Miracle-dz/UniFAR-FTUs huggingface dataset:
H_ Index for Linear Time-Varying Markov Jump Stochastic Systems and Its Application to Fault Detection

In this paper, the <italic>H</italic>_ index for Markov jump linear time-varying stochastic systems and its application to robust <italic>H</italic>_ fault detection filter (FDF) are under consideration. First, a set of finite horizon backward generalized differential Riccati equations (GDREs) and a set of matrix inequalities are introduced. Based on the introduced backward GDREs and matrix inequalities, for nominal Markov jump linear time-varying stochastic systems, two necessary and sufficient conditions for the finite horizon <italic>H</italic>_ index larger than a given prescribed level <inline-formula> <tex-math notation="LaTeX">$\beta > 0$ </tex-math></inline-formula> are given. Second, for norm uncertain Markov jump linear time-varying stochastic systems, sufficient conditions are presented in terms of matrix inequalities. As applications, two equivalent conditions for the existence of robust <italic>H</italic>_ FDF are obtained for nominal linear stochastic systems. In particular, under the case of norm uncertainties, a robust <italic>H</italic>_ FDF is designed based on the feasibility of linear matrix inequalities.
Single Object Tracking Using Estimation Algorithms

The application of Kalman Filter in the process of state estimation and thereby tracking a single object in motion is explored in this paper. A collection of images consisting of 200 different instances of the single object's position has been taken into consideration, whose location has been found with the help of background subtraction technique. The actual trajectory has been obtained by connecting the centroid locations of the obtained images of moving object. This paper incorporates the use of traditional Kalman filter to estimate the position and the trajectory of the single object in motion. The performance of the traditional Kalman filter has also been compared with a proposed modified version of Kalman filter for this challenging job. An exponential function has been multiplied with the Kalman gain in the modified Kalman filter. The performance evaluation shows that the modified Kalman filter generates improved results with high convergence rate and low tracking error compared to Kalman filter. The work presented here has enormous potential in the field of object tracking and navigation for different practical applications.
Reduced Order Sliding Mode Observer for a Class of Linear Systems with Relatively Slower Unmeasured State Variables

In this paper, we have designed a new observer utilizing the fact that some class of systems possesses at least one fast varying measured state variable along with some sluggish unmeasured plant state variables. This observer is designed with introducing a new variable which is proportional to the combination of unmeasured state variables and the plant output. A velocity estimation problem with a finite dimensional linear time-invariant model of a brushed permanent magnet DC (PMDC)motor has been considered as a case study. Simulation result shows the effectiveness of the proposed reduced order sliding mode observer over the widely used Utkin observer.
Simulation of Network Interaction Between Base Stations and Subscribers in 5G

Modeling is widely used to develop new telecommunication systems and network protocols. Network simulators are available to simulate an arbitrary network, simulating the behavior of both the nodes’ network and communication channels. To develop network level algorithms, it is necessary to develop a simulation model to verify the correctness of the developed algorithms. This article presents the results of the work of the developed model for testing the performance of the network and channel level algorithms in 5G systems. This model was developed in the simulator NS-3, and the results of this model are presented in the form of diagrams.
Can Forensic Detectors Identify GAN Generated Images?

Generative adversarial network (GAN) has shown its powerful capability in generating photorealistic images. Although the generated images can fool human eyes, it is not clear whether they can evade the detection of forensic detectors, which aim to identify the originality and authenticity of images. In this paper, we investigate how forensic detectors perform in differentiating between GAN generated images and real images. We consider two kinds of approaches, one is intrusive and the other is non-intrusive, based on whether the GAN architecture is needed for performing detection. We have conducted extensive experiments on a celebrity face image dataset to evaluate the effectiveness of different approaches. The results and analyses show that the intrusive approach can detect GAN generated images but with a relatively high false alarm rate. The non-intrusive approach with features extracted from a VGG network is very effective for detecting GAN generated images when the training data is sufficient, but it still faces challenge when the training data and testing data are mismatched.
Inflammation and epithelial cell injury in AIDS enteropathy: involvement of endoplasmic reticulum stress

Immunosuppressive lentivirus infections, including human, simian, and feline immunodeficiency viruses (HIV, SIV, and FIV, respectively), cause the acquired immunodeficiency syndrome (AIDS), frequently associated with AIDS enteropathy. Herein, we investigated the extent to which lentivirus infections affected mucosal integrity and intestinal permeability in conjunction with immune responses and activation of endoplasmic reticulum (ER) stress pathways. Duodenal biopsies from individuals with HIV/AIDS exhibited induction of IL‐1β, CD3ε, HLA‐DRA, spliced XBP‐1(Xbp‐1s), and CHOP expression compared to uninfected persons (P<0.05). Gut epithelial cells exposed to HIV‐1 Vpr demonstrated elevated TNF‐α. , IL‐1β, spliced Xbp‐1s, and CHOP expression (P<0.05) together with calcium activation and disruption of epithelial cell monolayer permeability. In addition to reduced blood CD4+ T lymphocyte levels, viral loads in the gut and plasma were high in FIV‐infected animals (P<0.05). FIV‐infected animals also exhibited a failure to gain weight and increased lactulose/mannitol ratios compared with uninfected animals (P<0.05). Proinflammatory and ER stress gene expression were activated in the ileum of FIV‐infected animals (P<0.05), accompanied by intestinal epithelial damage with loss of epithelial cells and leukocyte infiltration of the lamina propria. Lentivirus infections cause gut inflammation and ensuing damage to intestinal epithelial cells, likely through induction of ER stress pathways, resulting in disruption of gut functional integrity. —Maingat, F. , Halloran, B. , Acharjee, S. , van Marle, G. , Church, D. , Gill, M. J., Uwiera, R. R. E. , Cohen, E. A. , Meddings, J., Madsen, K. , Power, C. Inflammation and epithelial cell injury in AIDS enteropathy: involvement of endoplasmic reticulum stress. FASEB J. 25, 2211‐2220 (2011). www.fasebj. org
Discriminative Feature Extraction Based on Sequential Variational Autoencoder for Speaker Recognition

This paper presents an extended version of the variational autoencoder (VAE) for sequence modeling. In contrast to the original VAE, the proposed model can directly handle variable-length observation sequences. Furthermore, the discriminative model and the generative model are simultaneously learned in a unified framework. The network architecture of the proposed model is inspired by the i-vector/PLDA framework, whose effectiveness has been proven in sequence modeling tasks such as speaker recognition. Experimental results on the TIMIT database show that the proposed model outperforms the traditional i-vector/PLDA system.
Applying Complex-Valued Neural Networks to Acoustic Modeling for Speech Recognition

Complex-valued neural networks (CVNNs) are well suited to speech signal processing because they can naturally represent amplitude and phase. In this paper, we explore applying an acoustic model with multiple complex-valued layers (multiple-CVNN-AM) and spliced features to speech recognition. First, we focus on multiple-CVNN-AM with unspliced input features and investigate an appropriate architecture from the viewpoint of the activation function, bias, and number of complex-valued layers. We also propose batch amplitude mean normalization for more quickly and stably training complex-valued layers. We then investigate an appropriate architecture for multiple-CVNN-AM with spliced input features and compare it with a real-valued neural network acoustic model without complex-valued layers (RVNN-AM) and complex linear projection (CLP) models, which can be considered acoustic models with single complex-valued layers. We show that under noise conditions, multiple-CVNN-AM outperforms RVNN-AM and CLP models by up to 7.45% and 11.90%, respectively.
Cannibalism enhances biofilm development in Bacillus subtilis

Cannibalism is a mechanism to delay sporulation in Bacillus subtilis. Cannibal cells express the skf and sdp toxin systems to lyse a fraction of their sensitive siblings. The lysed cells release nutrients that serve to feed the community, effectively delaying spore formation. Here we provide evidence that the subpopulation of cells that differentiates into cannibals is the same subpopulation that produces the extracellular matrix that holds cells together in biofilms. Cannibalism and matrix formation are both triggered in response to the signalling molecule surfactin. Nutrients released by the cannibalized cells are preferentially used by matrix‐producing cells, as they are the only cells expressing resistance to the Skf and Sdp toxins. As a result this subpopulation increases in number and matrix production is enhanced when cannibalism toxins are produced. The cannibal/matrix‐producing subpopulation is also generated in response to antimicrobials produced by other microorganisms and may thus constitute a defense mechanism to protect B. subtilis from the action of antibiotics in natural settings.
An Analysis of Speaker Diarization Fusion Methods For The First DIHARD Challenge

In this paper, we introduce the attempts of our fusion methods during the first DIHARD challenge. To our knowledge, this is the first launch in speaker diarization domain which aims to evaluate the performance of the state-of-the-art system in realistic adverse acoustic environments. Besides speech preprocessing modules including speech denoising and speech activity detection, our attention has been focused on back-end clustering algorithms, especially in system fusion. Consensus clustering is adopt to combine both original speech and denoised speech, for purifying unreliable clusters. Moreover, a score-level fusion is conducted between GMM-UBM-based i-vector and CNN-based i-vector. Finally, our system achieves diarization error rates (DERs) of 36.05% on the evaluation sets, which is the second place in the DIHARD challenge.
Control of Quadrotors for Tracking and Landing on a Mobile Platform

Quadrotors have become very popular, both commercially and as research platforms. Numerous studies have been carried out on these aerial vehicles, covering different aspects of their dynamics and control. Many of the interesting applications of quadrotors arise when they are used as part of a cooperative robotic system, consisting of aerial and ground robots. Two essential steps toward realizing autonomous teams of aerial and ground robots are achieving position coordination between them, and autonomous landing of aerial robots on ground platforms. The goal of this paper is to tackle the problems of tracking and landing of an aerial robot on a mobile platform. Two approaches are presented for the control of a quadrotor with the purpose of tracking a mobile platform and landing on it while in motion. First, a sliding mode controller is proposed for the tracking application. The controller is then improved to an adaptive controller for the landing phase, taking into account an aerodynamic phenomenon known as the ground effect. Formal proof of stability is provided for both controllers using the Lyapunov stability theorem. Simulations are also conducted to assess the performance of the proposed control schemes, which demonstrate good tracking and landing behavior with realistic control action. The presented work was done as part of a bigger project aiming to develop a system consisting of two aerial and ground robots capable of coordinated tracking and landing.
Nearest-First: Efficient Relaying Scheme in Heterogeneous V2V Communication Environments

Today, there exist two types of vehicular communication technology, namely, Long Term Evolution-based vehicle-to-vehicle (LTE-V2V) and dedicated short-range communications (DSRC). Although many studies dealing with vehicular communication have been conducted, the situation where separate groups of vehicles using different communication technologies coexist has never been studied. In the coexistence situation, the communication inability issue between the vehicles using different communication technologies is raised. To resolve the problem, we propose a relaying system, called Nearest-first, where hybrid user equipments (UEs), which are equipped with both DSRC and LTE-V2V modules, are considered. When a hybrid UE receives a cooperative awareness message (CAM), the UE relays the CAM using the other communication technology. We conduct simulations in various situations incorporating realistic mobility model and road topologies. We demonstrate that the Nearest-first outperforms existing relaying schemes with the performance gain of up to 91% in terms of effective communication distances.
VV-Couplet: An open source Chinese couplet generation system

Chinese couplet is a special form of poetry involving a pair of lines that adhere to certain rules. This paper presents VV-Couplet, an open source automatic couplet generation system. This system is based on the attention-based sequence-to-sequence neural model that ‘translates’ the first line to the second line. Particularly, it relies on a local attention mechanism that learns the basic rules of couplet generation. Moreover, entity names such as person names and addresses are particularly treated. We open an online couplet generation service, and the entire source code and data for interested readers to reproduce our work.
Optimized Tone Mapping of HDR Images via HVS Model-Based 2D Histogram Equalization

We propose an optimized human visual system (HVS) response model-based tone-mapping algorithm to display high dynamic range (HDR) image content on standard dynamic range (SDR) displays. We first measure the HVS response differences using a 2D histogram when an HDR image is displayed on an HDR device and its tone-mapped image on an SDR device. Then, we formulate an optimization problem to minimize the difference. By efficiently solving the optimization problem with a closed-form solution, we obtain an optimal tone-mapping curve. Experiment results demonstrate that the proposed algorithm provides higher image quality than a conventional algorithm in the HDR10 standard.
Implementation of a Battery-Free Wireless Sensor for Cyber-Physical Systems Dedicated to Structural Health Monitoring Applications

This paper addresses the concept of a wirelessly powered and battery-free wireless sensor for the cyber–physical systems dedicated to the structural health monitoring applications in harsh environments. The proposed material architecture is based on a smart mesh wireless sensor network composed of sensing nodes and communicating nodes. The sensing nodes are used to sense the physical world. They are battery-free and wirelessly powered by a dedicated radiofrequency source via a far-field wireless power transmission system. The data collected by the sensing nodes are sent to the communicating nodes that, between others, interface the physical world with the digital world through the Internet. A prototype of the sensing node—using a LoRaWAN uplink wireless communication and temperature and relative humidity sensor—has been manufactured, and the experiments have been performed to characterize it. The experimental results prove that the periodicity of measurement and communication can be controlled wirelessly by using only the wireless power transmission downlink. In this paper, we highlight the performance of this complete implementation of a wirelessly powered and battery-free wireless sensing node—not yet integrated or miniaturized—designed for implementing complete cyber–physical systems and based on the simultaneous wireless information and power transfer. Finally, an investigation of comparable implementations of the battery-free sensing nodes for the cyber–physical systems is carried out.
Tibetan acoustic model research based on TDNN

Deep neural network (DNN) has been significantly improved in Tibetan speech recognition tasks, however, it still requires improvement when compared with that in Mandarin, English, or other languages. This paper examines a Tibetan acoustic model based on deep neural network and extracts the i-Vector features by modeling the speaker in the feature space. After combining the MFCCs and i-Vector features, we train a time-delayed neural network (TDNN) based Tibetan acoustic model, compared to deep neural network, it can get better performance. At the same time, we study the transfer learning from Mandarin to Tibetan and prove its effectiveness.
9.2 A 192-Virtual-Receiver 77/79GHz GMSK Code-Domain MIMO Radar System-on-Chip

MIMO radars transmit selected waveforms on N TX physical antennas: when they receive them back on M RX physical antennas, they are able to reconstruct an array with (N × M) virtual receive elements, thereby obtaining a finer spatial/angular resolution. Fully integrated mm-wave radar transceivers such as [1–3] implement up to 12 Virtual Receivers (VRX) and can be expanded to larger time-domain MIMO arrays only by relying on costly PCB-based implementations.
Improved Deep Persian Named Entity Recognition

Named Entity Recognition (NER) is a challenging task specifically for low resource languages like Perisan. In this work we evaluate the result of deep learning structures in NER. Specifically we use the only publicly available corpus (ArmanPer-soNER) to train a model in which features are extracted using recurrent and convolutional neural networks. Extracted features vectors are then passed to a simple fully connected network and finally a conditional random field layer is used to find the best sequence tag for the input word sequence. Results show statically significant improvement over the available results on the corpus. Specifically the best structure has 81.50% and 76.79% word level and phrase level f1 score respectively. We also conduct an error analysis on the output of the best model and show that this corpus also have much errors and must be reviewed to have a more accurate one to further improve the performance.
DksA and ppGpp directly regulate transcription of the Escherichia coli flagellar cascade

The components of the Escherichia coli flagella apparatus are synthesized in a three‐level transcriptional cascade activated by the master regulator FlhDC. The cascade co‐ordinates the synthesis rates of a large number of gene products with each other and with nutritional conditions. Recent genome‐wide studies have reported that flagellar transcription is altered in cells lacking the transcription regulators DksA or ppGpp, but some or all reported effects could be indirect, and some are contradictory. We report here that the activities of promoters at all three levels of the cascade are much higher in strains lacking dksA, resulting in overproduction of flagellin and hyperflagellated cells. In vitro, DksA/ppGpp inhibits the flhDC promoter and the σ70‐dependent fliA promoter transcribing the gene for σ28. However, DksA and ppGpp do not affect the σ28‐dependent fliA promoter or the σ28‐dependent fliC promoter in vitro, suggesting that the dramatic effects on expression of those genes in vivo are mediated indirectly through direct effects of DksA/ppGpp on FlhDC and σ28 expression. We conclude that DksA/ppGpp inhibits expression of the flagellar cascade during stationary phase and following starvation, thereby co‐ordinating flagella and ribosome assembly and preventing expenditure of scarce energy resources on synthesis of two of the cell's largest macromolecular complexes.
Auxiliary Structure for Convolutional Neural Network Training

Despite recent breakthroughs in the applications of deep neural networks, one challenge remains for deep model training when only limited training data available. New structure may be different from the previous models in different tasks. In addition, there may be insufficient labeled or unlabeled data to train or adapt a deep architecture to new tasks. In view of this, we propose the auxiliary structure for deep model learning with insufficient data via additional alignment layers to migrate the weight of the auxiliary model to the new model. The experimental results demonstrate that the auxiliary structure reduces the overfitting problem and can improve the accuracy using only a few training samples.
Joint Back Projection and Residual Networks for Efficient Image Super-Resolution

Benefiting from the great power of graphic processor units, researchers can now come up with more and more sophisticated and complex deep learning structures to solve computer vision problems in various fields with excellent results. However, real-time performance is the bottleneck for deep learning in some applications, like image super-resolution. In this paper, we propose an image super-resolution making use of both the advantages of Back Projection and Residual Networks (BPRN). It generalizes the residual networks as a hierarchical back projection process. We use both convolution and deconvolution to down- and up-sample images to feedback the residues for super-resolution. Furthermore, we come up with a Lighter BPRN (L-BPRN) model to achieve similar state-of-the-art PSNR but fewer network parameters. The testing process is much faster and also accurate for image super-resolution with different scaling factors. Compared with recent deep learning based image super-resolution approaches, experimental results show that our proposed methods can achieve the state-of-the-art PSNR and SSIM performance as well as fast realization.
Outage Probability of Wireless-powered Multi-antenna Cooperative Spectrum Sharing Networks with Full-duplex and NOMA Transmissions

We consider a cooperative spectrum sharing non-orthogonal multiple access (NOMA) network in which a wirelessly powered secondary transmitter assists transmission from a primary transmitter to a primary receiver with poor channel conditions, and simultaneously the secondary transmitter transmits to a secondary receiver with strong channel conditions. The secondary transmitter is assumed to be full-duplex enabled and is equipped with multiple antennas. In order to cancel the self-interference at the secondary transmitter two zero-forcing (ZF) based beamforming schemes and a maximum ratio combining/maximum ratio transmission scheme are designed and the corresponding outage probability analysis of the primary and secondary networks, are derived. Our results suggest that by using ZF-based beamforming schemes depending on the number of transmit/receive antennas at the full-duplex secondary transmitter significant performance improvement can be achieved. Moreover, our results indicate that proposed ZF-based schemes achieves a zero-diversity order.