Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Demonstration of a Ferroelectric HfO2-Based Content Addressable Memory Cell

In this work, we present an experimental demonstration of a content addressable memory (CAM) cell based on ferroelectric HfO2 field effect transistors (FeFETs). Our proposed ferroelectric CAM (FeCAM) utilizes a CMOS-compatible ferroelectric material, hafnium zirconium oxide (HZO), as the gate dielectric. We discuss operation of the FeCAM cell and propose a suitable architecture to realize in-memory computation as well as single clock cycle content-driven search. In addition, the HZO FeFET is analyzed for its intrinsic memory characteristic, and design considerations are identified for improving device and therefore projected system-level performance. Our results indicate that FeCAM is well-suited to accommodate demanding modern computational needs by sealing the gaps between conventional memory, logic, and continued device scaling.
Hierarchical and reconfigurable optical/electrical interconnection network for high-performance computing

Compared with electrical packet switches, optical switching technology could enable a more desirable high-performance computing (HPC) system with lower power consumption, lower delay, higher bandwidth, and more flexibility. In this article, we designed a hierarchical and reconfigurable optical/electrical HPC interconnection network. The traffic matrix of the target task can be decomposed into multiple matrix groups that are executed in parallel, and the network topology in each layer can be reconfigured according to the subtraffic matrix associated with this layer. For interlayer cross-connection, we use a shuffle network to offer a direct optical bypass for a huge aggregated amount of interlayer communication requirements, as well as multiple light paths. We propose a reconfiguration optimization algorithm and routing algorithm to optimize network performance. Simulation results show that, with the proposed architecture and algorithms, the average path length per unit of communication intensity is minimized by 13.7%–52.4%, the delay is reduced by more than 14.8%, and throughput is improved by 33% at least compared with Mesh, Torus, and Dragonfly.
Research on the Electricity Energy Pool Trading and Its Price Mechanism

As a regional power grid with the highest penetration of renewable energy, the China Northwest Power Grid is now facing with high curtailment of renewable energy, caused by the inefficient energy utilization, the serious marketization barriers between provinces and the immature market mechanism. To address these problems, this paper proposes an electricity energy pool (EEP) trading applied to the real-world case in China that is on the marketization process. Firstly, the EEP trading model is established, based on the economic dispatch model in two provinces whose power resources are complementary. Secondly, the price mechanism of the EEP is made up of two parts: system marginal price calculation and settlement of the unbalanced electricity (UE) within the whole month. Then, the benefit of the EEP trading is presented by comparing the rate of the curtailment in the wind and solar generation whether there is the EEP trading between the Shaanxi Power Grid and the Gansu Power Grid from day to month to year. Finally, the monthly UE of the EEP trading and its price are calculated, which verifies the effectiveness of the EEP trading and its price mechanism.
Localization in 2D PBR With Multiple Transmitters of Opportunity: A Constrained Least Squares Approach

A new algorithm for Passive Bistatic Radar (PBR) localization exploiting multiple illuminators of opportunity is proposed. To capitalize a-priori information on the receiving antenna main-lobe extent, specific constraints are forced to the target localization process. At the estimator design process the elliptic positioning problem is formulated according to the constrained Least Squares (LS) framework. Hence, the resulting non-convex optimization problem is globally solved providing a closed-form estimate to the target Cartesian coordinates. At the analysis level, the performance of the new estimator is assessed in terms of Root Mean Square Error (RMSE) behavior. The results highlight that interesting MSE improvements with respect to some counterparts available in the open literature can be achieved especially at low Signal to Noise Ratio (SNR) values.
Microgrid Transactive Energy: Review, Architectures, Distributed Ledger Technologies, and Market Analysis

Prosumer concept and digitilization offer the exciting potential of microgrid transactive energy systems at distribution level for reducing transmission losses, decreasing electric infrastructure expenditure, improving reliability, enhancing local energy use, and minimizing customers’ electricity bills. Distributed energy resources, demand response, distributed ledger technologies, and local energy markets are integral parts of transaction energy system for emergence of decentralized smart grid system. Hence, this paper discusses transactive energy concept and proposes seven functional layers architecture for designing transactive energy system. The proposed architecture is compared with practical case study of Brooklyn microgrid. Moreover, this paper reviews the existing architectures and explains the widely known distributed ledger technologies (blockchain, directed acyclic graph, hashgraph, holochain, and tempo) alongwith their advantages and challenges. The local energy market concept is presented and critically analyzed for energy trade within a transactive energy system. This paper also reviews the potential and challenges of peer-to-peer and community-based energy markets. Proposed architecture and analytical review of distributed ledger technologies and local energy markets pave the way for advanced research and industrialization of transactive energy systems.
Design of a Planar Transmission Line Balun Based on Novel Phase Inverter

In this paper, a planar ultrawideband (UWB) transmission line balun based on a novel phase inverter is proposed, which consists of three coaxial connector to double-sided parallel-strip line (DSPSL) transitions, a two-section Wilkinson divider, and a phase inverter. In the phase inverter, a 90-degree rotating transition is applied to realize field inversion. It maintains good structural uniformity of the two outputs of the balun and ensures the characteristics of equal energy division and a stable 180-degree phase difference in wide frequency band. Compared with other designs, the proposed balun has the advantages of easy to design, UWB operation, low reflection, equal power division and a stable phase difference. The presented balun works in the 0.52 GHz to 2.32 GHz band, covering 4.46 octaves. The measurements can achieve the reflection, amplitude imbalance and phase imbalance characteristics below −15 dB, ±0.46 dB and ±4 deg, respectively. The designs including the phase inverter and balun are fabricated and measured to verify the performance, and the measurements are found to be in good agreement with the simulations. In addition, the UWB potential of the porposed balun is studied. From an example based on a 1:2 balun, we can conclude that the proposed balun has the potential to work in a wider frequency band within the range of 0.1 GHz to 3GHz.
Size-Dependent Photoresponse of Germanium Nanocrystals-Metal Oxide Semiconductor Photodetector

In this article, size-dependent photoresponse characteristics and low-frequency noise behavior of high-speed germanium nanocrystals (nc-Ge) photodetector have been reported. The nc-Ge embedded in SiO<sub>2</sub> matrix with different sizes have been synthesized by annealing the cosputtered Ge-SiO<sub>2</sub> thin films at 800 °C–900 °C. It has been observed that the photoresponse of the detector increases with the increasing size of nc-Ge. The sample annealed at 900 °C showed maximum responsivity (3.5 A/W) with the fast response time. The low-frequency noise spectral power density in the current fluctuation (<inline-formula> <tex-math notation="LaTeX">${S}_{\text {I}}$ </tex-math></inline-formula>) have been measured for the fabricated nc-Ge photodetectors, and lowest noise equivalent power (NEP) and highest detectivity (<inline-formula> <tex-math notation="LaTeX">${D} ^\ast $ </tex-math></inline-formula>) have been observed in the nc-Ge detector synthesized at 900 °C. Tunable responsivity in nc-Ge has been observed due to size-dependent light absorption, electric field-driven carrier separation, and tunneling through the oxide barriers. The transient photoresponse behavior has also been studied and the best rise time of <inline-formula> <tex-math notation="LaTeX">$6.2~\mu \text{s}$ </tex-math></inline-formula> was observed for nc-Ge synthesized at 900 °C. These results suggest that the nc-Ge are a promising contender for high-performance integrated optoelectronic devices that are fully compatible with silicon technology in terms of fabrication and thermal budget.
High-Performance InGaAs-on-Silicon Technology Platform For Logic and RF Applications

An InGaAs-on-Si MOSFET technology platform using a CMOS-compatible fabrication flow is demonstrated. Several channel heterostructure designs are explored, with the use of thin InP barrier layers resulting in significant enhancement of mobility. MOSFETs with ft/fmax of ~350/350 GHz are demonstrated. Within the same technology platform, logic FinFET devices are also demonstrated, with record-high on-currents of 350 µA/µm. The use of S/D spacers to mitigate the parasitic bipolar effect is also explored, leading to significant reduction of off-state leakage currents. Finally, 3D sequential integration of InGaAs MOSFETs on SOI CMOS wafers is reported, showing no degradation of CMOS performance post top-level fabrication. The results indicate the strong potential for integrated InGaAs FETs towards high-performance logic and mixed-signal applications.
Optimal Bidding Strategy for a RES Virtual Power Plant in the Spot Market

Recently, the penetration of renewable energy source(RES) have been growing globally, encouraged by environmental policies and low-carbon energy policies. Due to the volatility and uncertainty of renewable energy output, it is very difficult to bid in the market alone. To solve this problem, combining wind power(WP), photovoltaic(PV) and power storage units to form a virtual power plant(VPP) is an effective path to stabilize the output deviation and promote renewable energy consumption. In this paper, a VPP consists of wind power generation, photovoltaic generation, power storage units and load are enabled to participate in the wholescale markets, including day-ahead(DA) market and real-time(RT) market. The objective of the VPP in this paper is to maximize the profit on DA and RT market. To achieve this goal, an optimal bidding model is proposed. In this model, several important market uncertainties are considered, consisting of RES generation, DA market price and RT market price. A fuzzy optimization method is applied to deal with the uncertainty of RES generation. Moreover, the uncertainties of DA market price and RT market price are handled by a robust optimization. Finally, an example is given to verify the effectiveness of the bidding strategy.
Review of Digital Wallet Requirements

The financial industry has proven to be an important factor in our daily lives through the adaption of new technology. Fintech companies are driven towards enhancing financial services currently being provided by consecutive financial institutions. Digital wallet is the latest invention of finance technology, which is a great tool for making our payment transaction very easily and fast. Many digital wallet applications have already been developed and implemented in payment transactions. An effective number of cyber threats targetting the monetary system have made security an imperative component of the banking system. This paper outlines the digital wallets’ threats and also provides the requirements of digital wallets that technically try to address the fear of security customers as usual providers,,while assisting in the successful implementation of digital wallets.
Selection of sub-optimal feature set of network data to implement Machine Learning models to develop an efficient NIDS

With the rapid increase in the dependency on technology and the internet in our personal and professional life, the computer networks have become very congested, and the frequency of presence of an intrusion in a network has also increased. An active IDS (Intrusion Detection System) protects the network from intrusions and provide security to the system. Machine learning techniques are the most efficient technologies to develop IDS as substantial network data can be easily trained and tested using ML models. Any general machine learning models work in three phases: Data pre-processing, Feature selection and training, and testing the developed models. The major contribution of this paper is the extraction of sub-optimal feature set from NSL-KDD data set having 41 features and then implementing different ML models to find the best suitable model using this set of features. It is observed that the ML models SVC and MLPClassifier performed better as compared to CNN in terms of complexity, accuracy and training time when trained and tested using the selected optimal feature set. CNN is an excellent deep learning algorithm that gives good results for image data perform better in comparison to simple text data machine learning models like SVC and MLP Classifier. MLP Classifier gave a higher accuracy of 98.19%.
Merged LSTM Model for emotion classification using EEG signals

The applicability of contemporary deep learning techniques have seen considerable improvements in the field of biomedical signal analysis. Emotion analysis using EEG signals is one such problem that has been studied and worked upon extensively in recent times. In this paper we have proposed a novel methodology to classify emotions using signal processing techniques such as wavelet transform and statistical measures for feature extraction and dimensionality reduction followed by developing state of the art neural architecture for the classification task. A merged LSTM model has been proposed for binary classification of emotions. The model's applicability and accuracy has been validated using DEAP dataset which is the benchmark dataset for emotion recognition.
Reduced order controller for FO-IMC with desired phase margin and gain cross-over frequency

Fractional order (FO) controller provide more number of tuning parameters than integer order (IO) counterpart. In this paper, a fractional order (FO) internal model control (IMC) is considered which provides two tuning parameters $(\lambda$ and $\beta)$ as compared to integer order (IO) counterpart. These two parameters are tuned to get the desired $\varphi_{m}$ and $\omega_{g}$ for higher order systems. Usually, we use IO approximation of FO terms in the controllers. It is observed that 5-15 order IO approximation provide almost accurate behavior for the FO terms. The problem with such approximation is the resulting higher order controller. In this work, a balanced truncation method is used to get a reduced order controller which retains important properties of higher order controller. The viability of reduced order controller is measured in terms of maximum sensitivity $(M_{s})$ and the frequency at which the system is most sensitive $(\omega_{m})$. Further, the results are compared in terms of rise time $(T_{r})$, settling time $(T_{s})$, maximum overshoot (%$M)$, integral square error (ISE), integral absolute error (IAE) and integral of the time weighted absolute error (ITAE). The importance of using reduced order controller is checked using three examples: first, a minimum phase (MP) system, second, a non-minimum phase (NMP) system with right hand plane (RHP) zero and third, a first order plus time delay (FOPTD) system. It is observed that the lower order controller can be used for higher order controller.
FATORES DE RISCOS CARDIOVASCULARES: ESTUDO COM PESSOAS HIPERTENSAS, DE UM BAIRRO POPULAR NA REGIÃO AMAZÔNICA

Cross-sectional study, retrospective and quantitative character that aimed to estimated the cardiovascular risk factors of 145 users with hypertension of a Amazon community in Belem-PA. The collected data were from the Registration Form of HIPERDIA. The U test, by Mann-Whitney was applied for comparison between genders and the chi-square test to identify the influence of some variable. The software SPSS. 16® was used on the descriptive analysis and MINITAB. 15® to perform the tests. Were identified: high blood pressure, both systolic (89.4%) and diastolic (64.1%), increased abdominal circumference (male: 63.2% female: 88.6%), presence of cardiovascular family history (45.5%), smoking (27.6%), sedentary (57.9%) and overweight and obesity (75.2%). The U Mann-Whitney test showed that showed the average risk factor is equal for men and women and that there is greater influence of sedentary lifestyle in people who had a stroke. The coexistence of risk factors with hypertension is present in 91.1% of the sample. These findings underscore the need for more effective action to control the disease in the study group such as, reduce and control body weight; recommend and encourage proper nutrition and physical activity, controlling blood pressure values, as well as global ratings of living conditions, which would sustain the best recommendations to this group.
Motion Planning Algorithm for continuum Robots Bending over Obstacles

Continuum robots are manipulators able to bend at any point of their structure. This feature allows continuum robots to avoid unwanted collisions in a complicated working area or grasp objects using their body. In this regard, they are used as instruments in industry and medicine. For continuum robots, a collision with an obstacle does not always mean that further motion is impossible. A part of continuum robot can be bent around the obstacle and pushed further. In connection with this point, we propose a planar motion planning algorithm for continuum robots considering the ability to bend over obstacles. We made two assumptions for the proposed algorithm. For the first assumption, friction is not affecting the robot shape while it is pushed forward. The second assumption suggests that a collision point divides a single section of the robot into two sections which can be described by the constant curvature approach. These assumptions allow us to present a single-section continuum robot going from one collision point to another as a multi-section continuum robot. The algorithm searches for the configurations going through collision points without crossing the obstacle borders. Obtained simulation results show that the proposed algorithm with the bending-over-obstacle feature copes well with the motion planning and is able to find multiple solutions less than 0.1 seconds.
A Semantic Search Capability for a Grid Model Repository

The operation of electric grids is becoming more challenging due to continued increases in wind and solar generation, the proliferation of decentralization generation resources, and cyber security threats. To support the needed research, new large scale realistic public grid models have been developed and placed in a repository for access by researchers. To assist researchers in easily finding grid models that are suitable for their specific research needs, an approach to searching numerous large grid models was evaluated, prototyped and developed. The research identified multiple possible approaches to using graph database techniques and a natural language processing for the search algorithm. Results of the prototype, the final approach for production repository and the search algorithm formulation are described.
Unsupervised Conditional Reflex Learning Based on Convolutional Spiking Neural Network and Reward Modulation

Automatic decision and lane-keeping tasks are the most fundamental but important tasks in robot controlling researches. Concerned about the computing limitations of mobile robot platforms, an easily trainable method with low computational consumption and low latency is needed to solve this problem. An unsupervised conditional reflex learning network was proposed in this paper, which uses the conditional pattern to learn the conditional pattern and make decisions in an unsupervised manner. We used the convolutional spiking neural network to extract hidden features of road lanes, and then used the dopamine modulation mechanism to learn the decision-making information from the acquired features. In order to evaluate the quality of automatic decision-making models, two metrics were designed which are total deviation distance per second (TDDPS) and target achievement rate during training (TAR). In the process of training, neither labels were given to the convolutional spiking neural network, nor artificial decision-making information was assigned to the dopamine modulation layer. Simulation experiments showed that the proposed model has a state-of-the-art performance in a relatively complicated scenario and solves three limitations mentioned in previous works. Our work brought more biological inspiration into decision support systems, with the hope that the proposed method can promote the development of the bionic decision support system in hardware, especially in neuromorphic hardware.
Broadband PLC on Medium Voltage - Performance Measurements

The currently used GPRS/LTE communication solution for distribution automation cannot be deployed in places where there is no signal (cellar spaces, etc.) or the utility prefers a custom solution (independent of the mobile operator). Therefore, the use of the existing medium voltage lines for Broadband Power Line (BPL) communication as an independent communication path is an ideal and affordable solution. The article focuses on the methodology proposal for BPL communication used on medium voltage lines. Further, the article focuses on measuring the performance of particular BPL modem with IEEE 1901 standard in laboratory environment.
Path Tracking Control Using Imitation Learning with Variational Auto-Encoder

This paper presents a path tracking algorithm for autonomous driving that learns an action command from high-dimensional input state vector, e.g., grid-maps. The learning framework is built upon a variational auto-encoder (VAE) and takes advantage of the efficient path tracking results that already exist or can be obatined from a human expert. The VAE is known to give smooth latent represetations of the input data and we make one of the latent attribute follow the expert's command. We implement an autonomous driving system on the open source robot simulator (Webots) and collect the demonstration data for training the VAE. Numerical results show that the proposed tracking method can drive a vehicle robustly without explicitly detecting the road features.
Experience with Impact of GIC on Noise Performance of Large Power Transformers

Two large 3-phase 345 / 115 kV Autotransformers of the same design, located at two different locations in the Northeast region of USA, have been experiencing occasional high sound level excursions of short duration. Detailed analysis performed over several months has found a high degree of correlation between these sound level excursions and magnitudes of the Geomagnetically Induced Current (GIC) flowing into the windings of these transformers. This paper presents detailed measurements and analysis performed by the authors. Results obtained, and the conclusions reached, in regard to the nature of the relationship between transformer noise and magnitudes & signature of the GIC are also included.