Miracle-dz/UniFAR-FTUs huggingface dataset:
Compression-Aware ATPG

The remarkable growth of the circuit size and complexity is primarily due to the advances of VLSI design and manufacturing technologies. The on-chip linear sequential test compression has become the de facto industrial mainstream DFT methodology in reducing the overall cost of testing large chips. In this paper, we propose a novel and efficient compression-aware ATPG method to significantly boost the performance of ATPG and reduce pattern count. The proposed approach first analyzes the intrinsic dependency of the equations determined by a linear sequential decompressor to build Maximal Linear Independent Group (MLIG). Next it computes the implied values given ATPG generated test cubes based on MLIG. The implied values can significantly improve the test compaction and reduce the ATPG run time due to the reduction of value conflicts between ATPG and compression. The proposed approach does not affect test coverage, requires no extra hardware support, and can be applied to any linear sequential compression scheme. Experimental results on several industrial designs demonstrate that on average the proposed approach can reduce the number of ATPG patterns by 7.57% and the ATPG run time by 28.4%.
Orbital Proper Motions in the Protobinary System L1527/IRAS 04368+2557?

Using high angular resolution 7 mm Very Large Array (VLA) observations, we show that the low-mass protostellar object IRAS 04368+2557 in L1527 is composed of two radio sources separated in projection by 25 AU. One of the two components is extended in the direction perpendicular to the outflow powered by IRAS 04368+2557 and is, most likely, a compact accretion disk (R ~ 20 AU), similar to those found in L1551 IRS 5 by Rodríguez et al. As in L1551 IRS 5, the disk found here is small compared to those around T Tauri stars. Tidal interactions with nearby companions provide a natural way of truncating disks, and we argue that the two millimeter-wavelength sources in L1527 trace a compact binary system, where the disk surrounding one of the components has been truncated by the tidal influence of the other. A comparison between observations obtained in 1996 and 2002 reveals large proper motions, which can be only partly attributed to the overall large-scale motion of the region in which IRAS 04368+2557 is located. The remaining "residual" proper motions might trace the orbital motion of the binary and would suggest a total mass for the system larger than 0.2 M☉ and likely of the order of 0.5-2 M☉. This mass (only a small fraction of which is in the disk) is of the same order as that of the extended surrounding envelope of gas and dust traced by far-infrared and submillimeter observations (Menv ~ 0.5 M☉), implying that the stars that will eventually form out of IRAS 04368+2557 would have already acquired a significant fraction of their final masses. It is worth noting that multiepoch VLA studies of nearby protobinary systems similar to that presented here could provide direct mass estimates in most nearby star-forming sites. Combined with submillimeter observations of the surrounding envelopes, this would provide a more reliable measure of the evolutionary status of binary protostars.
User Pairing Effects on Load-Aware DPS CoMP in NOMA Systems

As a key radio access technique in 5G systems, Non-Orthogonal Multiple Access (NOMA) is aimed at offering high spectral efficiency to support massive connectivity for current and future applications. As for the network deployment, Heterogeneous Networks (HetNets) are by default the 5G use cases. Coordinated Multipoint (CoMP) techniques have been integrated in 5G technology for interference mitigation among network tiers via network coordination. This work presents the study of user pairing effects, which are the result of NOMA implementation, on the 5G NOMA systems embedded with our proposed actual traffic based load-aware DPS CoMP. Simulation results show that the NOMA system embedded with our proposed DPS CoMP offers system throughput enhancement over the conventional NOMA systems especially in the load imbalanced situations and the suitable user pairing can further improve fairness and throughput performance especially for the cell edge users.
Robust discrimination of human footsteps using seismic signals

This paper provides a statistical analysis method for detecting and discriminating different seismic activity sources such as humans, animals, and vehicles using their seismic signals. A five-step process is employed for this purpose: (1) a set of signals with known seismic activities are utilized to verify the algorithms; (2) for each data file, the vibration signal is segmented by a sliding-window and its noise is reduced; (3) a set of features is extracted from each window of the signal which captures its statistical and spectral properties. This set is formed as an array and is called a feature array; (4) a portion of the labeled feature arrays are utilized to train a classifier for discriminating different types of signals; and (5) the rest of the labeled feature arrays are employed to test the performance of the developed classifier. The results indicate that the classifier achieves probability of detection (pd) above 95% and false alarm rate (pfa) less than 1%.
Analysis of the statistical significance of 3D texture features in MRI images toward the detection of Tourette’s Syndrome

Tourette Syndrome (TS) is a genetically induced disorder that is believed to be caused by morphological alterations in brain structure, resulting in involuntary movements known as tics. The current clinical standard for diagnosing TS is by clinical assessments performed by physicians. Mild stages of TS, however, commonly go underdiagnosed as tics are infrequent or often suppressed. Brain imaging has been suggested to be a reliable tool to detect brain alterations and possible biomarkers that correspond to neurological disorders. In Magnetic Resonance Imaging (MRI), anatomical brain changes can be identified in the scan by variation in texture patterns of certain regions. The main goal of this work is to identify the statistical significance of texture features in specific brain regions to distinguish TS from control subjects. The proposed approach consists of four main steps: (i) image acquisition, where the data is also organized using demographic information; (ii) brain segmentation, where the structural MRI is parcellated into anatomical regions; (iii) registration, where functional MRI is aligned to structural MRI; (iv) obtaining texture features and statistical analysis, where texture features are extracted from the anatomical brain regions. We adopted 68 subjects aged between 6 to 14 years, divided equally into TS and Normal Control groups. We evaluated the texture features in a statistical manner, where our main findings are: (i) After False Discovery Rate (FDR) correction, only one texture feature was significant $(p$-value $\lt 0.08)$ in structural MRI; (ii) Following FDR correction, eight texture features in functional MRI for three anatomical regions were considered significant; (iii) The right amygdala presented significance in distinct texture features, matching its importance in the literature. Texture features aligned with the literature can serve as a reliable tool to identify imaging changes, which can lead to future work applied to clinical studies.
Characterization of residual stress in metallic films on silicon with micromechanical devices

Al and Cr cantilever microbeams, microbridges and suspended microrings were fabricated by isotropic etching of silicon with a SF6/O2 plasma after film patterning. They were used for the characterization of both compressive stress, extensive stress and stress gradients in the metallic films. It is shown that valid residual stress measurements with such micromechanical devices must take into account stress changes due to process fabrication, underetching at the clamped ends, edge effects and stress gradients. This is demonstrated by bucking threshold measurements of microbridges and microrings of various sizes and 3D finite element linear static and linear buckling analyses including underetching or stress gradients. Cantilever microbeam profile measurements shows that temperature rise during etching must be carefully minimizes to avoid thermally induced stress gradients. Stress gradients in the deposited films have no effect on microbridges critical buckling stress but lead to a distortion of the microrings before the occurrence of buckling.
Approximation in Learning Theory

This paper addresses some problems of supervised learning in the setting formulated by Cucker and Smale. Supervised learning, or learning-from-examples, refers to a process that builds on the base of available data of inputs xi and outputs yi, i = 1,. ,m, a function that best represents the relation between the inputs x ∈ X and the corresponding outputs y ∈ Y. The goal is to find an estimator fz on the base of given data z := ((x1,y1),. ,(xm,ym)) that approximates well the regression function fρ (or its projection) of an unknown Borel probability measure ρ defined on Z = X × Y. We assume that (xi,yi), i = 1,. ,m, are independent and distributed according to ρ. We discuss the following two problems: I. the projection learning problem (improper function learning problem); II. universal (adaptive) estimators in the proper function learning problem. In the first problem we do not impose any restrictions on a Borel measure ρ except our standard assumption that |y|≤ M a.e. with respect to ρ. In this case we use the data z to estimate (approximate) the L2(ρX) projection (fρ)W of fρ onto a function class W of our choice. Here, ρX is the marginal probability measure. In [KT1,2] this problem has been studied for W satisfying the decay condition εn(W,B) ≤ Dn-r of the entropy numbers εn(W,B) of W in a Banach space B in the case B = C(X) or B = L2(\rhoX). In this paper we obtain the upper estimates in the case εn(W,L1(ρX)) ≤ Dn-r with an extra assumption that W is convex. In the second problem we assume that an unknown measure ρ satisfies some conditions. Following the standard way from nonparametric statistics we formulate these conditions of the form fρ ∈ Θ. Next, we assume that the only a priori information available is that fρ belongs to a class Θ (unknown) from a known collection {Θ} of classes. We want to build an estimator that provides approximation of fρ close to the optimal for the class Θ. Along with standard penalized least squares estimators we consider a new method of construction of universal estimators. This method is based on a combination of two powerful ideas in building universal estimators. The first one is the use of penalized least squares estimators. This idea works well in the case of general setting with rather abstract methods of approximation. The second one is the idea of thresholding that works very well when we use wavelets expansions as an approximation tool. A new estimator that we call the big jump estimator uses the least squares estimators and chooses a right model by a thresholding criteria instead of the penalization. In this paper we illustrate how ideas and methods of approximation theory can be used in learning theory both in formulating a problem and in solving it.
Broadband Wavelength Conversion for Hybrid Multiplexing Signals Based on a Parallel Dispersion-Engineered Silicon Waveguide

Broadband all-optical wavelength conversion (AOWC) for hybrid wavelength- and mode-division multiplexing (WDM-MDM) signals is experimentally demonstrated based on degenerate four-wave mixing in a silicon chip with a parallel dispersion-optimized multimode nonlinear waveguide and mode (de)multiplexers. By simultaneously coupling two modes into the waveguide using an optical fiber array, the intermodal crosstalk is measured to be as low as −25.9 dB for the fundamental mode of the transverse electric mode (TE0) (or −23.6 dB for the first-order mode TE1), and the conversion efficiency is −25.4 dB for TE0 (or −26.3 dB for TE1) mode. A wide conversion bandwidth of ∼68 nm is measured except for the influence of the crosstalk, which is the first time to experimentally demonstrate the broadband wavelength conversion for MDM signal. Using a 4 × 10 Gbit/s on-off keying (OOK) hybrid WDM-MDM signal, four AOWC channels are obtained on the idlers and the power penalty of each channel is less than 2.7 dB at the bit-error-ratio of 1 × 10−9.
Schottky barrier diodes on 3C‐SiC

Schottky barrier contacts have been made on n‐type 3C‐SiC epitaxially grown by chemical vapor deposition, and their characteristics were studied by the capacitance and photoresponse measurements. By evaporating Au onto chemically etched surfaces of 3C‐SiC, good quality Schottky barrier junctions have been obtained. The barrier height determined by the capacitance measurements is 1.15 (±0.15) eV, while the height by the photoresponse measurements is 1.11 (±0.03) eV. These values are about a half of the energy band gap Eg at room temperature.
Dual-Terminal Voltage Feedforward Based Direct Power Control Scheme and Stability Analysis of Dual Active Bridge Converter in DC Microgrid Systems

The dual active bridge (DAB) dc–dc converter plays an important role in energy exchange between dc microgrids integrated with energy storage systems. To directly control the power flow between the adjacent dc microgrids rapidly and accurately, a dual-terminal voltage feedforward based direct power control scheme (DVF–DPC) with single-phase-shift control is proposed for the DAB dc–dc converter. The strategy makes the converter realize rapid and precise output power regulation under the following conditions, including the startup process, load step-change, the desired output power step-change, and voltage fluctuations. An accurate discrete-time model is applied to analyze the system's dynamic characteristics, and a discrete-time small-signal model of the system is established to design the controller's parameters. In addition, a comprehensive and systematic analysis of all the possible unstable phenomena for adjacent dc microgrids is conducted based on the discrete-time model. Accordingly, the underlying mechanisms are revealed by analyzing the relationships among the state variables, the Floquet multipliers, and system parameters. Hence, the stability-oriented parameter design method is given, which can provide guidance in practice. The theoretical analysis is verified in both the time domain and frequency domain. Finally, by comparing the DVF–DPC method with the traditional PI feedback control, the superiority of the proposed strategy is verified through theoretical analysis and experimental results, which demonstrates its application prospect in the field of electric energy dispatch control of the dc power supply network.
STheReO: Stereo Thermal Dataset for Research in Odometry and Mapping

This paper introduces a stereo thermal camera dataset (STheReO) with multiple navigation sensors to encourage thermal SLAM researches. A thermal camera measures infrared rays beyond the visible spectrum therefore it could provide a simple yet robust solution to visually degraded environments where existing visual sensor-based SLAM would fail. Existing thermal camera datasets mostly focused on monocular configuration using the thermal camera with RGB cameras in a visually challenging environment. A few stereo thermal rig were examined but in computer vision perspective without supporting sequential images for state estimation algorithms. To encourage the academia for the evolving stereo thermal SLAM, we obtain nine sequences in total across three spatial locations and three different times per location (e.g., morning, day, and night) to capture the variety of thermal characteristics. By using the STheReO dataset, we hope diverse types of researches will be made, including but not limited to odometry, mapping, and SLAM (e.g., thermal-LiDAR mapping or long-term thermal localization). Our datasets are available at https://sites.google. com/view/rpmsthereo/.
Uniform Global Exponential Stabilizing Passivity-Based Tracking Controller Applied to Planar Biped Robots

This paper presents a novel control approach, based on the interconnection and damping-assignment passivity-based control (IDA-PBC), to achieve stable and periodic walking for underactuated planar biped robots with one degree of underactuation. The system's physical structure is preserved by assigning a target port-Hamiltonian dynamics to the closed-loop system, which also ensures passivity. The control design ensures that the tracking error to the desired periodic gait converges exponentially to zero, and the convergence rate can be adjusted via gain tuning. Besides, through the hybrid zero dynamics, the stability of the full-order system can be retrieved from the stability of the orbit created in a lower-dimensional manifold. The proposed approach is the first example of a tracking controller based on the IDA-PBC applied to underactuated biped robots. Numerical simulations on a five-link planar biped robot with unactuated ankles validate the approach and show the performance of the closed-loop system.
A Markov Regenerative Model of Software Rejuvenation Beyond the Enabling Restriction

Software rejuvenation is a proactive maintenance technique that counteracts software aging by restarting a system or some of its components. We present a non-Markovian model of software rejuvenation where the underlying stochastic process is a Markov Regenerative Process (MRGP) beyond the enabling restriction, i.e., beyond the restriction of having at most one general (GEN, i.e., non-exponential) timer enabled in each state. The use of multiple concurrent GEN timers allows more accurate fitting of duration distributions from observed statistics (e.g., mean and variance), as well as better model expressiveness, enabling the formulation of mixed rejuvenation strategies that combine time-triggered and event-triggered rejuvenation. We leverage the functions for regenerative analysis based on stochastic state classes of the ORIS tool (through its SIRIO library) to evaluate this class of models and to select the rejuvenation period achieving an optimal tradeoff between two steady-state metrics, availability and undetected failure probability. We also show that, when G EN timers are replaced by exponential timers with the same mean (to satisfy enabling restriction), transient and steady-state are affected, resulting in inaccurate rejuvenation policies.
Leveraging Multi-Level Modelling to Automatically Design Behavioral Arbitrators in Robotic Controllers

Automatic control design for robotic systems is becoming more and more popular. However, this usually involves a significant computational cost, due to the expensive and noisy evaluation of candidate solutions through high-fidelity simulation or even real hardware. This work aims at reducing the computational cost of automatic design of behavioral arbitrators through the introduction of a two-step approach. In the first step, the structure of the finite state machine governing the behavioral arbitrator is optimized. To this purpose, a more abstracted model of the robotic system is leveraged in order to significantly reduce the computational cost. In the second step, the close-to-hardware, behavioral parameters are fine-tuned using a high-fidelity model. We show that, for a scenario involving a single robot and multiple tasks to be solved sequentially, using the proposed method results in a significant decrease of the computational cost while reaching the same controller performance both in simulation and reality.
Customers’ Usage and Brand Experience Toward Branded Mobile Payment Improve Continuous Usage Intention

Launching branded mobile payment becomes an emerging trend, so how to attract consumers’ attention and retain customers has become a tough task. Therefore, we investigate how to keep customers using the branded mobile payment. The research model is based on the customer engagement model and considers rational and affective routes inducing continuous usage intention. The customer engagement model describes the process from customers’ experience of products/services to loyalty. Usability and brand experience are considered at the initial stage. Perceived benefit and inertia are outcomes of the evaluation and then induce customer engagement and continuous usage. This study conducted an online survey. Findings are drawn from analyzing 399 usable collected data. At the stage of experiential product/service, usability is more important than brand experience. In comparison with the influence of rational and affective evaluation, the effect of brand benefit is greater than inertia. Looking into usability, consumers emphasize the navigation of the user interface and user interface graphics design. Consumers pay more attention to functional and symbolic benefits. Consumers’ affective-based inertia contributes more to inertia. Theoretical and managerial implications are also listed.
The size of cloud droplets in layer-type cloud

The rate of growth of cloud droplets round a salt nucleus is examined and it is shown that, with nuclei of the size quoted in published measurements, the drop radius achieved in the period before the droplet is carried out of the cloud layer by turbulent diffusion is in reasonable accord with published measurements of the mean drop radius. It is also shown that the maximum drop radius reached when the droplet leaves the cloud owing to fall under gravity is of the same order as the largest cloud-drop measurements. The effect of temperature is considered.
COST-EFFECTIVE ENERGY MANAGEMENT SYSTEMS STRATEGY IN OPTIMIZATION OF PHOTOVOLTAIC FOR GRID-CONNECTED SYSTEM

Renewable Energy Source (RES) based Distributed Generation (DG) like Photovoltaic (PV) is widely integrated into the distribution network, particularly for residential. With proper planning, the installation of optimal PV in the network is capable of minimizing the dependency on the power grid generation. However, the optimum use of solar energy has been limited by the weather and the load variation. At the particular time, the generated PV output is not fully utilized during the minimum load. The excessive generation of PV power occurs and causes an increase in the cost of electricity consumption. Therefore, the purpose of this paper is to optimize the PV size for the grid-connected system considering the Battery Energy Storage System (BESS) and the proper Energy Management System (EMS) Strategy in order to reduce the grid power consumption. BESS is introduced to store the excess PV power generated during peak hours, while the cost-effective EMS strategy is proposed to ensure the RES is fully employed. The number of PV panels is optimized using Particle Swarm Optimization (PSO) technique. The implementation of PSO in optimising PV size can reduce the number of PV panels by 21% compared to the conventional method.