Miracle-dz/UniFAR-FTUs huggingface dataset:
Wideband Circularly Polarized MIMO Antenna for High Data Wearable Biotelemetric Devices

This paper presents a miniaturized circularly polarized multiple-input multiple-output (MIMO) antenna for wearable biotelemetric devices. The proposed MIMO antenna consists of four elements, which are placed orthogonally to the adjacent elements. The proposed antenna has a wideband response [10-dB bandwidth of 2210 MHz (fractional bandwidth (FBW) = 92.08%) in free space and 10-dB bandwidth of 2200 MHz (FBW = 91.66%) when worn on human-body], this frequency range covers the important and unlicensed industrial, scientific and medical (ISM) band (2.40–2.48 GHz). The antenna exhibits a wideband 3-dB circularly polarized bandwidth of 1300 MHz (FBW=54.16%) and 1040 MHz (FBW=43.33%) in free space and when worn on the body, respectively. The optimized antenna in free space (on-body) has an envelop correlation coefficient (ECC) less than 0.21 (0.23), a diversity gain (DG) greater than 9.77 dB (9.71 dB), a multiplexing efficiency (ME) greater than −0.85 dB (−0.63 dB), and a channel capacity loss (CCL) less than 0.13 bps/Hz (0.13 bps/Hz). The stable radiation, high gain, high efficiency, and good MIMO properties in free space and on human-body make the proposed antenna a suitable choice for use in high data wearable biotelemetric devices.
Surface Remeshing using Quadric based Mesh Simplification and Minimal Angle Improvement

Surface remeshing intends to yield high quality, high regularity and low complexity meshes that are geometrically faithful to original models and free from the low-quality elements. Unfortunately, attaining balance between quality, regularity, complexity and approximation error becomes tedious during remeshing. In the work presented here, authors propose a surface remeshing techniques based on quadric based simplification and high-quality approximation that tries to attain balance of remeshing goals. Given a triangular mesh and user assigned approximation error, the output mesh (remesh) achieves a higher minimal interior angle and low mesh complexity with implicit feature preservation. The proposed approach recapitulates in two-steps. First, the mesh complexity is optimized to lessen the extent of vertices, and then followed by enhancement of the quality of the elements using local operators. This approach can be efficiently incorporated in preprocessing for numerous applications. Investigations have demonstrated that the proposed approach is efficient and robust. The results of proposed approach attain high quality triangles along with preservation of the features of the original geometry.
New Measurement Method for Self-Heating in Silicon-on-Insulator MOSFETs Based on Shared Series Resistance

A new method using temperature dependent shared series resistance in a pair of devices with common drain configuration is proposed to measure self-heating (SH) in advanced MOSFETs. The proposed measurement technique depends on the intrinsic series resistance and is not limited to the spatial proximity between the heater and the sensor. The developed method demonstrated on the silicon-on-insulator (SOI) MOSFETs reveals that the more accurate SH assessment can be obtained compared to other methods that are sensitive to the distance from heat source. Furthermore, the extraction of intrinsic series resistance allows to quantify the process-induced variability of the contact resistance.
Luenberger-Like Observer-based Feedback Linearization with Robust Controller for Pseudolinear Systems

This paper discusses an observer-based robust control scheme for perturbed pseudolinear systems, which is a nonlinear system that can fully feedback linearization. The aforementioned robust control scheme involves an observer-based input–output exact feedback linearization (EFL) controller, a robust feedback controller, and an observer. The gain of the robust feedback controller and the observer is a binomial coefficient-type gain. In addition, through an quasi-similarity transformation (QST), the robust control scheme is applied to an observer-based approximate feedback linearization(AFL) method. The state variables are estimated using a Luenberger-like nonlinear observer. The simulation results show that this robust control scheme provides good robustness to the system's parameter uncertainty and mode error and confirms that the QST between the AFL and EFL occurs correctly.
Optimization of Monthly Power Generation Plan for Thermal Power Units Considering Access of Large-scale New Energy

The new energy output power has the characteristics of randomness and intermittence, and its large-scale grid-connected power generation makes the power system dispatching decision face new challenges. In this paper, with monthly scheduling as the research object, based on the actual production situation, a two-stage optimization method for solving the unit combination problem of new energy system is proposed. The method decomposes the solution process of the unit combination problem into two sub-problems: discrete start-stop optimization and continuous economic scheduling. The first stage is based on the experience summarized in the scheduling work, formulating heuristic rules, solving the monthly unit startup mode through integer programming; the second stage is economic scheduling through linear programming by combining actual production standards. Finally, a new energy consumption assessment model based on optimal power flow is established, which is solved by the mature GLPK commercial software package, which proves the effectiveness of the two-stage monthly unit combination scheme under the tens of thousands of variable levels of the provincial power grid company.
High-frequency modeling of PMSM by the VF-PSO in electric vehicles

This electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. *CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstra Permanent magnet synchronous motor (PMSM) has been widely applied in Electric vehicles. But PMSM is a main propagation path for CM and DM conducted emission of the drive system, many high-frequency harmonic components generated by dv/dt (di/dt) can be find in the motor terminals. In order to research the conducted EMI in the drive system, a PMSM modeling is proposed, the parameters of high frequency model of motor are extracted by VF method and optimized by PSO algorithm. The calculated results is compared with the experimental impedance measured on a 45 kW PMSM, which shows fairly high accuracy in the frequency range of 100 kHz-110 MHz.
PAPR reduction based on deep autoencoder for VLC DCO-OFDM system

DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) is widely used in visible light communication (VLC) systems to reduce the effects of inter-symbol interference (ISI) and to achieve high speed data transmission. However, DCO-OFDM VLC systems suffer from high peak-to-average power ratio (PAPR) issue which causes serious degradation of system performance. In this paper, a novel deep autoencoder-based PAPR reduction scheme for VLC DCO-OFDM is proposed. The autoencoder could adaptively adjust the constellation symbols in order to minimize the PAPR while maintaining an acceptable bit error rate (BER) for VLC transmission. The simulation results show that PAPR is reduced to 6. 5dB by comparing with conventional methods.
Deep Convolutional Auto-Encoder based Indoor Light Positioning Using RSS Temporal Image

Visible light communication is a promising candidate for achieving low-cost, high data rate and massive coverage in future wireless network. To fulfill the multimedia or broadcasting service, the prerequisite is to determine the user's position. However, traditional RSS fingerprint based positioning approach suffers the random signal fluctuation problem which significantly limits the positioning performance. To tackle this problem, this paper presents a deep learning scheme which utilize a regression DNN combined with convolutional auto-encoder (CAE) to provide robust indoor positioning. Instead of using the separate fluctuated RSS reading for positioning, the proposed method takes the RSS temporal image (RTI) composed from a set of consecutive RSS readings as input. By leveraging the spatial and temporal dependency of RTI data with stacked denoising CAE, the proposed network is expected to learn more complex and consistent features from the fluctuating RSS readings and perform better location estimation. Simulation results have shown that the proposed method is capable of providing more stable positioning result from fluctuating RSS data than traditional DNN based method.
A 1.9-mW SVM Processor With On-Chip Active Learning for Epileptic Seizure Control

This article presents a support vector machine (SVM) processor that supports both seizure detection and on-chip model adaptation for epileptic seizure control. Alternating direction method of multipliers (ADMM) is utilized for highly parallel computing for SVM training. From the algorithm aspect, minimum redundancy maximum relevance (mRMR) and low-rank approximation are exploited to reduce overall computational complexity by 99.4% while also reducing memory storage by 90.4%. For hardware optimization, overall hardware complexity is reduced by 87% through a hardware-shared configurable coordinate rotation digital computer (CORDIC)-based processing element array. Parallel rotations and folded structure for the approximate Jacobi method reduce overall training latency by 98.6%. The chip achieves a detection performance with a 96.6% accuracy and a 0.28/h false alarm rate within 0.71 s with the power dissipation of 1.9 mW. The proposed SVM processor achieves the shortest detection latency compared with the state-of-the-art seizure detectors. It also supports real-time model adaptation with a latency of 0.78 s. Compared with previous designs, this work achieves a <inline-formula> <tex-math notation="LaTeX">$22\times $ </tex-math></inline-formula> higher throughput and a <inline-formula> <tex-math notation="LaTeX">$162\times $ </tex-math></inline-formula> higher energy efficiency for SVM training.
Realization of Circular Polarization and Gain Enhancement for Implantable Antenna

This paper proposes and investigates an implantable circularly polarized (CP) antenna with an enhanced gain. The proposed antenna consists of a pin-loaded patch and via walls around the patch. The pin-loaded patch has a pair of orthogonal normal modes instead of degenerated modes, and two open-ended slots etched on the patch make resonant frequencies of the two modes close and introduce a 90-deg phase difference. The via walls protect the antenna against a gain loss due to lossy tissues. Simulation results show that the proposed antenna has impedance and CP bandwidths of 23.1% and 12.8%, and a gain improvement of 1.5 dBi due to the via walls. Finally, the proposed antenna is fabricated and measured, and measured results agree well with simulated one.
An Empirical Study of HPC Workloads on Huawei Kunpeng 916 Processor

The ARM-based server processors have been gaining momentum in high performance computing (HPC). While not designed specifically for HPC, Huawei Kunpeng 916 processor has 32 ARMv8 cores and is tempting for HPC workloads. However, its potential remains unknown. To throughly understand the potential, we conducted a systematic evaluation in three steps by using: 1) three well-known benchmarks (HPL, STREAM, and LMbench); 2) three typical scientific kernels (SpMV, N-body, and GEMM); 3) three widely used mini-apps (TeaLeaf, Neutral, and SNAP) and a real-world application GTC-P. We compared the performance results of Kunpeng 916 with that of Intel Xeon E5-2680v3/4 (Haswell/Broadwell). The evaluation results show that Kunpeng 916 has higher memory bandwidth than the two Intel processors, thus it can achieve compelling performance for running memory bound HPC applications.
Dataset Recommendation via Variational Graph Autoencoder

This paper targets on designing a query-based dataset recommendation system, which accepts a query denoting a user’s research interest as a set of research papers and returns a list of recommended datasets that are ranked by the potential usefulness for the user’s research need. The motivation of building such a system is to save users from spending time on heavy literature review work to find usable datasets.We start by constructing a two-layer network: one layer of citation network, and the other layer of datasets, connected to the firstlayer papers in which they were used. A query highlights a set of papers in the citation layer. However, answering the query as a naive retrieval of datasets linked with these highlighted papers excludes other semantically relevant datasets, which widely exist several hops away from the queried papers. We propose to learn representations of research papers and datasets in the two-layer network using heterogeneous variational graph autoencoder, and then compute the relevance of the query to the dataset candidates based on the learned representations. Our ranked datasets shown in extensive evaluation results are validated to be more truly relevant than those obtained by naive retrieval methods and adoptions of existing related solutions.
A 32-Gb/s Simultaneous Bidirectional Source-Synchronous Transceiver With Adaptive Echo Cancellation Techniques

Increased bandwidth per pin is necessary to support the growing data throughput in mobile systems. This article presents a low-power simultaneous bidirectional (SBD) transceiver employing a novel voltage-mode driver with a resistor-transconductance (R-gm) hybrid subtractor to enable concurrent transmission and reception of data on a single differential channel. A finite impulse response (FIR) filter-based adaptive echo cancellation system that cancels both near- and far-end echoes allows for the support of a wide range of channels, while a continuous-time linear equalizer (CTLE) efficiently compensates for channel loss. The quarter-rate source-synchronous transceiver utilizes a low-complexity phase interpolator-based clock and data alignment system to set the optimum data sampling point. Fabricated in 28-nm CMOS, the 32-Gb/s SBD transceiver achieves 1.83 mW/Gb/s and compensates 10.2-dB loss at a bit error rate (BER) < 10−12.
Grape Disease Identification Using Convolution Neural Network

In this paper, we present a CNN model to identify the disease in grapes plant in early stage by analyzing the leaf images. The model is illustrated on publicly available PlantVillage dataset and the performance compared with traditional machine learning models and pre-trained convolution neural network models. The performance of algorithms has been compared on various evaluation metrics i.e. accuracy, precision recall, running time of model, storage space and AUC-RoC. From the experimental results, it has been observed that the performance of proposed model is better than traditional machine learning algorithms as well as pre-trained models by achieving an accuracy of 99%.
Scalability of a Machine Learning Environment for Autonomous Driving Research

We study scalability of machine learning environments in the context of mixed collaborative driving. Mixed collaborative driving includes both human controlled vehicles and vehicles controlled by AI (Artificial Intelligence) that share the physical road resources (e.g., intersections and roundabouts). Many such driving situations cannot be easily created nor replicated in the real life. Therefore, development and testing of AI systems is often done with simulators.Machine learning environments must maintain a real-time understanding of their traffic situation. Scaling of the machine environment to multiple distributed nodes is required to support larger number of participating vehicles. Our experimental environment consists of the CARLA simulator, custom AI implemented with the TensorFlow framework, and a corner case search subsystem. With the corner case search subsystem we can automatically evaluate the AI in different driving scenarios. In this paper, we present how scaling of the environment to multiple distributed nodes affects its performance.
Multi-Stage Bi-Level Planning of Energy Storage Considering Cycling Degradation

Energy storage is an effective solution to the problems caused by wind power uncertainty. However, frequent charge-discharge cycling for power balance and optimal economic dispatch can accelerate battery degradation. In the long term, the degradation in storage capacity cannot cope with the growth of wind power and load. This paper purposes a multi-stage bilevel planning model to identify the optimal siting and sizing of energy storage systems. The model is mainly used to deal with degradation in ESS capacity and the growth of wind power generation and load, with the upper-level for planning and the lower-level for operating. The validity of the proposed model is proven by case study results.
A Decentralized Voltage Control Scheme for Distribution Networks with High Penetration of Distributed PVs

To meet the great demand for high penetration of distributed photovoltaics’ (PV) friendly integration and its coordinated control with distribution networks (DNs), this paper is to develop a decentralized dynamic zonal clustering voltage control scheme to address the overvoltage issues. Firstly, an improved community detection algorithm is used to dynamically partition a DN into several clusters under different triggering mechanisms. Then the proposed scheme decomposes the voltage control problem into two control layers, the outer voltage control layer applies On-load Tap Changers (OLTC) and Shunt Capacitors (SC) to adjust the voltage in the desired range, the inner voltage control layer adjusts the reactive and active outputs of PVs to keep the voltages within the limit. The developed scheme obtains the compensated reactive power, reductive active power of PVs and action number of conventional voltage regulation equipment’s’ by an analytical method. Finally, a real distribution network is studied in detail to show the validity of proposed scheme.
Background Characterization of Anomalies Using Hyperspectral data

Anomaly detection algorithms identify pixels which appear different with respect to immediate neighborhood. However, the output may contain many false alarms and also does not provide any information about the background against which the pixels are flagged as anomalous. This paper describes a methodology which is two-step process, first identifying anomalies using RX detector and reducing the false alarms by using probabilistic distribution. In the second step background against which the filtered anomalous pixels appear is characterized as vegetation, water, soil or snow based on combination of spectral indices. For characterizing water, soil and vegetation narrow band vegetation index is used and likewise for snow also. The developed methodology is tested on multiple datasets acquired by recently launched HYSIS satellite.
Lane-Changing Risk Analysis in Undersea Tunnels Based on Fuzzy Inference

Lane-changing in undersea tunnels has a negative impact on the normal traffic flow, and even lays hidden dangers for the occurrence of traffic accidents. Lane-changing behavior in undersea tunnels was divided into free, compulsory, and collaborative lane-changing types according to the characteristics of traffic flow to explore lane-changing risk in undersea tunnels. A fuzzy inference analysis on the three lane-changing behaviors was conducted on the basis of the behavior characteristics of fuzzy uncertainty of drivers. The most representative influencing variables, including speed difference, initial space of vehicles, traffic density, and distance for minimum lane-changing, were selected as fuzzy input variables, and lane-changing risk was used as an output variable to construct fuzzy rules for different lane-changing behavior. Risks of the three lane-changing behaviors were simulated by MATLAB/Simulink. Results demonstrated that the compulsory lane-changing in undersea tunnel was the riskiest, followed by collaborative and free lane-changing. Slope considerably influenced lane-changing risk. Specifically, the lane-changing risk at the downhill section was the highest, and the lane-changing risk at the uphill section was the lowest. The lane-changing risk at the flat section was between them.
A Study on Image-based Moving Direction Detection and Camera Control for Construction Machine Automatic Tracking

It's necessary to recover quickly after disasters occur. However, the workers can't work in seriously damaged area because there is a possibility of secondary disaster. Therefore, the remote controlled construction machines are used to restore the damaged area. But now, the operator of these machines needs to be helped by a person who operates camera. The camera is manually operated by the operator. So, there is a problem that some persons are assigned to the camera operators and a difference in work efficiency is caused depending on their skill level. In order to solve this problem, we are developing an automatic tracking camera control system for construction machine operators by using image processing. It works well like manual camera operation. However, the reproduction of the skilled camera operation is still not enough. Therefore, we aimed to improve the system. In this paper, to improve the system, we recognized the moving direction of the construction machine from the image by image processing. In addition, using this recognition results, we improved the system to display more suitable image to machine operator according to the recognized moving direction.
News Recommendation Systems - Accomplishments, Challenges & Future Directions

News publishers have decreased disseminating news through conventional newspapers and have migrated to the use of digital means like websites and purpose-built mobile applications. It is observed that news recommendation systems can automatically process lengthy articles and identify similar articles for readers considering predefined criteria. The objectives of the current work are to identify and classify the challenges in news recommendation domain, to identify state-of-the-art approaches and classify on the application domain, to identify datasets used for evaluation and their sources, the evaluation approaches used and to highlight the challenges explicitly addressed. The literature is thoroughly studied over the time span of 2001–2019 and shortlisted 81 related studies, broadly classified into six categories and discussed. The analysis showed that 60% of news recommendation system adopted a hybrid approach, 66% studies little talk about datasets, and addresses a few challenges from a long list of challenges in the news domain. This article is the first in the field to draw a comprehensive big picture of news recommendation and explore different dimensions covered in the studies. The last section presents the future research opportunities that lead to improving the recommendation of news articles in the news domain.