various LCs on the cost functions are weighted. Optimization tool called as Teaching Learning Based Optimization (TLBO) is then implemented to find out the optimized solution. The proposed algorithm has been illustrated on a low voltage DC MG which comprises of three sources and two loads. Simulation results show effectiveness of proposed method over conventional droop control method in terms of reduced current sharing error and voltage deviations across DC bus.
Adaptive Backstepping Control Of Cascaded H-Bridge Multilevel DC/AC Converters

This paper aims at highlighting on the importance of the cascaded H-bridge multilevel inverter in overcoming the disadvantages of the conventional multilevel inverter by combining three H-bridge modules together so as to increase the power level. The three H-bridge modules, which are empowered fundamentally by an H-bridge module, generate seven voltage levels as output. This topology can be used for large-scale PV conversion systems (> 10MW), electric vehicles (EVs) or hybrid EVs through a hybrid control of cascaded H-bridge multilevel inverters, where each module of the inverter can be supplied using a DC source, found mainly in fuel cells, batteries, photovoltaic systems or ultra capacitors. Our main focus in this paper is to provide sinusoidal voltages with very low harmonic distortion to be used in an Uninterruptable Power Supply (UPS) system. The three-cell H-bridge inverter is commanded by PWM generators and controlled by two types of regulators. The first one is the nonlinear backstepping approach and the second one is the adaptive backstepping approach. The structure under investigation is analyzed using MATLAB/SIMULINK environment.
Multiobjective-Based Approach for Microblog Summarization

In recent years, social networking sites such as Twitter have become the primary sources for real-time information of ongoing events such as political rallies, natural disasters, and so on. At the time of occurrence of natural disasters, it has been seen that relevant information collected from tweets can help in different ways. Therefore, there is a need to develop an automated microblog/tweet summarization system to automatically select relevant tweets. In this article, we employ the concept of multiobjective optimization in microblog summarization to produce good quality summaries. Different statistical quality measures namely, length, tf-idf score of the tweets, antiredundancy, measuring different aspects of summary, are optimized simultaneously using the search capability of a multiobjective differential evolution technique. Different types of genetic operators including recently developed self-organizing map (a type of neural network) based operator, are explored in the proposed framework. To measure the similarity between tweets, word mover distance is utilized which is capable of capturing the semantic similarity between tweets. For evaluation, four benchmark data sets related to disaster events are used, and the results obtained are compared with various state-of-the-art techniques using ROUGE measures. It has been found that our algorithm improves by 62.37% and 5.65% in terms of ROUGE–2 and ROUGE–L scores, respectively, over the state-of-the-art techniques. Results are also validated using statistical significance t-test. At the end of the article, extension of the proposed approach to solve the multidocument summarization task is also illustrated.
Introducing a Fuzzy Model for Cost Cognizant Software Test Case Prioritization

Testing ensures the delivery of a product in stipulated amount of time. According to Test Case Prioritization (TCP), test cases with higher priority get executed earlier. Enhancing the fault detection rate, capturing high priority requirements, reducing the cost and time of prioritization mechanism are several goals of TCP techniques. In this paper, we have proposed a fuzzy model that quantifies the savings generated by the prioritization mechanism. The model considers the effect of fault detection rate and number of test cases. Our first research question concentrates on implementing this fuzzy model while our second research question explores which technique for prioritization generates how much savings. Analysis of 10 C programs and 6 Siemens programs from Software Infrastructure Repository (SIR) shows that the technique of combining branch coverage and modified condition and decision coverage (MC/DC) yielded 2.01 times savings over non-prioritized test suite. Results also indicated that only branch coverage based TCP yielded 1.26 times savings over non-prioritized test suite. We hope our cost aware fuzzy model and these savings analysis will make test case prioritization more applicable with better planning. As several stages of software development do not have exact calculation of parameters, we realized that building a fuzzy model is of utmost importance.
Histogram Matching based Optimized Energy Redistribution for Near End Listening Enhancement

Speech enhancement techniques are broadly classified into far-end, and near-end approaches. Unlike far-end speech enhancement techniques, the near-end speech enhancement approaches have access to both clean speech and noise for processing. The current work focusses on near-end listening enhancement, and aims to prevent the speech signal delivered in a noisy environment from losing its comprehensibility. The proposed work combines, proposed speech histogram matching based energy redistribution followed by Wiener filter based post processing. Particle swarm optimization (PSO) is applied during histogram matching in order to maintain the total energy in the matched signal with respect to the energy of the noisy speech signal. The proposed algorithm provides intelligibility improvement especially in the weak speech regions (i.e. consonants such as fricatives, stops, etc.,). The performance of the proposed algorithm is evaluated for a stationary noise condition at 0 dB and 5 dB noise levels, in terms of quality and intelligibility using perceptual evaluation of speech quality (PESQ), and coherence speech intelligibility index (CSII) and short time objective intelligibility (STOI) measures respectively. The algorithm is evaluated for a stationary noise condition. The results indicate that the proposed technique achieves greater intelligibility with no decrement in quality.
Toward Non-CPU Activity in Low-Power MCU-Based Measurement Systems

This article evaluates the benefits of having peripheral-triggered peripherals in a microcontroller unit (MCU) intended for low-power sensor applications. In such an architecture, the functionality is moved from the central processing unit (CPU) to the peripherals so that a peripheral is able to trigger another peripheral with non-CPU intervention. For the sensor data logging application under study, both energy consumption and measuring time are reduced by a factor of 2 with respect to the case of applying an interrupt-based approach that requires the CPU intervention.
Multimodal Patient Satisfaction Recognition for Smart Healthcare

The inclusion of multimodal inputs improves the accuracy and dependability of smart healthcare systems. A user satisfaction monitoring system that uses multimodal inputs composed of users’ facial images and speech is proposed in this paper. This smart healthcare system then sends multimodal inputs to the cloud. The inputs are processed and classified as fully satisfied, partly satisfied, or unsatisfied, and the results are sent to various stakeholders in the smart healthcare environment. Multiple image and speech features are extracted during cloud processing. Moreover, directional derivatives and a weber local descriptor is used for speech and image features, respectively. The features are then combined to form a multimodal signal, which is supplied to a classifier by support vector machine. Our proposed system achieves 93% accuracy for satisfaction detection.
Permission Issues in Open-Source Android Apps: An Exploratory Study

Permissions are one of the most fundamental components for protecting an Android user's privacy and security. Unfortunately, developers frequently misuse permissions by requiring too many or too few permissions, or by not adhering to permission best practices. These permission-related issues can negatively impact users in a variety of ways, ranging from creating a poor user experience to severe privacy and security implications. To advance the understanding permission-related issues during the app's development process, we conducted an empirical study of 574 GitHub repositories of open-source Android apps. We analyzed the occurrences of four types of permission-related issues across the lifetime of the apps. Our findings reveal that (i) permission-related issues are a frequent phenomenon in Android apps, (ii) the majority of issues are fixed within a few days after their introduction, (iii) permission-related issues can frequently linger inside an app for an extended period of time, which can be as high as several years, before being fixed, and (iv) both project newcomers and regular contributors exhibit the same behaviour in terms of number of introduced and fixed permission-related issues per commit.
Action based Video Summarization

The enormous growth of digital multimedia has resulted in increased complexity of time and data management. Many researchers have developed techniques for managing this tremendous amount of data or longer videos effectively. Video summarization is one of the most important research areas for effective comprehension of video content by selecting the informative frames of the video as representatives. This paper presents two-step action based video summarization approach. First, video frames are extracted and content based key frames are selected. Second, video summary is generated by removing redundant frames by employing feature extraction and classification technique. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods are used for feature extraction. Convolution Neural Network (CNN) is used for classification of frames and video summary is generated using $K$-Means clustering and Bayesian model. Experiments are conducted on SumMe [1] video dataset by varying feature extraction, classification and summarization methods. Summary of the video is compared with summarized video time and number of reduced frames. Summary is generated by retaining key features of the video.
Design and Modeling of Millimeter-Wave Transformer in Silicon: A Tutorial (Invited)

A comprehensive review of transformer design and modeling techniques in silicon technology is presented in this paper. Compact, distributed and coupled T-line models of the transformer are established and compared in terms of common-mode and differential-mode behaviors. By use of the modeling technique, the bandwidth and the insertion loss of weakly- and strongly-coupled transformers are explored, and corresponding design examples are provided.