Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Adaptive Observer-Based Control for Output Consensus of Heterogeneous MASs With Input Saturation Constraint

Without assuming that the global connectivity information of the interaction graph is available to any agent, fully distributed saturated controller design for output consensus of heterogeneous multi-agent systems (MASs) under directed leader-follower communication topologies is addressed in this paper. The main difficulty of the problem lies in the coupling of the nonlinearities caused by the input saturation and the adaptive feedback control. To decouple these two types of nonlinearities, a distributed adaptive observer is first designed for each follower to realize asymptotical estimation of leader’s state, where the adaptive coupling gain is embedded to estimate the graph connectivity, and then an extra tracking error observer is introduced to tackle input saturation constraint. The controller is then constructed based upon these two observers, which is fully distributed with only the information of the distributed adaptive observer transmitted between neighboring agents and the local output information of each agent involved. To reduce the order of the controller, an in-depth analysis of the proposed controller is conducted, and the novel controller is designed by removing the tracking error observer and modifying the multi-level saturation control parameters for each follower. Furthermore, to remove the limitation of using absolute output information of each agent, novel controller is proposed with tracking error estimation designed in a distributed manner, which can achieve output consensus tracking for heterogeneous non-introspective MASs with undirected subgraph among followers.
Transfer Correlation Between Textual Content to Images for Sentiment Analysis

In social media, images and texts are used to convey individuals’ attitudes and feelings; thus, social media has become an indispensable part of people’s lives. To understand social behavior and provide better recommendations, sentiment analysis on social media is helpful. One sentiment analysis task is polarity prediction. Although current research on visual or textual sentiment analysis has achieved quite good progress, multimodal and cross-modal analysis combining visual and textual correlation is still in the exploration stage. To capture a semantic connection between images and captions, this paper proposes a cross-modal approach that considers both images and captions in classifying image sentiment polarity. This method transfers the correlation between textual content to images. First, the image and its corresponding caption are sent into an inner-class mapping model, where they are transformed into vectors in Hilbert space to obtain their labels by calculating the inner-class maximum mean discrepancy (MMD). Then, a class-aware sentence representation (CASR) model assigns the distributed representation to the labels with a class-aware attention-based gated recurrent unit (GRU). Finally, an inner-class dependency LSTM (IDLSTM) classifies the sentiment polarity. Experiments carried out on the Getty Images dataset and Twitter 1269 dataset demonstrate the effectiveness of our approach. Moreover, extensive experimental results show that our model outperforms baseline solutions.
Predominant Instrument Recognition Based on Deep Neural Network With Auxiliary Classification

Instrument recognition plays very important roles in music information retrieval, sound source separation and automatic music transcription. However, due to different playing styles and audio qualities, this task cannot be accomplished easily. Simultaneous existence of multiple instruments in polyphonic music increases the challenge to a greater extent. This article mainly focus on the identification of the predominant instruments in polyphonic music. We propose to construct a network with an auxiliary classification designed based on the onset groups and instrument families. The principal classification and the auxiliary classification enable the network to learn the instrument categories and groups jointly in a pattern of multitask learning. The IRMAS dataset is adopted in the experiment to extract the mel-spectrogram and six other types of features. The micro and macro average of precisions, recalls and F1 measures are used to evaluate the classification results. The effect of multitask learning, batch normalization and center loss in the predominant instrument recognition are demonstrated by various experiments. By selecting the loss ratios through a development set, the micro and macro F1 measures of our proposed network can reach 0.685 and 0.597, which are 10.7% and 16.4% higher than those obtained by the baseline, the ConvNet presented in [1].
Charge Transport in Vertical GaN Schottky Barrier Diodes: A Refined Physical Model for Conductive Dislocations

Charge transport mechanisms of forward and reverse leakage currents in vertical GaN Schottky barrier diodes are investigated by measuring the temperature-dependent current–voltage characteristics. The results show that the leakage current is primarily governed by dislocation-associated thermionic field emission (TFE). The primary transport path is the reduced, localized conduction band around the dislocation core rather than the continuum defect states. A refined phenomenological physical model is developed for conductive dislocations in GaN, emphasizing that: 1) surface donors, surrounding the core of dislocations, can significantly shrink the barrier region after ionization, causing severe TFE leakage; 2) the <inline-formula> <tex-math notation="LaTeX">$\text{O}_{N}$ </tex-math></inline-formula> donors likely to be responsible for TFE have a typical density of <inline-formula> <tex-math notation="LaTeX">$\sim 1\,\,\! \times \! \,\,10^{{18}}$ </tex-math></inline-formula> cm<inline-formula> <tex-math notation="LaTeX">$^{-{3}}$ </tex-math></inline-formula> at 300 K and activation energy of 78 meV; and 3) the barrier height at donor sites is ~0.65 eV at 300 K, which is reduced by ~0.4 eV with respect to the dislocation-free region.
An Efficient Accountable Privacy-Preserving Scheme for Public Information Sharing in Fog Computing

Fog computing is a new paradigm which extends cloud-computing services into the edge of the network. However, this new architecture creates new challenges in relation with data security and privacy. In fact, due to the emergence of data externalization technologies, as Fog computing, privacy has become a major concern for all users. There have been several proposals in the literature that treated privacy issues using existing anonymization techniques, but few of them considered accountability service. Whereas, when security systems do not adopt accountability mechanisms, full anonymity may encourage users to act maliciously. In this paper, we propose a novel accountable privacypreserving solution for public information sharing in fog computing architecture. Based on signatures, our scheme allows fog servers to authenticate any user in the system without violating its privacy. In case of usersâ€™ misbehavior, our architecture considers an authority that is able to trace any user in the system. Our solution ensures privacy preserving and accountability services in a completely distributed manner, without resorting permanently to the authority. Finally, we show through experimentation that our solution outperforms existing solutions.
Employing Higher Order Cladding Modes of Fiber Bragg Grating for Analysis of Refractive Index Change in Volume and at the Surface

In this work, a detailed study on volume and surface refractive index (RI) sensitivity of cladding modes for a fiber Bragg grating (FBG) based sensor is presented. Surface RI sensitivity of the cladding mode of FBGs has been illustrated and quantified with the concept of add-layer sensitivity for the first time to the best of our knowledge. A detailed investigation of mode transition of higher-order cladding modes has been revisited and important characteristics of the cladding modes are observed which could open a new designing path of fabrication and innovative way of the use of this family of optical fiber grating-based sensors. The effect of “mode transition” of higher-order cladding modes, higher operating wavelength for respective cladding mode and “mode stretching” effects are combined together to achieve higher volume and surface RI sensitivity of cladding mode of FBG. It has been shown numerically that with proper designing, sub-nanometer (∼0.04 nm) attachment of target analyte could be recognized by cladding mode of FBG which is quite promising for application in optical fiber grating bio-sensors. This critical designing method of FBG based surface refractometer would be very helpful in case of the fabrication of highly sensitive sensors for distinct biochemical applications.
Quasi-Vertical GaN Schottky Barrier Diode on Silicon Substrate With 1010 High On/Off Current Ratio and Low Specific On-Resistance

In this letter, we report a quasi-vertical GaN Schottky barrier diode (SBD) fabricated on a hetero-epitaxial layer on silicon with low dislocation density and high carrier mobility. The reduction of dislocation is realized by inserting a thin layer with high density of Ga vacancies to promote the dislocation bending. The dislocation density is <inline-formula> <tex-math notation="LaTeX">${1.6}\times {10}^{{8}}$ </tex-math></inline-formula> cm<sup>−2</sup> with a GaN drift layer thickness of <inline-formula> <tex-math notation="LaTeX">$4.5~\mu \text{m}$ </tex-math></inline-formula>. The fabricated prototype GaN SBD delivers a high on/off current ratio of <inline-formula> <tex-math notation="LaTeX">$10^{{10}}$ </tex-math></inline-formula>, a high forward current density of 1.6 kA/cm<sup>2</sup>@3 V, a low specific on-resistance of 1.1 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot \text {cm}^{{2}}$ </tex-math></inline-formula>, and a low ideality factor of 1.23.
Comparative Analysis on Big Data Tools: Apache Solr Search and Hibernate Search

In the following paper, an in-depth comparison of Apache Solr Search and Hibernate Search is presented. In the modern-day scenario, a lot of software enterprises require high performance and efficient searches in SQL based databases which consist of billions of entities. Since SQL's ‘like’ clause cannot provide these functionalities in the most efficient manner, several other libraries came into existence. Apache Lucene is one of the many common libraries used for the same in the software industry. It is a very powerful yet complex library with a wide variety of applications and advantages over conventional searches. Apache Solr Search and Hibernate Search are both implementations of Lucene with a variety of optimizations from the developers so as to facilitate its performance as per different requirements whilst making it user-friendly. In this paper, the search query execution times for both of the frameworks have been compared for several kinds of search methodologies on a test Big Data(110 Million Entries in MySQL) database. Upon comparison of the execution times, Solr Search was clearly performing better with Full-Text Searches, Fuzzy Searches, Wildcard Searches, etc. while Hibernate Search trumped when it came to Spatial Searches, Range Searches, etc. As both Solr and Hibernate Searches provide similar functionalities to some extent, software firms often struggle with picking a suitable one to best suit their needs. The following paper is directed towards helping such companies out by providing them with verified analysis and results on different types of searches, thereby helping them select the platform yielding most optimal results for them.
Artificial Noise Aided Hybrid Precoding Design for Secure mmWave MIMO System

This paper exploits the potential of millimeter wave (mmWave) system, where large-scale antenna arrays are allowed to implement in small physical dimension. We investigate a novel hybrid beamforming design for joint data and artificial noise (AN) precoding and power fraction selection in massive multi-input multi-output (MIMO) system. We aim at the secrecy rate maximization problem with respect to hybrid precoders design. The challenge of this problem lies in its non-convexity. To address this issue, we decouple the design for analog and digital precoders. We conduct analog precoder to maximize corresponding channel gain. For digital data precoder design, we first remove the non-convex codebook constraint and propose an iterative algorithm for optimal equivalent digital precoder design. Then, reconsidering the constraint, we conduct the digital data precoder to approach to the optimal design. Next, aiming to maximize AN power aligned at the eavesdropper, AN precoder design is optimally derived in closed form. Finally, we get power fraction by one-dimensional (1-D) search. Simulation results indicate that our proposed AN- aided hybrid precoding scheme achieves better secrecy performance compared with existing hybrid precoding schemes.
Deep Reinforcement Learning-Based Computation Offloading in Vehicular Edge Computing

Inspired by mobile edge computing (MEC), vehicular edge computing (VEC) enables vehicle terminals to support resource-hungry on-vehicle applications with significantly lower latency and less energy consumption. In this paper, we investigate the computation offloading problem in a typical VEC scenario, where a vehicle offloads its computation tasks to the VEC servers deployed in the road side unit (RSU) to minimize its long-term user cost. The mobility of the vehicle coupled with the high dynamics of the environment makes the problem particularly difficult. To tackle this challenge, a deep reinforcement learning (DRL) based offloading method is proposed, which approximates the offloading policy (OP) by a deep neural network (DNN) and trains the DNN with the proximal policy optimization (PPO) algorithm without a priori knowledge of the environment dynamics. Extensive simulation experiments and comprehensive comparison with six baseline algorithms demonstrate that it can achieve the lowest user cost in most cases.
A Deep Learning-Based Approach for Accurate Segmentation of Bladder Wall using MR Images

In this paper, a deep learning-based convolution neural network (CNN) is developed for accurate segmentation of the bladder wall using T2-weighted magnetic resonance imaging (T2W-MRI). Our framework utilizes a dual pathway, two-dimensional CNN for pathological bladder segmentation. Due to large bladder shape variability across subjects and the existence of pathology, a learnable adaptive shape prior (ASP) model is incorporated into our framework. To obtain the goal regions, the neural network fuses the MR image data for the first pathway, and the estimated ASP model for the second pathway. To remove noisy and scattered predictions, the CNN soft output is refined using a fully connected conditional random field (CRF). Our pipeline has been tested and evaluated using a leave-one-subject-out approach (LOSO) on twenty MRI data sets. Our framework achieved accurate segmentation results for the bladder wall and tumor as documented by the Dice similarity coefficient (DSC) and Hausdorff distance (HD). Moreover, comparative results against other segmentation approaches documented the superiority of our framework to provide accurate results for pathological bladder wall segmentation.
A 200–240 GHz Sub-Harmonic Mixer Based on Half-Subdivision and Half-Global Design Method

For the terahertz harmonic mixer, a design model based on Half-Subdivision and Half-Global Design Method (HS-HGDM) with a single functional unit is proposed. The single functional unit circuit of the mixer, such as LO or IF Low Pass Filter (LPF), is designed by Subdivision Design Method (SDM), and the rest circuits of the mixer are designed by Global Design Method (GDM). The harmonic mixer based on this model is characterized by simple circuit structure, low conversion loss, small circuit size and high stability. In this paper, an improved Compact Suspended Micro-strip Resonators (CSMRs) filter is developed for the IF part of sub-harmonic mixer. The remaining circuits consist of some basic transmission units, such as the High-Z Low-Z impedance Transmission Lines (H-Z L-Z TLs) applied for main circuit, and full/reduced height WR4.3/WR8 applied for providing RF/LO signal. In RF frequency range 200–240 GHz, a Single Sideband (SSB) conversion loss of 7.36±0.76 dB operating at IF frequency 1.8 GHz, and a Double Sideband (DSB) noise temperature below 1034 K with the LO power of 2–5 mW and the LO frequency of 107 GHz were achieved. This broadband sub-harmonic mixer operates at room temperature, which makes it applicable to sub-millimeter wave or terahertz wireless communication systems and imaging inspection systems.
GPM-Derived Climatology of Attenuation Due to Clouds and Precipitation at Ka-Band

Attenuation from clouds and precipitation hinders the use of Ka-band in SARs, radar altimeters and in satellite link communications. The NASA-JAXA Global Precipitation Measurement (GPM) mission, with its core satellite payload including a dual-frequency (13.6 and 35.5 GHz) radar and a multifrequency passive microwave radiometer, offers an unprecedented opportunity for better quantifying such attenuation effects. Based on four years of GPM products, this article presents a global climatology of Ka-band attenuation caused by clouds and precipitation and analyses the impact of the precipitation diurnal cycle. As expected, regions of high attenuation mirror precipitation patterns. Clouds and precipitation cause two-way attenuation at 35.5 GHz in excess of 3 dB about 1.5% of the time in the regions below 65°, peaking at as much as 10% in the tropical rain belt and the South Pacific Convergence Zone and at circa 5% along the storm tracks of the North Atlantic and Pacific Oceans. Confirming previous findings, the diurnal cycle is particularly strong over the land and during the summer period; while over the ocean, the diurnal cycle is generally weaker some coherent features emerge in the tropical oceans and in the northern hemisphere. Results are useful for estimating data loss from (sun-synchronous) satellite adopting active instruments/links at a frequency close to 35 GHz.
Backscatter-Aided Hybrid Data Offloading for Wireless Powered Edge Sensor Networks

In this paper, we consider a backscatter-aided hybrid data offloading scheme for a battery-less wireless sensor network. All sensor devices on the edge are coordinated by a hybrid access point (HAP), while also provides power for them via wireless power transfer. Co-located with the HAP, an edge computing server is set up to provide the computation and caching capabilities for the edge devices with insufficient power and computation resources. Each node is allocated a fixed time- slot for data offloading via either the conventional active communications or the passive backscatter communications. Such a hybrid data offloading scheme can flexibly control the trade- off between power consumption and data rate in offloading. We aim to minimize the total energy consumption by optimizing the offloading strategy of each edge device and the HAP's wireless power allocation over different edge devices. We show that the energy minimization problem exhibits a convex reformulation. For practical consideration, we devise a distributed algorithm to solve the problem. The numerical results demonstrate that the distributed algorithm can achieve a near- optimal performance. With a fixed transmit power at the HAP, our proposed hybrid offloading scheme provides a higher offloading throughput compared to the state-of-the-art data offloading schemes.
U-Net Architecture Application to Nuclei Cell Detection

In this paper U-Net neural network architecture and its’ variations for biomedical image processing is described. The application of the U-Net to the task of nuclei cell detection in particular is analyzed. The features and difficulties of the task are addressed. Several techniques to improve training and performance of the network are studied and, as a result, the model with specific parameters for the task of nuclei cell detection is proposed.
Efficient Hierarchical Multiple Access for Ambient Backscatter Wireless Networks

Ambient backscatter communication (AmBC) enables information delivery over an ambient RF signal without carrier generation and has emerged as a promising technology to build up the self- sustainable Internet-of-Things (IoT). However, when a strong ambient signal appears, multiple backscatter nodes may initiate data transmission simultaneously, causing severe contention and wasting the precious transmission opportunity. The nondeterministic and sporadic nature of ambient signals makes it a great challenge for efficient multiple access design in ambient backscatter aided wireless network (AmBWN). Moreover, the stringent energy supply and ultra-low-cost design of the backscatter transmitter makes most multiple access schemes no longer suitable for AmBWN. To fully share carrier resources for backscattering, we resort to the non-orthogonal multiple access (NOMA) to allow multiple devices in the same regime to transmit over an ambient signal with low latency. Moreover, we propose a hierarchical multiple access scheme, which allows beamforming based spatial division multiple access among groups, and NOMA for multiple users access within a group. The evaluation result shows latency and SINR can be significantly improved with minimal overhead at the transmitter.
The Development of Capacitive Nonlinear Transmission Lines and Its Performance Limits

The generation of radiofrequency through Nonlinear Transmission Lines (NLTLs) have been investigated as an alternative way to build RF generators for application in a variety of areas such as telecommunication, medical and defensive electronic countermeasures systems. Two main configurations of NLTLs have been reported in the literature: the discrete lines that comprise a network of LC sections built with nonlinear components and a gyromagnetic line that consists of a coaxial transmission line loaded with ferrite-based magnetic cores. Gyromagnetic lines produce high voltage microwave oscillations with frequency ranging from few hundred of MHz up to less than 10 GHz, however, an external magnetic polarization is required. On the other hand, discrete lines are suitable for RF generation in a lower frequency range from a few MHz to hundreds of MHz, having a better prospect for the use in compact systems. Capacitive NLTLs require the use of voltage-dependent components such as a ceramic capacitor or varactor diodes. While lines built with ceramic capacitors show a maximum operating frequency around tens of MHz requiring high input voltage, the use of silicon varactors diodes allows the construction of low voltage lines. On the other hand, by using carbide silicon Schottky diodes, the output of capacitive NLTLs can reach a few kV. This paper presents some experimental results on the development of the capacitive NLTLs at the Plasma Laboratory (LABAP) of the National Institute for Space Research (INPE) in Brazil. The analysis of the experimental results points out that performance limits of capacitive NLTLs are closely related to the characteristics of the nonlinear component used in their construction, leading to the conclusion that an improvement of their performance requires the development of new nonlinear components that present simultaneously voltage-dependent nonlinear capacitance, low losses, and thermal stability.