Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure Deep Neural (SeDeN) Framework for 5G Wireless Networks

The current generation network is being rapidly replaced by programmable 5G network. 5G wireless network is also expected to be core network for various industrial use cases. Any kind of security breach will lead to disruption of service to the users, increase in expenditure to the service providers and many other unfavourable effects. So to prevent the disruption of networks, we have proposed a secure deep learning based framework that is intelligent to detect attacks and prevent propagation of attack. The framework was tested on emulated wireless network and the result showed an accuracy of 97.8% which proved the efficiency of the framework.
Hardware-Assisted Flow Monitoring for High Speed Networks

In today's high-speed networks, network flow monitoring has become prevalent method for monitoring network traffic. Flow based monitoring system involves tasks like packet classification, flow generation and management, flow export and flow analysis. We have proposed a hardware-assisted architecture for flow monitoring for high speed networks. Our architecture maps packet classification and flow generation tasks into FPGA hardware. Remaining tasks are performed in software. By doing this bifurcation we have tried to exploit best of both hardware and software domains. The proposed architecture is implemented on Altera Stratix-IV FPGA board and the hardware details for the same are provided here. The implemented architecture was thoroughly tested and maximum performance of 20.2 Gbps was achieved.
Improving Performances of Enhancement-Mode AlGaN/GaN MIS-HEMTs on 6-inch Si Substrate Utilizing SiON/Al2O3 Stack Dielectrics

Enhancement-mode (E-mode) GaN-based MIS-HEMTs still suffer from undeniable gate leakage or low gate breakdown voltage due to the low quality of gate dielectrics, resulting in a notorious tailing effect of the off-state current. In this letter, a gate scheme featuring SiON/Al<sub>2</sub>O<sub>3</sub> stack dielectrics and partially recessed gate barrier has been employed in the AlGaN/GaN MIS-HEMTs. A high on/off current ratio over <inline-formula> <tex-math notation="LaTeX">$10^{{9}}$ </tex-math></inline-formula> and a small threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V} _{\text {th}}$ </tex-math></inline-formula>) hysteresis less than 20 mV are achieved in the fabricated E-mode devices with a <inline-formula> <tex-math notation="LaTeX">${V} _{\text {th}}$ </tex-math></inline-formula> around 2.5 V, mainly owing to the reduction of the net positive fixed charge density in the SiON/Al<sub>2</sub>O<sub>3</sub> gate stack confirmed by the <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> measurements. Meanwhile, a good performance uniformity on 6-inch wafer is achieved which demonstrates the promising scheme for fabricating GaN-based E-mode MIS-HEMT products.
Virtual Reality Based Collaborative Multiplayer Task Platform for Children with Autism

Children with autism are characterized by deficits in fluid interpersonal social communication due to difficulties in understanding other's perspectives and line-of-thought during interaction. With technological advancement, investigators have shown the potential of computer-based multiplayer collaborative environments to promote social interaction among these children. However, none of the existing research have emphasized the use of subtle implicit aspects of social communication e.g., understanding of other's perspective and line-of-thought before taking an action, crucial for social interaction. This paper presents the design of virtual reality based collaborative task platform that offered a context requiring one to pick up the line-of-thought and perspective of a partner for the task to be successful. Results of a study with 10 pairs of typically developing participants and 3 pairs with autism indicate variations in the (i) interaction abilities of the two groups of participants and (ii) cognitive effort as evident from physiology.
In – House Early Detection of Flat Foot and High Ankle Foot Using Image Processing

To study structural foot abnormality here we use a homemade setup to take snap shot of the front view and rear view of the foot ankle to figure out the level of abnormality regarding flat foot and high arch foot. After processing those images the Brucken Index is calculated and based upon this index abnormalities are classified in three different types: Normal Foot, Flat Foot and High Arch Foot. Our setup is not only able to differential about these abnormalities but also can detect the level of abnormalities.
Designing an Efficient Fingerprint Recognition System for Infants and Toddlers

Authentication and identification of infants and toddlers is a major challenge in various organizations. Fingerprint recognition system works well in authenticating and identifying the adults. However, in case of infants and toddlers, it faces the challenges like low quality of fingerprint images and improper enrollment, and thus reducing the recognition accuracy. To address these problems, we design an efficient fingerprint recognition system for infants and toddlers. The proposed system performs image enhancement on the fingerprints of infants and toddlers. Then it applies preprocessing on the enhanced images followed by the improved Gabor filter to derive the finger codes of the fingerprints. These finger codes are later matched with the test fingerprints using the Euclidean distance method in order to perform authentication and verification. We evaluate the efficiency and performance of the proposed system using existing CMBD and NITG fingerprint datasets.
Implementation of a Low Cost Smart Home Based on Standard 802.11 b/g/n WiFi

With the continuous development of different technologies related to the internet of things, as well as the communication that must be established within a WiFi network, this document proposes a low cost intelligent virtual assistant, this assistant provides a domotic control through: applications in Android, web applications, voice recognition and visual interfaces based on HMI. The communication between the server and the different installed nodes uses a UDP based discovery protocol with a continuous process of reading data that will be stored and obtained from the database. The remote access to the home consists of web page and application in Android which read the data stored in the database, showing them in real time. Several connectivity tests are performed as well as communication in different locations of a wireless network of sensors and actuators (WSAN) within a home automation system.
Spectrum Assignment using Prediction in Elastic Optical Networks

Future flex grid technology is very promising compared to fixed grid WDM technology, because of flexibility and high spectrum efficiency. Routing and Spectrum Assignment (RSA) is an important aspect of Elastic Optical Network (EON), which is used in providing efficient ways for best utilization of spectrum. We proposed a technique for spectrum provisioning based on the prediction in this paper. Back Propagation Neural Network (BPNN) is used for the prediction of the arrival and holding time of incoming connection requests. Simulation results show that the proposed technique gives better results in comparison to other existing traditional algorithms.
Helicopter Classification Using Time-Frequency and Tomographic Imaging Techniques

Helicopter targets, due to its low altitude flying, are mostly detected at short ranges. Radar systems capability to classify them to a particular class or model based on the returned radar signal characteristics of the main rotor blades, enables the operator to take early and confident decisions. This paper presents the L/N quotient method for classification and two different algorithms, joint Time-Frequency analysis and Tomographic imaging techniques, to estimate the required parameters from the return signals. In joint time-frequency analysis for blade flash detections, we used only two filter outputs from STFT of target radar returns and inverse Radon transform for the tomographic imaging algorithm. Both the algorithms are evaluated for different numbers of blades using a lab fan setup.
Analysis of a Novel Non-Volatile Look-Up Table (NV LUT) Controller Design with Resistive Random-Access Memories (RRAM) for Field-Programmable Gate Arrays (FPGA)

This paper studies the simulation results of a novel controller circuit design for non-volatile (NV) look-up tables (LUT) combined with an electrical model of a resistive random-access memory (RRAM)—an emerging non-volatile memory (NVM) device. The Write and Read schemes of the controller is tested for a 2x2 RRAM array. The selected RRAM successfully switches between low ‘0’ and high ‘1’ while the unselected the RRAM remain undisturbed, demonstrating the elimination of the intrinsic sneak-path current problem in RRAMs. Reading of a selected RRAM is also performed with agreeable results.
Low Power IoT System Node Design

There are many sensor nodes in the environment like soil moisture sensors, temperature, pollution sensors, etc. The operation of these sensors is controlled by the microcontrollers that they are interfaced with. The main limitation of these sensor nodes is their battery life. If the battery life is just a few weeks, then the battery has to be replaced several times a year. The approach here is to minimize the current consumption of sensor nodes like the esp8266 Wi-Fi module that will be interfaced with sensors so as to work under very low power. The methods that need to be adopted so as to reduce power consumption of sensor nodes to achieve power efficient employment of Internet of Things (IoT) is discussed in this paper.
IoT Based Interface Device for Automatic Molding Machine towards SMART FOUNDRY-2020

In the modern days, computing technology has been demanding which is called an Internet of Things (IoT). This IoT technology can be united with a variety of electronic and mechanical devices through cloud computing networking. IoT is a smart device which provides a solution for connecting, interacting and communicating to machine-to-machine using sensor network and Radio Frequency Identification (RFID) technologies, etc. Using IoT, the device can be connected easily and the generated data can be stored. Using the command and control system, the data can be utilized for appropriate applications which makes human life easier and safer. Also, it provides a huge impact on the industry. In this paper, a novel IoT based interface device for automatic molding machine (AMM) towards SMART FOUNDRY 2020 is proposed where this IoT based interface device solves the problem of the man-machine interface in the industry. A Graphical User Interface (GUI) based software is designed which interfaced the different sensors, actuators, and electronics motors, etc. SMART FOUNDRY-2020 is regulated using a GUI for its manual and automatic control. GUI is designed to interact with the automatic molding machine with the users. Subsequently, this data is connected to the cloud platform to store data and analyze the application functionality of automatic molding machine. This shows the potential of IoT in SMART FOUNDRY.
PataNET: A Convolutional Neural Networks to Identify Plant from Leaf Images

Plants are everywhere around the Earth. Plant identification is a very important problem for environmental protection and exploration. But getting to know various plants requires taking into account a large number of features which might be easy for a botanist but not easy for ordinary people. So having an automatic Plant identifier which uses a leaf to identifying plants will help many people. Here, a method is proposed where Convolutional Neural Network (CNN) technique is used to classify plants using leaf images. Using Adam optimizer and automatic Learning Rate reduction technique the model gave promising accuracy. This system was trained on 3600 RGB leaf images of 2 categories for 6 different plant species. The model reported promising results with validation accuracy was 95.86% and training accuracy was 96.54%. Different pre-processing techniques such as background whitening, noise removal are used. In the convolutional networks activation function ReLU is used in the hidden layer and Softmax for output layer.
Review of Machine Learning Methods for Windows Malware Detection

Malicious software or malware is one of the most critical cyber threats intentionally designed to cause damage, disrupt and gain unauthorized access to the system. The system can be a computer, server and computer network. Windows operating systems are widely used operating systems. It is easy for hackers to spread malware in these operating systems and exploit its vulnerabilities. Malware detection has always been a challenging issue and major concern for the data privacy. Many signature-based malware detection methods have been presented that work at a certain level and fail to detect unknown malware executable files, therefore the aim is to investigate a novel approach which can detect the new and unseen malware. In this paper, a simple and efficient malware detection model is introduced which distinguish between benign and malicious executable files by extracting features from the PE (portable executable) headers. Different machine learning methods such as Support Vector Machine, Decision Tree, Random Forest and Naive Bays classifiers are used for the classification. Random forest classifier among the different classifiers has achieved the highest accuracy result with the dataset of file, optional and section header.
ReTra: Reinforcement based Traffic Load Balancer in Fog based Network

As the trend moves along the use of IoT devices and greater dependence on the internet which creates a huge amount of data, the traditional architecture consisting of cloud and the networking devices fail to provide real-time decisions. Thus to satisfy the need of the real-world applications, a paradigm of computing and an intelligent routing environment was introduced. The era of fog computing and Software-defined networking(SDN) increased the performance of the traditional cloud-based architecture. With the increasing data bombardment from various sources, without proper load balancing, the refined architecture also cannot render maximum performance to the application. In view of this our paper deals with the real-time fog computing environment created with the support of SDN. On top of this platform, a novel approach for load balancing based on reinforcement learning has been proposed. The algorithm understands the behavior of the network and balances the load to provide the maximum possible availability of the resources. The distributed nature of this architecture also makes the network resilient. This paper gives an overview of the built architecture and the proposed novel architecture.
Classical Equivalent Quantum Based Pattern Matching Algorithms

The pattern matching problem is imperative in diversified field of computer science, and it finds occurrences of the pattern string(s) of any arbitrary length “M” in the large text string of length “N”. Since past decades the solutions to a problem have been suggesting through efficient algorithms. Classical benchmark algorithms such as Knuth Morris Pratt and Boyer Moore locates such solution in O(N) time, equivalent quantum algorithms can utilize quantum computations which are inherently parallel and obtains computational speedup by providing the solution in $\mathbf{O}(\sqrt{\boldsymbol{N}})$ time. The quantum pattern matching algorithm uses Grover's search logic that finds an element existence in the large unstructured text data of “N” items in $\mathbf{O}(\sqrt{\boldsymbol{N}})$ time, instead classically it takes O(N) time. The article comprises of introductory pattern matching tactics, quantum basics, Grover's search method, quantum based pattern matching algorithms, their illustration followed by complexity analysis, and finally concludes with the possible algorithmic variations and relevant applications.
Performance Analysis of Deficient Length Quaternion Least Mean Square Adaptive Filters

Quaternion adaptive filters have been widely used for processing 3D and 4D phenomena. Deficient length quaternion adaptive filters are explicitly or implicitly used in many practical applications where the length of system impulse response is large or unknown. However, their statistical behaviors are yet to be fully understood. As theoretical results on the class of “full length” quaternion least mean square (QLMS) algorithms do not necessarily apply to their deficient length versions, this article fills this void and analyses the mean and mean square convergence of the deficient length QLMS algorithms, both for the strictly linear and widely linear cases. Transient and steady-state performance is characterised by exploiting the augmented statistics of noncircular quaternion random vectors. A novel decorrelation technique in the quaternion domain is shown to allow for the development of intuitive closed-form solutions for correlated quaternion Gaussian inputs, thus unveiling the relationship between the algorithm behaviour and the noncircularity of quaternion input data. The analysis also provides a general framework whereby the strictly linear and semi-widely linear QLMS algorithms can be seen as “deficient-length” versions of the widely linear QLMS. Numerical simulations validate the accuracy of the theoretical results and support the behaviour of the considered algorithms.
Financial and Non-Stationary Time Series Forecasting using LSTM Recurrent Neural Network for Short and Long Horizon

Multi step ahead (long horizon) forecasting in a time series is a difficult task for various engineering applications in finance, geology, and information technology, etc. It is observed that different approaches mentioned in literature are at some disadvantage to give long term prediction for a non linear time series. Long Short-Term Memory (LSTM) network a type of Recurrent Neural Network (RNN) can be used to solve aforementioned problem and it may replace many practical implementations of the time series forecasting systems. This paper presents a novel LSTM model to give short and long horizon forecasting for a time series data. The LSTM method is preferable over other existing algorithms as LSTM network is able to learn non-linear and non-stationary nature of a time series which reduces error in forecasting. Also, long horizon forecasting is targeted in this paper which give wide range of its applicability. Comparative experiments with existing models demonstrate that our proposed LSTM ensemble method achieves state-of-the-art forecasting performance on four different real-life time series datasets publicly available.
Big Data based Enhanced Ambient Assisted Living System Framework

The current developments in the fields of networking and sensor technology enable more and more number of devices of the real world to be an integral part of Internet of Things (IoT). This leads to the generation of huge volumes of data on the scale sufficient enough to qualify as an instance of big data. This big data includes various types of hidden information that are useful for different kinds of services and applications. In this paper, an enhanced big data based framework for Ambient Assisted Living (AAL) system is presented. Where the prediction accuracy of the existing state-of-art work is enhanced by using map-reduce genetic algorithm. The results show that the proposed framework is more accurate when compared with the existing works.
A Circularly-polarized Linear Array of Maple-leaf shaped Antennas for C-band Applications

A two-element linear array of microstrip patch antennas in which the single element is a maple-leaf shape antenna has been presented in this paper. The proposed antenna is circularly polarized and designed to operate in C-band with the center frequency (fr) of 7.45 GHz. The single element is a nature-inspired maple-leaf shape antenna having symmetric geometry. Three diagonal slots inside the geometry have been implemented in order to resonate the structure at 7.45 GHz frequency as well as to get circular polarization (CP) from the structure. The feed network of the array has been designed using modified Wilkinson power divider which further improves the CP characteristics. The obtained simulated absolute bandwidth of the antennas covered ≈ 83.5% of the entire bandwidth of the C-band. The obtained RHCP gain of the antenna and the arrays are of 6.0, 8.72 and 8.65 dBic respectively.
Implementation of Linear Structuring Element in OpenCV for Blood Vessel Segmentation from Color Fundus Images

This paper presents an improved blood vessel segmentation technique from color fundus images using morphology operation. More accurate blood vessel segmentation from fundus images plays key role for screening of diabetic retinopathy and glaucoma. This paper has made significant contributions by developing linear structuring element for blood vessel detection using OpenCV. The proposed method involves three stages namely; pre-processing, generation of linear structuring element and detection of blood vessels from fundus image, In first stage, color fundus images are pre-processed or enhanced as these images often suffer from uneven illumination, low contrast and noise. In second stage, twelve linear structuring elements are generated and finally, in the third stage, blood vessel segmentation algorithm is applied to improve the extraction of diagnostic features such as microaneurysms and hemorrhages, leading to more accurate detection of diabetic retinopathy.
Two-Step Codeword Design for Millimeter Wave Massive MIMO Systems With Quantized Phase Shifters

In this paper, a two-step codeword design approach for millimeter wave (mmWave) massive MIMO systems is presented. Ideal codewords are first designed, which ignores the hardware constraints in terms of phase shifter resolution and the number of RF chains. Based on the ideal codewords, practical codewords are then obtained taking the hardware constraints into consideration. For the ideal codeword design in the first step, additional phase is introduced to the beam gain to provide extra degree of freedom. We develop a phase-shifted ideal codeword design (PS-ICD) method, which is based on alternative minimization with each iteration having a closed-form solution and can be extended to design more general beamforming vectors with different beam patterns. Once the ideal codewords are obtained in the first step, the practical codeword design problem in the second step is to approach the ideal codewords by considering the hardware constraints of the hybrid precoding structure in terms of phase shifter resolution and the number of RF chains. We propose a fast search based alternative minimization (FS-AltMin) algorithm that alternatively designs the analog precoder and digital precoder. Simulation results verify the effectiveness of the proposed methods and show that the codewords designed based on the two-step approach outperform those designed by the existing approaches.