Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Uniform Secret Sharing

An important problem in secret sharing schemes is minimizing the share size. For (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>)-threshold schemes and (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>)-ramp schemes, constructions that minimize the share size are known. This paper presents optimal constructions for a more general class of access structures in which subsets with the same cardinality have the same amount of information about the secret. We refer to schemes with such uniform access structures as uniform secret sharing. We first derive a tight lower bound for share entropy and then present an optimal construction. Our lower bound exceeds that previously reported. The optimal construction encodes the secret value using one or more ramp schemes.
An hybrid simulation tool for autonomous cars in very high traffic scenarios

This article introduces an open source tool for simulating autonomous vehicles in complex, high traffic, scenarios. The proposed approach consists on creating an hybrid simulation, which fully integrates and synchronizes two well known simulators: a microscopic, multi-modal traffic simulator and a complex 3D simulator. The presented software tool allows to simulate an autonomous vehicle, including all its dynamics, sensors and control layers, in a scenario with a very high volume of traffic. The hybrid simulation creates a bi-directional integration, meaning that, in the 3D simulator, the ego-vehicle sees and interacts with the rest of the vehicles, and at the same time, in the traffic simulator, all additional vehicles detect and react to the actions of the ego-vehicle. Two interfaces, one for each simulator, where created to achieve the integration, they ensure the synchronization of the scenario, the state of all vehicles including the ego-vehicle, and the time. The capabilities of the hybrid simulation was tested with different models for the ego-vehicle and almost 300 additional vehicles in a complex merge scenario.
Separating Optical and Language Models Through Encoder-Decoder Strategy for Transferable Handwriting Recognition

Lack of data can be an issue when beginning a new study on historical handwritten documents. To deal with this, we propose a deep-learning based recognizer which separates the optical and the language models in order to train them separately using different resources. In this work, we present the optical encoder part of a multilingual transductive transfer learning applied to historical handwriting recognition. The optical encoder transforms the input word image into a non-latent space that depends only on the letter-n-grams: it enables it to be independent of the language. This transformation avoids embedding a language model and operating the transfer learning across languages using the same alphabet. The language decoder creates from a vector of letter-n-grams a word as a sequence of characters. Experiments show that separating optical and language model can be a solution for multilingual transfer learning.
Converse Results for the Downlink Multicell Processing With Finite Backhaul Capacity

In this paper, we study outer bounds on the capacity region of the downlink multicell processing model with finite backhaul capacity for the simple case of two base stations and two mobile users. It is modeled as a two-user multiple access diamond channel. It consists of a first hop from the central processor to the base stations via orthogonal links of finite capacity and the second hop from the base stations to the mobile users via a Gaussian interference channel. The outer bound is derived using the converse tools of the multiple access diamond channel and that of the Gaussian MIMO broadcast channel. Through numerical results, it is shown that our outer bound improves upon the existing outer bounds greatly in the medium backhaul capacity range, and as a result, the gap between the outer bounds and the rate of the time-sharing of the known achievable schemes is significantly reduced.
Dynamic Performance Analysis of IEEE 802.15.4 Networks under Intermittent Wi-Fi Interference

A ZigBee (802.15.4) lighting network is potentially vulnerable to interference by other wireless technologies that operate in the same Industrial, Scientific and Medical (ISM) frequency band, such as Wi-Fi (802.11). Therefore the study of coexistence between IEEE 802.15. 4 and IEEE 802.11/Wi-Fi is becoming more relevant than ever. In this area, literature approaches to the co-existence issue currently fall into two main categories: experimental or simulation results and analytical models which are either asymptotic (e.g., using Bianchi's assumption) and/or stationary. In contrast, in this paper we present an analytical framework in which a comprehensive study of the dynamic influence of Wi-Fi interference on ZigBee networks of arbitrary size becomes possible. We first present an application of mean field approximation to an intractable Markov model of ZigBee networks. We use this modeling approach to investigate the effect of the Wi-Fi traffic pattern on the ZigBee network performance. By taking this dynamic interaction into account, we notice major performance differences depending on the type of traffic. It also reveals that even light traffic patterns, that offer long gaps of inactivity, can hamper the ZigBee performance significantly more than previously reported Wi-Fi coexistence studies suggest.
Distribution Network Scheduling with Distributed Energy Resources Based on Power Scenarios

With the development of distributed energy resources (DER) in distribution network, system operations such as peak shaving and voltage stability face more challenges. Battery energy storage (BES) has been widely accepted as the most potential application to cope with the challenge of high penetration of DER. To cope with the uncertainties and variability of DER, a stochastic distribution network scheduling method is proposed. The overall economy is achieved by fully considering the cell degradation, DER, electricity purchasing and active power losses. DER power scenarios are generated to consider the uncertainties and correlations based on the Copula theory. Finally, numerical studies for the day-ahead distribution network scheduling in the IEEE 123-node test feeder are presented to demonstrate the merits of the proposed method. Results show that the problems caused by DER are greatly alleviated by capturing the uncertainties and correlations.
Distributed Cooperative Droop Control for Seamless Islanding of an Autonomous Microgrid

The droop control under peer-to-peer (P2P) control mode, which does not need communication exchange and modes transfer, is more widely adopted recently. Especially, the distributed secondary control based on fully decentralized primary droop control attracts more attentions and deserves further investigation. Hence, a new distributed cooperative droop control is proposed to realize seamless islanding and maintain stability of the autonomous microgrid (MG) using pinning consensus of multi-agent system (MAS) in this study, it converts the secondary control to a pinning-based distributed control instead of a centralized control, and the control processing can be transformed into keeping track of synchronization, so as to reach the expected consensus through pinning and recover the frequency to rated value with zero steady-state error. Multiple cases are carried on to demonstrate the applicability and availability of the proposed control scheme.
Performance Gains of Three-Dimensional MIMO Antenna Structures

In order to face rising traffic demands and reduce the rigid requirements for antenna array design and operation, antenna placement geometry is being embraced by multiple automotive and cellular companies. Despite being supported in IMT2020 and 3GPP standards, MIMO antenna placement has been limited to two dimensional arrays, restricting antenna orientation flexibility and lack of scalability for Multi-user MIMO capabilities. This paper highlights the benefits of arbitrary placement of MIMO elements in 3D and introduces reconfigurable antenna structures for the base station as well as the UE, represented as a vehicular antenna array to expand the technology to Internet-of-Things (IoT) devices. The parameters of the patch antenna used are outlined for novel vehicular antenna structures. A 40% improvement is observed in user throughput for urban and suburban areas over typical 2D grid arrays.
A Compact Mobile Image Quality Assessment Using A Simple Frequency Signature

This paper addresses the task of automatic cleaning up of photo collections on low computing devices like mobile phones. That is, the task of automatically detecting and rejecting the blur, near duplicate and aesthetically poor images. To this end, a compact signature is presented with the constraints of being compact, simple to compute and use. One key characteristic of the proposed signature is that the same representation is used for all three different tasks that reduces the battery consumption and the storage requirements of a mobile device. The effectiveness of our proposed MIQA signature is experimentally validated by various experiments on several public datasets with subjective image quality evaluation. The proposed representation is optimal for all three IQA.
A High-Selective Frequency Selective Surface With Hybrid Unit Cells

In this paper, a novel frequency selective surface (FSS) is proposed. It has two metal layers and the configurations of the unit cells of these two layers are different, so-called hybrid unit cells. The designed FSS has three transmission zeros and steep roll-offs at the edges of the passband. In our design process, the top layer is used to achieve two transmission zeros at the edges of passband and the bottom layer is used to achieve wideband performance and transmission zero at the right side of the passband. The effects of the geometrical parameters on the frequencies of transmission poles and zeros are analyzed. By establishing an equivalent circuit, the matching impedance network is obtained quickly by Microwave Office and the flatness and insertion loss of the passband are improved after introducing the matching impedance network. The prototype of the proposed FSS is manufactured and tested. The measured −1.5-dB passband is from 13.2 to 19 GHz, and its relative bandwidth can achieve 36%. The measured results verify the design. This FSS is a good candidate to strengthen the performances of <inline-formula> <tex-math notation="LaTeX">$Ku$ </tex-math></inline-formula>-band satellite and <inline-formula> <tex-math notation="LaTeX">$Ku$ </tex-math></inline-formula>-band active radar.
Channel Estimation for Massive MIMO Uplink Transmission over Frequency Selective Fading Channels

In this paper, a low-complexity compressive channel estimation method is proposed for massive multiple-input multiple-output (MIMO) uplink transmission over frequency selective fading channels. Based on the physical channel model, the structured sparsity in the beam-delay domain is investigated and the overcomplete discrete Fourier transform matrix is utilized to mitigate channel power leakage in the beam-delay domain. Specifically, a nonorthogonal uplink pilot design scheme is employed to reduce the pilot overhead, then the compressive sensing model is established. By exploiting the structured sparsity and energy concentration property of the beam-delay domain channel, the energy-concentration based channel estimation algorithm is proposed by utilizing the sparsity level of channels. Simulation results demonstrate that the newly proposed algorithm outperforms other existing estimators with relatively low complexity.
Application of a Bio-inspired Localization Model to Autonomous Vehicles

In this paper, we propose an approach to tackle the localization challenge for autonomous vehicles by taking inspiration from biological models. We present a neural architecture based on a neurobotic model of the place cells found in the hippocampus of mammals. This model is based on an attentional mechanism and only takes into account visual information from a mono-camera and the orientation information to self-localize. Such a localization model has already been integrated in a robot control architecture which allows for successful navigation both in indoor and small outdoor environments. The contribution of this paper is to study how it passes the scale change by evaluating the performance of this model over much larger outdoor environments. Six experiments, taken from the KITTI datasets, using real data (image and orientation) grabbed by a moving vehicle are studied. The results show the strong adaptability of the model to different kinds of environments.
QL-Net: Quantized-by-LookUp CNN

Convolutional Neural Networks (CNNs) have achieved a state-of-the-art performance in the different computer vision tasks. However, CNN algorithms are computationally and power intensive, which makes them difficult to run on wearable and embedded systems. One way to address this constraint is to reduce the number of computational operations performed. Recently, several approaches addressed the problem of the computational complexity in the CNNs. Most of these methods, however, require a dedicated hardware. We propose a new method for the computation reduction in CNNs that substitutes Multiply and Accumulate (MAC) operations with a codebook lookup and can be executed on the generic hardware. The proposed method called QL-Net combines several concepts: (i) a codebook construction, (ii) a layer-wise retraining strategy, and (iii) a substitution of the MAC operations with the lookup of the convolution responses at inference time. The proposed QL-Net achieves a 98.6% accuracy on the MNIST dataset with a 5.8x reduction in runtime, when compared to MAC-based CNN model that achieved a 99.2% accuracy.
Concept of Modal Repulsion for Examining the Subsynchronous Oscillations Caused by Wind Farms in Power Systems

This paper investigates the torsional subsynchronous oscillations (SSOs) of synchronous generators caused by the grid-connected wind farms. The investigation does not rely on the representation of a wind farm by an aggregated wind generator and is based on a multi-input and multi-output closed-loop model. The model is comprised of the wind farm subsystem, which includes all the wind farms, and the subsystem of rest of power system (ROPS). Theoretical analysis proves that the open-loop SSO modal coupling of wind farm subsystem and ROPS subsystem results in the closed-loop SSO modal repulsion on the complex plane. Subsequently, damping of torsional SSOs decreases and growing SSOs may occur when the closed-loop modal repulsion is strong. Hence, closed-loop modal repulsion explains and reveals the mechanism about why the wind farms may cause the torsional SSOs in the power system. In addition, a method of open-loop modal analysis is proposed to detect the SSO instability risk brought about by the wind farms. The detection is based on the estimation of degree of closed-loop modal repulsion and only needs the modal information of open-loop subsystems. An example power system with wind farms is presented to demonstrate analysis and conclusions made in the paper.
Recurrent Variational Autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers

This paper explores two useful modifications of the recent variational autoencoder (VAE), a popular deep generative modeling framework that dresses traditional autoencoders with probabilistic attire. The first involves a specially-tailored form of conditioning that allows us to simplify the VAE decoder structure while simultaneously introducing robustness to outliers. In a related vein, a second, complementary alteration is proposed to further build invariance to contaminated or dirty samples via a data augmentation process that amounts to recycling. In brief, to the extent that the VAE is legitimately a representative generative model, then each output from the decoder should closely resemble an authentic sample, which can then be resubmitted as a novel input ad infinitum. Moreover, this can be accomplished via special recurrent connections without the need for additional parameters to be trained. We evaluate these proposals on multiple practical outlier-removal and generative modeling tasks involving nonlinear low-dimensional manifolds, demonstrating considerable improvements over existing algorithms.
A cm-Scale 2.4-GHz Wireless Energy Harvester With NanoWatt Boost Converter and Antenna-Rectifier Resonance for WiFi Powering of Sensor Nodes

The widespread use of WiFi in the 2.4-GHz ISM band creates the potential for RF powering using WiFi radios. This paper presents the design of a cm-scale RF-energy harvester including antenna, rectifier, and dc–dc boost converter for RF powering in the 2.4-GHz ISM band. The rectifier and boost converter are designed while considering cold-start operation and nanowatt-scale available power from the rectifier for RF incident power <−33 dBm. Low-power design in the power management unit and converter leads to ~1-nW quiescent power. The harvester implementation in 65-nm CMOS occupies 1.6 mm<sup>2</sup> while the antenna occupies 1.27 cm<sup>2</sup>. The proposed design optimization approach leads to a sensitivity of −33 dBm in cold start and −36 dBm in the primary mode for 1-V output, translating to ~1.4<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> and ~2<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> improvement in range compared to prior art. Measurements using a commercial WiFi TX demonstrate the ranges of up to 1.25 m for 14-dBm output power with 1.3% TX duty-cycling in WiFi access-point mode, demonstrating the feasibility of powering sensors from RF power beacons in the 2.4-GHz ISM band as well as from background WiFi transmissions.
Online Identification of Transient Stability and Unstable Generators Based on Deep Learning

Machine learning methods have been considered as a promising method for online transient stability analysis. This paper presents a deep learning approach for transient stability prediction and unstable generator identification based on convolutional neural networks (CNNs). The post-disturbance dynamic responses of all the generators are utilized as the input features. A binary classification CNN model is generated for transient stability prediction. And a multi-class classification CNN model is built for unstable generator identification. Moreover, the multi-class CNN model can provide the unstable probability of each generator using the probability outputs and the label information. Comprehensive studies are conducted on the New England 10-machine 39-bus system to verify the effectiveness of the proposed approach.
On Fixed Beamforming in Sectored mmWave Ultra Dense Networks at 73GHz

We investigate the use of fixed analogue transmit beamforming for downlink communication in millimetre-wave (mmWave) band ultra-dense networks (UDNs) at 73GHz. Our simulation study is based on 3D radiation patterns using a real design of large planar antenna arrays with different horizontal beamwidths and full characterisation of interference. The effects of network densification are captured through different ratios of the number of access points (APs) to the number of users. We quantify the system's performance by evaluating outage and achievable rate. The results show that users with rates around the mean benefit more from the densification than from narrowing the beamwidth, but for users with low rates, narrow beams also provide a significant increase in throughput.
Short Text Classification with A Convolutional Neural Networks Based Method

The traditional machine learning algorithms are easily affected by datasets in short text classification tasks, so they have weak generalization ability when confronted with new situations. This paper presents a new method SVMCNN by combining Convolutional Neural Networks and Support Vector Machine. Training the SVMCNN model with labeled datasets, and using the collected Twitter data for classification test. The results show that the SVMCNN, especially pre-trained SVMCNN has good performance in short text classification, which gets the high Precision rate, Recall rate and F1-measure.
Resource Allocation for Uplink NOMA and D2D Links with MLWDF Scheduling Discipline

One of the key features of the fifth-generation (5G) cellular network is non-orthogonal multiple access (NOMA) which improves the spectrum efficiency and increases the network connectivity. A second key ingredient is device to device (D2D) communication which increases spectrum efficiency and off-loads the cellular network. A proper exploitation of both NOMA and D2D requires adequate resources allocation algorithms to manage the interference. Moreover, to support various quality of service (QoS) requirements, other key metrics beyond the rate should be taken into account, such as queue stability and head of line (HoL) packet delay. In this paper, we consider resource block (RB) allocation and power control for both uplink NOMA and D2D communications. An overlay spectrum sharing paradigm is adopted, where an RB is exclusively allocated to either the cellular or D2D communications. The solution uses the modified largest weighted delay first (MLWDF) scheduling discipline and reduces the HoL packet delay, with negligible reduction of the achieved sum rate with respect to the pure maximum-rate solution in typical cellular scenarios.
Three-Phase ZVR Topology and Modulation Strategy for Transformerless PV System

Leakage current reduction is crucial for operating transformerlss photovoltaic (PV) systems. In this letter, a new three-phase topology and modulation strategy is proposed. It is derived from the single-phase zero-voltage state rectifier topology, but the operation mechanism is quite different. Therefore, a new modulation strategy based on the Boolean logic function is proposed to achieve the constant common-mode voltage, so as to eliminate the leakage current. Finally, the experimental tests are carried out to verify the feasibility and effectiveness of the proposed solution.
Signature and Logo Detection using Deep CNN for Document Image Retrieval

Signature and logo as a query are important for content-based document image retrieval from a scanned document repository. This paper deals with signature and logo detection from a repository of scanned documents, which can be used for document retrieval using signature or logo information. A large intra-category variance among signature and logo samples poses challenges to traditional hand-crafted feature extraction-based approaches. Hence, the potential of deep learning-based object detectors namely, Faster R-CNN and YOLOv2 were examined for automatic detection of signatures and logos from scanned administrative documents. Four different network models namely ZF, VGG16, VGG_M, and YOLOv2 were considered for analysis and identifying their potential in document image retrieval. The experiments were conducted on the publicly available "Tobacco-800" dataset. The proposed approach detects Signatures and Logos simultaneously. The results obtained from the experiments are promising and at par with the existing methods.