Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Data Transmission Model for Internet of Things Application

WSN plays a very important role for provisioning Internet-of-things (IoT) applications. The IoT enabled sensor network (IoT-SN) is composed of large number of battery powered sensor devices connected to IoT gateways. These sensor device are put forth in hazardous location for carrying out different operation such as sensing and transmitting. Preserving battery of sensor device is very critical. Existing data transmission model predominately focused on reducing energy consumption of device. However, modern application requires energy efficiency with low latency service delivery. Enhancing lifetime performance and network reliability for collecting sensed data is the major objective of IoT enabled sensor network(IoTSN). The work focuses on cluster-Head to reduce the energy consumption and to increase the lifetime of network. Thus, this paper present multi-objective metric for designing efficient data transmission model for IoT-SN. Experiment result shows EDT model is efficient increasing lifetime of network with less latencies.
ES-FEM with ABC for Accurate Parameter Extraction of High Speed Interconnects

An accurate edge-based smoothed finite element method (ES-FEM) with the absorbing boundary condition (ABC) to model high-speed interconnects is introduced in this paper. In the ES-FEM, the edge-based smoothing domain is first constructed based on the background triangle meshes. Then, by incorporating with the smoothing gradient techniques, the gradient of shape functions is replaced by the smoothed counterparts. After the boundary conditions are applied exactly the same as those in the FEM, the parameters of high-speed interconnects can be accurately extracted.
Four-Level Three-Phase Inverter With Reduced Component Count for Low and Medium Voltage Applications

This paper proposes a novel three-phase topology with a reduced component count for low- and medium-voltage systems. It requires three bidirectional switches and twelve unidirectional switches for producing four-level voltages without using flying capacitors or clamping diodes, reducing the size, cost, and losses. Removing flying capacitors and clamping diodes allows it to simplify control algorithms and increase the reliability, efficiency, and lifetime. A modified low-frequency modulation (LFM) scheme is developed and implemented on the proposed topology to produce a staircase voltage with four steps. Further, a level-shifted pulse width modulation (LSPWM) is used to reduce the filter size and increase the output voltage controllability. In this study, a voltage balancing control algorithm is executed to balance the DC-link capacitor voltages. The performance of the proposed topology is numerically demonstrated and experimentally validated on an in-house test setup. Within the framework, the power loss distribution in switches and conversion efficiency of the proposed circuit are studied, and its main features are highlighted through a comparative study.
Interferometric Reduction of Induced Phase Variation in Stimulated Brillouin Processing of Light Waves

Phase manipulation is crucial for radio frequency (RF) and optical signal processing. In this work, we introduce an interferometric phase-reduction scheme to flexibly suppress the phase change while simultaneously amplifying the signal. We develop a numerical model to evaluate the characteristics of this processing scheme. The concept is further demonstrated with a proof-of-principle experiment that modifies the phase and gain profiles induced by stimulated Brillouin scattering in an optical fiber. This scheme provides an additional freedom to control the signal phase and amplitude in RF/optical processing functionalities.
Field Effects of 5% Acetamiprid ME on Aphis gossypii Glover

The experiment was conducted in 2008 to evaluate the effect of 5% acetamiprid ME on control of Aphis gossypii Glover in field in Yucheng Shanxi.The results showed that 5% acetamiprid ME with dosages of 15.1 to 22.5 g/hm2 had an excellent effect to control the aphids.The effects were 96.48% to 99.35% and 96.48% to 99.57% in the 3rd to 7th days after spraying treatment respectively,and the persistent effect reached over 7 days.
Research on the Gate Oxide Layer Aging Trend of Power Electronic Device

The introduction of fully electric vehicles (FEVs) into the mainstream has raised concerns about the reliability of their electronic components such as Insulated Gate Bipolar Translator (IGBT). At present, only the transient thermal resistance curve in the datasheet and the initial thermal model of the experimental test are used to evaluate the life of the IGBT module, while it is well known that IGBT parameters are affected by its degree of aging. Thus, the development of research for the aging process of IGBT is of key importance. The aging process of IGBT is proposed to be a gradual aging process, verified by the accelerated aging experiment. Firstly, the mechanism between the relevant aging parameters and the degree of IGBT aging is studied in this paper. Secondly, the switching parameters in different degrees of aging are measured and compared by accelerated experiments. Finally, the stepwise linear regression algorithm is used to screen parameters and the Mann-Kendall test method is used to analyze the IGBT aging process curve. The results show that the aging process of IGBT is gradual, and the aging process model of IGBT is finally established. On this basis, the remaining using life of IGBT can be accurately measured.
Adaptive Cloud-Based Extended Reality: Modeling and Optimization

Extended Reality (XR) — which includes Virtual Reality and Augmented Reality — promises to bring the virtual and telepresence experience to another level. Unfortunately, solutions leveraging these technologies require special high-performance computing platforms that degrade the cost-benefit balance. Moving processing to the cloud solves this problem but imposes strict requirements on data transmission reliability, bandwidth, and delays. The satisfaction of these requirements becomes an extremely challenging problem in the presence of other types of delay-sensitive traffic, such as remote control, industrial automation, or the control commands of the Cloud XR application itself. This article studies the joint service of the adaptive Cloud XR traffic with other high-priority delay-sensitive traffics. The paper develops an analytical model of the considered communication system. The model represents the system as a discrete state Markov chain and estimates the quality of experience for Cloud XR users in various scenarios. Using the model, the paper estimates the network capacity for the Cloud XR traffic and optimizes the bitrate adaptation function of the Cloud XR video streaming application. The goal of the optimization is to improve the visual quality of the virtual environment observed by the users, subject to the constrained probability of image impairments due to excessive delivery delays. Numerical results demonstrate the high accuracy of the developed model and the benefits provided by the optimization.
Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. We also provide extensive simulation results to demonstrate the validity of the proposed approach.
Pre-Filtering Techniques for Spectrum Narrowing Caused by Optical Node Traversal in Ultra-Dense WDM Networks

To improve the spectral efficiency and increase network capacity, wavelength signals must be multiplexed densely, i.e., ultra-dense wavelength-division-multiplexing (WDM) networks. However, the spectrum of each WDM signal is narrowed by the wavelength-selective switches placed in optical nodes, so the transmissible distance and node hop count of the signal are strictly limited. To counter this spectrum narrowing problem caused by optical node traversal in ultra-dense WDM networks, pre-filtering techniques have been proposed. This paper comprehensively investigates the performance of four pre-filtering techniques: conventional Nyquist filtering, pre-emphasis filtering, partial-response filtering, and the combination of pre-emphasis and partial-response filtering. We conduct extensive computer simulations and discuss the optimality of pre-filtering techniques. The simulation results show that the adaptive use of pre-filtering can substantially extend the maximum attainable transmission distance and hop counts of 4/16/64-QAM signals.
DEMO: top-k cardinality estimation with HyperLogLog sketches

A recurring task in security monitoring consists in finding scan-type flows, namely flows which exhibit a large cardinality in terms of number of distinct source/destination addresses, or in most generality packet-level identifiers (e.g. ports, header fields, etc). But cardinality estimation requires to “remember” the identifiers seen in the past, and becomes quite challenging when the goal is to implement per-flow distinct count at wire speed, while maintaining high processing throughput and limited memory footprint. In this demo, we will show how to use HyperLogLog sketches to implement an efficient and innovative top-k cardinality estimation algorithm, called FlowFight. The algorithm has been tested and integrated in a full-fledged software router such as Vector Packet Processor.
A Second-order C-bandpass Frequency Selective Surface

A novel C-bandpass frequency selective surface(FSS) exhibiting miniaturized periodic parts is developed in this study. Such FSS acts as a bandpass filter at 4.03--7.87GHz. The proposed FSS exhibits miniaturization characteristics with the unit-cell dimension 0. 12λ*0. 12λ and the total thickness less than λ/28. Meantime, the simulation results reveal that the second-order FSS exhibits better stability under a variety of incident directions and polarization modes.
Cardiac Signals Based Methods For Recognizing Heart Disease: A Review

In recent time span among the number of untimed human death reasons cardiovascular related disease are foremost reasons. Life can only be saved by detection of such diseases at early-stage. The professionals related to medical field consider different physiological signals like ECG, PPG and PCG for getting an information of Cardiovascular health and its related diseases, such process is very effective to discover the information of related diseases at an early stage. This paper discusses on various cardiovascular diseases detection methods along with recent research gaps and future directions to enhance the competence of contemporary methods.
FlexState: Flexible State Management of Network Functions

Network function (NF) developers have traditionally prioritized performance when creating new packet processing capabilities. This was usually driven by a market demand for highly available solutions with differentiating features running at line rate, even at the expense of flexibility and tightly-coupled monolithic designs. Today, however, the market advantage is achieved by providing more features in shorter development cycles and quickly deploying them in different operating environments. In fact, network operators are increasingly adopting continuous software delivery practices as well as new architectural styles (e.g., microservices) to decouple functionality and accelerate development. A key challenge in revisiting NF design is state management, which is usually highly optimized for a deployment by carefully selecting the underlying data store. Therefore, migrating to a data store that suits a different use case is time-consuming as it requires code refactoring and adaptation to new application programming interfaces, APIs. As a result, refactoring NF software for different environments can take up to months, reducing the pace at which new features and upgrades can be deployed in production networks. In this paper, we demonstrate experimentally that it is feasible to introduce an abstraction layer to decouple NF state management from the data store adopted while still approaching line-rate performance. We present FlexState, a state management system that exposes data store functionality as configuration options, which reduces code refactoring efforts. Experiments show that FlexState achieves significant flexibility in optimizing the state management, and accelerates deployment on new scenarios while preserving performance and scalability.
Autonomous Transportation in Emergency Healthcare Services: Framework, Challenges, and Future Work

In pandemics like Covid-19, the use of autonomy and machine learning technologies are of high importance. The Internet of Things (IoT)-enabled autonomous transportation system (ATS) envisions a fundamental change in the traditional transportation system. It aims to provide intelligent and automated transport of passengers, goods, and services with minimal human interference. While ATS targets a broad spectrum of transportation (cars, trains, planes, etc.), the focus of this article is limited to the use of vehicles and road infrastructure to support healthcare and related services. This article offers an IoT-based ATS framework for emergency healthcare services using autonomous vehicles (AVs) and deep reinforcement learning (DRL). The DRL-enabled framework identifies emergency situations smartly and helps AVs make faster decisions on providing emergency health aid and transportation services to patients. Using ATS and DRL for healthcare mobility services will also contribute toward minimizing energy consumption and environmental pollution. This article also discusses current challenges and future works in using ATS for healthcare services.
Optimizing Labor Allocation in Modular Construction Factory Using Discrete Event Simulation and Genetic Algorithm

Modular construction is gaining popularity in the USA due to several advantages over stick-built methods in terms of reduced waste and faster production. Since modular construction factories operate as an assembly line, the number of workers at various workstations dictates the efficiency of the overall production. This paper presents a resource allocation framework combining discrete event simulation (DES) model and genetic algorithm (GA) to facilitate data-driven decision making. The DES model simulates the process of building modular units in the factory, and the GA optimizes the number of the worker at different workstations yielding to minimum makespan. A case study with a real-world modular construction factory showed that optimizing the assignment of available workers can reduce the makespan by up to 15%. This study demonstrates the potential of the proposed method as a practical tool to optimize resource allocation in the uncertain work environments in modular construction factories.
Convolutional Neural Networks for Malignant and Benign Cell Classification using Dermatoscopic Images

In general, cancer is considered as the second most fatal disease across the globe. In the past decades, skin cancer is increasing at a high rate as it is a major type of cancer. Diagnosis of unknown pores and skin cancer is very significant for delivering proper treatment. Curable with early analysis, simplest extraordinarily trained dermatologists that capable of appropriately understand skin lesions as malignant or benign. Skin disease is analyzed by utilizing clinical screening, dermoscopic investigation, histopathological assessment and a biopsy. As number of biomedical images collected and stored are rapidly increasing so a need of a technology to meet the requirement. As understanding is in restrained supply, systems classify skin lesions automatically as of benign or malignant cancer are beneficial as preliminary screening gear. The proposed methodology presents a CNN model, trained on feature extracted from a highway CNN pretrained on images of dermoscopic of pores and skin lesions. Further, the model is optimized using two different optimizers i.e., RMSPROP and ADAM individually and then comparison of accuracies is done, and conclusion would be based on the good optimizer. Further, it doesn’t price a good deal for computational energy in order to teach the model. It is a basic scratch model, in which this research work has worked only upon one DL algorithm and then in future, different DL algorithms will be analyzed to achieve a better accuracy.
Spreading Operation Frequency Ranges of Memristor Emulators via a New Sine-Based Method

The operation frequency of memristor seriously affects its applications in high-frequency working conditions. In this article, a new method using a sine-based function instead of the linear term in memristor models to spread the operation frequency ranges of memristor emulators is proposed. Via using this method on a flux-controlled memristor with smooth continuous cubic function as an example, research shows that the operation frequency range of this improved flux-controlled memristor is spread greatly. In particular, the improved memristor can still display an excellent pinched hysteresis loop even when the operation frequency is up to a few gigahertz, which is higher than most of the previously reported memristor emulators. The typical dynamic characteristics and the operation frequency range of the improved memristor are studied theoretically and numerically. The circuit simulations and physical experiments agree well with the theoretical analyses, which together demonstrate the effectiveness of the proposed methodology. Then, the improved memristor is applied to realize a chaotic oscillator to show its practical applications. In addition, the new method has universality, and it can be used by many other existing memristor emulators for spreading their operation frequency ranges.
Optical field manipulation for highly efficient wireless laser power transmission

We investigate the influence of laser incident at different angles on the output characteristics of PV cells. Meanwhile an inverse design method is proposed that generates optical field from the geometry of photovoltaic (PV) cells by using a spatial light modulator (SLM). The optimized optical field can be geometrically arranged and adapted to various configurations of any PV cells. The optic-electric (O/E) conversion efficiency of the optimized light field is about 33.14%, which is more than 204% higher than the Bessel, Airy and Gaussian optical field. In addition, optical field manipulation can also pave a new way for power transmission of moving objects by dynamic object tracking.
A New Structure for Broadband Transition Between Differential Stripline and Differential GCPW

In this paper, a new structure for broadband transition between differential stripline (DSL) and differential grounded coplanar waveguide (DGCPW) is presented for probe test and high-speed data transmission application. The proposed structure is designed under low temperature co-fired ceramic (LTCC) technology. A stepwise structure in the transition is utilized to eliminate additional vias on signal traces. Low insertion loss and low reflection up to 60 GHz are demonstrated by full-wave simulation.
Sentiment Analysis Using Deep Learning

Emotion recognition from text is crucial Natural Language Processing task which can contribute enormous benefits to different areas such as artificial intelligence, human interaction with computers etc. Emotions are physiologic thoughts engendered in human reactions to the events. Analysis of these emotions without facial and voice modulation are critical and requires a supervisory approach for proper interpretation of emotions. In spite of these challenges, it’s essential to acknowledge the human emotions as they progressively communicate using mistreatment text through social media applications such as Facebook, Twitter etc. In this paper, we propose a sentimental classification of multitude of tweets. Here, we use deep learning techniques to classify the sentiments of an expression into positive or negative emotions. The positive emotions are further classified into enthusiasm, fun, happiness, love, neutral, relief, surprise and negative emotions are classified into anger, boredom, emptiness, hate, sadness, worry. We experimented and evaluated the method using Recurrent Neural Networks and Long short-term memory on three different datasets to show how to achieve high emotion classification accuracy. A through evaluation shows that the system gains emotion prediction on LSTM model with 88.47% accuracy for positive/negative classification and 89.13% and 91.3% accuracy for positive and negative subclass respectively.
Small-sized Circularly Polarized Patch Antenna Designed with Isotropic Circular Fishnet Metasurface

Isotropic circular fishnet metasurface (CFM) is designed to realize a small-sized circularly polarized (CP) patch antenna. One numerical example shows that a patch antenna with patch length of 30mm can resonate at 2.529GHz when loaded with the CFMs on the ground plane. The substrate is with dielectrics constant of 2.2. The electrical patch length is 0. 37λg which is beyond the 0. 5λg limitation for a conventional antenna. In addition, the new CFM CP antenna is observed with improved impedance and axial ratio (AR) bandwidth performances over the conventional design. These results make the CFM CP patch antenna very promising in the portable wireless applications.
Optical Sensor based on hollow core negative curvature fiber for simultaneous detection of Multiple Gases

This paper proposes the design of an optical gas sensor based on a hollow-core negative-curvature fiber (HC-NCF) that guarantees the simultaneous detection of three hydrocarbon gases (C<inf>2</inf> H<inf>2</inf>, C<inf>2</inf> H<inf>4</inf> and CH<inf>4</inf>) in the mid-IR region.