Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN)

Time series based model has been widely applied to estimate the future stock price, and aids investors’ decisions and trades. However, due to high rate of volatility and non-linearity of time series, affecting stock market forecasting. To address this, artificial neural network (ANN) and deep neural network (DNN) have been applied in the area of stock price forecasting by various researchers. However, existing model use ANN and DNN with back propagation fails to provide flexible linear or nonlinear relationship among variables and they are difficult to train. The objective of this work is to present a modified back propagation neural network (MBNN) model that can handle huge density of nonlinear data, their relationship and give an optimal strategy for computationally hard problem. Experiments are conducted to evaluate performance of proposed MBNN over existing model in terms RMSE and MAPE. The outcome shows significant performance improvement by MBNN over state-of-art approach.
A Compact Dual-Band and Dual-Polarized Open-Ended ZOR Antenna with AMC Ground Plane for 4G-LTE/WLAN/WiMAX Applications

This work explains the design and development of a dual-band and dual-polarized metamaterial (MTM) antenna incorporating artificial magnetic conductor (AMC) ground plane for higher gain and circular polarization (CP) characteristics. Antenna compactness is due to the zeroth order resonance (ZOR) mode of composite right/left-handed (CRLH) transmission line (TL) and the application of AMC ground plane. The electrical size of the intended MTM antenna is 0.19 $\boldsymbol{\lambda}_{0}\times 0.23\ \boldsymbol{\lambda}_{0}\times 0.012\ \boldsymbol{\lambda}_{0}$ at 2.38 GHz ($\boldsymbol{k}\boldsymbol{a}=0.97 < 1$). The MTM antenna with AMC ground plane provides a bandwidth of 11.46% and 26.18% for the first and second frequency bands centered at 2.35 and 5.5 GHz. The antenna shows CP radiation in the second band with an axial ratio bandwidth (ARBW) of 3.80%. The antenna provides a higher gain of 6.15 dBi and 6.96 dBi for both the bands. The antenna provides higher gain, good radiation efficiency, wider bandwidth, and compact size. The antenna can be suitable for working in 4G-LTE, WLAN, and WiMAX application systems.
Immutable and Democratic Data in Permissionless Peer-to-Peer Systems

Conventional data storage methods like SQL and NoSQL offer a huge amount of possibilities with one major disadvantage, having to use a centralized authority. This authority may be in the form of a centralized or decentralized master server or a permissioned peer-to-peer setting. This paper looks at different technologies on how to persist data without using a central authority, mainly looking at permissionless peer-to-peer networks, primarily Distributed Ledger Technologies (DLTs) and a combination of DLTs with conventional databases. Afterwards it is shown how a system like this might be implemented in two prototypes which are then evaluated against conventional databases.
A Secure Fog-Cloud Based Architecture for MIoT

Medical Internet of Things (MIoT) offers innovative solutions to a healthier life, making radical changes in people's lives. Healthcare providers are enabled to continuously and remotely monitor their patients for many medial issues outside hospitals and healthcare providers' offices. MIoT systems and applications lead to increase availability, accessibility, quality and cost-effectiveness of healthcare services. On the other hand, MIoT devices generate a large amount of diverse real-time data, which is highly sensitive. Thus, securing medical data is an essential requirement when developing MIoT architectures. However, the MIoT architectures being developed in the literature have many security issues. To address the challenge of data security in MIoT, the integration of fog computing and MIoT is studied as an emerging and appropriate solution. By data security, it means that medial data is stored in fog nodes and transferred to the cloud in a secure manner to prevent any unauthorized access. In this paper, we propose a design for a secure fog-cloud based architecture for MIoT.
Internet of Things Based Monitoring of Large Rotor Vibration With a Microelectromechanical Systems Accelerometer

In rotating machinery, excessive vibration can affect the lifetime of the machine and, for example, in paper machines, it can directly affect production quality. Hence, it is important to monitor vibrations. Typically the vibrations are measured with piezo-based accelerometers attached to the bearing housing. Installation of the accelerometers and especially cabling is laborious and expensive in the case of existing machines that have several components to monitor. In continuous process maintenance, a break may be required as well. Therefore, typically only the critical rotors will be monitored, if at all. This research focuses on applying wireless microelectromechanical systems (MEMS) accelerometers to the measurement of large rotor vibration. The results indicated that MEMS accelerometers combined with wireless communication can offer a viable alternative to more expensive piezo-based accelerometers with traditional wire-based communication. The combination provides a flexible and cost-effective method for the collection of vibration data from large rotors and rotor systems.
Monte Carlo model of resistance evolution in embedded PCM with Ge-rich GST

This work presents an optimized model for resistance evolution in PCM cells with Ge-rich GeSbTe (GST) alloy as active material. Unlike conventional Ge2Sb2 Te5, the low-resistance (set) state of Ge-rich GST shows a resistance drift to high resistance R, similar to the high resistance (reset) state, which could be a potential risk for data reliability. We develop a Monte Carlo (MC) model which predicts the time evolution of R at the statistical level of a memory array at various temperature T. The model is validated against variable temperature annealing, such as the soldering profile in embedded PCM, supporting the good reliability of Ge-rich GST at high T.
Solution for eliminating defects caused by improper operation starting execution

Engineering industry represents everything that is developing, manufacturing and selling cars, being one of the most important economic sectors in the world to bring income. In general, companies in this sector are multinationals that produce in their country of origin or to countries that have purchased or built production plants. The main objective of this paper was to study on improving the operation of the post of starting diesel engine. Failure to comply with the priming operation adversely affects the quality indicators on the plant. All these problems have forced to find solutions for their elimination. In order to eliminate the problems, a thorough analysis of the post has been started. Difficulties in finding the solution consisted primarily in the lack of funds to purchase a new priming plant, as the current installation is an integral part of the tank filling facility. This study aims to find solutions to eliminate failures due to improper execution of this operation, defects which are registered in the program that handles the top factory defects and which is calculated based on one of the most important indicators of quality.
Methodologies in Face Recognition for Surveillance

Face recognition being the most significant part of the biometrics and video surveillance systems has to deal with many challenges like pose, illumination, distance, expression variations along with occlusion, low resolution and noise. This paper analyses the latest approaches and methodologies adapted by Face Recognition systems in overcoming the challenges with the focus on their performance on publicly available bench-mark face databases. Few of the approaches discussed in this paper are Local Binary Pattern technique, Reference Face Graph framework, Principal Component Analysis algorithm, Linear Discriminant Analysis algorithm, MLP Neural Networks, 3D Modeling, Back Propagation Neural Network, Local Pavithra S G Bhat Gradient Hexa Pattern descriptor, FPGA based architecture, Conditional Generative Adversarial Networks and Trunk-Branch Ensemble Convolutional Neural Networks. This paper provides an insight for selection of an approach for future researchers and practitioners for real world implementation.
Visual Loop Closure Detection over Illumination Change

In the Simultaneous Localization and Mapping (SLAM) problem, loop closure detection is a task of whether the robot has visited the area or not, when robot has traveled a long distance, and then revisits the previous travel route. Bag-of-visual-words method, one of the popular and fast visual loop closure detection method, converts a query image into a descriptor and compares it with the descriptors of the whole database images to determine loop closure. However, bag-of-visual words method has lower performance when the illumination is changed while a long driving time because the characteristic of the image is changed due to the illumination difference. In this paper, we propose a novel loop closure detection method robust to illumination change through fusing the results of parallel loop closing detection in original color space and illumination invariant space both by generating illumination invariant space image based codebook. Experimental results show that the proposed algorithm robustly performs loop closure detection over illumination change than conventional method.
AE and SAE Based Aircraft Image Denoising

Images are corrupted during transmission and acquisition. De-noising is an important image restoration operation which determines the accuracy of interpretation and recognition stages. Time and often traditional methods have been used for image de-noising. Lately, there has been considerably interest on learning aided image de-nosing. As deep learning has lately been established as the most efficient learning aided mechanism, it is increasingly being used for a range of image processing and computer vision applications. This paper focuses on the design of Auto-encoder (AE) and Stacked Auto-encoder (SAE) based approaches for de-noising of certain military aircrafts as part of an automatic target recognition (ASR) system. Five image types are taken for the work which are mixed with Gaussian, Poisson, Speckle, Salt and Pepper noise. For each of these image sets signal to noise ratio (SNR) variation between -3 to 10 dB are taken. Experimental results have show that the SAE based approach is more reliable despite showing higher computational latency.
FinalFilter: Asserting Security Properties of a Processor at Runtime

& IN AN IDEAL world, it would be possible to build a provably correct and secure processor. However, the complexity of today’s processors puts this ideal out of reach. The complete verification of a modern processor remains intractable. Statically verifying even a simple security property—for example, “hardware privilege escalation never occurs”—remains beyond the state of the art in formal verification. Testing can complement formal verification methods, yet testing is incomplete and bugs in the hardware that leave it vulnerable continue to elude test suites. Further, a crafty malicious actor can evade typical testing coverage metrics. Recent efforts, including that of three of the authors, have explored the use of static analysis on the design files (e.g., hardware description level source code or gate-level netlists) to find suspicious circuitry. These techniques rely on heuristics to define patterns that indicate a likely trojan and then search for instances in the design that match the pattern. However, malicious circuitry that does not match the pattern will be missed, as will inadvertent bugs that open vulnerabilities. By the time the weakness is uncovered, the hardware is already in the end user’s hands and vulnerable to attack. In the absence of a full proof of correctness, what is needed is a final filter: a runtime verification technique that works—postdeployment—to detect and respond to security property violations as they occur during execution. In this article, we make the case for final filters using our tool, FinalFilter, as a case study.
Reliability Assessment of Heterogeneous Drone Fleet With Sliding Redundancy

Reliability analysis of system is a complex problem. One important step is reliability analysis is development of mathematical representation of the investigated system. Modern system is composed by many components with different attributes and properties that should be taken into account in his reliability analysis. Therefore, it is important to choose suitable approach for representation of the system behavior. One such approach is known as structure function. In this paper we will show how the structure function of the system can be used to acquire the reliability function. The specific aspect of this approach is the possibility to use for system with homogenous and heterogeneous components. This will be shown on the reliability analysis of the drone fleet.
Joint Encryption and Compression of Audio Based on Compressive Sensing

The increasing amount of sensible information transmitted through insecure communications channels requires, the development of algorithms with the capacity of simultaneously compress and encrypt digital information. To solve this problem, this paper proposes an algorithm based on compressing sensing, which simultaneously compress and encrypt audio signals. In the proposed scheme the audio signal is segmented in frames of1000 samples which are then transformed in sparse frames using the DCT. Next each sparse frame is compressed using a different sensing matrix in each frame, to assure that the proposed system satisfies the Extended Wyner Secrecy (EWS) criterion. Evaluation results show that the proposed scheme allows the secure communication of audio signals.
Design and Analysis of a Novel Trans-inverse DC-DC Converter

This paper explores the coupled-inductor architectures for a trans-inverse DC-DC converter. The parasitics (e.g., leakage inductance and AC resistance) will inevitably affect the performance of the coupled-inductor and thus, the entire system. More specifically, when ignoring these parasitics, the operation principle of the DC-DC converter changes and the boosting capability is degraded. Considering the application in a trans-inverse DC-DC converter, three winding arrangements are explored in detail to demonstrate their advantages and disadvantages. Finite element analysis (FEA) on the magnetics is conducted in this paper to simulate the leakage inductance and AC resistance. Finally, the performance of the trans-inverse converter with the designed winding arrangements is verified by experimental tests.
Optimum Capacity over Power Consumption Requirements in MIMO Systems

In this research work we present the evaluation of the optimum capacity to the total energy consumption in the case of multiple antenna systems (MIMO). The proposed methodology is based on a general distance function, named the General Evaluation Factor (GEF). This method was applied to MIMO networks that operate over Rayleigh fading channels with different antenna nodes. The implementation of this method was based on different capacities and cost values. The GEF indicator regarding the power consumption and the capacity factors of different MIMO systems is calculated. The channel is modelled using the Rayleigh fading approach, which is the most representative case for non-line of sight radio propagation.
An Iterative Distributed Approach for Optimal Power Dispatch in a Smart Grid Environment

An iterative distributed algorithm is proposed for existing power system optimization problems. Here the centralized optimal dispatch problem is explained in a decentralised manner. The line flow constraints along with generation limits are distributed into different nodes of the given network. In order to achieve a globally optimal solution, the incremental cost and the penalty factor for congestion are varying iteratively. The distributed algorithms provide flexibility, robustness, and scalability. The algorithm is tested for optimal power dispatch in an IEEE 39-bus system and welfare maximization problem in a 3-bus system. The results are validated with the standard centralised approach.
Study of Blockchain with Bitcoin based Fund Raise Use case using Laravel Framework

Blockchain is a deliberately spreading set of data called as Blocks, which are associated and assured by the use of Cryptography. Each Block of this Blockchain consists of cryptographic hash of the preceding block, a timestamp and transaction information. Modification of Data by fraudsters is highly challenging in Blockchain by its design. It is an accessible shared register that can store activities between two internet users securely. Such a technology is an easy way for one person using internet to send fragment of digital property to other person who is also using internet, such that transfer of data or communication will be very secure and none of the sensitive details would be leaked. With the usage of Blockchain technology, several people can write the data into the Record and also sector of users can manage how the Record should be added with new entries. This technology is now being employed in a variety of applications like digital currency, supply chain management, medical information systems etc. In this paper, we have studied the technology behind Blockchain and the application of the same in several use cases. We have also implemented fund raise use case using Laravel PHP Framework to illustrate the steps of using Blockchain to assure secure transactions
Surround view algorithm for parking assist system

Modern vehicles use different advanced driver-assistance systems (ADAS) in order to make driving safer and more comfortable. One of them is a system that uses a set of in-vehicle cameras and provides the driver a top-view image of the space around the vehicle, thus helping the driver in parking. The main part of that system is the algorithm which processes the frames acquired simultaneously from four in-vehicle cameras located at different sides of the vehicle and creates the final top-view image of the space around the vehicle. In this paper an new algorithm for that purpose is designed, which consists of two main parts, one for performing camera calibration, the other for generating the top-view image. The implemented camera calibration is based on calibration patterns and calculates camera parameters which are then used to eliminate image distortion due to usage of fish-eye cameras. For generating the top-view image of the space around the vehicle, different geometric operations including distortion correction, perspective transformation, and image stitching, are applied to the input frames acquired by different cameras. Algorithm performance are tested using four real automotive fish-eye cameras fixed to the vehicle model and connected to the ADAS development board.
Digital Predictive Peak Current-Mode Control for Three-Level Buck Converters

This paper addresses the application of digital predictive current-mode control (DPCMC) to three-level flying-capacitor Buck converters (3LFC). While DPCMC has been extensively studied for basic dc-dc topologies, its reliable application to multilevel converters is subject to the constraint that the flying capacitor voltage(s) remains stable. This work clarifies the stability properties of predictive peak current-mode control for three-level converters when operated in single-sampling and multisampling mode. A specific implementation of the multisampled DPCMC through a fast-update of the duty cycle command is also disclosed and analyzed which guarantees system stability and FC voltage balancing while significantly extending the dynamic capabilities of the converter. Results are validated in simulation and experimentally on a low-voltage 3LFC prototype.
Network Traffic Classification Techniques-A Review

With the growth in the amount of devices associated with the internet; the data that is getting circulated over the internet is also increasing. It is an undeniable fact that this data has significant presence for individuals as well as for organizations. A network needs to handle this massive amount of data traffic which contains malicious data as well. Therefore, it is very essential to distinguish between normal and abnormal traffic by analyzing the network traffic. A number of network traffic classification techniques are available. The researchers are trying to find the traffic classification techniques that do not depend on port numbers or that do not read the packet payload contents. In this study, an analysis of various traffic classification techniques and the application of several Machine learning techniques for traffic classification is carried out. This survey paper also presents a brief review of various machine learning techniques for traffic classification.
Analysis of Different Gate Dielectric Materials in Carbon Nanotube Field Effect Transistor (CNFET) Using Optimization Technique

The main objective of this paper is to select the suitable gate dielectric material for the carbon nanotube field effect transistor (CNFET) using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The selection of dielectric material is based on various material indices which include relative dielectric constant $(\varepsilon_{\mathrm{r}})$, conduction band offset (CBO), energy band gap $(\mathrm{E}_{\mathrm{g}})$ and coefficient of thermal expansion (CTE). Based on the analysis it was observed that $\text{La}2\mathrm{O}_{3}$ is the most suitable material followed by HfO2 and $\mathrm{ZrO}_{2}$ for gate dielectric material in CNFET.
Crosstalk Prediction of Cables Based on Cosserat Elastic Rod Theory

The position and morphology of cables may affect the calculation of distribution parameters between muti-conductors, and then impact crosstalk prediction of cables. Therefore, a novel method based on Cosserat elastic rod theory for predicting crosstalk of cables is proposed in this paper. Consider the physical characteristics of cable, which can be closer to reality, a central line function is applied into cables modeling. The crosstalk of the cable physical model are analyzed by Method of Moment. The results illustrate that crosstalk prediction will be affected due to various degree of cable deformation, and the Cosserat model is of great significance for crosstalk prediction of cables.
Terrain Adaptive Walking of Biped Neuromuscular Virtual Human Using Deep Reinforcement Learning

There have been some biomechanics-based control systems that have achieved better realistic virtual human motion. Yet their abilities to adapt the changing environments are weaker than the traditional control systems with characters driven by proportional derivative actuators directly. In our method, we build a hierarchical neuromuscular virtual human (NMVH) motion control system that consists of a low-level spine reflex layer and a high-level policy control layer. The spine reflex layer uses a feedback net to map sensory information to excitations, which stimulate muscles to generate joint torques. The policy control layer includes a deep neural network, which provides a learned action policy to spine reflex layer for achieving terrain-adaptive motion skills. The particle swarm optimization algorithm is used to optimize the gain factors of the feedback net for finding out a basic policy to make the virtual human walk on the flat terrain autonomously. The proximal policy optimization algorithm is employed to train the deep neural network in policy control layer for learning how to modulate the actions to adapt to the changing terrain. The simulation results in Matlab show that virtual human can walk smoothly and better adapt to the given terrain changes. It demonstrates that our control system improves the terrain-adaptive walking skill of the neuromuscular virtual human.