Miracle-dz/UniFAR-FTUs huggingface dataset:
A Type of Energy-Balanced Tree Based Data Collection Strategy for Sensor Network With Mobile Sink

In order to improve the sensing efficiency of the sensor network, a type of energy-balanced tree-based data collection strategy with mobile Sink (ETDC) is proposed. Based on the minimum cost data gathering tree, several sub-trees with similar scales and small differences in energy consumption are built for data gathering. Moreover, the roots of these sub-trees are regarded as the traversal points, and the moving trajectory of Sink is obtained with the help of these points. In addition, to further reduce energy consumption on communication and the end-to-end delay, roles of some traversal nodes and relay nodes are exchanged. The experimental results show that, compared with the typical mobile Sink based data collection methods such as VGDD and VGDR, ETDC performs well in reducing path length, enhancing the success rate of data transmission, and prolonging the network lifetime.
Impact of three-dimensional current flow on accurate TCAD simulation for trench-gate IGBTs

TCAD simulation has been recognized as a powerful design tool for insulated gate bipolar transistors (IGBTs). In this work, excellent agreement between 3D TCAD simulations and experimental current-voltage characteristics were obtained in the up to 1000 A/cm2 region for IGBTs with scaled trench-gates. The results of 2D and 3D simulations are compared to discuss the difference in current-voltage characteristics and their physical origins. A method to evaluate the saturation current ($\boldsymbol{J}_{\mathbf{Csat}}$) using a 2D simulation is also presented with an appropriate correction.
Solving the more difficult aspects of electric motor thermal analysis

With the ever increasing pressures on electric motor manufactures to develop smaller and more efficient electric motors, there is a trend to carry out more thermal analysis in parallel with the traditional electromagnetic design. It has been found that attention to the thermal design can be rewarded by major improvements in the overall performance. Technical papers published to date highlight a number of thermal design issues that an difficult to analyze. This paper reviews some of these difficult issues and gives advice on how to deal with them when developing algorithms for inclusion in design software.
Merging Graphics and Vision for 3D Face Recognition

This paper presents a new approach to automatic 3D face modelling from unstructured point cloud data. An efficient B-Spline surface-fitting algorithm is used to obtain an initial parametric surface for each face point cloud data set. Knot vectors for each individual face surface are then standardised to produce a set of uniform knot vectors so that all the surfaces can be seen as fitted with the same set of knot vectors. Mapping from object space to shape space can then be established so that each 3D face can be described by a small number of shape descriptors. The use of shape descriptors allows automatic registration between face models. More importantly, it allows dynamic facial variation to be modelled and analysed via 3D warping, resulting in a powerful approach to quantifying the differences among individuals required for face recognition. 3D warping is often used in simulations in computer graphics. This paper explains, for the first time, how 3D warping can be exploited for face recognition based on multi-resolution analysis of warping fields. The methodology allows the quantitative study of variation in characteristics previously only described from a qualitative perspective.
Pseudo-coevolutionary genetic algorithms for power electronic circuits optimization

This paper presents pseudo-coevolutionary genetic algorithms (GA's) for power electronic circuit (PEC) optimization. Circuit parameters are optimized through two parallel co-adapted GA-based optimization processes for power conversion stage and feedback network, respectively. Each process has tunable and untunable parametric vectors. The best candidate of the tunable vector in one process is migrated into the other process as untunable vector through a migration controller, in which the migration strategy is adaptively controlled by a first-order projection of the maximum and minimum bounds of the fitness value in each generation. Implementation of this method is suitable for systems with parallel computation capacity, resulting in considerable improvement of the training speed. Optimization of a buck regulator for meeting requirements under large-signal changes and at steady state is illustrated. Simulation predictions are verified with experimental results.
Control Scheme for Bidirectional DC/DC Converter in PV-battery System based on Active Disturbance Rejection Control

The increasing penetration of non-synchronous renewable generation is changing power system frequency performance, including its inertial response. In this paper, we argue that PV-battery systems owned by end users can be used as a source of fast frequency response, which can mitigate the impact of the reduced inertia. To that end, we propose a control scheme for the bidirectional DC/DC converter that is used to connect the battery to the DC-link capacitor to provide the energy needed for fast frequency response. The proposed scheme, based on active disturbance rejection control, avoids the external switching signal during mode switching and rejects the “total disturbance” of the system, thereby improving the response speed and enhancing the stability of the system. Simulation results are provided to verify the feasibility of the proposed scheme.
Analysis of Malware Hidden Behind Firewalls with Back Scans

Malware infection and propagation in Local Area Network(LAN) have became a critical security consideration in IoT systems. Recent cases happen when hosts are observed to be infected even protected by firewall. If we go deep into the analysis on infected hosts, we can measure on the possibility of this situation. When we are aware of status of ports on the source host, we can determine how intrusion happens based on classification on manner of infection. We propose SB-MSS (scan back to malicious source scan source), a network measurement method on malware behaviors in infection on hosts even protected by firewall. This includes passive analysis and active back scan, i.e., port back scanning and vertical back scanning methods towards malicious sources. We conducted 2-week experiment and provide our results in attack distribution on different factors, possible port entrances for malware intrusion and classification result on different infection type. We found in 82.52% cases, malware infection bypasses firewalls.
Rate Splitting for Asynchronous Uplink NOMA Systems with Cyclic Prefixed Single Carrier

To alleviate the strict synchronization requirements and avoid the high peak-to-average power issue, asynchronous cyclic prefixed single carrier (CP-SC) transmission is applied for an uplink NOMA communication system. In this paper, we investigate rate splitting (RS) to improve the outage performance of the considered system. Two RS schemes, namely, the fixed RS and cognitive RS schemes, are proposed under back-off power control. With the aid of frequency-domain equalization, successive interference cancellation (SIC) is applied at the base station for recovering the desired signal. Under frequency-selective fading channels, closed-form expressions for the outage probabilities are derived for both near and far users achieved by asynchronous CP-SC transmission with and without RS, respectively. Motivated by the cognitive radio concept, the cognitive RS scheme implements the power allocation to sequentially guarantee the outage performance of the virtual primary, secondary, and tertiary users according to the SIC processing order. The enhanced outage performance achieved by the fixed RS and cognitive RS schemes are corroborated by Monte Carlo simulation results.
Semantics of interactions in UML 2.0

The recent major revision of the UML has introduced significant changes and additions. In particular, message sequence charts (MSC) according to the ISO standard have been integrated. In UML, the concept underlying these notations is called interaction. In this paper, the author shall look at its definition, defining a formal, yet straightforward trace semantics, including time.
Effects of fault detection and isolation to the stability of fault tolerant control systems

In this paper the effect of detection delays and errors on the stochastic stability of Fault Tolerant Control Systems with Markovian Parameters (FTCSMP) is studied. A mathematical representation for the detection delays of a Fault Detection and Isolation (FDI) process is developed. FDI processes employing memory-less statistical and sequential tests are considered. The dynamic programming principle is used to minimize a stochastic cost function and to obtain an optimal control law. Both the failure process and the FDI process are assumed to be available for the controller. Therefore, the designed controller allows the consideration of errors in fault detection. It is shown that the stability of FTCSMP may be lost in the event of large detection delays or errors in detection. A numerical example is used to demonstrate the theoretical developments.
Queue-Aware Cell Activation and User Association for Traffic Offloading via Dual-Connectivity

With the objective of reducing energy cost, we study the stochastic optimization of traffic off-loading via dual-connectivity by joint cell activation and user association. Particularly, explicitly considering the dynamic effects of time-varying and random traffic arrivals, we formulate a stochastic problem that quantifies the tradeoff between energy cost and queuing delay. Employing two-time-scale Lyapunov optimization, the formulated problem is transformed as a user association problem at each time slot and a small-cell activation problem at each frame. However, the user association problem turns out to be a mixed-integer nonlinear programming problem and is difficult to be transformed into a convex problem, and traditional game-theoretic solutions, such as Nash equilibrium, cannot be used to solve it. Then, we introduce a two-sided many-to-one matching game and propose a distributed user association algorithm that converges to a local optimum. On the other hand, the cell activation problem belongs to the class of maximum facility location problems that are generally NP-hard. Additionally, its objective function is not a submodular one. Thus, we present an iterative and heuristic algorithm that finds the best set of active small-cell BSs by repeatedly solving the user association problem with different sets of active small cells in each iteration. Furthermore, an online two-time-scale joint cell activation and user association algorithm are developed. Finally, numerical results show the effectiveness of the matching-based user association algorithm on traffic off-loading improvement, the heuristic cell activation algorithm on trading off between energy cost reduction and network throughput enhancement, and the online joint algorithm on balancing between energy cost reduction and queuing delay performance.
Saliency-Driven System With Deep Learning for Cell Image Classification

This paper describes our automatic cell image classification algorithm that explores expert’s eye tracking data combined to convolutional neural networks. Our framework selects regions of interest that attract cytologists attention, then it focuses computation on cell classification of these specific sub-images. Our contribution is to fuse deep learning to saliency maps from eye-tracking into an approach that bypasses segmentation to detect abnormal cells from Pap smear microscopy under real noisy conditions, artifacts and occlusion. Preliminary results show high classification accuracy of $\sim 90$% during tasks of locating and identifying critical cells within three levels: normal, low-risk disease and high-risk disease. We validate our results on 111 images containing 3,183 cells and obtained an average runtime of 4.5 seconds per image.
The optimization research of formation control for multiple mobile robots

In order to achieve formation control, an individual robot adopts the motor schema-based architecture and four primitive behaviors are introduced including: move to goal, keep formation, avoid static obstacle and avoid robot behavior. The behavioral decision to direct the movement of robots is made by the combination of primitive behaviors. The genetic algorithm is used in this paper to solve the problem of selecting control parameters that underlie the behaviors because of its difficulty. The simulation results obtained testify the feasibility of the proposed approach.
Data Augmentation for Chest Pathologies Classification

Diagnosis of lung pathologies from CXRs is one of the main tasks in modern image-based diagnosis. Automation of lung pathology diagnosis is greatly facilitated by recent developments in deep learning-based clinical decision making. The performance of deep learning solutions has the tendency to improve with the growing number of training X-rays, which can be artificially increased by augmentation of training X-rays. Commonly, different augmentation approaches are greedily applied to the available training data without investigating the necessity and actual contribution of individual augmentation. Our work aims to fill this gap in computerized lung pathology diagnosis and evaluate the contribution of different data augmentation approaches by leveraging the publicly available ChestX-ray14 dataset.
Special Session: Reliability of Hardware-Implemented Spiking Neural Networks (SNN)

The research work presented in this paper deals with the fault analysis in hardware-implemented Spiking Neural Networks with special emphasis on circuits designed to perform unsupervised, on-line learning. The paper describes the benefits of such neuromorphic systems, the possibilities of their hardware integration, but more importantly, it underlines the main concerns related to their resilience face to different types of faults. An overview of pertinent fault models and a methodology for conducting fault injection campaigns is described and different scenarios of faulty behaviors occurring after/before the STDP learning are shown.
Quasi-Postan Logic Hazard of Postan Signals

Every quasi-Postan function is a coproduct of a chain and quasi-Boolean functions. The quasi-Postan logic hazard of Postan signals are described by means of quasi-Boolean logic hazard of Boolean signals. The proof that elimination of the quasi-Postan logic hazard is achievable by eliminating the quasi-Boolean logic hazard form Boolean signals is provided in the paper.
Component-based Assembling Tool and Runtime Engine for the Machine Learning Process

Machine Learning has gradually become a hot industry, widely used for data mining, computer vision, natural language processing, biometric recognition, search engine, medical diagnosis and so on. Due to the complex processing processes and various methods of machine learning, it often takes a lot of time in practical applications. For this problem, an easy-to-use assembling tool and a runtime engine for the machine learning process is of great benefit. In this paper, we propose a component-based assembling tool and a runtime engine that can be used to quickly build self-executing machine learning processes and monitor process execution. First of all, we introduce the algorithm components, which includes many machine learning methods. Then, we present the assembling tool. The purpose of the assembling tool is the layout, which is the design phase of the process. After that, we introduce the runtime engine. The engine is responsible for executing the designed process, which is the running phase of the process. Finally, we evaluate and discuss how to use this tool to speed up the efficiency of machine learning processing.
Kinship Verification based Deep and Tensor Features through Extreme Learning Machine

Checking the kinship of facial images is a difficult research topic in computer vision that has attracted attention in recent years. The methods suggested so far are not strong enough to predict kinship relationships only by facial appearance. To mitigate this problem, we propose a new approach called Deep-Tensor+ELM to kinship verification based on deep (VGG-Face descriptor) and tensor (BSIF-Tensor & LPQ-Tensor using MSIDA method) features through Extreme Learning Machine (ELM). While ELM aims to deal with small size training features dimension, deep and tensor features are proven to provide significant enhancement over shallow features or vector-based counterparts. We evaluate our proposed method on the largest kinship benchmark namely FIW database using four Grandparent-Grandchild relations (GF-GD, GF-GS, GM-GD and GM-GS). The results obtained are positively compared with some modern methods, including those that rely on deep learning.
Photon-Enhanced Conductivity Modulation and Surge Current Capability in Vertical GaN Power Rectifiers

In this work, we investigate the temperature-dependent forward conduction performance, the reverse recovery behavior and surge current capability of the vertical GaN Schottky barrier diode (SBD) and PN diode (PND). Compared with the SBD, the vertical GaN PND can deliver photon-enhanced conductivity modulation (PECM), temperature-independent differential ON-resistance, and higher surge current capability. It is experimentally verified that the PECM and zero reverse recovery can be simultaneously realized in the vertical GaN PND.
Map-Matching on Big Data: a Distributed and Efficient Algorithm with a Hidden Markov Model

In urban mobility, map-matching aims to project GPS points generated by moving objects onto the road segments representing the actual object positions. Up to now, map-matching has found interesting applications in traffic analysis, frequent path extraction, and location prediction. However, state-of-art implementations of map-matching algorithms are either private, sequential or inefficient. In this paper, we propose an extension of an existing serial algorithm of known efficiency by reformulating it in a distributed way, in order to achieve great scalability on real big data scenarios. Furthermore, we enhance the robustness of the algorithm, which is based on a first order Hidden Markov Model, by introducing a smart strategy to avoid gaps in the matched road segments; indeed, this problem may occur under sparse GPS sampling or in urban areas with highly fragmented road segments. Our implementation is based on Apache Spark and is publicly available on Github. The implementation is tested against a dataset with 7.8 million GPS points in Milan.
Novel 2000 V Normally-off MOS-HEMTs using AlN/GaN Superlattice Channel

We demonstrate for the first time a GaN-based metal oxide semiconductor high electron mobility transistor (MOS-HEMT) with AlN/GaN superlattice (SL) channels. This new channel structure allows for superior voltage blocking capabilities and thermal stability than conventional GaN channels, as well as higher electron mobility than AlGaN channels. State-of-the-art static and dynamic performance has been achieved in this new MOS-HEMT, including a breakdown voltage over 2000 V, a high ON current density of 768 mA/mm, a threshold voltage (VTH) of 1.0 V, a specific on-resistance ($\boldsymbol{R}_{\mathbf{ON}}$) of $7.7\ \mathbf{m}\mathbf{\Omega}\cdot \mathbf{cm}^{2}$, thermal stability up to 225 °C and good dynamic RON. These results show the great potential of our novel AlN/GaN-SL-channel MOS-HEMTs for highvoltage and high temperature power switching applications
Design of a stopband filter based on a magnetic photonic bandgap material

In this paper, we present a stopband filter synthesis using a new class of photonic bandgap (PBG) materials, in which the magnetic permeability varies periodically. This magnetic PBG (MPBG) is fabricated using arrays of ferromagnetic nanowires. The MPBG design procedure, which will be described, is divided into two stages. First, we perform an analytical prediction of the filter properties for given MPBG parameters. Then, the frequency response of the MPBG is simulated using a variational-chain matrix model, which is validated by measurements.
A Real-Time Simulation Framework for System Protection in Smart Grid Applications

Analysis and simulation of power systems are crucial for a successful deployment of power system networks. However, introduction of smart grid concepts and integration of information and communication technologies (ICT) in operation of power systems has been a challenging topic for studying of future power systems. In this paper complexities related to simulation of smart grids and its requirement for research and development studies have been discussed. In addition to that, a real-time simulation framework for system protection in smart grids has been established to verify the simulation results. The proposed methodology is based on co-simulation of power system components and integration of ICT components in real-time. In summary, the outcomes of this research can be highlighted to address feasibility studies in smart grid operation and capability to integrate Distributed Artificial Intelligence (DAI) for reliable system protections.
SPEK: a storage performance evaluation kernel module for block level storage systems

In this paper we introduce SPEK (storage performance evaluation kernel module), a benchmarking tool for measuring and characterizing raw performance of data storage systems at block level. It can be used for both DAS (direct attached storage) and block level networked storage systems. Each SPEK tool consists of a controller, several workers, and one or more probers. Each worker is a kernel module generating I/O requests to lower level SCSI layer directly. Compared to traditional file system I/O and disk I/O benchmarking tools, SPEK is highly accurate and efficient since it runs at kernel level and eliminates file system overheads. It is specially suitable for accurately measuring raw performance of data storages at block level without influence of file system cache or buffer cache. Using SPEK, a user can easily simulate realistic workloads and produce detailed profiling data for networked storage as well as DAS. We have built a prototype on Linux and our experiments have demonstrated its accuracy and efficiency in measuring block level storage systems.
Design of Quad-Edge-Triggered Sequential Logic Circuits for Ternary Logic

We propose a quad-edge-triggered flip-flop which captures and propagates a ternary data signal at four-edges of a ternary clock signal. The proposed circuit uses carbon nanotube FETs and consists of four types of logic gate: ternary clock driver, standard ternary inverter, binary inverter, and transmission gate. HSPICE simulation result has confirmed that power consumption of QETFF is lower than conventional single-edge-triggered flip-flop. The average power consumption is reduced by 31 % in flip-flop and 75 % in clock tree. We designed a ternary serial adder using QETFF and the energy efficiency of the proposed circuit is significantly improved compared to the previous design of ternary serial adder.