Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Perpendicular and Diagonal Unparking Using a Multi-Sensor-Based Control Approach

This paper explores the feasibility of a Multi-Sensor-Based Control (MSBC) approach for addressing forward nonparallel (perpendicular and diagonal) unparking problems of car-like vehicles as an alternative to classical approaches (e.g. path planning based, etc.). The results of individual cases are presented to illustrate the behavior and performance of the proposed approach as well as results from exhaustive simulations to evaluate the convergence and stability. The results presented in this work increase the versatility and validity of our MSBC approach towards a fully autonomous parking system.
Contract-Based Resource Allocation for Low-Latency Vehicular Fog Computing

Low-Iatency communication is crucial to satisfy the strict requirements on latency and reliability in 5G communications. In this paper, we firstly consider a contract-based vehicular fog computing resource allocation framework to minimize the intolerable delay caused by the numerous tasks on the base station during peak time. In the vehicular fog computing framework, the users tend to select nearby vehicles to process their heavy tasks to minimize delay, which relies on the participation of vehicles. Thus, it is critical to design an effective incentive mechanism to encourage vehicles to participate in resource allocation. Next, the simulation results demonstrate that the contract-based resource allocation can achieve better performance.
The Study of Closure Domain Structure Dynamics in Bistable Microwires Using the Technique of Three-Level Field Pulses

The process of release of a single domain wall from the closure domain structure at the microwire ends and the process of nucleation of the reversed domain in regions far from the microwire ends were studied using the technique that consists in determining the critical parameters of the rectangular magnetic field pulse (magnitude—<inline-formula> <tex-math notation="LaTeX">$H_{\mathrm {pc}}$ </tex-math></inline-formula> and length—<inline-formula> <tex-math notation="LaTeX">$\tau _{c}$ </tex-math></inline-formula>) needed for free domain wall production. Since these processes can be influenced by the magnitude of the magnetic field before or after the application of the field pulse (<inline-formula> <tex-math notation="LaTeX">$H_{p}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\tau$ </tex-math></inline-formula>), we propose a modified experiment in which the so-called three-level pulse is used. The three-level pulse starts from the first level, then continues with the second measuring rectangular pulse (<inline-formula> <tex-math notation="LaTeX">$H_{p}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\tau$ </tex-math></inline-formula>), which ends at the third field level. Based on the results obtained in experiments using three-level field pulses, it has been shown that reversed domains are not present in the remanent state in regions far from the microwire ends. Some modification of the theoretical model of a single domain wall trapped in a potential well will be needed for an adequate description of the depinning processes.
Paper as a Substrate for Sensor Applications: A Review

Paper-based sensor is a new alternative technology to build low cost and simple fabricated sensor applications. This paper described fabrication process technologies of paper-based sensors and their applications for force, tactile, oxygen, and humidity sensor. Paper-based sensors are much easier in fabrication and can reduce production cost, such as the clean room requirement. The piezoresistive effect is applied in force and tactile sensor by using low-cost paper substrate. Meanwhile, the paper-based substrate is used in the oxygen and humidity sensor as a new method to enhance adhesive between substrate and sensing material.
SWIFT: Switch-Level Fault Simulation on GPUs

Current nanometer CMOS circuits show an increasing sensitivity to deviations in first-order parameters and suffer from process variations during manufacturing. To properly assess and support test validation of digital designs, low-level fault simulation approaches are utilized to accurately capture the behavior of CMOS cells under parametric faults and process variations as early as possible throughout the design phase. However, low-level simulation approaches exhibit a high computational complexity, especially when variation has to be taken into account. In this paper, a high-throughput parallel fault simulation at switch level is presented. First-order electrical parameters are utilized to capture CMOS-specific functional and timing behavior of complex cells allowing to model faults with transistor granularity and without the need of logic abstraction. Furthermore, variation modeling in cells and transistor devices enables broad and efficient variation analyses of faults over many circuit instances for the first time. The simulation approach utilizes massive parallelization on graphics processing units by exploiting parallelism from cells, stimuli, faults, and circuit instances. Despite the lower abstraction levels of the approach, it processes designs with millions of gates and outperforms conventional fault simulation at logic level in terms of speed and accuracy.
Decision-Behavior Based Online Shopping

The explosive popularity of e-commerce sites has reshaped users' shopping habits and an increasing number of users prefer to spend more time shopping online. This evolution allows e-commerce sites to collect rich data about users. The majority of traditional recommender systems have focused on the macro interactions between users and items, particularly the purchase history of customers. However, decision support only achieved limited performance due to the mismatch between the causality and the interaction sequence. It is especially challenging for products with low purchase frequency, such as refrigerators, or new users with little history data. To address the problem, we investigated how to leverage the heterogenous information, including decision making information to improve recommender systems, helping users approach their items easier and more accurately. Specifically, we propose to model users' purchasing reason information and knowledge graph of items to provide personalized recommendation. The new recommend model, called Decision-Behavior Knowledge Graph (DBKG), captures the decision-making knowledge during users' online purchasing, and update the decision making knowledge in the process of supporting users' purchase decision.
Building Occupancy Detection from Carbon-dioxide and Motion Sensors

Occupant detection using carbon-dioxide sensors is prevalent but its accuracy is restricted by the inherent sensing delays. This paper proposes an indoor occupant detection method using real-time carbon-dioxide and Pyroelectric Infrared (PIR) sensor measurements overcoming the sensing delays. The occupancy detection problem is formulated as a classification problem wherein the classifier learns from offline carbon-dioxide data and the actual occupancy measurements of the room. While the classifier can provide realtime occupancy detection, the delays in carbon-dioxide sensors influence their accuracy. To overcome the delays, observations from PIR sensors are combined with the results of the single-layer feedforward neural network (SLFN) based classifier. The classifier works in four steps: (i) data-preprocessing, (ii) feature-selection, (iii) learning, and (iv) validation. The data is preprocessed by smoothing and several features are selected as input to the SLFN. Then, the classifier is validated with realtime experiments. Our results demonstrate that the proposed approach provides accuracy up to 99.79% and also overcomes the delays found in carbon-dioxide sensors.
Online Discovery of Congregate Groups on Sparse Spatio-temporal Data

The pervasiveness of location-acquisition technologies leads to large amounts of spatio-temporal data, which brings us opportunities and challenges to discover interesting group patterns from these individual's trajectories. In this work, firstly, we propose a novel group pattern called congregate group, which captures various congregations by exploiting trajectory streams. Then, we design a discovery framework which contains three main stages including trajectory preprocessing, crowds generation and congregate groups discovery to detect congregations. Meanwhile, an interpolation method is proposed to handle missing points on sparse data. Besides, a set of optimization techniques is applied to reduce computational costs. Finally, our extensive experiments based on real cellular network dataset and real taxicab trajectory dataset demonstrate the effectiveness, efficiency and scalability of our proposed approach.
Transient Stability Assessment of Power Systems Using Cost-sensitive Deep Learning Approach

This paper presents a deep learning approach with cost-sensitive loss function for transient stability assessment (TSA). Firstly, multiple generator variables after disturbance are utilized as the original inputs. Secondly, the convolutional neural network (CNN) is applied for automatic feature extraction and regression model building for TSA. The generated TSA model can provide more accurate transient stability index than the fully-connected neural network and support vector regression model. Moreover, a cost-sensitive loss function is proposed to reduce the misdetection of instability. Case studies are conducted on the New England 10-machine 39-bus system to verify the effectiveness of the proposed approach.
A Proactive and Survivability-Constrained Operation Strategy for Enhancing Resilience of Microgrids Using Energy Storage System

A resilience-constrained operation strategy is proposed in this paper using a battery energy storage system (BESS) as a resilience resource. A proactive operation scheme is proposed for the pre-disturbance phase by formulating resiliency cuts for the state of charge (SoC) of BESS units. These resiliency cuts can assure the survivability of critical loads for n intervals after the occurrence of an event. A resilience constraint index is formulated to access the feasibility of the operation model for each selected value of n. In the emergency mode, different possible scenarios are analyzed and a unified survivability enhancement strategy is proposed, which comprises of dynamic penalty costs and target SoC. Dynamic penalty costs can potentially avoid the unnecessary load shedding by shifting it toward the possible end of the scheduling horizon. Similarly, the inclusion of target SoC for the last interval of the scheduling horizon can mitigate the load shedding of critical loads on the following day. A two-step adaptive robust optimization scheme is adopted to incorporate the prevailing uncertainties in loads and renewables in the operation model. Finally, the impact of various decision parameters, involved in the problem formulation, is analyzed with reference to the proposed resilience-constrained operation strategy for hybrid microgrids.
Phase Identification of Smart Meters by Spectral Clustering

Phase identification is important for the safe operation of distributed power grid, and a data-driven phase identification approach has been proposed in this paper, in which the OpenDSS (Open distribution system simulator) is used to perform power flow analysis on a real distribution feeder-European Low Voltage Test Feeder. The spectral clustering is used to identify phases of users/smart meters. Verification by European Low Voltage Test Feeder in different settings. The phase identification accuracy of proposed method is over 95% when the voltage sampling period is no more than 1 hour in the case of complete data set. Results indicate that the proposed method performs better than the constrained k-means method when the sampling period exceeds 15 minutes.
A Low Quiescent Current, Low THD+N Class-D Audio Amplifier With Area-Efficient PWM-Residual-Aliasing Reduction

An area-efficient pulsewidth modulation (PWM)-residual-aliasing reduction technique is proposed for Class-D audio amplifiers by utilizing a low-complexity feed-forward path to cancel the PWM high-frequency components inside the feedback loop. The proposed technique reduces the PWM-residual-aliasing distortion without increasing the required switching frequency or power-hungry controller circuits, thereby lowering both the quiescent current and total harmonic distortion plus noise (THD+N) in Class-D audio amplifiers. Measurement results show that the proposed technique improves the THD+N of second-order Class-D amplifiers by 16.2 dB with a 1-kHz input; in addition, the Class-D amplifier of this paper achieves a minimum THD+N of 0.0032% while operating at a 215-kHz switching frequency, resulting in a reduction in quiescent current of more than 33%. Compared with other state of the arts, this paper implemented in 0.5-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS technology features a competitive THD+N while consuming the lowest quiescent current of 0.96 mA and occupying the smallest active area of 0.49 mm<sup>2</sup>.
Decentralized Optimal Reactive Power Dispatch of Optimally Partitioned Distribution Networks

This paper proposes a two-stage decentralized optimal reactive power dispatch (D-ORPD) framework, which considers the network partitioning influence in distributed optimization. The first stage is to divide the distribution networks (DNs) into several high-intra-cohesion and low-coupling sub-networks while the mathematical model is constructed based on partition indexes. This optimal partition designation reduces information exchange between adjacent sub-networks. Then, with the reduced information exchange, the distributed computation would be accelerated. Based on the network optimal partition, the D-ORPD model is constructed in a “decomposition-coordination” pattern during the second stage. To enhance the consistency between distributed and centralized optimization, the D-ORPD is improved by the proposed virtual load, which simulates the load characteristic of sub-networks. The alternating directions method of the multipliers (ADMM) algorithm is utilized to solve the mathematical model effectively. Moreover, case studies on the PG&E 69 bus test system and the IEEE 123 bus test system are executed by the MATLAB platform to demonstrate the validity and effectiveness of the proposed method.
Educational Functions and Design of Badge Systems: A Conceptual Literature Review

In today's technological era, emerging educational technologies, such as digital badges, have shown the potential for fostering student learning. To examine the major considerations undergirding the design of digital badges used in an educational context, a conceptual literature review of multidisciplinary electronic databases oriented towards computing and education was undertaken. The three core design dimensions found in our analysis were the specific function of badges, the structure of badge systems, and the different types of design and interaction features used with badges. We highlight the main effects of studies addressing these dimensions and provide recommendations and suggestions for further research to educational stakeholders who seek to better understand how to implement badges within their own institutions.
An Enhanced Radio Tomographic Imaging with CSI-MIMO Measurements

As a cost-effective localization scheme, radio tomographic imaging (RTI) has become a hot topic in the field of indoor positioning. In most previous works, the received signal strength (RSS), which is extremely unstable in indoor multipath environments, is used as the measurement. In this paper, we propose to leverage the fine-grained channel state information (CSI) for RTI. Further, by incorporating the multiple input and multiple output (MIMO) technique, we establish the CSI-MIMO attenuation model which utilizes the benefits of frequency diversity and spatial diversity. In addition, we also change the signal frequency to a higher one which can enhance the shadowing loss. We conduct experiments to evaluate the performance of CSI-MIMO based RTI. Experimental results show that the localization accuracy of the proposed method can be considerably improved compared with the RSS-based RTI methods.
Estimation of Rician Channels From Indoor Measurements at 26 GHz

The aim of this work is to evaluate amplitude distributions of narrow-band channels based on sounding measurements made at 26 GHz. The setup is with an omni-directional antenna moving in different indoor environments with an access point (AP). Rician channel model parameters are obtained via maximum likelihood (ML) estimation and the k-factors are obtained as well as mean power levels. Depending on the type of environment and the location of the mobile with respect to the used AP, the found k-factors varies from about −10 dB up to about 9 dB.
Improved Control Strategy of Bi-Directional Buck/Boost Converter with Constant Power Load

This paper is based on the study of Bi-directional DC/DC converter, aimed at resolving the question of constant power load with negative impedance characteristics, researched the output impedance of the converter and proposed a virtual impedance method to improve the cascade system's stability, virtual impedance consists of virtual resistor, and locates at the converter output capacitor branch. The stability of the cascade system is analyzed by using the impedance theory. Simulink results verify the correctness of the theoretical analysis and the controller design.
Introduction of Information Technologies: Training Methodology

The lack of a systematic approach to minimizing risks and sufficient competency models leads to the inefficiency of projects for introducing new information technologies. The purpose of the study is to develop a training methodology that minimizes the risks of professional activity in the implementation of information technology. The basis of the methodology consists of invariant models and methods of network planning of personal trajectories of development and design of a system for the safe implementation of information technology. Invariant models of competences, composition and evaluation of measures have been constructed, which allow minimizing labor costs and staff errors when adapting the system for training personnel to work with a specific type of information technology in a particular enterprise or organization. Based on the competency model that takes into account the level of education, work experience, certificates and duties, a method for designing personal development paths for working with information technologies is proposed, minimizing economic risks. Based on the composition models and the evaluation of measures, a method was proposed for designing a system for the safe implementation of information technologies, taking into account the system approach to risk accounting, as well as the competences and interests of all participants in the process of implementing information technologies. Application of the research results at the Chelyabinsk Forge-and-Press Plant, in the South Urals and Priamursky State Universities resulted in cost savings related to staff training for the safe implementation of the 1C platform products.
Adaptive Impedance Control Applied to Robot-Aided Neuro-Rehabilitation of the Ankle

Robot-aided neuro-rehabilitation is a potential solution to help resolve the increasing demand for post-stroke therapy. In this letter, we propose an assistive-resistive approach to promote active patient participation during robotic therapy. In order to automatically determine the most appropriate level of task difficulty, we recommend a novel approach for real-time adaptation of the robotic assistance according to the instantaneous patient's participation and performance. First, we estimated the dynamic con tribution of the patient from robot's torque and kinematic data. Second, we adapted the stiffness parameter of the robot's impedance control according to the purported patient's participation. And third, we adapted the robotic assistance according to his/her performance while playing a serious game. We present the results of a feasibility study in which we evaluated our methodology with four persons with impairments due to stroke in a single session using the ankle robot. Our proposed metric for performance and participation has potential implications for real-time assessment during robot-assisted therapy. The proposed adaptive strategy, when compared to a fixed-stiffness approach, allows more kinematic variability in patient-led movements without compromising the overall performance during therapy.
Solutions for SmartCities: proposal of a monitoring system of air quality based on a LoRaWAN network with low-cost sensors

The continuous exposure to high levels of atmospheric pollution can cause harmful effects on the population health and environment. Technology advances in communication networks and air pollution sensors allow the development of low-cost systems for the management of the air quality in cities. The deployment of a large number of sensors provides a better geographic and temporal resolution, and it complements the traditional monitoring networks. In this work, we present an experience in the city of La Plata, Argentina, where an air quality monitoring system for urban areas was assembled. This system is based on a LoRaWAN network, and uses free hardware and software environments. All the sensed information is transmitted to a scalable platform for IoT in the cloud, where we can process the received data, monitor the network state and generate statistics. In this first experience, we test 3 different models of sensors (Nova SDS011, Nova SDS021 y SHINYEI PPD42) in combination with a low volume sampler of particulate matter, MiniVol TAS Airmetrics. The results obtained until the moment provide a contribution to the construction of future Smart Cities.
An Odd Phase CDR With Phase Interpolator Trimming

This brief presents an odd phase Bang–Bang phase detector-based CDR architecture that has a unique property that the clock phases sampling the data automatically become aligned to the center of the data. This eliminates both the residual phase error of the quadrature correction and the quadrature corrector loop itself. An adaptive closed loop weight trimming algorithm is proposed to suppress the non-linearity of phase interpolator (PI). With this algorithm, very high PI linearity is achieved. Additionally, the resulting linearity is maintained over process, voltage, temp. (PVT). A compensation method for the phase offsets of the multiphase clock going into the PI is proposed. Phase offset compensation reuses the same sensing block, which is used for PI trimming. After settling, a phase alignment error of of ≤ 1.7° is achieved. At 2.67-GHz clock frequency, the PI trimming achieves DNL ≤ 0.45 LSB and INL ≤ 0.43 LSB across PVT. The phase offsets to PI input are corrected to within ±2.5% of the ideal phase spacing.