Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Dual-Band Bandpass Filter based on Substrate Integrated Coaxial Line

In this paper, a novel dual-band bandpass filter based on the technology of substrate integrated coaxial line (SICL) is proposed. The proposed bandpass filter which can be realized in standard printed circuit boards (PCB)-exhibits the advantages of compact size, low cost, high selectivity low profile and easy integration with other planar circuits in the future. Since substrate integrated coaxial line (SICL) is a kind of enclosed structure, the filter using this technology will reduce the radiation loss effectively. The simulated results show that the proposed bandpass filter based on SLCL promises to be useful in the design of future microwave and millimeter wave circuits and systems. Index Terms—bandpass filter (BPF), dual band, substrate integrated coaxial line (SICL), compact size.
Multi-Level Modulation for High-Speed Wireless and Wireline Transceivers

Multi-level modulations are important techniques to achieve high data rate in wireless transceivers which bandwidth is limited by regulations. In wireline transceivers, data rate has been increased by expanding the bandwidth which is not limited by regulations. However, it is difficult to expand the bandwidth anymore for technical issues and multi-level modulations are introduced in wireline transceivers. In this paper, multi-level modulation examples used in wireless and wireline transceivers are presented.
Generalized Preference Learning for Trust Network Inference

Trust inference is essential in a plethora of data mining and machine learning applications. Unfortunately, conventional approaches to trust inference assume trust networks are available, while in practice they must be derived from social network features. This is however a difficult task which has to cope with challenges relating to scarcity, redundancy and noise in the available user interactions and other social network features. In this work, we introduce the new problem of Trust Network Inference (TNI), that is, inferring a trust network from a sequence of timestamped interaction networks. To solve the TNI problem, we propose a principled approach based on a preference learning paradigm, under a preference-based racing formulation. The proposed approach is suitable for addressing the above challenges, moreover it is versatile (i.e., independent from the social network platform) and flexible w.r. t. the use of topological and content-based information. Extensive experimental evaluation focusing on two distinct ground-truth scenarios, has provided evidence of the meaningfulness and uniqueness of our TNI approach, which can be regarded as key-enabling for any application that requires to handle a trust network associated with a social environment.
Application-Oriented LPWA Network with A Moving Gateway and Stationary End-Nodes

Recently, low-power and wide area (LPWA) network is one of the hottest wireless technologies to realize low-cost Internet of things (IoT) systems. Many proprietary LPWA schemes have been actively developed, some of which have been utilized in commercial services. However, the LPWA schemes sacrifice data rate to provide low-power and wide coverage features. Even if the communication link supports low data rate, it is necessary to investigate a novel networking that accommodates a huge number of LPWA clients distributed in very large geographic area. If the coverage area can be made movable in certain schedule, effective communication area of LPWA network can be easily expanded. Therefore, this paper proposes an application-oriented LPWA network with a moving gateway and geo-located stationary clients. The proposed network can reduce the total communication traffic in the network and shorten the total length of moving path by skipping to visit unnecessary clients. This network also can utilize the application-specific knowledge for traffic reduction and scheduling of the moving gateway. Based on the proposed network, we implemented a notification system for house-to-house trash collection using LoRa gateway and LoRa clients. Through some experimental measurements and discussions, we reveals the feasibility of the system deploying the proposed network.
Low Complexity and High-Resolution Line Spectral Estimation Using Cyclic Minimization

The line spectral estimation problem has applications in radar, wireless communications, spectroscopy, and power electronics, among others. The signal is modeled as a sparse linear combination of complex sinusoids and the problem target is to estimate the number of sinusoids in the mixture, and respective parameters of each individual sinusoid, such as magnitude, phase, and frequency. In this work, we first introduce a novel formulation of the rank function, which involves the solution of a multi-convex optimization problem. Using the multi-convex reformulation of the rank function an over-parameterization of the line estimation problem is proposed together with a cyclic minimization procedure to obtain a solution. The cyclic method iterates between the optimization of the signal subspace and null-space and stops when the two are orthogonal. Every limit point of the sequence of iterates is shown to be a stationary point of the original problem. Numerical experiments show that only a small number of iterations is required for convergence. The signal subspace optimization is a semidefinite program (SDP) and the null-space optimization has a closed-form solution. The cost per iteration of a general-purpose interior point method (IPM) to solve the SDP is a quartic function of the problem dimension. It is shown that by exploiting the problem structure the cost per iteration of the IPM may be reduced to a cubic function of the problem dimension, comparable to the cost of the alternating direction method of multipliers (ADMM). However, contrarily to the latter, the former achieves the precision necessary for fine frequency localization. A large set of numerical experiments show the effectiveness of the proposed approach.
Project R-CASTLE: Robotic-Cognitive Adaptive System for Teaching and Learning

Robots are already present in people’s lives as receptionists, caregivers, and tutors. In human–robot interaction, social behavior is not only expected but often associated with users’ confidence. Although several studies have been researching in this direction, the robot adaptation and the existing gap between the system and nonprogramming designers still need more effort to achieve success. In this article, a cognitive architecture is proposed and implemented into a humanoid robot. The aim is to offer a framework programmable for controlling the robot’s resources, approaching previous knowledge, and new content in educational interactive activities. Furthermore, the system adapts the robot’s behavior according to objective measures of users, attention and engagement during the activity. After the interactive sessions, these measures are provided in a graphical interface for students, skills evaluation. Functions of visual classification, speech processing, autonomous web search for new content, and attention detectors were tested and analyzed separately. This approach shows effectiveness in basic and medium condition levels from a set of sceneries for each module.
Consequence of Stress on Cognitive Performance: An EEG and HRV Study

Frontal cortical activity can be used as an indicator of mental stress. Using the frontal alpha activity in the electroencephalogram (EEG) as a basis, the Frontal Brain Asymmetry, Resting Brain Asymmetry, Frontal Asymmetry Index and Alpha Band Power along with Heart Rate Variability have been used as features in the present work to analyse the level of stress in subjects. Eleven healthy subjects participated in the experiments with 3 tests each based on 4 trials, with each trial posing an added cognitive challenge. Trials consisted of the Hopkins Verbal Learning Test, Trial Making Test, and Corsi Block Tapping Test. They were tested on factors based on verbal understanding, memory, attention, focus and overall cognitive performance. Using a rule-based classification model and combining the results from the features, 3 categories (low, medium and high) of stresses were identified. Partial consistency through the various extracted features indicate that subjects vary in their ability to manage stress. Overall, 2 subjects were indicative of a low stressed state, 6 subjects showed medium stress levels and 3 subjects were categorized under the high stress state.
Force Rebalance with Multi-bit Sigma-Delta implemented with FPGA for Precision Rate Sensor

A novel processing and readout electronics for high dynamic range (106) rate measurement using two degree-of-freedom Dynamically Tuned Gyroscope (DTG) based on Multi-bit Sigma-Delta architecture is developed. The control loop is configured as a multi-bit delta modulator loop to obtain predefined frequency response, tracking error and margins. The control blocks are designed digitally and implemented in a ruggedized custom developed FPGA board. ENOB of 20 bits is obtained by oversampling ratio of 560 and 12 bit ADC & DAC. Results obtained indicate readout resolution of 0.2 deg/hr and stability of 0.06 deg/hr over long duration (windowed average).
Applications of Big Data Analytics in Financial Auditing- A Study on The Big Four

Big data analytics (BDA) is being applied intensively in many industries to gather, transform, and analyze enormous datasets to identify patterns, support arguments and decisions. However, the auditing industry seems to be lethargic and reluctant to embrace the potentials BDA might bring to the auditing work. It is understandable that a sector with such stern regulations and tight control will tend to hold a conservative and cautious attitude toward new and infant technologies. In this paper, we have used a mixed method approach through, first conducting interviews with front-line auditors, and second, conducting a survey in the Big Four auditing firms in Taiwan. This study provides a preliminary understanding of auditors’ perceptions about applying BDA into financial auditing and recognize the hidden components that facilitate or hinder the adoption of BDA. Based on our findings, a recommendation of the business approach on how other auditing firms can take to fasten the process of integrating BDA into their auditing practices and the methods to reduce the barriers associated with it is presented in this study.
A survey of zero-day malware attacks and its detection methodology

The recent malware outbreaks have shown that the existing end-point security solutions are not robust enough to secure the systems from getting compromised. The techniques, like code obfuscation along with one or more zero-days, are used by malware developers for evading the security systems. These malwares are used for large-scale attacks involving Advanced Persistent Threats(APT), Botnets, Cryptojacking, etc. Cryptojacking poses a severe threat to various organizations and individuals. We are summarising multiple methods available for the detection of malware.
DoLaR: Double Layer Routing for Bufferless Mesh Network-on-Chip

Network on Chip (NoC) is embraced as an interconnect solution for the design of large tiled chip multiprocessors (TCMP). Bufferless NoC router is a promising approach due to its simple router design, energy and hardware efficiency. NoC, which rely on underlying network architecture, is characterized by performance measures like latency, deflection rate, throughput and power. In this paper, we come up with DoLaR architecture to raise performance of standard bufferless 2D mesh NoC by stacking two similar layers of 8×8 meshes one above the other. DoLaR employs standard 5-port bufferless router architecture and the unused ports of edge routers are utilized to make vertical interconnections between the layers. Simulation results show that our proposed design surpasses existing state-of-the-art 5-port 2D mesh and torus bufferless router designs in terms of better network saturation point and minimized deflection rate, average flit latency and power consumption.
Predicate Proximity in Failure: An MLP based Fault Localization approach

Fault localization (FL) is a time consuming and tedious task during program debugging. Most of the existing FL methods use statement coverage information to prioritize the statements based upon a computed suspiciousness score. We use predicate level execution trace to train a multilayer perceptron neural network model for effective fault localization. After prioritizing the fault at predicate level, we search the statements bounded by the predicates. Also, dynamic slicing is used to reduce the search space. We have experimentally studied the performance of our approach over Siemens suite and Space program and found that it is performing on an average 39.12% more effectively than DStar, a state-of-the-art bug localization technique.
Scalable Mean-Field Sparse Bayesian Learning

Sparse Bayesian learning is a powerful framework for expressing compressed sensing problems in the language of Bayesian inference, but its principal algorithm—the variational relevance vector machine—is matrix-bound and scales poorly to larger problem sizes. Recent approaches to construct more computationally efficient methods of variational inference in the sparse Bayesian learning framework either scale poorly with the problem size or fail to minimize the correct variational objective. This work demonstrates an efficient matrix-free algorithm for sparse Bayesian learning that scales well to large problems, converges at a rate competetive with existing fast methods, and provably descends on the correct mean-field objective.
Topic Modeling of Scientific Publications in the Specialty of “Dentistry”

The intellectual analysis of texts and the task of identifying the thematic structure of documents allows us to solve various applied problems associated with documental retrieval, automatic selection of keywords and annotation of documents. In the parsing task of the dynamics of topics in a large number of scientific works, the automatic determination of text details of a work or article becomes fundamentally important. The use of machine learning methods and thematic modeling allows us to solve such problems. This paper analyzes the multiple data of the Russian Science Citation Index (dynamics of topics and keywords (phrases)) in the scientific specialty of Dentistry based on the analysis of more than 15,000 Russian texts of scientific articles published from 2005 to 2015. Thematic modeling of the corpus of available documents has been carried out on the base of the papers titles analysis. Using the methods of extracting keywords in the document and identifying the main topics in the corpus of documents, the dynamics and distribution of the top keywords and phrases, as well as the change of thematic focus of articles in the analyzed period have been analyzed.
Massive MIMO Channels Under the Joint Power Constraints

The capacity and optimal signaling for a fixed Gaussian MIMO channel under the total power constraint (TPC) are well-known. When per-antenna power constraint (PAC) are added to the TPC, the problem becomes significantly more complicated and no closed-form solution is known in the general case, while some special cases (MISO and full-rank MIMO) have been solved. Here, we consider a massive MIMO Gaussian channel under the joint (TPC+PAC) power constraints and obtain a closed-form solution for its optimal covariance matrix under the favorable propagation conditions. The impact of joint constraints on the optimal signaling and capacity is discussed. In particular, while optimal signaling under the joint constraints has some similarities to the standard WF, it also reveals a number of significant differences. A water-filling-type interpretation of the obtained solution is given. The number of active streams as well as the number of active PACs are determined. A closed-form solution for the optimal dual variable under the joint constraints is given.
Removal of Speckle Noise in Images through a Hybrid Methodology with Application in Optical Coherence Tomography (OCT) Images

A novel methodology is being introduced to reduce the speckle noise in the image. The main objective is to use the algorithm on Optical Coherence Tomography (OCT) images. It is difficult to obtain an OCT image without any noise. The method is first validated on natural pictures of cats and dogs obtained through openly available database. The algorithm is tested against both scalar and adaptive filters to reduce the effect of speckle noise. Correlation Coefficient, Structural Similarity Index (SSIM), Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Naturalness Image Quality Evaluator (NIQE) and Blur Index are used as parameters. The algorithm is applied on OCT images and it reduces the noise while preserving clinically essential retinal layer information.
Estimation of Functional Connectivity in Cognitive Impaired Brain Using Non-Homogeneous Dynamic Bayesian Model

Probabilistic graph models have been viewed as an essential tool for learning networks from data. The dynamic functional connectivity using graph models depends on time-varying networks of Functional MRI data. The structure of the brain organization has been studied by exploring functional network analysis. Network-oriented studies on whole-brain connectivity analysis are helpful to infer the dynamics of the brain. Estimation of functional connectivity is a challenging task for large dimensional data. Understanding and discovering the arrangement of network data about the complicated human brain is fundamentally significant for diagnosis. The template-based graph models such as Dynamic Bayesian Networks (DBN) provides a single compact model which captures characteristics of a system dynamics and distributions over time. In this work, non-homogeneous DBNs have been constructed for Default Mode Network (DMN) of three category subjects such as Alzheimer's Disease (AD), Early Mild Cognitive Impairment (EMCI) and Control Normal (CN) subjects. The connectivity interactions and weights between the eleven Region of Interests (ROI) of DMN for three category subjects show a significant difference in the form of information flow between the ROIs.
Greedy Kernel Change-Point Detection

We consider the problem of detecting abrupt changes in the underlying stochastic structure of multivariate signals. A novel non-parametric and model-free off-line change-point detection method based on a kernel mapping is presented. This approach is sequential and alternates between two steps: a greedy detection to estimate a new breakpoint and a projection to remove its contribution to the signal. The resulting algorithm is able to segment time series for which no accurate model is available: it is computationally more efficient than exact kernel change-point detection and more precise than window-based approximations. The proposed method also offers some theoretical consistency properties. For the special case of a linear kernel, an even faster implementation is provided. The proposed strategy is compared to standard parametric and non-parametric procedures on a real-world data set composed of 262 accelerometer recordings.
Higuchi and Katz Fractal Dimension for Detecting Interictal and Ictal State in Electroencephalogram Signal

Epilepsy is a neurological disorder which may occur in every human being. The existence of a seizure showed as the characteristic of this disorder. International League Against Epilepsy (ILAE) mentioned that the diagnose of epilepsy required a minimum of two seizure event in 24 hours. A standard tool used by the neurologist to diagnose epilepsy was Electroencephalogram (EEG). An automatic seizure detection in EEG signal may help them to identify the pattern of ictal condition. Since the characteristic of the EEG signal were dynamic and nonstationary, it was very challenging to interpret the signal pattern. In this study, we analyzed the use of Higuchi and Katz fractal dimension as a feature extraction method to detect an interictal and ictal state in the EEG signal. These two states were essential in the term of seizure detection and prediction system. EEG signal extracted into five frequency bands called delta, theta, alpha, beta, and gamma. Each frequency showed a different characteristic of brain-behavior in a specific condition. The extracted features then fed into a support vector machine (SVM) to classify between normal with interictal and ictal states. The proposed method able to determine normal vs. ictal state with 100% of accuracy. On the other hand, the best accuracy obtained for detecting normal vs. interictal state was 99.5%.
A New Hybrid XGBSVM Model: Application for Hypertensive Heart Disease

The changes in people’s life rhythm and improvement in material levels that happened in recent years increased the number of people suffering from high blood pressure in the world. Therefore, as a cardiac complication of hypertension, the prevalence of hypertensive heart disease has increased annually, it has seriously endangered the safety of human life, and the effective prediction of hypertensive heart disease has become a worldwide problem. This paper uses the newly proposed XGBSVM hybrid model to predict whether hypertensive patients will develop hypertensive heart disease within three years. The final experiment proves that through this model, hypertensive patients can learn their risk of hypertensive heart disease within 3 years and then undergo targeted preventive treatment, thereby reducing the psychological, physiological and economic burden. This paper confirms that the machine learning can be successfully applied in the biomedical field, with strong real-world significance and research value.
Enhanced Schedulability via Minimal Routing with Mapping and Priority Assignment for Real-Time Network-on-chip

Multi-processor System-on-Chip (MPSoC) based on Networks-On-Chip (NoC) as a communication paradigm becoming an industry standard in design of embedded system. Some class of embedded systems are designed for executing real-time services which are having stringent deadlines, failing to meet these deadlines may lead to catastrophic failure. NoC not only requires specialized architecture to deal with real time requirement but also problem of task mapping and flow priority assignment becomes more crucial. Real-time schedulability analysis is performed assuming worst-case to get the lower bound of schedulability for tasks and flows. The term schedulability defines the number of tasks and flows meeting the deadlines. Flow of data packets from source to destination node depends on the network topology of NoC and routing strategy at the routers. It improves on end to end schedulability by considering all possible minimal paths on place of fixed X-Y routing. It also considers deadlock avoidance in routing.
SWIPT Techniques in Multi-tier D2D Networks for Energy Efficiency

Energy efficiency is an important metric in the upcoming 5G scenario due to the increasing energy costs and the focus on sustainable technology. Paradigms like device-to-device communication and heterogeneous networks help in achieving high data rates, user capacity, and coverage promised by 5G, while simultaneous wireless information and power transfer (SWIPT) offers a green technique to have dual usage of the incoming RF wave in transferring energy and information simultaneously. SWIPT also helps in converting the harmful interference, incurred by the frequency reuse of D2D tier underlaying the Hetnet, into energy that can be stored for the future. This paper presents the use of two techniques of SWIPT, namely power splitting SWIPT and time splitting SWIPT, and gives a resource allocation scheme so as to improve the energy efficiency of the D2D tier. Minimum data rates are ensured for all users, cellular and D2D. Maximum bipartite matching is used for subcarrier allocation to cellular users and a stable many-to-one matching game is used for subcarrier allocation to D2D users. Extensive simulations are done to compare the performance of the two approaches.