Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling early stage fretting of electrical connectors subjected to random vibration

Fretting corrosion induced by vibration is a topic of major concern for automotive applications, often leading to increased contact resistance and connector failure. Presently, modeling of the behavior of connectors during fretting corrosion is a difficult matter, requiring many parameters, and is generally highly nonlinear in nature. Experimental testing of sample connectors is currently the only practical method of evaluating connector performance; however, testing can be a time-consuming and inexact task. Prior work by the authors studied the fretting behavior of connectors subjected to single frequency vibration. Correlation of experimental results with simulated behavior showed that, for the primary mode of connector interface motion observed (rocking-type motion), the relative moment at the interface was a good indicator of the observed fretting rate. It was also shown that the moment applied as the result of a given excitation level and frequency could reasonably be predicted via simulation. The current work extends this approach to random vibration profiles, which are a more realistic representation of the connector application environment. A simple model is developed which relates the early stage fretting corrosion rate to the threshold vibration levels for the connector, the dynamic characteristics of the connector/wiring configuration, and the vibration profile. A high degree of consistency between this model and the experimental data was demonstrated. Interestingly, regardless of the excitation profile applied to the overall system the existence of a characteristic vibration threshold at the connector interface was observed.
Hybrid Performance Modeling for Optimization of In-System-Structural-Test (ISST) Latency

In-System-Test (IST) is one of the most advanced feature of autonomous drive platforms to monitor the semiconductor chip failures due to field defects. This is achieved by application of structural-tests (Memory BIST and/or Logic BIST) during key-on and/or key-off functional events. The time duration for application of these structural tests, detection of defects, drive platform reaction, and leading it to a fail-safe state must be short enough to avoid hazards caused by defects in safety modules (SMs). The optimization of diagnostic test interval (test application and result analysis) is important to reduce the overall in-system-structural test latency. In this paper, we present the hybrid performance modeling methodology adopted for IST architecture to optimize the design implementation with lowest possible and acceptable diagnostic test latency for key-on and/or key-off application of structural-test in Drive platform.
A high performance wide-band CMOS transimpedance amplifier for optical transceivers

This paper describes a novel low noise CMOS current feed-back transimpedance amplifier using a low-cost Agilent 0.5 /spl mu/m 3M2p CMOS process technology. The bandwidth of the amplifier was extended using inductive peaking technique and simulation results indicated a -3 dB bandwidth of 3.5 GHz with a transimpedance gain of /spl ap/ 60 dB ohms. The dynamic range of the amplifier was high enough to enable an output voltage swing of around 400 mV for a test input current of 100 /spl mu/A. The output noise voltage spectral density was 12 nV/sqrt(Hz) (with a peak of /spl ap/ 25 nV/sqrt(Hz)), while the input referred noise current spectral density was below 20 pA/sqrt(Hz) within the amplifier frequency band.
Exploring Features and Attributes in Deep Face Recognition Using Visualization Techniques

Deep convolutional neural networks (CNNs) currently have achieved state-of-the-art results on face recognition; yet, the understanding behind the success of the deep face model is still lacking. In particular, it is still unclear the inner workings of deep face model. What effective features does a deep face model learn? What do these features represent and what is the sematic meaning of them? This work explores this problem by analyzing the classic network VGGFace using deep visualization techniques. We first explore features computed by neurons, investigating characters of features like diversity, invariance, discrimination. It’s worth noting that the middle layer is the least robust to transform, which contradicts the conventional view that robustness to transform increases as the network going deeper. The most significant phenomenon we find is that high level features are correspond with complex face attributes which human could not describe using a few words. We present a quantitative analysis on these face attributes perceived by deep CNNs, understanding them and the complex relationships between them. Additionally, we also focus on the significant point, the pose invariance in face recognition. Our research is the first work to understand the inner works of deep face models, elucidating some particular phenomena in deep face recognition.
Building blocks for future dual-channel GaN gate drivers: Arbitrary waveform driver, bootstrap voltage supply, and level shifter

Capitalising on the high-speed switching capability of 650 V GaN FETs in power-electronic bridge-legs is challenging. Whilst active gate driving has previously been shown to help overcome adverse switching behaviour, the best results are likely to be achieved through a combination of uncompromised circuit layout and active gate driving. A fully integrated dual-channel driver would minimise external circuitry and allow power devices to be placed as close together as possible. This would facilitate simultaneous minimization of parasitic inductances in the gate-drive and power-circuit loops. Other benefits would include ease of use, lower BOM cost, and providing a step towards full integration of driver and power stage. This paper presents three circuit blocks vital to the implementation of a fully integrated dual-channel gate driver – A 100 ps resolution, digitally-controlled active gate driver IC, a sub-ns propagation delay level shifter with 200 V/ns slew-rate immunity, and a regulated bootstrap supply that maintains its output voltage regardless of any switch-node undershoot during switching events. Measurement results show the efficacy of the high-resolution active gate driver in a GaN bridge leg, and the sub-ns propagation delay of the level shifter, both fabricated in a 50 V CMOS process. Simulation results demonstrate the slew-immunity of the level shifter, and operation of the bootstrap supply. It is also inferred how to increase the voltage rating of the level-shifter and bootstrap without adversely affecting performance.
A Comparative Study on Virtual Private Networks for Future Industrial Communication Systems

The future industrial networks will not be created from scratch. Rather, they will grow from existing installations without displacing legacy components. The secure integration of these legacy machines and networks will become an important building block in order to realize the vision of Industry 4.0. Secure and high performance virtual private networks (VPNs) will be necessary for that purpose.Therefore, we investigated and compared various VPN solutions. Their performance was tested on multiple hardware platforms ranging from very resource constrained to very powerful. Non-functional aspects, relating around security, manageability and ease of use, were discussed in order to assess their suitability for future use cases.We arrive at clear recommendations on which software VPN solutions to choose for future industrial setups.
PDP-SAG: Personalized Privacy Protection in Moving Objects Databases by Combining Differential Privacy and Sensitive Attribute Generalization

Moving objects databases have become an enabling technology for location-based applications. They mostly focus on the storing and processing of data about moving objects. Privacy protection is one of the most important concerns related to such databases. In recent years, some mechanisms have been proposed to answer statistical queries over moving objects databases, while satisfying differential privacy. However, none of them consider the case where a moving objects database contains non-spatiotemporal sensitive attributes other than spatiotemporal attributes. Besides, most of them do not support the personalized privacy protection requirements of different moving objects. In this paper, we address these problems by presenting PDP-SAG, a differentially private mechanism that combines the sensitive attribute generalization with personalized privacy in a unified manner. By this combination, we aim to provide different levels of differential privacy protection for moving objects that have non-spatiotemporal sensitive attributes as well. In this regard, we generalize the sensitive attribute values of trajectory data records based on their privacy descriptor and define a new personalized differentially private tree structure to keep different noisy frequencies for each trajectory according to the generalized sensitive attribute values of trajectory data records passing through that trajectory. We also propose intra- and inter-consistency constraints enforcements to make noisy frequencies consistent with each other. The extensive experiments on synthetic and real datasets verify that PDP-SAG significantly improves the utility of sensitive query answers and provides the required level of privacy protection for each moving object, in comparison to the case when no personalization and generalization are permitted.
Elastic Net Regularization in Diffuse Optical Tomography Applications

Diffuse optical tomography (DOT) uses near-infrared light to obtain quantitative information about the optical coefficients in biological tissues. Such an information can be clinically exploited for diagnostic purposes. In DOT, the surface of the investigated tissue is illuminated with a light source and the emerging light is measured at various locations on the surface itself. The minimization of the discrepancy between the observed data and the corresponding field generated by a mathematical model of light propagation is then performed, yielding an estimated distribution of the optical parameters in the tissue. This problem is characterized by severe ill-conditioning, so that appropriate regularization techniques must be applied to obtain a meaningful solution. To do this, the use of $\ell_{2}$ (Tikhonov) penalization has been generally advocated in this context; more recently, $\ell_{1}$ (LASSO)-norm penalization has also been proposed to detect the existence of sparsity patterns. Both approaches are classical regularization techniques and are often favored in DOT over other methods, when robustness with respect to geometry and need for almost real-time results are an issue. The goal of the present contribution is to explore the “elastic net” regularization technique originally introduced by Zou and Hastie [1], that shares the desirable properties of both the $\ell_{2^{-}}$ and $\ell_{1}$-norm penalization approaches. Whilst this technique has been largely used as a learning theory tool in different applications, at the best of our knowledge, this paper presents its first use in DOT applications. Numerical simulations are performed here on a simple $2D$ geometry, to assess the potentialities of this approach. The results show that this technique may be a good choice for our target application, where DOT is used as a cheap, first-level and almost real-time screening technique for breast cancer detection.
A Smart Supervisory System for Distribution Transformers

Traditionally the determination of the health and life of a distribution transformer (DT), has relied on modelled rather than measured data. This can lead to inaccurate assessment of transformer health and non-optimised transformer utilisation. This paper presents a smart supervisory system for DTs incorporating self-protection, communications and comprehensive monitoring features. The monitoring includes fibre-optic winding hot-spot temperature (θHS), sensing, top oil temperature (TOT), sensing and dissolved gas analysis (DGA), through monitoring hydrogen levels. Self-protection is implemented through the direct control of the medium voltage (MV), switchgear, according to sensed θHS. Communication allows for transformer health data to be sent to a remote monitoring centre. This system provides accurate and dynamic monitoring of DTs, improving reliability and utilisation levels, and simplifying asset management.
Analyzing group performance in small group interaction: Linking personality traits and group performance through the verbal content

In this paper, we investigate the link between the personality traits and group performance in terms of the verbal content. We further study the variability in the verbal interaction between different performance groups. Towards this goal, we extract topics representing the content of meetings as well as term-frequencies of items that play a critical role in the decision task. We use a dataset where each group performs the winter survival task, in which the task is to decide on the ranking of different items with respect to the importance of each item for their survival. In the experiments, we contrast the ranking of items with respect to their term frequencies and compare the differences between topics both for distinct personality traits and group performances. Results of the term-frequency based approach show that influential people put correct emphasis on items more than dominant people. The topic-based method reveals that influential people consider the majority of items by providing usage instructions for alternative scenarios and that dominant people focus only on a small subset of items by stressing their significance. High-performance groups assess items in a similar manner to influential and dominant people, i.e. a wide range of items are considered and their importance is explained. Low-performance groups, on the other hand, concentrate on the situation they are in rather than the items and their usages.
Thermal modeling and measurement of AlGaN/GaN FETs built on sapphire and SiC substrates

We present thermal modeling and measurement results of AIGaN/GaN Heterojunction Field Effect Transistors (HFETs) fabricated on sapphire and SIC substrates. The temperature profiles on the device surface were calculated using in-house codes PAMICE and UNITHERM. These 3-D codes allow us to compute temperature at any location on and inside the device. Thermal measurement was performed using nematic liquid nystal thermography. This technique is nondestructive and can be applied while the device is in operation condition. It has suhmicron spatial resolution and +l0C temperature accuracy. In addition to temperature measurement, the technique can also be employed to detect hot spots that are possibly caused by defects. For the devices studied, current versus voltage was first measured. The results indicate that these devices behave nicely. Measured peak temperatures agree well with the calculated ones. Since sapphire has much lower thermal conductivity than Sic, AlGaN/GaN/sapphie device has higher thermal resistance than AlGaN/GaN/SiC device. A linear relationship between temperature and power was observed for both types of devices. AIGaNiGaN devices are known to have superior high power capability at high frequency. Thus, thermal modeling and measurement are valuable in determining the junction temperature and subsequently to modify device and package design so as to keep the junction temperature within acceptable range. lotroduction
Toward a new CAD method for MIMO multi-rate digital controllers

The author presents a novel computer-aided-design method for multi-rate sampled-data feedback systems (MRSDFSs) with many nice features including naturalness and effectiveness. Naturalness means that a control engineer can express specifications in his terms, by referring directly to signal peaks, overshoots, RMS powers, pulse responses, etc., rather than other, less direct (and more opaque) expressions (e.g. a quadratic integral in LQR or LQG). Effectiveness means that if a controller meeting the specifications exists, then the method will find it. The three main concepts for the proposed MRSDFS CAD method are: (1) shift-invariant representations for multi-rate systems; (2) parametrization of stabilizing controllers and convexity of specifications; and (3) machine intelligence and control specification language compilers. Rather than rigorously discussing each topic in depth and full generality, the author chooses a simple example and discusses each concept as it arises in the course of designing a MRSDFS. <<ETX>>
Pose Estimation by Key Points Registration in Point Cloud

In 6D pose estimation, both template based or learning based models need template/training data corresponding to different poses. We propose a new model based on point clouds. It needs less training data and therefore is lighter, faster and precise enough for texture-less objects. We first extract key points automatically from the point cloud of the object and then generate rigid transformation invariant point-wise features of the cloud as input feature. Then we use a hierarchical neural network architecture to predict the key points coordinates corresponding to the reference pose. At last we can calculate the relative transformation between the current and the reference poses. The hierarchical structure takes into account the symmetry or invariance problem of certain object geometries.
Clock Drift Impact on Target Wake Time in IEEE 802.11ax/ah Networks

In the Internet of Things scenarios, it is crucially important to provide low energy consumption of client devices. To address this challenge, new Wi-Fi standards introduce the Target Wake Time (TWT) mechanism. With TWT, devices transmit their data according to a schedule and move to the doze state afterwards. The main problem of this mechanism is the clock drift phenomenon, because of which the devices cease to strictly comply with the schedule. As a result, they can miss the scheduled transmission time, which increases active time and thus power consumption. The paper investigates uplink transmission with two different TWT operation modes. With the first mode, a sensor transmits a packet to the access point (AP) after waking up, using the random channel access. With the second mode, the AP polls stations and they can transmit a packet only after receiving a trigger frame from the AP. For both modes, the paper studies how the average transmission time, the packet loss rate and the average energy consumption depend on the different TWT parameters. It is shown that when configured to guarantee the given packet loss rate, the first mode provides lower transmission time, while the second mode provides lower energy consumption.
Design of quasi-lumped-element LTCC filters and duplexers for wireless communications

Multilayer integrated circuits using the low temperature cofired ceramics (LTCC) technology are considered as applied to design of microwave filters and duplexers for wireless applications. Different structures of miniature LTCC filters based on quasi-lumped-element resonators are suggested with a special emphasis to filters with additional transmission zeros. Original design of LTCC duplexer for IMT-2000 handsets is presented.
Rerouting Based Congestion Control in Data Center Networks

Data center networks (DCNs) are widely deployed to provide infrastructure backbone for various cloud services. Meanwhile, the emergence of Software-Defined Networking (SDN) makes it easy to monitor the global status of DCNs. In DCNs, traffic bursts or uneven traffic distribution may lead to network congestion, thus deteriorating the overall network performance. Rerouting is an effective method to mitigate network congestion by rerouting flows to paths with sufficient bandwidth. In this paper, we propose an efficient rerouting algorithm by leveraging the SDN technique. We firstly formulate the rerouting problem as a Multi-Commodity Flow (MCF) problem with the objective to minimize the maximum link utilization in the DCN. Since the problem is a NP-hard problem, we propose a heuristic algorithm that jointly considers the current path utilization and the path criticality to approach the optimal solution. Our simulation results demonstrate the superiority of the proposed algorithm in mitigating the network congestion and guaranteeing the performance of the network, over the classical Equal-Cost Multi-Path (ECMP) algorithm and Global First Fit (GFF) algorithm.
International Journal of Recent Trends in Engineering , Vol 2 , No . 5 , November 2009 8 Ensemble Voting System for Anomaly Based Network Intrusion Detection

The growing dependence of modern society on telecommunication and information networks has become inevitable. Therefore, the security aspects of such networks play a strategic role in ensuring protection of data against misuse. Intrusion Detection systems (IDS) are meant to detect intruders who elude the “first line” protection. Data mining techniques are being used for building effective IDS. In this paper we analyze the performance of some data classifiers in a heterogeneous environment using voting ensemble system with the purpose of detecting anomaly based network intrusions. Experimental results using KDDCup 1999 benchmark dataset demonstrate that the voting ensemble technique yield significantly better results in detecting intrusions when compared to other techniques.
The opinions and attitudes of students – future IT teachers – on the use of VR and AR in teaching

Recent technological advances in the field of Virtual Reality (VR) have resulted in market release of multiple head mounted displays such as Oculus Rift, Sony Gear VR and HTC Vive. This new technology brings great possibilities in education in terms of new methods of learning, and its applications in teaching have already been studied in several studies worldwide. Augmented Reality (AR), as an emerging technology, is predicted to have even more potential usages than VR, both in education and other areas. The advantages of AR are increased freedom, lack of simulator sickness, and broader application range. The aim of this paper was to investigate the opinions and attitudes of students who will, presumably after their graduation, teach ICT in primary education schools. The research showed that even though a majority of students have positive opinions and expectations of these technologies, such as immersive learning and pupils’ motivation, only a few of them feel competent enough to use them in classroom. They also have some concerns regarding health issues of using AR/VR technologies, as well as students losing their concentration in class.
A Dynamic Array of Sub-Array Architecture for Hybrid Precoding in the Millimeter Wave and Terahertz Bands

In this paper, a dynamic array of sub-array (DAoSA) architecture is proposed for hybrid precoding in the millimeter wave (mmWave) and Terahertz (THz) bands. Switches are inserted in the hybrid precoding architecture, to dynamically adjust the connection between RF chains and sub-arrays, i.e., each RF chain connects to m sub-arrays. In particular, the fully-connected (FC) and array of sub-array (AoSA) architectures can be realized by setting m as the number of RF chains and 1, respectively. Based on the DAoSA architecture, the intractable precoding problem is decomposed into multiple tractable FC precoding problems, to which the solutions are developed based on a low-complexity column-by-column (CBC) algorithm. In the mmWave and THz wireless channel, simulation results show that compared with the FC architecture, the power consumption of the DAoSA architecture can be substantially reduced with a negligible spectral efficiency loss. In contrast to the AoSA architecture, the spectral efficiency is significantly improved.