Miracle-dz/UniFAR-FTUs huggingface dataset:
Task Scheduling with Load Balancing on Automotive Multicore ECUs

In order to achieve greater performance with low power dissipation at moderate clock rate which is required to reduce the electromagnetic interference and compatibility issues with high speed processors, multicore processors have been implemented in automotive electronic control units(ECUs). Multicore processors enable the automobile manufacturers to add more functionality into one ECU rather than integrating more number of single functional ECUs. With the introduction of multicore processors in automotive ECUs, complexity in the design, development, and verification of the applications software have been increased tremendously. Even though both the hardware and software have parallelizability, there are certain design challenges such as: integrating various application software and scheduling the application tasks to balance the load across the cores. It requires to sequencing threads within the chained tasks, segregating those into thread sets and allocating to the available processing cores. In this paper, we address the problem of clustering numerous task threads and allocating them on multiple identical cores.We present a low-complexity modified heuristic algorithm to partition and build thread sets and map those on to cores so as to balance the load across the cores. It is observed that, minimum average load of the total load is distributed to each core to achieve a balanced load with task scheduling in simulating environment. The performance of the algorithm on dual core, tri core and quad core processors have been verified and compared to the same on single core processor.
A Flow-based IDS for SDN-enabled Tactical Networks

Tactical Mobile Ad hoc Networks (TMANET) have received much attention in recent years due to their ability to provide network connectivity to tactical mobile nodes in a battlefield without any infrastructure. Additionally, they provide decentralization, self-organizing, robustness and scalability to tactical nodes. Research in the tactical network domain indicates that network security remains a critical and continuous issue that needs attention. Researchers have identified the lack of a central management and control unit as one of the limitations in such networks. That is, there is no single management system by which the network and security services can be configured and provisioned. This paper proposes a flow-based Intrusion Detection System (IDS) framework for TMANETs. The proposed flow-based IDS framework leverages Machine Learning (ML) and software-defined network (SDN) to achieve anomaly detection in TMANETs. The paper also discusses the envisaged deployment scenarios and the capabilities of the proposed system.
A Novel Real-time Semantic-Assisted Lidar Odometry and Mapping System

Recently, rich semantic information has proven to be an enabling factor for a wide variety of applications in mobile robots. In this paper, we explore the integration of semantics into lidar odometry and mapping approaches and present a novel real-time semantic-assisted system. To this end, a sparse 3D-CNN model is designed to perform per-frame semantic segmentation of lidar points. Transformations are then estimated by jointly minimizing the geometric and semantic distances between correspondences. At last, new points are transformed into the world coordinate system and used to update predicted labels in the global semantic map. Experiments show that our system has a better performance in pose error compared with the geometry-based method.
An ultra-wideband conformal antenna with halfved Vivaldi

An ultra-wideband conformal antenna with halved Vivaldi antenna is presented for the 2GHz-18GHz. Halved Vivaldi is used and placed on the vehicle to keep conformal with the vehicular, which is suitable to vehicular communication. The prototype of this proposed antenna with a size of 65*30*0.508 mm3 was designed and simulated. Simulation results show that the input reflection loss is below 10 dB over 4.2 GHZ −18 GHz, and below 7 dB over 2–4.2 GHz
Intermolecular Interaction and Electric Field Dependence of Reverse Micelle on Water Tree Initiation in Polyethylene

It has been experimentally found that when amphiphilic surfactants are added to polyethylene (PE), water tree initiation, especially that of bow-tie trees (BTTs), is suppressed. In this study, we analyze the intermolecular interactions among PE, amphiphilic surfactants, and water molecules by molecular dynamics (MD) simulation and quantum chemical calculation. Moreover, we confirm that the surfactants and water molecules form a reverse micelle, and investigate the effect of the presence or absence of an electric field.
A Novel Data Packing Technique for QC-LDPC Decoder Architecture applied to NAND flash controller

This paper presents a data packing technique for Quasi-Cyclic LDPC codes decoder applied to NAND flash controller which has the challenge of long code length implementation due to high complexity and routing congestion on floor planning. Firstly, we introduce a proposed shift network to reorder the channel data sequence. The data packing mechanism is designed to be generic. Secondly, the proposed LDPC architecture is desgined to overcome complicated routing problems caused by random accessing of massage passing within the LDPC decoder. Based on the proposed architecture, the hardware description has been synthesized on Design Compiler using a TSMC 0.18um model and an FPGA-based floor planning with the implementing result has also been provided. Synthesis results also show that the area and throughput of the proposed decoder has desirable performance for the application of NAND flash controller.
Multi-Input Boost DC-DC Converter with Continuous Input-Output Current for Renewable Energy Systems

A new multi-input boost dc-dc converter topology is proposed in this paper. The input current waveforms of all input sources are continuous that makes it suitable for renewable energy applications. In addition, the output current of the converter is continuous which is desired in case of connection of the output terminals to the inverter. The steady-state analysis, the small-signal modelling of the converter and the control-output transfer functions of the system were calculated for the controller design. The performance of the converter was validated using simulation results.
Open Set Recognition of Timber Species Using Deep Learning for Embedded Systems

Reliable and rapid identification of timber species is a very relevant issue for many countries in South America and especially for Peru, which is the second country with the largest extent of tropical forest, and that is because this issue is a necessity in order to develop an effective management of the forest resources, such as inspection and control of the timber commerce. Since current methods of identification are based on a closed set recognition approach, they are not reliable enough to be used in a practical application because scenarios of identification of timber species are by nature an open set recognition problem. For that reason, in this work we propose a convolutional neural network that has two main characteristics, being able to run in a real-time embedded system and being able to handle the open set recognition problem, that is, this model can discriminate between known and unknown species. In order to evaluate it, tests are performed in two timber species datasets and some experiments are developed in the embedded system Raspberry Pi3B+ to measure energy consumption. The results present high metrics, which means that it manages to discriminate the unknown species with accuracy and F1 score above 91% for two sets of images used. In addition to this, our proposed model obtain lower maximum power value (10-12%) and computational resource usage (5-13%) than a classical convolutional model and MobileNetsV2 measured on the Raspberry Pi3B+.
How to Evaluate High Level Fusion Algorithms?

Evaluating high level information fusion algorithms is a tricky problem. Most of the time, situations monitored through high level information fusion are complex, composed of multiple objects or entities, having heterogeneous properties and in relation with each other's. Many criteria have to be taken into account within the evaluation. In this paper, we define several performance evaluation criteria. We focus on criteria related to functional evaluation, namely the correctness, the completeness and the precision of the result, as well as the level of management of uncertainty of information. Our criteria rely on the comparison of the result, given by the evaluated fusion algorithm, with the expected result of a given set of information provided as an input benchmark. We then present a proposition to aggregate them together with the 2-additive Choquet integral to obtain a single evaluation score.
Simulation Of Filter And Load Influence On Single Phase Inverter Against Voltage And Current Harmonic

The inverter using for power supply provides constant voltage, current and frequency generation advantage although there are changes in the load. However, inverter has weakness too. It will generate large harmonic. To get a high efficiency from an inverter, a sinusoidal voltage output is needed and total harmonic distortion (THD) is minimized. Several factors influence the harmonic magnitude on 1 phase inverter, namely load type and presence of filter. This study discussed about simulation of 1 phase inverter with and without LC filters, using R and RL series load type against voltage (V) and current (I) THD. The study results with LC filter and series RL load indicated THD reduction. The LC filter installation did not only affects harmonic reduction only but also output voltage and current of load.
Interpersonal Ties and the Social Link Recommendation Problem

Studies on evolving social network is a prevalent topic in today's society. Imagine an individual who tries to enter a social network through socialization. A key concern of the individual involves studying which other members of the social network he or she should interact with, so that the individual may gain the highest positional advantage, i.e., social capital. We ask the question: what ties to make with members of a dynamic social network for an individual who is trying to enter the network, to achieve a better position in the dynamic network? We formalize the dynamic social link recommendation problem. The focus of the problem is placed on modeling the evolution of social networks and the strategies for the target individual to create new ties with others. The result will be a recommendation mechanism that suggests potential members of the network whom the target individual could establish ties with. The goal is not only for the target individual to adopt the recommended output but the recommended output is beneficial to the target individual in terms of gaining a higher positional advantage. The main challenge in building such a recommendation mechanism is to identify potential links that are beneficial while taking into account the network dynamics. We aim to utilize multiple link prediction algorithms and propose edge establishing strategies to solve this specified problem. We evaluate these two sorts of strategies for average recall rate and average closeness centrality metrics, as well as other structural properties such as diameter, distribution of clustering coefficient.
Optimal Data Traffic and Computer Processing by a Generalized Network Flow Model with Gains and Losses

Using of a generalized network flow model with gains and losses is proposed for solving the problem of optimal network traffic and data processing from sources to the consumers. The problem for finding traffic of minimal cost is reduced solving the linear network flow problem. A method for network flow optimization is proposed for finding the maximal data flow from sources to consumers. It is shown that this flow is equal to the minimal cut of the network between sources and consumers. Theoretical results, obtained in the present work are confirmed by two appropriate numerical examples.
Representing, Sampling and Reconstructing Diffusion MRI Signal in Space and Time

This paper presents a novel 4D-basis, sampling grid and corresponding transforms for representing, sampling and reconstructing the diffusion MRI signal that is a function of both the diffusion wave vector q and diffusion time $\tau$. The vast majority of diffusion MRI sampling schemes treat diffusion time as a constant and only focus on the wave vector q, however the incorporation of diffusion time is important for estimating axon diameter and packing density. Our proposed sampling scheme is only the second to include diffusion time, and the first to provide an accurate reconstruction transform and corresponding sampling recommendations. Unlike the first scheme proposed by Fick et al., the proposed scheme requires a number of samples which is equal to the number of degrees of freedom in the proposed basis, the smallest number required for accurate reconstruction. We demonstrate the proposed basis and sampling scheme enables machine precision reconstruction accuracy of band-limited signals and high accuracy (below the noise floor) for synthetic diffusion MRI signal measurements.
Geographical NOMA-Beamforming in Multi-Beam Satellite-Based Internet of Things

In this paper, a geographical non-orthogonal multiple access based multiuser beamforming (NOMA-BF) scheme is investigated to improve spectral efficiency in multi-beam satellite-based Internet of Things (IoT), where the primary user per beam needs to be timely served with a small targeted data rate while other users per beam are opportunistically served by using the NOMA concept. We first design a novel user scheduling to enhance spectrum efficiency. Then, a multi-layer beamforming (BF) is developed to mitigate inter-beam interference, augment the difference of the effective channel gains of the users, and perform inter-beam power allocation in a separate manner. Additionally, a dynamic intra-beam power allocation policy is conceived to further improve spectrum efficiency, while meeting predefined target data rates of the primary NOMA users. Numerical results show the capability of our proposal.
Distributed Learning over Time-Varying Graphs with Adversarial Agents

In this work, we study the problem of non-Bayesian learning in time-varying (dynamic) networks when there are some adversarial (faulty) agents in the network. The set of faulty agents is fixed across time. The connectivity graph of the network is changing at each time step and is unknown to the agents. In every time step, each non-faulty agent collects partial information about an unknown state of the environment. Each non-faulty agent tries to estimate the true state of the environment by iteratively sharing information with its neighbors at each time step. We first present an analysis of a distributed algorithm in static communication network with faulty agents which does not require the network to achieve consensus. Existing algorithms in this setting require that all non-faulty agents in the network should be able to achieve consensus via local information exchange. We then extend this analysis to dynamic networks with relaxed network condition. We show that if every non-faulty agent can receive enough information (via iteratively communicating with neighbors) to differentiate the true state of the world from other possible states then it can indeed learn the true state.
Computation Offloading Decision Bounds in SWIPT-Based Fog Networks

Computation sharing is one of the most promising services in fog computing allowing the Fog Nodes (FNs) to share among themselves data and tasks to be computed. In case of battery powered-FNs, energy consumption becomes an issue. Simultaneous Wireless Information and Power Transfer (SWIPT) is a recently introduced technology enabling data and power transfer through microwave links among different nodes. In this work, we have considered the presence of a battery powered FN able to simultaneously share data and harvest energy from a Fog Access Point (F-AP), supposed to be plugged to the electrical network. The aim of this work is to define two suitable bounds able to drive the offloading decision to be taken by the battery powered FN, based on the estimated packet generation time, with the aim of having a stable energy system. We have further studied the impact of bandwidth and packet size on the two bounds. Simulation results demonstrate the impact of SWIPT-based offloading decision algorithm on network in terms of latency and network lifetime.
All-Digital Current-Sensorless Multi-Mode DC-DC Converter for Battery Powered Applications

This paper presents an all-digital current-sensorless multi-mode DC-DC converter for portable applications. All-digital load current detector, operation mode detector and multi-mode controller are implemented. The multi-mode controller can seamlessly change modes between pulse frequency modulation (PFM) and pulse width modulation (PWM) with the aid of load current detector and operation mode detector. The cooperation of PWM and PFM can maintain good output voltage regulation and light-load efficiency over wide load range. The digital controller, power MOSFET and driver are fabricated in TSMC 90-nm standard CMOS process. The experimental results show that the proposed architecture can achieve high efficiency over a wide load range.
1D Convolutional Neural Network for Detecting Ventricular Heartbeats

This paper shows a novel approach for detecting ventricular heartbeats using a 1D Convolutional Neural Network (1D-CNN). The algorithm input is the raw ECG signal, i.e., no signal pre-processing nor feature extraction are involved. The output of the 1D-CNN is filtered using a combination of linear and nonlinear filters to produce the final output. The MIT-BIH arrhythmia database was used for both algorithm training/tuning and evaluation. The assessment methodology followed the interpatient paradigm, where the algorithm was trained and evaluated using independent subsets. The performance of the proposed method was evaluated for two tasks; QRS detection, and heartbeat classification. QRS detection resulted in a sensitivity of 99.0% and a positive predictivity of 96.5%. The performance assessment of the ventricular ectopic beat detection resulted in a sensitivity of 85.8% and a positive predictivity of 64.5%. Although there is still room for improvement, the results suggest that convolutional neural networks are a promising approach for building heartbeat classifiers.
Effect of Gate Engineering and Misalignment on the RF Performance of DG HFET

In the current era of MOS Technology, DGMOS HFETs (Double Gate Metal Oxide Semiconductor Heterostruc-ture Field Effect Transistors) are one of the front runners in high speed switching applications. This paper presents the implementation of DGMOS HFET and effect of gate metal misalignment on the device parameters. An extensive simulation based study have been analysed on the back gate of the DGMOS HFET models by taking Drain Side Misalignment (DSM) and Source Side Misalignment (SSM). Various RF parameters and figure-of-merits of the misaligned structures of gate engineered DGMOS HFETs have been found out from the 2D simulation using commercial Sentaurus TCAD tool. These models are Single Material Double Gate (SMDG), Double Material Double Gate (DMDG), Triple Material Double Gate (TMDG) structure.
High-Efficiency Low Voltage Inverse Class-F Power Amplifier Design Based on Harmonic Control Network Analysis

A high-efficiency inverse Class-F (Class-F−1) power amplifier (PA) with a low voltage pHEMT is presented in this paper. The design process is based on a novel harmonic control network (HCN), consisting of a bias circuit (BC) and a low pass filter (LPF). The goal is reducing the impact of fundamental frequency reflection on DC supply, elimination of harmonics, improvement of power added efficiency (PAE), shaping the voltage and current waveforms along with enhancing the performance of the PA. The simulation is performed based on the harmonic balance analysis in order of 9. The implemented Class-F−1 PA is simulated and measured at 2.4 GHz. The maximum measured PAE of 76% and the drain efficiency of 82% are achieved, with a low drain voltage of 3 V and 11.2 dB gain under a 16 dBm input power. The proposed PA is fabricated and measurement results validated the simulations.
An Active Transmission Matrix-Based Nonlinear Analysis for Folded Waveguide TWT

A large signal beam-wave interaction model for the analysis of folded waveguide traveling wave-tube (FWG TWT) is proposed. The slow-wave structure is modeled as a sequence of coupled resonant cavities. The electron beam is treated as a current source that can induce electromagnetic waves in the cavity. The beam-wave interacting process is modeled as an active matrix. Compared with the other models only calculating the in-band characteristic of FWG TWT, our model can provide characteristic analysis in both in-band and near cutoff regions, and thus broaden operating bandwidth and suppress oscillation. As verification, two FWG TWT examples were designed by using our model. Its performances predicted by our code agree well with that obtained by the experiment and particle-in-cell software, respectively.
A Novel Doubly-Fed Flux Reversal Linear Machine With Armature Windings Wound on Both Stator and Mover Teeth

A novel doubly-fed flux reversal linear machine (DF-FRLM) is proposed in this paper, which has two sets of armature windings wound on stator and mover teeth, respectively. The proposed DF-FRLM can exhibit significantly higher thrust force than the conventional FRLM by fully utilizing the stator slot space. The configuration and operation principle of a 6/5 mover/stator-pole DF-FRLM are described. In order to obtain maximum thrust force, the major design parameters of both conventional FRLM and proposed DF-FRLM are optimized by 2D finite element (FE) method. The optimized DF-FRLM and FRLM are investigated and compared in terms of the electromagnetic performance. It is found that the average thrust force of the optimized DF-FRLM is 1.23 times that of the optimized FRLM. In addition, the optimized DF-FRLM can achieve 34.4% higher average thrust force compared to the conventional FRLM whose geometry parameters are the same as that of the optimized DF-FRLM. Furthermore, a periodic model of the DF-FRLM is used to analyze the influence of the longitudinal end effect on the machine electromagnetic performance. Finally, experimentation is carried out to verify the 2D FE simulation result.
Time-Related Network Intrusion Detection Model: A Deep Learning Method

Network Intrusion Detection Systems (NIDS) have become a strong tool to alarm attacks in computer and communication systems. Machine learning, especially deep learning, has made huge success in fields of industry and academic. Network intrusion activity can be a time series event. In this paper, we adopt a time-related deep learning approach to detect network intrusions. A stacked sparse autoencoder (SSAE) is first built to extract the features with the greedy layer-wise strategy. And then, we propose a time- related intrusion detection system based on the variants of Recurrent Neural Network (RNN). We study the performance of proposed approach on the binary classification with a benchmark dataset UNSW- NB15. Based on the study of parameter time steps, it is proved that our time- related model is effective for intrusion detection. The experiment results show that the accuracy of the proposed approach reaches over 98% and the false alarm rate is as low as 1.8%. The performance of our model is superior to that of the standard RNN- based approach and approaches based on Deep Neural Network and shallow machine learning.