Miracle-dz/UniFAR-FTUs huggingface dataset:
Header Compression Across Entire Network Without Internet Protocol Saves Bandwidth and Latency

Session Bridging, a new form of header compression, significantly reduces bandwidth requirements in multiple use cases. This innovation separates connections into end segments marked by separate protocol instances using normal headers. A transport segment between them carries payloads with compressed headers. The process dramatically reduces the core bandwidth per connection for conversational applications and extends savings into access circuits as well. Session Bridging takes advantage of MPLS or virtual Ethernet connectivity in existing networks. Custom code on a working proof of concept confirms viability for conversational connectivity-real-time voice, live video conferencing, telemedicine, and gaming-applications where latency is a key quality measure. Extensions save additional bandwidth on links between routers and over a Radio Access Network.
Millimeter-wave power amplifier linearity characterization using Unequally Spaced Multi-Tone stimulus

This paper presents an implementation of Ka band linearity measurement. In this paper large signal measurements using the innovative Unequally Spaced Multi-Tone (USMT) wideband test signal have been performed in order to assess the in-band linearity degradation of non-linear microwave power devices. The proposed test bench is suitable for both on-wafer and connectorized devices characterization, using a sub-6GHz source and an RF mixer to up convert the signal into Ka band. In this paper a first result of comparison between NPR with a notch and USMT test signal has been demonstrated; it shows very similar behavior using similar signal statistics, bandwidth, peak-to-average, etc… at a 6 GHz frequency carrier. This comparison has been done with simulations and experimentally on the same test bench, to ensure the same conditions.
Monthly Load Forecasting Based on an ARIMA-BP Model: A Case Study on Shaoxing City

Load forecasting is a basic issue in grid planning and operation arrangements, and the forecasting accuracy varies from system to system in terms of the local load demand characteristics and different external influencing factors. This paper contributes a monthly load forecasting method based on an autoregressive integrated moving average (ARIMA) model and the classical back-propagation neural network (BPNN). The original sequential monthly energy demand records are firstly decomposed into regular static component, seasonal component and irregular nonlinear component by multiplicative trend decomposition. After that, an ARIMA model is optimally selected and trained to emulate the trend component in terms of its suitable feature of describing linear time series. On the other hand, a three-layer BPNN is employed to govern the connection between the city-specific external meteorological factors with the nonlinear irregular components. the proposed ARIMA-BP model is validated against a representative deep-leaning method–-EMD-LSTM model, through their applications to the whole society monthly demand prediction of Shaoxing city. The application results show that the proposed ARIMA-BP model is obviously superior to the EMD-LSTM model in terms of prediction accuracy.
Structuring Human-Robot Interactions via Interaction Conventions

Interaction conventions (e.g., using pinch gestures to zoom in and out) are designed to structure how users effectively work with an interactive technology. We contend in this paper that successful human-robot interactions may be achieved through an appropriate use of interaction conventions. We present a simple, natural interaction convention—"Put That Here"—for instructing a robot partner to perform pick-and-place tasks. This convention allows people to use common gestures and verbal commands to select objects of interest and to specify their intended location of placement. We implement an autonomous robot system capable of parsing and operating through this convention. Through a user study, we show that participants were easily able to adopt and use the convention to provide task specifications. Our results show that participants using this convention were able to complete tasks faster and experienced significantly lower cognitive load than when using only verbal commands to give instructions. Furthermore, when asked to give natural pick-and-place instructions to a human collaborator, the participants intuitively used task specification methods that paralleled our convention, incorporating both gestures and verbal commands to provide precise task-relevant information. We discuss the potential of interaction conventions in enabling productive human-robot interactions.
Affect-Learn: An IoT-based Affective Learning Framework for Special Education

A learning disorder is associated with the ability of the child to process the information effectively. The purpose of special education is to provide equal access to education for all children to help them succeed in the regular curriculum through specialized services. Children with anxiety, hyperactive, or attention-deficit disorders require special assistance to help them stay at their normal level and thus effectively suit in a classroom setting. With the advancement in technology, the land-scape of special education is rapidly changing. The motivation for this research is to develop an Internet of Things-based affective computing framework, Affect-learn, that can help teachers in identifying the hyperactivity or inattentiveness in children, and help them improve the overall learning outcomes. The proposed research is validated with the help of commercially available off the shelf components. The measure of success in this research is the response time of the proposed framework and the efficiency of emotion elicitation.
Non-intrusive Load Decomposition Method Based on LSTM Considering Temperature Factor

Non-intrusive load decomposition is a key technology for customer-side energy management. In this paper, a Long Short-Term Memory neural network (LSTM) based method is proposed to load decomposition. We also take into account the impact of nonelectrical factors (temperature) on load decomposition, which is used as the input of LSTM together with the total energy consumption to improve decomposition accuracy. The performance of the proposed method was evaluated using the DRED dataset. The results show that the LSTM model has certain advantages in the energy decomposition of various types of load devices with multistate changing. Meanwhile, the decomposition accuracy of the temperature sensitive loads is improved by considering the temperature factor.
Knowledge Transfer for Long-term Voltage Stability Assessment Between Power Grids Based on Deep Domain Adaptation Networks

For machine learning techniques, it is difficult to transfer knowledge between different domains due to the system discrepancy and limited training data. In this paper, we implement a knowledge transfer system (KTS) for long-term voltage stability assessment between power grids. First, system behaviours are converted to heatmaps with a more general method to avoid negative transfer. Then, a deep domain adaptation network (DDAN) is introduced to learn domain-invariant representations with strong semantic separations by adding a maximum-mean-discrepancy calculator. The KTS based on DDAN is performed to transfer knowledge from IEEE 39-bus system to IEEE 14-bus system. Case studies are also given to show its potential under those scenarios of data shortage.
Design of Front-End of Wireless Data Link for Millimeter-wave Indoor Communications

The present work highlights the design and development of radio frequency (RF) front-end and baseband architecture for wireless data link at 60 GHz for high speed short range communications. The millimeter-wave (mm-wave) technology, especially the 60 GHz frequency band (57 - 64GHz) has been found as an attractive choice for 5G indoor communications. However, the special characteristics of mm-wave sets new challenges in the signal processing, circuit, antenna and communication technologies. The antenna beamforming technology employing multiple antennas is considered as a viable solution to increase the link capacity by allowing directional communication. This paper concentrates on the design of RF front end at 60 GHz including a realistic phased array antenna model with non-linear model of power amplifier. The link budget is calculated to establish the feasibility of the system design over the wireless link and also to evaluate the required gain of the phased array. The system performance is estimated in terms of bit error rate (BER).
A Compact Frequency-Tunable VGA for Multi-Standard 5G Transceivers

This paper presents a multi-standard digitally controlled variable gain amplifier (VGA) for 5G phased array transceivers, designed and fabricated in 120 nm SiGe BiCMOS process. A current steering technique is used to minimize the phase variation under different gain modes. Frequency tuning is achieved by utilizing variable artificial transmission lines, capable of shifting the peak gain frequency from 28 GHz to 39 GHz in discreet steps to obtain multi-band operation with minimal footprint. The proposed VGA achieves more than 12.5 dB measured peak gain, gain tuning range of 18 dB, gain resolution of less than 1 dB with <4.5° phase variation and OP1dB > 4.5 dBm. The IC occupies area of only 0.08 mm2 and consumes 45 mW.
Hybrid Bidirectional LSTM Model for Short-Term Wind Speed Interval Prediction

Wind speed interval prediction is gaining importance in optimal planning and operation of power systems. However, the unpredictable characteristics of wind energy makes quality forecasting an arduous task. In this paper, we propose a novel hybrid model for wind speed interval prediction using an autoencoder and a bidirectional long short term memory neural network. The autoencoder initially extracts important unseen features from the wind speed data. The artificially generated features are utilized as input to the bidirectional long short term memory neural network to generate the prediction intervals. We also demonstrate that for time series prediction tasks, feature extraction through autoencoder is more effective than making deep residual networks. In our experiments which involve eight cases distributed among two wind fields, the proposed method is able to generate narrow prediction intervals with high prediction interval coverage and achieve an improvement of 39% in coverage width criterion over the traditional models.
Demand Response-Based Voltage Regulation in Distribution Networks

Voltage control of distribution networks is one of the main tasks for network operators. Integrating high levels of renewable energy resources into power networks may cause many challenges on system operation. The traditional voltage control devices may fail to handle voltage control problems. With the development of the new technologies for the future networks (i.e. the controllable reactive power devices and demand response programs), it is possible to introduce an efficient control variable for voltage control. Thus, this work presents a technique for voltage control in distribution networks based on demand response and reactive power injection by the end-users. An optimization technique is performed to achieve the voltage control. The optimization technique aims to minimize the cost (i.e. the change in reactive power injection and demand curtailment) and the deviation in system voltages while fulfilling the network requirements. The standard IEEE 123–bus is used for this purpose. The results demonstrate that demand response programs and reactive power injections by end-users can successfully regulate the network voltages.
Speed Based Distributed Congestion Control Scheme for Vehicular Networks

The Internet of Vehicles (IoV) and vehicular clouds use sensor data and information from vehicles to implement an intelligent transportation system for future smart cities. The tremendous amount of data generated in IoV can lead to channel congestion, packet loss and delay of time-sensitive messages. This can have a serious impact on the performance of applications and services provided by a vehicular network, particularly safety applications that are time critical. As such, network congestion control is an topic in vehicular networks and various methods of controlling the message transmission rate and power have been explored to-date. In this paper we have proposed a new distributed congestion control algorithm which manipulates the transmission power based on a density estimation derived from the vehicle’s driving speed. The results indicate that the proposed approach is effective in reducing packet loss and improving the relevance of the received messages.
Analytical Study on the Breakdown Characteristics of Si-Substrated AlGaN/GaN HEMTs With Field Plates

The breakdown voltage model of Si-substrated AlGaN/GaN HEMTs with gate and drain field plates is proposed in this work. The silicon substrate and GaN buffer are considered as the depletion region in the modeling process. The analytical model shows great simplicity and veracity. It gives physical insights into the breakdown characteristics of the AlGaN/GaN HEMTs. The avalanche breakdown occurs at the field plate’s edge in lateral structure or at the interface in vertical structure. According to the analytical model, the relationship between the lateral breakdown and the vertical breakdown is demonstrated in this work. The breakdown location is affected by the variation of the structure parameters, including gate-to-drain distance, buffer thickness and field plates’ lengths. With the aid of the analytical model, effective guidance for device optimization to achieve high performance can be obtained.
An Ultra-Wideband Single-Polarization-Single-Mode Terahertz Photonic Crystal Fiber

A terahertz (THz) photonic crystal fiber (PCF) with an ultra-wide bandwidth and single-polarization-single-mode (SPSM) operation is designed and analyzed. Two air slots are introduced in the core region and epsilon-near-zero (ENZ) material is deposited in four specific air holes in the cladding of the PCF. The design achieves significantly different electric (E)-field distributions of the X-polarized (XP) and Y-polarized (YP) modes. The E-field components overlapping the ENZ material are attenuated because it is lossy. Gain material is then deposited in a rectangular slot in the core center to provide amplification of the E-field components overlapping this gain region. Changing the dimensions of the PCF modifies the amplification and attenuation rates to the wanted XP mode, the unwanted YP mode, and any unwanted higher order (HO) modes. The amplification of the wanted mode and the attenuation of the unwanted modes are maximized through optimization. The result is a PCF with an ultra-wide SPSM spectrum of 0.53 THz, from 1.00 to 1.53 THz. The minimum loss difference (MLD) across this bandwidth between the wanted mode and any unwanted modes is over 7.4 dB/cm. To the best of our knowledge, this is the widest SPSM bandwidth of a PCF fiber reported in THz regime.
Mobile Edge Computing Performance Evaluation using Stochastic Petri Nets

Mobile Edge Computing (MEC) is a network architecture that takes advantage of resources available at the edge of the network to enhance the mobile user experience by decreasing the service latency. MEC solutions need to dynamically allocate the requests as close as possible to their users to avoid high latency. However, the request allocation does not depend only on the geographical location of the servers, but also on their requirements. The task of choosing and allocating appropriate servers in a MEC environment is challenging because it involves many parameters. This paper proposes a Stochastic Petri Net (SPN) model to represent a MEC scenario and analyze its performance. The model focuses on parameters that can directly impact the service Mean Response Time (MRT) and resource utilization level. We propose case studies with numerical analyzes using real-world values to validate the proposed model. The main objective is to provide a practical guide to assist infrastructure administrators to adapt their architectures, finding a trade-off between MRT and resource utilization level.
SARS-CoV-2 Detection From Voice

Automated voice-based detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could facilitate the screening for COVID19. A dataset of cellular phone recordings from 88 subjects was recently collected. The dataset included vocal utterances, speech and coughs that were self-recorded by the subjects in either hospitals or isolation sites. All subjects underwent nasopharyngeal swabbing at the time of recording and were labelled as SARS-CoV-2 positives or negative controls. The present study harnessed deep machine learning and speech processing to detect the SARS-CoV-2 positives. A three-stage architecture was implemented. A self-supervised attention-based transformer generated embeddings from the audio inputs. Recurrent neural networks were used to produce specialized sub-models for the SARS-CoV-2 classification. An ensemble stacking fused the predictions of the sub-models. Pre-training, bootstrapping and regularization techniques were used to prevent overfitting. A recall of 78% and a probability of false alarm (PFA) of 41% were measured on a test set of 57 recording sessions. A leave-one-speaker-out cross validation on 292 recording sessions yielded a recall of 78% and a PFA of 30%. These preliminary results imply a feasibility for COVID19 screening using voice.
Energy-only vs. capacity markets: impact of hedging opportunities considering risk-averse investors

In this study, an agent-based model is developed to examine the impact of long-term bilateral contracts on the performance of energy-only markets and markets with capacity remuneration mechanism (CRMs). Specific emphasis is put on hedging via bilateral forward contracts. We found that bilateral contracts can indeed incentivize investments in new capacities and help to reach a relatively safe system margin, but at an increase in total system costs. Implementing CRMs, in the presence of bilateral contracts, lead to a higher and more stable system margin while maintaining the total system cost on a similar level.
Energy- and Spectral-Efficiency Tradeoff With $\alpha$-Fairness in Energy Harvesting D2D Communication

Energy Harvesting (EH) technology enables Device-to-Device (D2D) User Equipments (DUEs) to harvest energy from ambient energy, making contributions to green communications and breaking the single battery-powered and the regional limitations of device deployment. The joint problem of energy harvesting and resource allocation in EH-based D2D Communication Networks (EH-DCNs) is a challenge issue. In this paper, we investigate the joint problem of resource allocation and EH time slot allocation of DUEs in EH-DCNs, where the DUEs harvest energy and multiplex Cellular User Equipments (CUEs) uplink resources. A channel assignment, power allocation and EH time slot allocation problem in EH-DCNs is formulated. The goal is to maximize energy- and spectral-efficiency with <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>-fairness while guaranteeing the EH constraints of DUEs and the quality of service of CUEs. The formulated problem is a non-convex mixed-integer multi-objective optimization problem. In order to solve the formulated problem, the multi-objective optimization problem is transformed into a single-objective optimization problem based on the weight sum method. We propose a joint iterative algorithm based on Lagrangian dual decomposition for <inline-formula><tex-math notation="LaTeX">$\alpha >0$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\alpha =0$</tex-math></inline-formula>, respectively. Numerical results illustrate that the proposed algorithm achieves higher energy efficiency and spectral efficiency for different network parameter settings.
Subarray Design with Two Rectangular Elements for Massive MIMO System Development

Wireless communication for the future will use 5G standards, supported by an antenna system with Massive MIMO technology incorporating subarrays. A subarray microstrip antenna that has small element spacing will result in mutual coupling effects. This paper proposes design of a two rectangular subarray antenna with coaxial feeding for massive MIMO development and analysis the effect of mutual coupling at varying spacing element. Antennas are designed and simulated, namely a single antenna, a two element antenna arrayed in the E-plane and a two element antenna arrayed in the H-plane. The simulation results show that the mutual coupling of the two antenna elements arrayed in the E-plane for all element spacing is less than −20 dB, which means the mutual coupling effect can be ignored. Whereas the mutual coupling of the two antenna elements arrayed in the H-plane for all spacing elements is greater than −20 dB, which means the value of the mutual coupling must be considered. The bandwidth of the two element subarrays on E and H-plane is 77.3-88.5 MHz depending on the element spacing, which is still below 100 MHz required for 5G application.
Autonomous Vehicles: Building a Test-bed Prototype at a Controlled Environment

Rapid improvements in technology are making autonomous vehicles feasible. Researchers and industry are looking for different methods to implement the concept of smart cities. Autonomous Vehicles provide a unifying framework for several research topics (e.g., navigation, localization and Global Positioning System (GPS) positioning, security, and privacy). Despite significant progress, we believe that fully autonomous vehicles will be deployed initially in constrained environments (e.g., closed-loop areas, gated retirement communities). In this paper, we describe the development of a prototype autonomous golf-cart that can be used as a test-bed for a range of research projects. We explain the details of the conversion from a traditional golf-cart to an autonomously driven one. Our target application is a system of autonomously driven golf-carts for use within a retirement community. We show our testing results, and how Internet of Things (IoT) devices can be used to extend the WiFi coverage for log reporting and to request the golf-cart. Finally, we discuss the challenges and highlight improvements that can be performed on the system.
A hierarchical, market-based, non-cooperative game-theoretic approach to projecting flexible demand-side resources: Towards more realistic demand response-integrated, long-term energy planning models

This paper presents a non-cooperative, zero-sum, symmetric game approach to projecting the flexible demand-side resources to improve the reliability and robustness of long-term strategic energy planning models. A specifically devised distributed algorithm is put forward to determine the unique, pure-strategy Nash equilibrium of a non-cooperative game formulated for the delivery of aggregator-mediated demand response resources, at which no player can yield a higher payoff by deviating from its best-response strategy. The simulation for the operation of renewable energy systems is run over the course of a year. Furthermore, using a metaheuristic-based solution algorithm proposed for the optimal capacity planning of microgrids, the model was satisfactorily applied to the energy infrastructure planning of a test-case system conceptualized for the town of Ohakune, in New Zealand. The simulation results suggest that not only does the proposed market-directed, incentive-based, strategic demand-side management planning framework help elicit further contributions from the customers, which, in turn, reduces the reserve capacity requirements to meet the peak demand, it also guarantees the highest possible payoff for all players of the game. According to the simulation results, the test-case system’s life-cycle cost can be reduced by up to ~29% compared to the case, where no demand response is considered.