Miracle-dz/UniFAR-FTUs huggingface dataset:
Intent-Based Path Selection for VM Migration Application with Open Network Operating System

Application of virtual machine (VM) migration is one of the key applications in the management of a data center. The migration task is required for several reasons such as load balancing, maintenance, or power management. Migration task need to be optimized by improving networking (e.g. end-to-end path) between VM hypervisors. Migrating VM on the best path, will certainly give a smaller migration time. Intent (i.e., intent networking) is used as a basis in path selection on the SDN to identify better networking performance (i.e., higher bandwidth). The migration measurement is taken for three parameters which are migration time, downtime, and overhead time. The measurement result shows an improvement for three different bandwidth variation.
Speaker Recognition Techniques: A Review

Speaker Recognition is the process of recognizing the speaker from the individual's speech biometrics. The voice characteristics of every speaker are different and thus can be used to construct a model. This model is later used to recognize an enrolled speaker from the list of available speakers. The paper makes an effort to discuss different speaker modeling techniques like Vector Quantization (VQ), Gaussian Mixture Model (GMM). , Neural Networks (NN). , etc. Also. , different techniques for extraction of voice characteristics like Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding (LPC) are discussed. Further, an in-depth analysis of these surveyed techniques is made to identify their advantages and limitations. The work in the field of Speaker Recognition Systems began in the 1950's and is evolving since then, it has wide applications in the fields of security, forensics. , authentication etc.
A Novel 1.03 ppm/°C Wide-Temperature-Range Curvature-Compensated Bandgap Voltage Reference

This paper proposes a novel bandgap voltage reference with an accurate curvature compensation technique for completely compensating the thermal nonlinearity of the emitter-base voltage. With VIS 0.15 standard CMOS process, the proposed bandgap voltage reference circuit achieves temperature coefficient of 1.03 ppm/°C over the temperature range from −55°C to 155°C with the supply voltage of 1.8V. In addition, the circuit achieves line regulation performance of 0.02%/V with the supply voltage from 1.8V to 3V and the power supply rejection (PSR) levels are 83dB, 77dB, 58dB at ultra-low frequency (<200Hz), 1kHz and 10kHz, respectively.
Design Methodology Proposal of Digital Predistorter Using Matlab and Modelsim Cosimulation

This paper details the design of a Digital Predistorter (DPD) based on the Simplified Volterra Series (SVS) model. Our main contributions concern first the design of the predistorter unit using the Look Up Table (LUT) method without additional algorithms to decrease the high number of coefficients required for the PA model. Then, a Matlab and Modelsim cosimulation approach is discussed and performed to evaluate the proposed DPD architecture, in particular synthesis results are presented in terms of required Field Programmable Gate Array (FPGA) resources to implement the proposed predistorter. In addition, the performances of the proposed design are verified using a class AB GaN Power Amplifier (PA) driven by one carrier Long Term Evolution-Advanced (LTE-A) signal with 20 MHz channel bandwidth. It is proven that the LUT predistorter occupies only 55 % of the multipliers (DSP48E1) available in the Zynq-7000 FPGA. Also, the Adjacent Channel Power Ratio (ACPR) attains more than −45 dB.
Deep Learning Based Gujarati Handwritten Character Recognition

Optical character recognition is a method of the renovation of pictures of printed text, handwritten documents andtypewritten documents into a document recognized by the machine. This can be used for editing, reduction in storage space etc. So, basically, it relates to posses of teaching and recognition of data. Hence, here a hand written document which is in a form of some local language characters except than English. In this paper, we are using the Gujarati Characters for the recognition into the machine editable format. Deep learning is the method used for identity recognition which will depend on the man-made neural network. Deep learning involves a proper focus on right features of the image to recognize themselves with a negligible assistance from the program. Neural network evolved considering the concept of biological neurons. Accuracy and efficiency are improved by use of deep learning for character recognition.
Ribose-5-phosphate isomerase A overexpression promotes liver cancer development in transgenic zebrafish via activation of ERK and β-catenin pathways

Abstract Dysregulation of the enzymes involved in the pentose phosphate pathway (PPP) is known to promote tumorigenesis. Our recent study demonstrated that ribose-5-phosphate isomerase (RPIA), a key regulator of the PPP, regulates hepatoma cell proliferation and colony formation. Our studies in zebrafish reveal that RPIA-mediated hepatocarcinogenesis requires extracellular signal-regulated kinase (ERK) and β-catenin signaling. To further investigate RPIA-mediated hepatocarcinogenesis, two independent lines of transgenic zebrafish expressing human RPIA in the liver were generated. These studies reveal that RPIA overexpression triggers lipogenic factor/enzyme expression, steatosis, fibrosis and proliferation of the liver. In addition, the severity of fibrosis and the extent of proliferation are positively correlated with RPIA expression levels. Furthermore, RPIA-mediated induction of hepatocellular carcinoma (HCC) requires the ERK and β-catenin signaling pathway but is not dependent upon transaldolase levels. Our study presents a mechanism for RPIA-mediated hepatocarcinogenesis and suggests that RPIA represents a valuable therapeutic target for the treatment of HCC.
The Method of Multimodal MRI Brain Image Segmentation Based on Differential Geometric Features

Accurate segmentation of brain tissue in magnetic resonance images (MRI) is a diffcult task due to different types of brain abnormalities. Using information and features from multimodal MRI including T1, T1-weighted inversion recovery (T1-IR) and T2-FLAIR and differential geometric features including the Jacobian determinant(JD) and the curl vector(CV) derived from T1 modality can result in a more accurate analysis of brain images. In this paper, we use the differential geometric information including JD and CV as image characteristics to measure the differences between different MRI images, which represent local size changes and local rotations of the brain image, and we can use them as one CNN channel with other three modalities (T1-weighted, T1-IR and T2-FLAIR) to get more accurate results of brain segmentation. We test this method on two datasets including IBSR dataset and MRBrainS datasets based on the deep voxelwise residual network, namely VoxResNet, and obtain excellent improvement over single modality or three modalities and increases average DSC(Cerebrospinal Fluid (CSF), Gray Matter (GM) and White Matter (WM)) by about 1.5% on the well-known MRBrainS18 dataset and about 2.5% on the IBSR dataset. Moreover, we discuss that one modality combined with its JD or CV information can replace the segmentation effect of three modalities, which can provide medical conveniences for doctor to diagnose because only to extract T1-modality MRI image of patients. Finally, we also compare the segmentation performance of our method in two networks, VoxResNet and U-Net network. The results show VoxResNet has a better performance than U-Net network with our method in brain MRI segmentation. We believe the proposed method can advance the performance in brain segmentation and clinical diagnosis.
Energy Management Strategy for Supercapacitor Storage Using a Nonlinear Virtual Impedance

This paper proposes a new energy management strategy (EMS) used for charging and discharging a supercapacitor (SC). The method is based on a nonlinear impedance and resembles the droop control applied in microgrid structures. The droop control has the advantage of being decentralized so the proposed EMS also benefits this characteristic. Usually a tradeoff must be made between voltage regulation and load sharing for the droop control and a nonlinear resistor is often used to minimize this tradeoff. For charging and discharging a SC in droop controlled microgrids, a virtual impedance with an RC characteristic is usually used. The proposed method was created by merging together the two strategies, the nonlinear resistor and the virtual capacitive impedance, in order to benefit from the advantages provided by both methods. An overview of the most common strategies is also presented in this paper.
Increasing Bandwidth of Log Periodic Array Microstrip Antenna Using Parasitic Air Gap for Digital Video Broadcasting Application

This paper proposes a new design of a log periodic array microstrip antenna with parasitic air gap for Digital Video Broadcasting application with return loss value of ≤-10 dB and $\mathbf{VSWR} \leq 2$ at working frequency of 478 to 694 MHz. The proposed antenna consists of two layers of substrate where at the bottom layer is a log periodic microstrip antenna and at the top layer is a parasitic load separated by air gap. The bandwidth of the design antenna can be optimized by adjusting the distance of air gap between the top and bottom layers of the proposed antenna. From the simulation results can be obtained the distance gap (d) is 2 mm with the bandwidth of the antenna design is 339 MHz (421 MHz to 757 MHz). Using parasitic air gap the bandwidth of the proposed antenna can be increased until 31.49% compared to the conventional periodic log antennas.
A Novel System for WiFi Radio Map Automatic Adaptation and Indoor Positioning

WiFi fingerprinting-based indoor positioning system (IPS) using received signal strength (RSS) has been considered to be one solution for indoor positioning. However, there are two major bottlenecks that hamper its large-scale implementation. One widely recognized problem is the construction of a proper fingerprint database with high efficiency and accuracy. Second is to improve the online positioning accuracy on the basis of the fingerprint database. To address these issues comprehensively, this paper proposes a novel system—Digital navigation center IPS (DncIPS), an IPS that enables automatic online radio map construction, and step-by-step positioning, aiming for the high-accuracy RSS estimation and high-precision positioning. DncIPS can capture WiFi data packets transmitted in WiFi traffic so that they obtain the MAC addresses, frequency, and RSS of any WiFi access point (AP) at any point. DncIPS uses Gaussian process regression model based on a fireworks algorithm to approximate the RSS distribution of an indoor environment and to estimate the location of APs increasing the flexibility of DncIPS work environment. This system also consists of a coarse localizer detecting the outliers and dividing clustering area and a fine localizer followed to improve the online positioning accuracy. Extensive experiments results indicate the proposed system DncIPS leads to improvement on radio map updating and localization accuracy.
Optimization of Intelligent Neural Network Prediction Based on Particle Swarm

In view of the disadvantages of slow convergence and local optimality in traditional neural network learning algorithms, a neural network nonlinear function fitting system with improved particle swarm optimization (PSO) is designed for intelligent prediction accuracy. The intelligent optimization scheme integrates the immune PSO algorithm with BP neural network theory, and realizes the nonlinear function fitting algorithm of BP neural network, based on IPSO algorithm optimization. The new fitting algorithm first determines the structure of the BP neural network, and optimizes the initial weight and threshold with IPSO algorithm. Then, it improves the intelligent prediction method to verify the performance. Numerical experiments show that the algorithm proposed in this paper improves the fitting ability of BP neural network, reduces the fitting error and improves the fitting accuracy.
Gender Identification From Children's Speech

Children's speech can be characterized by higher pitch and format frequencies compared to the adult speech. Gender identification task from children's speech is difficult as there is no significant difference in the acoustic properties of male and female child. Here, an attempt has been made to explore the features efficient in discriminating the gender from children's speech. Different combinations of spectral features such as Mel-frequency cepstral coefficients (MFCCs), ΔMFCCs and ΔΔMFCCs, Formants, Linear predictive cepstral coefficients (LPCCs); Shimmer and Jitter; Prosodic features like pitch and its statistical variations along with Δpitch related features are explored. Features are evaluated using non linear classifiers namely Artificial Neural Network (ANNs), Deep Neural Network (DNNs) and Random Forest (RF). From the results it is observed that the RF achieves an highest accuracy of 84.79% amongst the other classifiers.
Performance Evaluation of a Vector Supercomputer SX-Aurora TSUBASA

A new SX-Aurora TSUBASA vector supercomputer has been released, and it features a new system architecture and a new execution model to achieve high sustained performance, especially for memory-intensive applications. In SX-Aurora TSUBASA, the vector host (VH) of a standard x86 Linux node is attached to the vector engine (VE) of the newly developed vector processor. An application is executed on the VE, and only system calls are offloaded to the VH. This new execution model can avoid redundant data transfers between the VH and VE that can easily become a bottleneck in the conventional execution model. This paper examines the potential of SX-Aurora TSUBASA. First, the basic performance is clarified by evaluating benchmark programs. Then, the effectiveness of the new execution model is examined by using a microbenchmark. Finally, the potential of SX-Aurora TSUBASA is clarified through evaluations of practical applications.
Performance Analysis of CSA Optimized PIDF Controller for Containing Frequency Fluctuation of Micro-Grid

Integration of renewable power generation in the modern power system requires intelligent and flexible control to ensure the balance between the generation and load demand. This paper addresses a frequency control model of an isolated micro-grid with plug-in hybrid electric vehicle (EV) and heat pump (HP) supplied by diesel engine generator (DEG) and renewable solar thermal power generation (STPG). Due to the interconnection of intermittent energy source like STPG and load variation, the micro-grid frequency fluctuate with different adverse effect on the power system. The PID and PID with filter (PIDF) are employed with EV, DEG and HP to control their power generation to maintain the desired frequency level under different contingencies. The parameters of PID and PIDF controllers are controllably tuned by Cuckoo search algorithm (CSA) for containing frequency fluctuation and their performances are compared thereafter. Different disturbed conditions have been realized in the form of step perturbations and random variations on the load and generation side. Investigation of the responses shows that Cuckoo search algorithm (CSA) based PIDF controller gives better response compare to the Cuckoo search algorithm (CSA) based PID controller.
A Review on Different Multilevel Topology Used in AC Drives to Mitigates the Total Harmonic Distortion

Sustainability and power quality are the measure concern when dealing with industrial and domestic applications. Motion control of mechanically rotated machines proved themselves as a reliable source for many applications. Power semiconductors devices play prime roll to achieve the speed control of AC as well as DC motor. The recent development and improvement in the same increases the dependability and reliability of the systems with cheaper cost but the association of power semiconductor devices to the system to achieve the requirement causes the Total Harmonic Distortion which affect the quality of power supply. To mitigate this problem in case of AC motor drives multilevel inverters with corresponding outputs had been utilizes. In this paper topologies of multilevel inverter, their hardware requirements and performance with respect to THD have been discussed.
Exploration of Secured Workflow Scheduling Models in Cloud Environment: A Survey

Cloud computing (CC) is a useful tool for executing complex applications. As a result of this, it has become so popular and used in diverse domains such as science, engineering, medicine. etc. CC structure is composed of a number of virtual machines(VMs) provisioned on demand and charged on a "Pay-as-you-go" basis, it is deployed in different form of access levels. Complex applications needed to be executed on clouds are represented as workflows. Workflow scheduling (WS) is one of the most important concepts in cloud computing. WS model contributes to minimizing cost, makespan and energy as well as maximize the quality of service(QoS) of applications in clouds. Despite the security constraints set by each provider, CC has become so critical due to the considerations of applications with sensitive intermediate data, this thereby requires a security level known as Secured workflow Scheduling(SWS). This security is on the level of executing workflows. It indicates that applications with sensitive interdependent data have to be protected during their execution across different cloud VMs. The addition of security in workflow execution generates time overhead, making it complex to meet up with the QoS required by the users. Some research works have proposed algorithms for providing the QoS requirements and security at the same time. In this work, we survey some existing works, by defining the factors needed in securing workflows during execution, clarifying the domains for security, sources of security threats and their solutions as well as cloud computing services that needs security and lastly classify the proposed algorithm depending cloud computing components.
Reactively Loaded AOA Estimation Coexistent with a 16 x 16 MIMO Beam Steering Array

To achieve a channel capacity of over 10 Gbps for use in forthcoming connected car systems, a $16 \times 16$ multiple-input multiple-output (MIMO) antenna system has been developed, which is a version-up approach of our $4 \times 4$ MIMO circular phased array technology. The angle of arrival (AOA) estimation function must be embedded into the phased array so that the MIMO system is fully capable of accomplishing the beam steering functions. The most challenging issue is the mitigation of phase perturbation due to electromagnetic (EM) coupling between the AOA antenna and the surrounding MIMO subarray elements. In this paper, we present an AOA estimation antenna with reactive load impedances for reducing the EM-coupling. The results show that the estimated angle error is less than 0.5 degrees with an omnidirectional radiation pattern.
A Multi-Label Text Classification Method Based on Labels Vector Fusion

In massive text information, text classification can better help people organize and manage the mass of text information. In real life, a text often belongs to a number of categories. For this classification scenario, it is called a multi-label classification. Existing text multi-label classification methods rarely take into account the correlation between labels, and lack the understanding of the label semantics. In this paper we propose a method of text multi-label learning based on label correlation, modeling between texts and label vector through neural networks, and capture the semantic correlation between text and labels. A large number of experiments show the effectiveness of the method, especially when the amount of data is large.
Nonvolatile Tunable Integrated Mid-Infrared GST-SiC Metasurfaces

We demonstrate an integrated mid-infrared 3C-SİC metasurface with relatively sharp transmission peaks associated with phonon-mediated magnetic polariton resonance. We further show the possibility of developing reconfigurable metasurfaces using such architectures by integrating a thin layer of phase-change material, Ge<inf>2</inf>Sb<inf>2</inf>Te<inf>5</inf>, on top of the SiC layer.
DRAGON: Breaking GPU Memory Capacity Limits with Direct NVM Access

Heterogeneous computing with accelerators is growing in importance in high performance computing (HPC). Recently, application datasets have expanded beyond the memory capacity of these accelerators, and often beyond the capacity of their hosts. Meanwhile, nonvolatile memory (NVM) storage has emerged as a pervasive component in HPC systems because NVM provides massive amounts of memory capacity at affordable cost. Currently, for accelerator applications to use NVM, they must manually orchestrate data movement across multiple memories and this approach only performs well for applications with simple access behaviors. To address this issue, we developed DRAGON, a solution that enables all classes of GP-GPU applications to transparently compute on terabyte datasets residing in NVM. DRAGON leverages the page-faulting mechanism on the recent NVIDIA GPUs by extending capabilities of CUDA Unified Memory (UM). Our experimental results show that DRAGON transparently expands memory capacity and obtain additional speedups via automated I/O and data transfer overlapping.
Probabilistic-Based Image Categorization Using Novel Visual Vocabulary

The visual words representation is widely applied to many multimedia applications. In this paper, we use a novel visual vocabulary developed in our previous work and propose a category-specific visual model for image categorization. The category-specific visual model is composed by macro and micro visual words description, respectively. We can categorize image by considering macro or micro content in a flexible way. Meanwhile, we will propose a probabilistic-based method to achieve effective and excellent image categorization. The performance evaluation for the proposed systems indicates that the new categorization scheme achieves promising results.
Separable Reversible Data Hiding in Encrypted JPEG Bitstreams

While most techniques of reversible data hiding in encrypted images (RDH-EI) are developed for uncompressed images, this paper provides a separable reversible data hiding protocol for encrypted JPEG bitstreams. We first propose a JPEG encryption algorithm, which enciphers an image to a smaller size and keeps the format compliant to JPEG decoder. After a content owner uploads the encrypted JPEG bitstream to a remote server, a data hider embeds an additional message into the encrypted copy without changing the bitstream size. On the recipient side, the original bitstream can be reconstructed losslessly using an iterative recovery algorithm based on the blocking artifact. Since message extraction and image recovery are separable, anyone who has the embedding key can extract the message from the marked encrypted copy. Experimental results show that the proposed method outperforms a previous work in terms of separation capability, embedding capacity and security.