Miracle-dz/UniFAR-FTUs huggingface dataset:
Stocktaking Robots, Automatic Inventory, and 3D Product Maps: The Smart Warehouse Enabled by UHF-RFID Synthetic Aperture Localization Techniques

In our age of global networks and the Internet of Things, communication and identification with a transponder tagged on goods and products have become increasingly important [1]. There is not only a need for the identification of the labeled objects but also for finding their exact positions in 3D space. Radio-frequency identification (RFID) offers both and is, in fact, already used in a vast field of applications, such as access control, goods logistics, indoor localization, automation, stocktaking, and many others [2]-[6]. Figure 1 illustrates a mobile robot platform for automatic inventory rounds that is used to acquire 3D product maps with ultrahigh-frequency (UHF)-RFID technology.
Current Mode Neuromorphic Implementation using Current Memory

Recently, neuromorphic research has been drawing attentions as there is a limitation of a serial processor with a Von Neumann architecture. In particular, spiking neural network (SNN) is one of the neuromorphic AI models that has been actively researched because it can be implemented with a low power consumption and a parallel signal processing system. Non-volatile semiconductors are currently used for SNN implementation, but they still have limitations due to problems with production cost and compatibility with silicon devices. Therefore, in this paper, a new SNN model using CMOS is proposed and proved through post simulation with cadence MMSIM. For the current mode neuromorphic implementation, the pattern recognition of the full current mode SNN system was confirmed by modeling the system with MATLAB SIMULINK
Miniaturized Broadband Planar Antenna Using Cross-shaped Inter-embedded Metasurface Structure

This work presents a miniaturized wideband antenna using a cross-shaped inter-embedded metasurface (MS) structure. By etching sawtooth slots on the radiation patch, the cross-shaped inter-embedded MS can be formed effectively. Compared with the conventional MS structure, the proposed MS structure can realize miniaturization due to introducing extra interdigital-like capacitances, which can decrease the resonance frequency accordingly. For demonstration, a C-band miniaturized antenna using the proposed technique is accordingly designed, achieving an impedance bandwidth of 35.08%, a high gain about 8.25dBi with a compact overall size of $0.66\lambda_{0}\times 0.66\lambda_{0}$. Moreover, one further miniaturized antenna using modified cross-shaped inter-embedded MS is also presented, featuring a larger impedance bandwidth of 41.35% and a high gain about 6.62dBi with a smaller aperture of $0.48\lambda_{0}\times 0.48\lambda_{0}$. Compared with conventional MS antenna, 42% overall size reduction and larger bandwidth with comparable gain can be obtained.
A Jitter-Tolerant Referenceless Digital-CDR for Cellular Transceivers

A half-rate jitter-tolerant referenceless digital clock and data recovery (D-CDR) circuit for cellular transceivers is presented. For a referenceless configuration, we introduced a half-rate jitter-tolerant digital quadricorrelator frequency detector (JT-DQFD). Also, we proposed a multi-bit decimator circuit that losslessly down-samples up/down data from a phase detector to reduce the recovered clock jitter. The down-sampled multi-bit phase information is processed by a digital loop filter to adjust a phase of the recovered clock. Fabricated in 28-nm CMOS technology, the test chip achieves a power efficiency of 1.3 pJ/bit at 10 Gb/s.
A 53.1-to-64.5 GHz In-Phase Coupled Quadrature Injection-Locked Oscillator with Transformer-Based I/Q-Phase Differential Injection Scheme

A in-phase coupled quadrature injection-locked oscillator (QILO) with Transformer-based I/Q-phase differential injection scheme is proposed in this paper. The proposed transformer-based orthogonal injection scheme contributes to widen the locking range considerably. The in-phase coupling scheme improves both phase noise and IQ mismatch. Designed in a 65nm CMOS technology, post-layout simulation results indicate that the QILO achieves a total locking range from 53.1-to-64.5 GHz.
A Study of Finger Movement Classification Based On 2-sEMG Channels

Electromyography signals are highly valuable bioelectric signals in diagnosing abnormal nerve and muscle problems. Besides, in recent decades, processing and classification of EMG signals has become a core issue in prosthetic control applications. The focus of this study is an investigation into individual and combined fingers movement recognition using surface EMG signals. The dataset was used belongs to ten different classes collected from ten subjects. There are several sequential steps obtained to analysis EMG signals in this paper (i.e. preprocessing, feature extraction, feature reduction, pattern recognition). At first, EMG signals have been segmented by the windowing process. After that, various feature sets were extracted from these segments. Feature vectors were then reduced by applying two different reduction methods: Principal Component Analysis and Bhattacharyya Distance. Finally, they were fed to two classifiers: Artificial Neural Network and Fuzzy Logic. Overall average classification accuracies of these two systems were 96.08(±0.9)% and 90.56(±3)% respectively.
Pin-Loaded Patch Antenna Fed With a Dual-Mode SIW Resonator for Bandwidth Enhancement and Stable High Gain

A pin-loaded patch antenna fed with a dual-mode substrate-integrated waveguide (SIW) resonator is proposed in this letter for enhanced bandwidth and stable high gain. The loading effect of shorting pins on quality factor of the radiator is investigated with characteristic mode analysis at first. As such, the pin location can be properly selected to enhance gain with the least negative effect on bandwidth. A SIW resonator operating in TE110 and TE120 modes is introduced to feed the patch through aperture coupling. The dual modes of SIW resonator increase the resonant order of the antenna with compact size, which significantly widens the bandwidth. Moreover, the undesired high-order mode is well suppressed with the proposed feeding scheme, which further contributes to steady radiation performance. Simulated and measured results demonstrate that the proposed patch antenna has obtained an enhanced bandwidth of 22% and stable high gain up to 11.9 dBi simultaneously.
An Improved Core Loss Model of Ferromagnetic Materials Considering High-Frequency and Non-Sinusoidal Supply

Energy-saving material such as soft magnetic ferrite has been widely adopted as the core material in power electronic devices. Due to the non-sinusoidal supply of power electronic devices, the core loss properties are quite complex so that it is more difficult to predict accurately under different supply waveforms. With the magnetic property measurement system for soft magnetic material, this study measures core losses of typical soft magnetic ferrite and nanocrystalline toroidal cores under the different supply waveforms, such as medium-frequency square, sinusoidal and PWM supplies. Further, in order to improve estimation accuracy of the Bertotti loss separation model under non-sinusoidal supply, the excess loss item is expressed by using the Barbisio algorithm, and the mathematical relationship between the excess loss and magnetizing frequency and strength is also proposed. Meanwhile, eddy current loss in the Bertotti model is derived further to fit for computing the eddy loss in the rectangular cross section of solid ferrite, and the proposed loss model is verified with the experimental data. Finally, the core loss of an intermediate frequency (IF) power electronics transformer is calculated under the square supply, the related experimental validation is also performed and the effectiveness of the proposed model are verified well. The achievement in this paper can provide the reference model for further optimization design and core performance analysis of electrical devices.
A GaN/SiC UHF PA for Particle Accelerators with 100-145V Quasi-Static Drain Modulation

This publication introduces a solid-state technology based on RF GaN/SiC High Electron Mobility Transistors (HEMT) operating at 100 VDC in CW mode, and up to 145 V in pulse mode. In pulse mode, it is an ideal solution for high efficiency using drain modulation as demonstrated in quasistatic mode characterization from 100 V to 145 V. Results based on a 50 mm single die RF transistor are reported here demonstrating 600 W CW at 100 V bias with 80% drain efficiency, and 1 kW at 145 V bias with a pulse width of 100 µs and 10% duty cycle, also with 80% efficiency. Quasi-static drain modulation achieves 3 dB peak power dynamic range with bias modulated from 100 V to 145 V, and 6 dB from 50 V to 145 V. The design employs harmonic tuning techniques for class E, F or F−1power amplifiers to achieve high efficiency and assembly techniques that overcome heat dissipation in such high-power density transistors. These devices and circuits have been designed to operate at 325 and 650 MHz in use at Fermi National Laboratory.
Strategic Prosumers based Peer-to-Peer Energy Market Design for Residential Microgrids

With the rapid popularization of photovoltaics (PVs), battery energy storage systems (BESSs) and electric vehicles (EVs) in residential microgrids, more and more "prosumers" have participated in actively trading energy with peers, which promotes the appearance of peer-to-peer (P2P) energy trading mode. Since this P2P trading could enhance the grid’s reliability and improve the social welfare in the system, trading mechanism research, in the context of P2P energy trading, have attracted considerable attentions. However, in recent studies, there still are some limitations, such as a lack of proper pricing strategies and a lack of consideration about forward energy trading. To solve these problems, this paper designs a P2P energy trading mechanism based on both hour-ahead market and real-time market. In the proposed mechanism, to increase P2P transactions and maximize social benefits at each market, some proper pricing strategies, such as a risk-implemented simultaneous game-theoretic approach and auction-based pricing strategy, have been adopted. Meanwhile, the proposed mechanism also considers a balance responsible party (BRP) to offer reserve services for realizing purposes. Moreover, the effectiveness of the proposed trading mechanism has been verified by several case studies.
Distributed Average Tracking for Heterogeneous Multi-Agent Systems

This paper studies the distributed average tracking problem for heterogeneous multi-agent systems with bounded derivatives reference signals. Two types of DAT signal estimators are devised for reference signals mentioned above by using the symbolic function method. It is proved that the estimator can eventually obtain to the average value of reference signals. Then, by using the output regulation techniques, a class of DAT control law is proposed such that the output of each agent tracks the average value of multiple reference signals. Finally, some simulation results are shown to prove the effectiveness of the proposed control law.
A New Continuous-Time Policy Iteration for Time-Varying Nonlinear Systems

A new optimization method for time-varying nonlinear systems named continuous-time policy iteration (CTPI) method is designed, which is a kind of adaptive critic design (ACD). Iterative control law in CTPI is established to solve the generalized Hamilton-Jacobi-Bellman (HJB) equation in each iteration. In order to avoid the partial differential equation, neural networks are used in CTPI algorithm. The properties of the CTPI are also analyzed. The monotonically non-increasing and convergence of CTPI are proven, which are the main contributions. Finally, numerical results show the CTPI's effectiveness.
Deep Learning Approach in Sentiment Analysis: A Review

Sentiment Analysis (SA) is the field that combines Natural Language Processing (NLP), Computational Linguistics (CL) and text analysis to study people’s opinions through, by extracting and analyzing subjective information from different resources as the Web, social media and similar sources and so help in drawing public’s sentiments or attitude toward certain people, products or ideas and extracting the contextual polarity of the information. This review focuses on recent work in SA using Deep Learning (DL)techniques in the sentiment classification process, it is based on the articles published through ScienceDirect and Springer databases in the interval from 2016 to 2020.It sheds the light on different DL algorithms used, different applications of SA systems. 58 articles studied in ScienceDirect While 26 articles in Springer satisfying the same criteria with the total of 84 articles studied and analyzed in this review. The review concerns with DL techniques, language, domain, and performance results.
Day-ahead optimal dispatch of integrated energy system considering wind power forecasting and integrated demand response

Due to natural conditions in northern China, heat power is the main energy supply, and there is a large proportion of wind power connected to the grid. During the heating period in winter, combined heat and power (CHP) unit operates in the "determining power generation by heat" mode, which coincides with the time when wind power is abundant, resulting in serious wind abandonment in the system. P2G is adapted to convert excess wind power to natural gas in order to promote wind power consumption and decoupling to "determine power generation by heat". Aiming at the uncertainty of wind power output, this paper uses Autoregressive Integrated Moving Average model (ARIMA) model and Latin hypercube sampling to generate a large number of scenes, and K-means clustering algorithm to form typical scene sets. Considering the structure of the distribution network and the gas network, a multi-energy flow calculation model is established. Based on the energy hub model, a day-ahead optimal dispatch model of the Electricity-Gas-Heat-Cooling Integrated Energy System (EGHC-IES) is established, taking into account the comprehensive demand response on the user side, wind power output forecast and energy storage. The results of the case studies shows that the optimized dispatching solution can promote the consumption of renewable energy and improve energy efficiency.
Sizing and Siting of Energy Storage Systems in a Military Based Vehicle-to- Grid Microgrid

Due to the absence of utility power grid infrastructure in remote military bases, on-site diesel generators serve as the primary sources for power demands. Increasing efficiency and preventing frequent start- up/shutdown operations of on-site diesel generators are therefore becoming a critical issue for reducing fuel cost. Application of vehicle-to-grid technology in a military based microgrid has embodies potential for significant fuel economy benefits since vehicles can act as mobile power sources that provides higher flexibility for supplying power demands. In addition, energy storage system integration is considered as an alternative solution for increasing on-site diesel generators efficiency and lessening their start-up/shutdown operations. To further improve fuel economy in a remote military based microgrid, this paper proposes a three-stage planning procedure for identifying the optimal locations and capacities of energy storage systems, considering multiple operating scenarios via stochastic programming. In the first stage, the optimal sizing and siting strategy of storage units for each individual operating scenario is determined. In the second stage, the expected value of optimal locations and capacities of energy storage systems for all the scenarios, which are determined the first stage are obtained as the near-optimal result. In the third stage, with fixed location and capacities of energy storage systems, the optimal operation of microgrid is simulated to demonstrate their benefits. It is assumed that, in the tested microgrid systems, several tactical military vehicles with onboard generators and energy storage units are deployed as alternative power sources. Note that on-site diesel generators and on-board vehicle generators support plug-and-play functionality, meaning their start-up/shutdown operations can be decided in real time. Furthermore, network-constrained AC unit commitment model is used to optimize operation of microgrids. The economic merits of vehicle-to-grid implementation and energy storage system integration in a military based microgrid are validated in the numerical studies.
Social Network Data Analysis of Romance of The Three Kingdoms based on Python

Our country has more than 5,000 years of cultural heritage, handed down a lot of precious literary works. Based on python data processing technology, this paper makes an in-depth analysis of the relationship between characters in Romance of The Three Kingdoms. After python crawler extracts the text of Romance of The Three Kingdoms on the web page, it uses harvesttext tool library built in jieba to finish fine segmentation of Chinese words and entity links, and then builds networkx-type character relationship network diagram. In the analysis of network graph, the importance ranking of weighted network graph nodes is carried out based on pagerank algorithm. Finally, louvain algorithm is used to classify the characters into community groups. The analysis results are helpful to the in-depth study of the classics by professional scholars and stimulate the interest of ordinary readers.
Influence of Particle Shape on the Ability of Plasmonic Metal Core-Silica Shell Nanoparticles Embedded within the Absorbing Layer to Enhance the Opto-electronic Performance of Thin-Film Solar Cells

This computational study investigates the enhancements in the opto-electronic performance of thin-film silicon solar cells due to the use of different shapes of plasmonic metal core-silica shell nanoparticles embedded inside the Si absorbing substrate. The different shapes of the silver core-silica shell nanoparticles that were investigated in this study were cubes, cylinders, pyramids, spheres and spheroids, respectively. Due to morphology-dependent properties, various shaped nanoparticles show unique optical characteristics. The most significant enhancements in the performance of the thin-film solar cells were obtained using a pyramid shaped silver nanoparticle core that was encompassed within a hollow pyramid-shaped silica shell. The conclusion reached in this study were made through rigorous finite-difference time-domain (FDTD) simulations that calculated the plasmon resonance of the different shaped metal core-silica shell nanoparticles, optical absorption enhancement studies, short circuit current density (Jsc), open-circuit voltage (Voc), fillfactor, output power and optical near-field enhancements. The study concludes with a quick investigation of the feasibility of using a ‘sandwich’ configuration where a spherical homogeneous metal nanoparticle was placed on top of the Si absorbing layer and a metal core-dielectric shell nanoparticle was embedded inside the Si layer to further enhance the opto-electrical performance of the thin-film solar cells.
A Ka-Band CMOS Variable Gain Amplifier with High Gain Resolution and Low Phase Variation

This paper presents a Ka-band variable gain amplifier (VGA) with high gain resolution and low phase variation for millimeter-wave (mm-wave) 5G communications in 65-nm CMOS technology. The VGA employs three-stage differential common source (CS) structure. The input and output stages provide fixed gain, while the middle stage provides variable gain control. Based on the capacitive cross coupled neutralization (CCCN) technique and asymmetric capacitor structure, the proposed three-stage differential VGA achieves a measured gain resolution of 0.2 dB with 7-bit digital gain control in $23 \sim 28$ GHz. It realizes the phase variation lower than 2.3° with 6.2 dB gain control range at 28 GHz. The measured peak gain is 29.4 dB with a 3-dB bandwidth of $23.5 \sim 27.5$ GHz. The core chip area is 0.36 mm2 excluding pads.
Joint Optimization of 3D Hybrid Beamforming and User Scheduling for 2D Planar Antenna Systems

We consider a single-cell network in which a base station (BS) serves a set of users. We assume 2D planar antenna systems at the BS in which the BS applies 3D hybrid beamforming to serve a set of scheduled users. Unlike the previous 3D hybrid beamforming, we consider a joint optimization of elevation beamforming and user scheduling to maximize the overall throughput. Extensive numerical evaluation is performed to demonstrate the performance of the proposed scheme. Specifically, simulations have been carried out by utilizing 3D channel models based on ray-tracing methodologies as describes by the 3rd generation project partnership (3GPP) TR36.873 document. Simulation results show that the proposed scheme provides an improved throughput compared to the conventional elevation beamforming schemes.
A 28 GHz and 38 GHz High-Gain Dual-Band LNA for 5G Wireless Systems in 22 nm FD-SOI CMOS

This paper presents a high-gain, dual-band low noise amplifier (LNA) for 5G wireless systems, which supports simultaneous operation at 28 GHz and 38 GHz. The circuit consists of two cascode stages, and is implemented in a 22 nm FD-SOI CMOS technology. To realize the dual-band operation, dual-band matching networks based on transmission lines and capacitors were used. The presented LNA draws a current of 7.1 mA from a 1.6 V supply, which results in a total power consumption of 11.4 mW. The LNA provides a gain of 19.3 dB and 24 dB at 28 GHz and 38 GHz, respectively. At the input of the LNA a dual-band matching network was implemented to obtain a simultaneous noise and power matching at 28 GHz and 38 GHz. The measured noise figure at 28 GHz and 38 GHz is about 5 dB. The presented LNA compares well against previously reported designs by showing one of the highest gain and the lowest power consumption while still having the comparable performance in the other figures of merit. To the best knowledge of the authors, this is the first LNA using dual-band matching technique to support dual-band operation at 5G millimeter-wave bands.
Multi-Stage Coordination Volt/VAR Control with CVR in Active Distribution Network in presence of Inverter-Based DG Units and Soft Open Points

The increasing penetration of renewable energy sources (RES) exacerbates the problems such as voltage rise, higher energy losses and energy consumption. The VAR support provided by inverter based distributed generators (IDG) and soft open points (SOP) devices is emerge as a potential solution for aforementioned problems. Therefore, a multi-stage coordination Volt/VAR control (VVC) with conservation voltage reduction (CVR) methodology incorporating IDG and SOP devices has been proposed in this paper. The proposed methodology includes three different stages namely; day-ahead scheduling stage, inner- day control stage, and real-time dispatch stage. In the day-ahead scheduling stage, a coordination optimisation model considering traditional VVC devices, IDG and SOP has been established; in the inner day control stage, a rolling optimisation control model based on model predictive control (MPC) is built, to reduce the influence of the predicted RES forecasted deviations; in the real-time dispatch stage, voltage violations occurred due to sudden change in generation/load are mitigated by regulating the reactive powers of IDG and SOP. To validate the developed scheme, a real time co-simulation framework using real time digital simulator (RTDS) and MATLAB-GAMS interface has been built. The proposed scheme has been tested and validated on a well-known 33 bus distribution system. The test results demonstrate the significance of the proposed scheme on volt/VAR control problems.