Miracle-dz/UniFAR-FTUs huggingface dataset:
Granularity Effects in Electromigration

The continued efforts to scale front-end-of-line transistors has resulted in simultaneous attempts to scale back-end-of-line coper metalization. However, just like is the case in scaled transistors, nanometer sized metal films behave quite differently to their thicker counterparts, primarily due to the increased influence that the microstructure and granularity plays on its conductive and reliability behavior. The grain boundaries and the roughness at the interface between a metal film and surrounding dielectric or isolation layers influence the movement of conducting electrons and diffusing atoms during electromigration. Therefore, fully understanding and thus modeling this phenomenon has become increasingly challenging, since the boundaries and interfaces must be treated independently from the grain bulk, for which continuum models are insufficient. In light of this, recent advances in modeling electromigration in copper nano-interconnects are described, which use spatial material parameters to identify the locations of the grain boundaries and material interfaces. This method allows to reproduce the vacancy concentration in thin copper interconnects properly.
Novel Framework for Outdoor Mobility Assistance and Auditory Display for Visually Impaired People

Outdoor mobility of Visually Impaired People (VIPs) has always been challenging due to the dynamically varying scenes and environmental states. Variety of systems have been introduced to assist VIPs’ mobility that include sensor mounted canes and use of machine intelligence. However, these systems are not reliable when used to navigate the VIPs in dynamically changing environments. The associated challenges are the robust sensing and avoiding diverse types of obstacles, dynamically modelling the changing environmental states (e.g. moving objects, road-works), and effective communication to interpret the environmental states and hazards. In this paper, we propose an intelligent wearable auditory display framework that will process real-time video and multi-sensor data streams to: a) identify the type of obstacles, b) recognize the surrounding scene/objects and corresponding attributes (e.g. geometry, size, shape, distance from user), c) automatically generate the descriptive information about the recognized obstacle/objects and attributes, d) produce accurate, precise and reliable spatial information and corresponding instructions in audio-visual form to assist and navigate VIPs safely with or without the assistance of traditional means.
Soft and Hard Robotics for Movement Rehabilitation, Analysis and Modelling

Dementia and stroke have been considered two major causes of disability in the UK. The field of rehabilitation robotics emerged since early 90’s as a tool to assist therapists with assessing stroke and dementia patients and providing the necessary therapy. The present article aims to offer a review of how current hard robotic devices utilized in upper and lower limb rehabilitation, and how successful they were in increasing the efficiency of the recovery process. Their bottlenecks and short comes are discussed and soft robotics are presented as alternative techniques to resolve them. Novel pneumatic Muscle Actuators (pMA) capable of bending in contrary to traditional pMA are studied here. In particular, we investigate and propose a novel exoskeleton soft robotic arm, which made of several novel pMA for elbow joint rehabilitation. For this purpose, analysis, design, integration and characterization of the proposed arm are presented and geometrical and numerical models are obtained. This study shows that how our proposed exoskeleton soft robotic arm with a lower cost can increase the efficiency of rehabilitation recovery process.
Fog Computing: A Comprehensive Architectural Survey

Fog computing is an emerging technology to address computing and networking bottlenecks in large scale deployment of IoT applications. It is a promising complementary computing paradigm to cloud computing where computational, networking, storage and acceleration elements are deployed at the edge and network layers in a multi-tier, distributed and possibly cooperative manner. These elements may be virtualized computing functions placed at edge devices or network elements on demand, realizing the “computing everywhere” concept. To put the current research in perspective, this paper provides an inclusive taxonomy for architectural, algorithmic and technologic aspects of fog computing. The computing paradigms and their architectural distinctions, including cloud, edge, mobile edge and fog computing are subsequently reviewed. Practical deployment of fog computing includes a number of different aspects such as system design, application design, software implementation, security, computing resource management and networking. A comprehensive survey of all these aspects from the architectural point of view is covered. Current reference architectures and major application-specific architectures describing their salient features and distinctions in the context of fog computing are explored. Base architectures for application, software, security, computing resource management and networking are presented and are evaluated using a proposed maturity model.
Applications of Internet of Things (IoT) – An Overview

Internet of Things (IoT) is a revolutionary communication paradigm which plays an essential role in the field of remote monitoring and control operations. This paper presents the overview of IoT based remote monitoring and control systems which potentially solve societal issues in the field of healthcare, environment, home automation, transportation, military, agriculture, solid waste management, smart metering, surveillance, consumer asset tracking, smart grid, vehicular communication system and pilgrims monitoring.
Deep Q-Network Based Energy Scheduling in Retail Energy Market

As a significant component of electric energy trades, retail electric market (REM) can effectively alleviate the pressure of load demand from the power grid. However, the load demand uncertainty of customers becomes a nodus because retail electricity providers (REPs) should predict the load demand when trading with wholesaler electricity provider (WEP) based on the interaction. Therefore, in this paper, we propose an optimal energy scheduling scheme in REM with consideration of the influence of decisions made in pre-purchase stages to situations in real-time stages. Firstly, we present a trading framework to analyze the strategies of REPs in REM, in which REPs conduct both pre-purchase trading with WEP, and real-time trading with customers. Then, to solve the scheduling problem caused by the demand uncertainty of customers, we design a power allocation mechanism based on the charging demand degree, by which REPs can minimize the operating cost while ensuring that each electric vehicle can be charged with sufficient energy. Next, to minimize the cost of REPs in the pre-purchase stage, we adopt deep Q-network (DQN) algorithm to implement the pre-purchase schedule. The charging station adjusts the pre-purchased schedule for each period through Q-learning and utilizes the optimal strategy to design the electricity schedule. Finally, simulation experiments show that the proposal can obtain the optimal strategy to significantly reduce the operating costs of REP.
Analysis of Phase Characteristics of Gyrotron Traveling-Wave Tubes

The phase characteristics of gyrotron traveling-wave tubes (gyro-TWTs) for high-resolution imaging radars have been investigated. Determinant equations of output phase are classified into the cold phase and warm phase in the cases of cold-field and hot-field regions, respectively. The phase–frequency characteristics of cold phase are examined regarding single-mode and multimode operations, respectively. The time stability and the sensitivity to external parameters of warm phase are also investigated. The efficient methods to make the output phase of gyro-TWTs controllable are proposed. The measured output phase of the input coupler and output taper of a Ka-band gyro-TWT is linear with respect to the frequency, without any distortion. Moreover, the warm output phase fluctuation introduced by the RF circuit is less than ±8°.
Effect of Thermal Annealing up to 200°C on SnO Thin Films Deposited at Room Temperature by Direct Current Magnetron Sputtering

The effect of thermal annealing at low temperature (≤200°C) on some structural, optical, and electrical properties of tin (Sn) layers, deposited at room temperature by direct current magnetron sputtering in an argon and oxygen reactive plasma, was studied to obtain tin monoxide (SnO). The Sn layers were 70±5 nm thick. The oxygen partial pressure (OPP) was fixed at 7% or 9%. Subsequently, deposited films were thermally annealed (TA) for 1 hour at temperatures between 160° C and 200° C in air environment. X-ray diffraction patterns of films deposited at 7% and 9% of OPP showed a dominant Sn phase oriented to different crystallographic planes. Once the deposited films were annealed at 200° C, a phase of polycrystalline SnO became predominant for the deposition process at 9% of OPP. The optical bandgap energy, the refractive index and the extinction coefficient of the annealed layers varied with the TA temperature. Finally, four-point-probe measurements showed the resistivity variation of the films from as-deposited to thermally annealed up to 200° C.
Signal Fusion for Multi-target on Distributed MIMO Radar System

Distributed MIMO radar system configured with the DTFR (defocused transmit-focused receive) mode can detect and track multiple targets. Due to the non-consistency of the received beams' direction, the system working in TWS (track while scan) mode is faced with the problem of targets' signal matching between different stations, it's difficult to accumulate multi-station's signal effectively. This paper proposes a novel multi-station signal association and fusion technique for multi-target based on sequential Bayesian algorithm that can associate multi-station 's signal from different scanning-beams, and further more implement multi- station signal fusion to achieve spatial diversity gain. Performance results with real data obtained from an experimental radar system demonstrate the practical effectiveness of the proposed method.
A Study on Fluid based Retinal abnormalities Analysis from OCT Images using SVM Classifier

Blindness is one of the most commonly occurring diseases in human. The reason being abnormality in the various layers of retina due to abnormal amount of fluid either excess accumulation or deficit. This paper aims at identifying and evaluating the such abnormality in the retinal layers by using classifier to diagnose the abnormality that may serve as prior stages to blindness. SVM Classifier was evaluated for around 114 images extracted from OCT Modality with common features using MATLAB. The SVM classifier was designed and evaluated to classify the input images as Normal and Abnormal, based on which the sensitivity and specificity were estimated to evaluate the system performance. Abnormalities taken into consideration are Cystoid Macular Edema (CME), Choroidal Neo Vascular Membrane (CNVM) and Macular Hole (MH) images for classification. The overall performances prove that the proposed system has an accuracy of 87.98%, say with sensitivity and specificity of 87.50% and 88.46% respectively.
Aalyzing and selecting the typical propulsion systems for ocean supply vessels

The shipbuilding industry and marine economic services can be considered as one of the potential industries, a spearhead and an important motive of the marine economy in coastal countries. For serving the marine economy and being able to exploit marine resources, an enormous industry has been developed, using floating rigs, boats and fixed production platforms or anchoring on the sea. These floating structures require different types of support activities and to provide the necessary assistance, many different types of ships have been produced. The common function of offshore construction serving vessels are to support the fields of energy exploitation, continental shelf exploitation, oil and gas exploitation, geological survey and some other offshore industrials and services. Each type of ship can be designed to perform a single specific task, but in usal, each type of ship is designed to perform a combination of tasks. When integrating multiple tasks in one type of vessel, the designer must come up with the most suitable plan to satisfy the conflicting requirements, and must meet the operational requirements and the corresponding safety codes. This paper focuses on the analysis and selection of typical propulsion system such as hybrid propulsion with both the diesel propulsion and the electric propulsion with generators for several types of mainly offshore construction serving vessels. Access and expand information on vehicles and ships serving offshore construction, statistical methods, data processing combined with comparison and control to select the optimality typical propulsion system.
Accelerating Balance Sheet Adjustment Process in Commercial Loan Applications with Machine Learning Methods

Financial analysts perform balance sheet adjustment that includes reductions, additions or movements of balances in accounts before applicants' credibility scores are calculated in the assessment of commercial loan applications. The analysts usually go through financial documents manually and it causes waste of time and labor for financial institutions. This paper presented a solution model that detects balance sheet items to be adjusted in order to reduce costs and accelerate the balance sheet adjustment process by helping financial analysts. Machine learning algorithms are the key elements for the solution model. Besides, a new feature set that can detect balance sheet items to be adjusted is proposed to be used for machine learning models. The proposed solution model and feature set were tested with experiments. The results show that Stacked Generalization model, Random Forest as meta-learner and LGBM, XGBoost and CatBoost as base learners, is the top performer model with the new feature set. The dataset used in experiments is obtained from one of the largest banks of Turkey.
Detection of Gas Harmful Effect using Fuzzy Logic System

The researcher focuses on determining the harmful effect of gases in human. Using fuzzy logic is the core process of the study. It has an ability to turn the computer to think as a human because of the rules that is embedded in the whole system. Carbon Monoxide, Cyanide, Formaldehyde, Ammonia, and Hydrogen Sulfide was use as a sample of harmful gases. There are certain level of concentration and length of time a human can tolerate the harmful gas. Without any detection of it a person will suffer a bad effect of the gas. The system can tell if the gas in certain level and time is harmful or not. The result of the system is correct in correlation with the rules set in the system and the graph that proves the validity of the system
Self-Concept and Social Networking of Students at Polytechnic College

The paper presents results that reveal interactions between indicators of self-concept and peculiarities of networking of students at the polytechnic college. It has been concluded that recreational and communication motives are main ones to turn to social networking for college students. It is shown that self-concept plays an essential role in identifying a pattern of social networking. Self-confidence, self-worth mediate a level of communication and creative self-realization in social media, whereas self-blame and motivation of social acceptance define self-disclosure, self-presentation in social media.
Features of Bacterial Cellulose Hydroxyapatite Nanocomposites Obtained by Two Different Techniques

Nanocomposite material based on bacterial cellulose and hydroxyapatite was obtained using the biomimetic mineralization method (imitation of the formation of the mineral component in natural bone tissue) and the synthesis of hydroxyapatite nanoparticles in the presence of fibrillar fragments of BC. The biocomposite BC/HA-1 obtained by biomineralization showed high physicochemical parameters compared with the composite obtained by synthesis of hydroxyapatite nanoparticles in the presence of fibrillar fragments of BC (BC/HA-2). The ultimate tensile strength of the composite BC/HA-1 was 92.07+1.4 MPa, the composite BC/HA-2 was 76.09+1.1 MPa. Young's modulus (the coefficient of proportionality between stress and strain) of the BC/HAP-1 composite was 59.04+0.6 MPa (BC/HA-1), which is 18.6% higher than that of the BC/HA-2 composite (48.03+0.8 MPa).
Performance Evaluation of a Two-Phase Interleaved Transition Mode Boost Converter Power Factor Correction with Phase Shedding

This paper evaluated a design of a two-phase interleaved boost converter operating in transition mode for power factor correction (PFC) application. Phase shedding was employed to further improve the efficiency at light-load conditions. While the design was simulated to assess the stability of the converter, a 350W-prototype was built to verify the analyses. Results showed that the prototype had a worst line regulation of 0.388% and load regulation of 0.451% while the simulation of the converter stability had a gain margin of at least -6dB and phase margin of at least 45°. While the power factor achieved was above 0.9 down to 20% load, the measured efficiencies peaked to 96.55% at nominal low-line and 97.92% at nominal high-line, making the PFC converter compliant to 80 Plus Gold and ErP Tier 1 Lot 3 requirements. As the phase shedding level was set to 20% of the nominal power, the measured efficiency remained above 91% down to 10% load at nominal lines. In terms of electromagnetic compatibility, the prototype passed the harmonic current emission standard EN61000-3-2 Class A requirement and EN55032 conducted EMI emission Class A requirement with sufficient margin. For these reasons, the design can be implemented for practical PFC applications.
Acquisition of english retroflex vowel [3] by EFL learners from Chinese dialectal regions- A case study of Beijing and Changsha

This paper investigates, through an extensive acoustic analysis, the acquisition of English retroflex vowel [3] by learners of English as Foreign Language (EFL) from Beijing (BJ) and Changsha (CS), which are representative dialectal regions in the north and south China respectively. In our analysis, formant and duration were selected as parameters. The results demonstrate that all the EFL learners involved in the study produced the onset target and offset target of [3] with a more backward tendency. For formant patterns, both native speakers and EFL learners present a similar tendency, namely the decline of F3 and the rise of F2. For CS speakers, due to the effect of their mother tongue, their F3 falls more slowly. Moreover, from the spectral perspective, the F3 changing rate of CS male learners is significantly smaller than that of native speakers. On the other hand, BJ learners, especially female learners, show more obvious changes in F3 than native speakers. In addition, we speculate that the language background and gender can affect the acquisition of retroflex vowels.
Comparative Study of Novel Doubly-Fed Linear Switched Flux Permanent Magnet Machines With Different Primary Structures

To enhance the thrust force density and move a part of copper loss from the primary to the secondary of linear switched flux permanent magnet machines (LSFPMMs), this paper proposes a novel kind of doubly-fed LSFPMMs (DFLSFPMMs) by adding another set of armature winding on the secondary of LSFPMMs. The design and working principles of the new winding are analyzed first. It is found that the optimal coil pitch depends on the primary structure. Then, the parameters of DFLSFPMMs with U-core, C-core, E-core, and multi-tooth primary structures are globally optimized, and their electromagnetic performances are investigated by 2D finite element method. The result shows that the average thrust force of U-core DFLSFPMM is 30% higher than that of conventional LSFPMM counterpart, and it is also 23%-29% higher than those of DFLSFPMMs with other three primary structures. Besides, the U-core machine with tubular structure is analyzed for the potential application of electromagnetic shock absorbers, which shows the U-core DFLSFPMM has 10% higher peak-to-peak damping force than linear spoken-type PM machine. Finally, a prototype of U-core DFLSFPMM is manufactured and tested to validate the analysis.
Joint Estimation of DOA and Frequency with Sub-Nyquist Sampling in a Sparse Array Radar System

Direction-of-arrival (DOA) estimation has been an important research topic in the field of radar signal processing for a long time. Recently, joint estimation of DOA and frequency has been extensively studied. This paper considers joint DOA and frequency estimation of multiple narrow-band signals received by array radars at sub-Nyquist rates. A new sub-Nyquist array architecture (a sparse array radar whose the reference unit connects to a sparse multi-coset structure and the other units connect to 2 channel multi-coset structure) and corresponding efficient technique (a joint estimation algorithm with an augmenting method) are proposed. The estimation algorithm can match frequencies up with corresponding DOAs, and the augmenting method can increase the capability of the number of identifiable signals from $M-1$ to $Q-1(Q$ is extended estimating degrees of freedom). We further analyze the minimum total sampling rate of achieving joint estimation. Simulations results verify the effectiveness and performance of this technique in this sparse array structure.
Electrical characterization of advanced MOSFETs towards analog and RF applications

This invited paper reviews main approaches in the electrical characterization of advanced MOSFETs towards their target analog and RF applications. Advantages and necessity of those techniques will be demonstrated on different study cases of various advanced MOSFETs, such as FDSOI, FinFET, NW in a wide temperature range, based on our original research over the last years.
Research on Information Collection Technology of Power Information Assets Based On NB-IOT

Power information and communication assets are an important guarantee for the reliable operation of power grid. At present, the life cycle management of power information assets has become the focus of research. For the collection and management of power information assets information, a low power consumption, low bandwidth occupancy technology is needed, and NB-IoT meets this technical characteristics. In this paper, NB-IoT technology is introduced into the whole life cycle information acquisition system of power information assets, and the application framework of NB-IoT in power information assets is designed. The key technology of NB-IoT application in this field is studied. A typical case of NB-IoT application in power information assets is analyzed. It provides reference for the practical application of the whole life cycle management of power information assets.