Miracle-dz/UniFAR-FTUs huggingface dataset:
Handwriting Behaviour Analysis for deception detection using SVM Classifier in virtualized Environment

There are various methods in practice to analyze deception by the law enforcing departments. Analyzing handwritten document is still considered to be one of the reliable methods of detecting deception. Handwritten documents are usual and reliable source of communication even in today's scenario. The handwritten text is considered to reflect human's body mind coordination. Researchers state that human behaviour can be analyzed by studying a person's handwriting. The paper consists of studies and implementation of online Handwritten Behaviour Analysis in proxmox-server virtualization Environment. C# language was used to implement the Behaviour Analysis part and python is used to validate the classification accuracy on the collected data. We have extended the previous work to client server mode to highlight the part of online Behaviour analysis to identify the uniqueness in characteristics of any person to differentiate deceptive and true statements. This paper will report the development and deployment of an online virtual server based handwriting behaviour analysis system using SVM based supervised Machine learning algorithm for classification.
Development of a Wireless Gateway for Industrial Internet of Things Applications

Industry 4.0 is a new concept representing the evolution of the current production systems through the convergence of new technologies of industrial automation and information systems. The Industrial Internet of Things (IIoT) stands out for the communication among equipment and systems, offering to users a variety of useful information for the management of improvement of production systems. This paper presents the development of a wireless gateway for IIoT applications. This IIoT gateway is based on open source solutions and enables the programming, network communication and supervision of the industrial equipment. The communication protocols supported by the gateway are: Modbus TCP/IP, MQTT and CoAP for connection between the remote modules and the gateway and HTTP using the REST standard with JSON format for external communication with the gateway. An innovation and differential of the gateway was adding the OpenPLC project in order to enable the programming and automation of industrial processes according to the IEC 61131-3. In addition, the gateway stores process information and makes it available online and accessible in real-time to any point connected to the network and/or the Internet. Several operation tests performed validated the development and demonstrated the efficacy of the gateway.
Performance Evaluation of IDMA-Based Random Access Considering User Detection and Channel Estimation

This paper evaluates the performance of interleave division multiple access (IDMA)-based random access in order to support massive machine-type communications (mMTC) in the fifth generation mobile communication system taking into account actual user detection and channel estimation using a preamble attached to the packet. To suppress the packet loss due to contention and achieve multi-packet reception, accurate user detection and channel estimation, which are used in the successive interference canceller (SIC)process in IDMA, are essential. Multiple preamble sequences, each of which has a one-to-one relationship with the interleaver pattern prepared in the system, are generated by applying a cyclic time shift and orthogonal masking operations to Zadoff-Chu (ZC) sequences. Based on the threshold decision on the correlation between the received signal and each preamble sequence, user detection and channel estimation are performed. Computer simulation results show the impact of the preamble overhead, miss detection and false alarm in the user detection, channel estimation error, and the case when multiple users simultaneously select the same interleaver on the achievable performance of IDMA-based random access.
Implementation of Link-16 based Tactical Data Link System Using Software-Defined Radio

Frequency-hopping and direct sequence are techniques of spread spectrum of Link-16 that have good anti-jamming ability. With good anti-jamming ability, this tactical data link is used by US Navy and NATO military for secure communication. The purposes of this research are to create a Link-16 frequency-hopping system based on software-defoned radio and implement it by transmitting some of text. This research use LabVIEW to design the system. USRP, 1 GHz antennas, and gigabit ethernet switch are used to implement this system.
Retrieval of Gas Temperature and Pressure Based on Rayleigh–Brillouin Spectrum

Optical detection methods such as Rayleigh–Brillouin scattering spectrum can be used for physical parameter measurement in solids, liquids, and gas. In this paper, a new method based on spectral characteristics is proposed to realize temperature and pressure retrieval simultaneously in gas. The characteristics of Rayleigh–Brillouin spectrum are firstly utilized to establish the retrieval model by fitting Tenti-S6 line shape with 3Voigt model at different temperatures and pressures, and then deriving the retrieval equations via regression fitting procedure. Through theoretical and measured error analyses, the characteristic retrieval model based on Rayleigh linewidth and the whole linewidth of Rayleigh–Brillouin scattering spectrum is shown to be optimal. Afterward, the retrieval model is verified using the experimental Rayleigh-Brillouin scattering spectra of N2 and air. The results indicate that, compared with the current method based on the whole linewidth or line shape of spectrum, the proposed method not only produces lower theoretical and retrieval errors but also can simultaneously retrieve the temperature and pressure of gases. The novel retrieval method provides a new idea for obtaining valuable information on gas parameters and promotes the application of Brillouin lidar remote sensing in meteorology and space science.
InDoCaS: A process for domain engineering in software production lines with a quality approach

This article proposes a process for domain engineering in software production lines based on Software quality called InDoCaS. The main disciplines of this process are the analysis, design and implementation of the domain. The process proposed here can be used in the software production line development approach and can be instantiated for a specific domain and the produced software assets can be reused to generate a product from a particular domain family. The concepts of: development processes, reuse of software assets, domain engineering, variability models, software production lines and Bayesian learning techniques are applied to define the way in which these applications are adapted at runtime to the applications. environment changes. These software variability and adaptability requirements represent an interesting challenge within software engineering. }{The contribution of this paper to the area of software development based on software production lines is to propose a process for domain engineering with a quality approach that will allow the specification of the quality requirements of the domain application families to be formalized, presenting details about the activities of analysis, design and implementation of the base architecture and its refinement.
Design Considerations of a Monitoring System of a Farm for Energy Efficiency Purposes

The monitoring of industry processes can optimize the use of resources and improve its efficiency. In dairy farms, several parameters from the processes must be monitored. This paper proposes the design of a monitoring system for a farm that produces dairy products and jams. Several similar studies are analysed. Based on this, and on the farm characteristics, a novel design is developed. The principal benefits of the system are also exposed.
RMB Real Effective Exchange Rate Affects the Economic Growth of China: Based on Building Open Ramsey Model of Foreign Debt's Flow Control

In order to research the effects of the real exchange on economic growth thoroughly, we should intensively study the decision process of microeconomic body, and pay close attention to the household resident's choice on consumption path as well as manufacturer's selective investment. This paper regards the real effective exchange rate as an important variate of affecting nation's net foreign debt, and which is introduced in open Ramsey model, based on building open Ramsey model of net foreign debt's flow regulation without full opening condition. It also discusses the equilibrium growth path of economy, and analyses the effect direction and mechanism of change in real exchange rate on equilibrium growth path and economic growth with numerical simulation technique.
Attendance System Based on Biometrics and RFID

Biometrics is one of the most vital technology that is growing and has been used to recognize an individual for security purposes. To determine and distinguish people identities, biological characteristics can be used. In this paper, two biometric features such as face recognition, fingerprint recognition have been combined and RFID is used all in one system. This paper provides an advanced way of taking attendance in universities, which is computerized and has advanced features. The system design including hardware and software have been provided.
An Improved Method of DDoS Attack Detection for Controller of SDN

For controllers of Software Defined Network (SDN), Distributed Denial of Service (DDoS) attacks are still the simplest and most effective way to attack. Aiming at this problem, a real-time DDoS detection attack method for SDN controller is proposed. The method first uses the entropy to detect whether the flow is abnormal. After the abnormal warning is issued, the flow entry of the OpenFlow switch is obtained, and the DDoS attack feature in the SDN environment is analyzed to extract important features related to the attack. The BiLSTM-RNN neural network algorithm is used to train the data set, and the BiLSTM model is generated to classify the real-time traffic to realize the DDoS attack detection. Experiments show that, compared with other methods, this method can efficiently implement DDoS attack traffic detection and reduce controller overhead in SDN environment.
Tunable Multiple Fano Resonances Based on the Partial Ring Resonator Connected with a Rectangular Cavity in a Plasmonic System

This paper proposed a compact structure in a plasmonic system. Multiple Fano resonances arose in its transmission spectrum and could be tuned. Because of the sharp asymmetry of Fano resonances, the proposed system was explored as a high efficient refractive index sensor (RIS) with a sensitivity of 1150 nm/RIU and figure of merit (FOM) of 5328.
AIoT Solution Survey and Comparison in Machine Learning on Low-cost Microcontroller

Neural Networks, especially Convolutional Neural Network [1] are becoming increasingly popular in IoT edge devices today for executing data analytics right at the source without transmitting to Cloud Computing centers. So that it will be reduced latency as well as energy consumption for data communication. In this paper, we will compare CMSIS-NN and uTensor: low energy consumption microcontrollers. Most classification tasks have always-on, and real-time requirements, which limits the total number of operations per neural network inference. So that, with image classification model, microcontrollers will execute lower frame per second than GPU and embedded CPU. CMSIS-NN is a collection of efficient kernels which was developed to maximize the performance and minimize the memory footprint of Neural Network applications. Allow deploy machine learning models on ARM Cortex-M processors for intelligent IoT edge devices.
Seizure Detection Based on EEG Signals Using Katz Fractal and SVM Classifiers

Epilepsy is a brain disorder characterized by the occurance of seizure. The International League Against Epilepsy describes that the least occurence of seizure is twice in 24 hours. There are more than 50 million people in the world suffering from this disease. Recurring seizures in epilepsy can be seen from changes in a pattern in EEG signals. EEG is one of the common tools that can be used to record brain activity rather than MRI and CT Scan. EEG recording is conducted by placing electrodes in the scalp (sEEG) or inside the cranium (iEEG). EEG signals are non-linear and non-stationary, so it is difficult to interpret them manually. In order to minimize the mistake during manual interpretation, various methods have been developed to analyse EEG signals in epilepsy patients using digital signal processing. In this study, we proposed a fractal-based method to detect normal, pre-ictal, and seizure from EEG signals. First, EEG signals were decomposed into five sub-bands: Alpha, Beta, Theta, Delta, and Gamma. The Katz fractal dimension (KFD) was calculated for each EEG signal sub-band to be used as features. A Support Vector Machine (SVM) with six different kernels was used as the classifier. The highest accuracy of 98.7% was achieved for three classes of EEG signal data.
Comparison of Forcasting Ability between Backpropagation Network and ARIMA in the Prediction of Bitcoin Price

Bitcoin is a peer-to-peer (P2P) electronic currency that allows online payments around the world without the management of a third party. Many studies have been conducted on the performance prediction of the stock market; in particular, the Autoregressive Integrated Moving Average model (ARIMA) is one of the linear regressive models widely used in the time series. Nevertheless, as artificial intelligence has become a heated research topic in modern days, A number of studies have also shown that the Back-propagation Neural Network (BPNN) is very effective in prediction. Hence, this paper compares the ARIMA model with the BPNN model in the prediction of Bitcoin price.
Design Area-time Efficient Pipeline Architecture for Finite Impulse Recursive System

The paper aims in developing a finite impulse response filter architecture that relies on the multipliers to overcome the shortcomings in the prevailing method and heighten the speed of the filters by employing the adders. The algorithm of the Vedic multiplier is generally utilized for the applications of higher bit length were the lower order bits work well with the ordinary multiplier. The Vedic multiplier and the ordinary multiplier is integrated to develop a multiplier of higher speed for an applications with the higher bit length. The bits of the remainders are eluded to reduce the issues faced in the prevailing architecture. The proffered algorithm is implemented using the Xilinx software Vertex-7.
Non-Volatile Integrated-Silicon-Photonic Switches using Phase-Change Materials

Current Mach-Zehnder switches primarily rely on the weak modulation of the refractive index (usually Δn < 0.01) from the free-carrier dispersion or thermo-optic effects, resulting in a large footprintand high power consumption. By utilizing the dramatic difference in the optical refractive index and extinction coefficient between the crystalline and amorphous phases of Ge2Sb2Te5 (GST), we demonstrate an all-optical nonvolatile switch in microring resonators with extinction ratio up to 33 dB. To extent the operating bandwidth, we demonstrate compact (∼30 pm), low-loss (∼1dB), and broadband (over 30 nm with CT < −10 dB) 1 × 2 and 2 × 2 photonic switches using the PCM, based on the asymmetric directional coupler (DC) switch design bypassing the high loss associated with the crystalline state. GST possees much lower values of extinction coefficient in the wavelength region of >2 µm.
Security Challenges and Solutions for Cloud Radio Access Networks

Millions of consumers and thousands of enterprises use cellular networks for mobile broadband services for high speed internet connectivity. This increases the immensity on various cellular network providers to afford the reliable and secure communication. The main purpose of 5G system is to become a reliable in communication standards and to provide an innovation platform for businesses and organizations to create and deploy new add-on services. Cloud radio access networks (CRAN) are an encouraging technology that will allow 5G mobile communication systems to satisfy the industry's needs and demands of end users with higher mobility and seamless connectivity. CRAN is characterized by centralized processing and control, providing collaborative access to the radio networks, and integrated with cloud based systems. Meanwhile, energy-efficient operation must be delivered together with cost savings in deployment and functioning in the network. However, CRAN technology in 5G mobile communication systems has to face multiple intrinsic security issues related with virtual system functions and cloud computing environment. This architecture allows coverage of maximum distance with fiber optic links up to 20 kilometers with low latency. Very high bandwidth collaboration between the interconnected systems in the network is possible in CRAN systems. Real time virtualization enforces the dynamic allocation of resources among the functional modules in accordance with the network capacity and the load distribution. This may obstruct the rate of successful establishment in the global market. It is critical to identify these challenges to enable C-RAN to function on its full potential and adoptive the deployment of future 5G mobile systems. This paper focuses on various security challenges and the privacy issues found in Cloud Radio Access Networks (CRAN). CRAN is characterized by centralized processing and control, providing collaborative access to the radio networks, and integrated with cloud based systems. The security issues at various levels and components are identified through several literatures and are tabulated in Table I. For this segregation, we have considered three major components of the systems as SDN, NFV and Cloud environment. We also have identified related security solutions for issues addressed and suggested some future guidelines for secure 5G systems.
Bandwidth-Aware Last-Level Caching: Efficiently Coordinating Off-Chip Read and Write Bandwidth

The last two decades have witnessed a large number of proposals on the last-level cache (LLC) replacement policy aiming to minimize the number of LLC read misses. Another independent large body of work has explored mechanisms to address the inefficiencies arising from the DRAM writes introduced by the LLC replacement policy. These DRAM scheduling proposals, however, leave the LLC replacement policy unchanged and, as a result, miss the opportunity of synergistically shaping and scheduling the DRAM write bandwidth demand. In this paper, we argue that DRAM read and write bandwidth demands must be coordinated carefully from the LLC side and hence, introduce bandwidth-awareness in the LLC policy. Our bandwidth-aware LLC policy proposal enables long uninterrupted stretches of DRAM reads while maintaining the efficiency of the last-level cache and controlling precisely when and for how long writes can demand DRAM bandwidth. Our proposal comfortably outperforms the state-of-the-art eager DRAM write scheduling proposals and bridges 75% of the performance gap between the baseline and a hypothetical system that deploys an unbounded DRAM write buffer.
A Transformation for Polar Code BP Decoding

A transformation technique has been designed, where the encoding result of the polar code can be transfered to an low-density parity-check (LDPC) code. The belief-propagation decoding can be applied to the received sequence according to the parity-check matrix of the LDPC code. The transfered polar code can provide the same error-rate of the LDPC code, and its performance can be further improved if the success-cancellation decoding is avaliable.
Robotic Bin-Picking under Geometric End-Effector Constraints: Bin Placement and Grasp Selection

In this paper we demonstrate how path reachability can be taken into account when selecting among predetermined grasps in a bin-picking application, where grasps are supplied independently of the robot at hand. We do this by creating a map of the workspace to optimally place the bin with regards to the existence of an inverse kinematic solution and a collision-free path, a necessary condition for systems with obstructions in the workspace. Furthermore, we densely re-map this region and based on this map predict whether a grasp is reachable by the robot. Moreover, an algorithm is implemented to weight the grasps in terms of path existence, length and time consumption. The algorithm was tested with grasps generated by the neural network in simulation and the results indicate that faster picking can be achieved when taking path reachability into consideration.
ScleraSegNet: an Improved U-Net Model with Attention for Accurate Sclera Segmentation

Accurate sclera segmentation is critical for successful sclera recognition. However, studies on sclera segmentation algorithms are still limited in the literature. In this paper, we propose a novel sclera segmentation method based on the improved U-Net model, named as ScleraSegNet. We perform in-depth analysis regarding the structure of U-Net model, and propose to embed an attention module into the central bottleneck part between the contracting path and the expansive path of U-Net to strengthen the ability of learning discriminative representations. We compare different attention modules and find that channel-wise attention is the most effective in improving the performance of the segmentation network. Besides, we evaluate the effectiveness of data augmentation process in improving the generalization ability of the segmentation network. Experiment results show that the best performing configuration of the proposed method achieves state-of-the-art performance with F-measure values of 91.43%, 89.54% on UBIRIS.v2 and MICHE, respectively.
Lung Nodule Classification in CT Images Using Convolutional Neural Network

Convolutional neural networks (CNN), due to its self-learning ability and deep feature extraction capability has created a new era to the biomedical applications and medical image analysis. Computer aided diagnosis system (CAD) using CNN’s helps radiologists in decision making and reduces their difficulty on screening lung nodules. This paper presents CAD system using pre-trained networks AlexNet, VGG16 and a new network is designed based on the inference obtained to eliminate the drawbacks of available network for the early prediction of lung cancer. Comparative analysis based on the performance parameters proved that the proposed CNN architecture outperforms the pre-trained networks. Experiments were evaluated using computed tomography (CT) scan images obtained from LIDC-IDRI dataset.
Dataset of Raw and Pre-processed Speech Signals, Mel Frequency Cepstral Coefficients of Speech and Heart Rate Measurements

Heart rate is an important vital sign used in the diagnosis of many medical conditions. Conventionally, heart rate is measured using a medical device such as pulse oximeter. Physiological parameters such as heart rate bear a correlation to speech characteristics of an individual. Hence, there is a possibility to measure heart rate from speech signals using machine learning and deep learning, which would also allow noninvasive, non-contact based and remote monitoring of patients. However, to design such a scheme and verify its accuracy, it is necessary to collect speech recordings along with heart rates measured using a medical device, simultaneously during the recording. This article provides a dataset as well as the procedure used to create the dataset which could be used to facilitate research in developing techniques to estimate heart rate accurately by observing speech signal.