Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of a shift of whispering-gallery modes caused by deformations and tensions

Abstract. The influence of centrifugal forces on angular velocity sensors that measure a spectral shift of whispering-gallery modes (WGMs) is investigated. Spherical WGM resonators of different materials are considered the sensing elements. The study is based on the results of the simulation in OOFELIE::Multiphysics software.
A new approach towards Image retrieval using texture statistical methods

Texture is a possession that represents the facade and arrangement of an image. Image textures are intricate ocular patterns serene of entities or regions with sub-patterns with the kind of brightness, color, outline, dimension, and etc.This article proposes a new method for texture characterization by using statistical methods (TCUSM). In this proposed method (TCUSM) the features are obtained from energy, entropy, contrast and homogeneity. In an image, each one pixel is enclosed by 8 nearest pixels. The confined in turn for a pixel can be extracted from a neighbourhood of 3x3 pixels, which represents the fewest absolute unit. We used four vector angles 0, 45, 90,135 to carry out the experimentation with the query image. A total of 16 texture values are calculated per unit. Compute the feature vectors for the query image by calculating texture unit and the resultant value is compared with the image database. The retrieval result shows that the performance using Canberra distance has achieved higher performance.
RPBV: Reputation-Based Probabilistic Batch Verification Scheme for Named Data Networking

As a promising implementation of Information Centric Networking, Named Data Networking (NDN) can facilitate content distribution with in-network caching and location-independent data access. However, the reliance on caches makes NDN vulnerable to content poisoning attacks, which waste network resources and decrease transmission efficiency. Most mitigating schemes follow the pattern that each content is repeatedly verified individually in each router and all producers have the same status, which wastes computation resources and degrades network performance. In this paper, we propose a Reputation-based Probabilistic Batch Verification (RPBV) scheme to address the issue, in which producers’ reputation is estimated according to verification results to distinguish different producers. We provide an adaptive probabilistic verification method based on reputation to avoid a lot of unnecessary verification operations. At the same time, we adopt an efficient batch verification algorithm to simultaneously verify multiple content, which reduces the overhead greatly. With the above mechanisms implemented only on the edge router to avoid repeated verification, we provide an optional probabilistic verification method on intermediate routers to strengthen the security. The extensive simulations show that RPBV achieves much lower computation overhead and shorter content retrieval time than the traditional schemes.
Synthesis of Simplified Cross Coupled Blocks with All Positive Couplings

In this work we present the synthesis of popular cross-coupled blocks used in the synthesis of filters with a cascaded topology to introduce transmission zeros in the response. The goal of the synthesis is to obtain the sign of all the couplings in the considered blocks positive (regardless of the position of the assigned transmission zeros). Furthermore, the elimination from the block topology of couplings that are difficult to implement is also realized. To solve this problem, we have exploited a recently introduced polynomial characteristic (namely the Reduced Chebycheff characteristic), which allows additional degrees of freedom with respect the classical Chebycheff response. The proposed synthesis technique has been validated by an experimental prototype, whose measured response is in good agreement with the theory prediction.
Deep Reinforcement Learning for Dependent Task Offloading in Mobile Edge Computing Systems

In mobile edge computing, there are usually relevant dependencies between different tasks, and traditional algorithms are inefficient in solving dependent task offloading problems and neglect the impact of the dynamic change of the channel on the offloading strategy. To solve the offloading problem of dependent tasks in dynamic network environment, this paper establishes the dependent task model as a directed acyclic graph. A Dependent Task Offloading Strategy (DTOS) based on deep reinforcement learning is proposed with minimizing the weighted sum of delay and energy consumption of network services as the optimization objective. The task-dependent offloading problem is transformed into an optimal policy problem under Markov decision processes. Multiple parallel deep neural networks (DNN) are used to generate offloading decisions, cache the optimal decisions for each round, and then optimize the DNN parameters using experience replay and incentive-driven mechanisms to obtain the optimal task offloading decisions. By using multiple parallel deep neural networks to generate offloading decisions, the impact of oscillations in the training results of individual neural networks on the algorithm is reduced. The experimental results show that, compared with the existing algorithms, the offloading decision generated by the algorithm can effectively reduce the delay and energy consumption of network services.
A study on high definition maps' standards and specifications for autonomous vehicles

Prior high definition maps are currently required in the perception stack of autonomous vehicles since they encode road-level features in centimetre accuracy. This rapid map establishment has led to a vast range of different map vendors, formats, structures and standards. In this study, the landscape of high-definition maps is being analyzed, evaluated and compared. Unlike other similar works, the maps in this study are categorized by their formats and structures since map vendors have started to provide single structure maps in multiple formats. Although this wide availability helps to expand the map usage, many aspects have to be considered and evaluated for choosing the most appropriate one. Apart from the detailed review, key issues and a discussion concerning the use of prior maps is provided to facilitate better map usage and adoption in the intelligent vehicles era.
A Mismatched Joint Source-Channel Coding Perspective of Probabilistic Amplitude Shaping: Achievable Rates and Error Exponents

Probabilistic Amplitude Shaping (PAS) is a novel method of reliable commmunication over channels with non-uniform capacity achieving input distribution. The encoder of PAS is a concatenation of a distribution matcher with systematic encoding of a Forward Error Correction (FEC) code whereas the PAS decoder is a concatenation of a mismatched FEC decoder and dematcher. In recent works PAS has been shown to close the shaping gap to Shannon capacity for discrete input AWGN channel for a wide range of SNR values. In this paper we modify the classical joint source-channel coding setup to include the salient features of PAS and we derive the Gallager style error exponent and consequently achievable rates for this setup.
Automatic Feeding System with High Accuracy Intelligent Product Defection Function

The combination of automation equipment and intelligence has become the trend of the automation industry. Among them, automated optical inspection (AOI) applications are applied in the field of automation due to the advantages of not being limited by working hours, rapid detection, and low labor costs. Because the content of the sample database is directly proportional to the detection accuracy, so in advance the established sample library becomes an important part, and the insufficient number of training samples are the main cause of poor detection accuracy. This research is integrated with industrial practice. In the automatic feeding system, the goal of high-accuracy product detection is achieved through the method of generative adversarial network (GAN). In this study, two kinds of generative adversarial networks are used to generate unknown flaw samples and import them into the database to improve the detection accuracy. In order to simulate the insufficient number of training samples, this paper uses a small number of samples for generation experiments. In addition, this study set up an automatic feeding system, using Ethernet communication architecture to integrate six-axis robotic arm, programmable logic controller (PLC), industrial computer and other equipment, with intelligent functions such as defect detection and so on. Experimental results show that the addition of GAN to expand the data set can improve the accuracy of sample detection. In addition, the graphical user interface (GUI) is also designed in the system to reduce the learning cost and operation difficulty of personnel and improve the utilization rate of equipment, thus forming a highly efficient intelligent mechanical system.
Instantaneous Frequency based estimation of time varying Directions of Arrival

This paper addresses the problem of estimating time-varying directions of arrival. It demonstrates how the concept of instantaneous frequency can be employed for this purpose. The proposed approach can localize more sources than the number of available sensors. It also theoretically allows the estimation of any angular time variation. Herein, we consider linear and hyperbolic time variations, which, in practice, take into account velocity and acceleration, respectively. Numerical experiments are conducted to validate the effectiveness of the proposed method.
Radar-Camera Fusion for Ground-based Perception of Small UAV in Urban Air Mobility

The resilient surveillance of cooperative and non-cooperative aerial targets is critical for the safety and security of urban air mobility operations. Accurate detection, tracking, and trajectory prediction are essential to the subsequent tasks, e.g. tactical conflict prediction and resolution. Meanwhile, the combination of radar and camera is a classic option to provide perception services in different challenging environments. In this paper, a deep semantic association network is proposed for building relationships between the image detections and raw radar points, which then contributes to subsequent tasks, e.g. detecting, tracking, and predicting the small UAV with networked radar and camera systems. Various flight trials are conducted for collecting multi-sensor data, finally, training and testing results on this dataset demonstrate the outstanding performance of the proposed fusion workflow in comparison to single-sensor performance. At the same time, the 2D predictions in the sensor network are reconstructed to 3D trajectories for comparison and also reveal the improvements of the radar-camera fusion approach.
High Sensitivity RF Energy Harvesting System with Self-calibrate Network

An effective codesign methodology of high sensitivity RF energy harvesting (RFEH) system for passive IoT applications is proposed, and chip implementation based on this solution was fabricated in standard TSMC 180 nm CMOS technology. By this systematic approach, a RFEH system including antenna, rectifier, power management unit (PMU) and impedance self-calibrate network were adopted to achieve an excellent sensitivity performance, which is characterized not only by high-Q factor but also large bandwidth. This work achieved high sensitivity performance of -32 dBm in 920~925 MHz for 0.7 V output voltage in anechoic chamber environment. Moreover, it can be powered by 36 dBm effective-isotropic radiated power source at a distance of 68 m outdoors.
Numerical simulation of guided wave propagation in reinforced concrete structures with debond damage

This paper numerically investigated the guided wave propagation along the reinforcing rebar in reinforced concrete structures. The finite element analysis was conducted to demonstrate the multi-mode and attenuation characteristics of guided waves in reinforced concrete structures, where the influences of thicknesses of concrete cover were also considered. The phenomenon of wave conversion resulting from interface debond between reinforcing rebar and concrete was specifically considered. It shows that the guided waves propagating in the integrated and debonded regions exhibit disparate behaviors in terms of the mode, group velocity and attenuation characteristics. The results indicate that the interface debond length can be determined by the amplitude and spectrum characteristics of the received waveform.
TGNRec: Recommendation Based on Trust Networks and Graph Neural Networks

In recent years, user-user trust relationships have played an important role in recommendation based on graph neural networks(GNNs). However, existing studies based on GNNs still face the following challenges: how to obtain more rating information of users’ trust from trust networks when using GNNs to learn the user latent feature. And how to effectively mine items’ relationships from the recommended data so that GNNs can better learn the item latent feature. To address the above challenges, in this paper, we propose a new model called TGNRec that accomplishes recommendation based on trust networks and graph neural networks. TGNRec consists of three modules: User Spatial Module, Item Spatial Module, Prediction Module. User Spatial Module considers both the rating information of users’ direct and indirect trust based on the transfer properties of trust relationships in trust networks. It mainly learns the user latent feature using user-item interactions and user-user trust relationships. Item Spatial Module establishes items’ similarity relationships based on the rating mean, which helps GNNs learn the item latent feature from user-item interactions and item-item relationships. Prediction Module realizes users’ rating prediction for unrated items by aggregating User Spatial Module and Item Spatial Module. At last, we conduct experiments on two real-world datasets, Film Trust and Ciao-DVD. The experimental results demonstrate the effectiveness of TGNRec for rating prediction in recommendation.
High Power Inductive Coupler Design From a Downscaled Model

Wireless power transfer systems are considered by many experts, possible solutions for smart charging of the batteries of future non-pollutant heavy duty electric vehicles, such as electric buses, electric trucks etc. The development of wireless power transfer system prototypes for heavy duty electric vehicles that are inherently characterized by high rated powers (hundreds of kW) is very expensive and their optimizations would entail huge costs. This paper analyzes the possibility of dimensioning a high-power inductive coupler for heavy electric vehicles based on a ~kW downscaled model so that a preliminary study can be carried out at acceptable price levels. The following main parameters were considered for the dimensional scaling: the distance between transmitter and receiver coils, the magnetic coupling factor, the operation frequency, the coupler area and the transmitted power. Keeping the same coupling factor for the real and downscaled models is a basic condition that is followed in this study for a transmitted power density at a given power ratio.
An Improved Extraction Method for the Trapping Time Constants in GaN HEMTs

With Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) being the preferred solution for radio frequency (RF) power applications, there is a growing need to have accurate models that encompass their trapping effects. For that, it is necessary to know the charge capture and emission time constants. As IDS has a nonlinear dependency on the trapping state, this paper proposes to use the static dependency with quiescent VDS (VDQ) to access the equivalent trapping state and, from there, extract the time constants. The proposed method was applied to a 2 × 200 μm GaN HEMT and the impact of extracting the time constants directly from IDS is shown.
Data-driven Vehicular Mobility Model introducing with Kinetic Car-Following Models for Traffic Simulation

Vehicular mobility model plays an important role in high-quality virtual traffic flow simulation. The neural network based method is capable to extract individual mobility models from traffic data, however, scenario simulation based on these models tends to go wild due to the error accumulation caused by the mixture of data from different driving states. In this paper, prior knowledge in vehicle kinetics will be introduced to the data-driven mobility model. A hierarchical framework is proposed with a Hidden Markov Model (HMM) layer to distinguish the driving states and a mobility model trained by specified data, along with an adaptation method to achieve the probabilistic representation of the kinetic car-following model. The partial knowledge on the indicated car-following driving state will also help initialize the HMM. The HMM delivers the temporal dependency and the labeling procedure provides more specified and precise data to the neural network. Finally, several experiments will be given to demonstrate the validity of the proposed method.
Comparison of Two Deep Learning Models for the Recognition of Parkinson’s Disease Gait Patterns

In this paper, a method based on gait analysis and deep learning was developed and explored for the recognition of Parkinson’s disease (PD). The study comprised 33 patients with PD and 15 healthy subjects (CTRL). Two deep learning models were designed and compared: convolutional neural network (CNN) and long short-term memory network (LSTM). The networks were trained on data that was recorded using a wireless and wearable system comprised of inertial and force sensors while performing a gait task. The influence of different data lengths on the performance of models was also examined. The best result was obtained using a CNN model fed with 8 seconds long walking sequences, with an accuracy of 92.49% for the classification of all PD patients and CTRL subjects. The obtained results showed that methods based on deep learning and gait analysis could contribute to objective and automatized recognition of Parkinson’s disease with high accuracy.
Evaluation of Machine Learning Prediction Models for Wifi based Indoor Positioning System

Thanks to the availability of wireless network infrastructure, wireless service providers can determine the device's position by analyzing the signal strength received from them in indoor scenarios. In this work, we propose to evaluate four machine learning prediction models for indoor positioning with an Open source Wireless Infrastructure deployed in an office. The performance evaluation is based on RMSE and R-Squared for the estimated two-dimensional (2D) positions. The first approach estimates the distance from the devices to the routers using a RSSI-based machine learning prediction model and uses the Min-Max positioning algorithm to determine the 2D device's position. As the second approach, we use two independent RSSI-based machine learning models to predict each coordinate of device position. The third approach predicts first the X coordinate using one ML model trained with three RSSI measurements from the routers and then predicts Y using the predicted X coordinate and the RSSI values by means of another machine learning model. The last approach is similar to the latter but this time the Y prediction is made first, followed the prediction of X coordinate based on the predicted Y coordinate and RSSI values. Finally, we compared these models to an indoor positioning model based on a path loss propagation model and the Min-Max positioning algorithm.
A V-band VCO with on-chip body bias voltage control technique using 40-nm CMOS process

Abstract This paper presents a V-band voltage-controlled oscillator (VCO) implemented in 40-nm CMOS Process. The on-chip body bias voltage control technique is adopted to improve phase noise and stability of the output signal and to design an LC tank and output buffer circuitry that compensates for the process, voltage, and temperature variations. The design was fabricated in RF Mixed-signal CMOS process with die size 0.078 mm2. Based on the silicon results, the proposed VCO achieve a phase noise of −86 dBc/Hz at 1 MHz offset. The VCO draws 17.8 mA current from a 1.2 V supply. Compared with the traditional structure VCO of the same batch, the measured figure of merit (FOM) is optimized from −165.4 to −169 dB. The novel VCO demonstrates better FOM performance compared to the traditional VCO structure. Moreover, the measured output signal power offset of novel VCO is reduced by 1.8 dBm, which will contribute to the improvement in the yield of chip manufacturing.
ELECTOR: Deterministic leader election algorithm for modular robots

A modular robot is an ensemble of programmable robotic modules which are linked together to form a bigger robot. Some applications like programmable matter are using a huge number of modules, potentially millions of modules, and there are many cases in which electing a leader is needed to optimize distributed algorithms. In these algorithms, modules act in collaboration with the condition, the status, and the decision of the leader. The main problem of current leader election algorithms is that most of them rely on random number generation which is difficult to implement in a distributed system. This paper proposes a new algorithm for electing a leader that does not take into consideration any probability approach such as random identifiers or coin flipping. Furthermore, the proposed algorithm reduces the probability of having a symmetric system since it uses the connected interfaces’ number to generate identifiers. The results show that the proposed algorithm, with a complexity of $\theta$(n), will end up with a leader with high probability, and works on all kinds of modular robots, 2D or 3D, despite the particle distribution and/or the system’s graph representation.
A SymPy derivation of Branin’s solution of uniform lossless transmission lines

Deriving Branin’s solution of uniform lossless transmission lines can be automated by using symbolic analysis and rewrite rules. This paper aims to show the derivation steps using SymPy, an application of this automation in SymPy on an electric circuit containing a uniform lossless transmission line, and the already existing applications of Branin’s equations in other software.