Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Approach for Lane Detection in Challenging Illumination Scenarios

Automated lane identification is an essential component of perception based driver assistance system. These systems employed in intelligent vehicles minimize the fatal accidents and improve safety of driver as well as passenger and enhance the traffic scenarios. In this article, a lane detection approach based on image processing that determines the painted lanes on road in challenging scenarios is proposed. In the preprocessing stage, unique technique is used to detect and minimize the shadow and other illumination effects on the road which impose a crucial problem while detecting the lane lines. Two different thresholds are utilized to identify the probable lane boundaries and the outliers are removed in the post processing stage. The credible lane edges are obtained and superimposed on the original image. The tested results show the efficacy of the presented algorithm and it is evident from the detected response that this technique is effective in detecting straight and curved lanes in challenging hilly road scenarios.
Brain Tumor Segmentation and Classification Using MRI Images via Fully Convolution Neural Networks

Charismatic timbre Imaging be the favorite picture modality intended meant pro assess brains tumor plus segmentation be essential designed on behalf of analysis plus action preparation. Consequently vigorous routine segmentation method is requisite. Mechanism education proposal anywhere the representation be educated as of information be pretty victorious. Hierarchical segmentation approach first section the complete brain tumor follows through intra growth hankie classification. Currently fully convolutional networks approaches for segmentation are very efficient. Exact growth segmentation is necessary plus vital pace intended for mainframe aid brain tumor analysis plus surgical arrangement. They are motionless opposite a few challenge such because inferior segmentation correctness demanding priori information otherwise require the being meddling. Subsequent plan the sorting effect to double picture the placement dispensation is implementing through morphological strain toward acquire the concluding segmentation. During organize near appraise the future technique the research be practical in the direction of section the brain tumor used for the authentic tolerant dataset. The ending presentation show to the future brain tumor segmentation technique be additional precise plus competent.
Malware Detection in Android Operating System

Android operating system has gained a wide popularity in the recent years due to the open environmental nature of the Android framework. This has given a tremendous flux to the Malware developers to target the Android users. Thus, personal privacy theft has become a major issue. Various mobile malware detection systems are proposed in the recent years to address this issue. Operation level, Analysis technique and Accuracy are the core design aspects for any detection method. This paper focuses on surveying the existing mobile malware detection systems. Also, it provides a detailed comparison to summarize and analyze the detection methods under concern.
Fractional-Order Controller for the Position Control of a DC Motor

This paper represents the position control of a DC motor using Fractional-order PID controller. DC motor has wide applications in industries and other fields of control theory. Concept of fractional calculus has been presented in this paper. The mathematical description of FOPID controller is described. A special toolbox named FOMCON for simulation in MATLAB has been studied here. An example of simulation using fractional-order PID controller along with integer-order PID controller has performed and comparative study has been done with simulation result.
A Novel Optic Disc and Blood Vessel Detection Algorithm

Optic disc (OD) and blood vessel detection is an essential measure while developing computerized diagnosing arrangement for diabetic retinopathy using retinal fundus image. This paper presents a morphological operator based methodology followed by a finding of OD boundary pixel location for segmenting the OD and adaptive thresholding algorithm for extracting blood vessel. The ultimate target is to assist the early detection of definite pathologies and to fully computerize the system that may help the medical persons to detect the irregularity easily at the earliest. The proposed method has been evaluated using DIARETDB0, DIARETDB1 and DRIVE database, containing color fundus images. This proposed method obtained high sensitivity, specificity and accuracy.
Adaptation of Proof-of-Stake-based Blockchains for IoT Data Streams

Proof-of-Work (PoW) in Blockchains (BC), which is a widely used consensus algorithm, suffers from high power consumption of miners and low transaction rates. This work demonstrates a Proof-of-Stake (PoS)-based BC called Bazo, which is specially designed and adapted for Internet of Things (IoT) data streams. Bazo displays enhanced performance in terms of energy consumption and transactions processing in comparison to PoW-based BC. To further improve performance of Bazo, sharding and transaction aggregation methods are employed. Moreover, IoT-BC adaptation helpers of a modular and layered architecture are provided to allow wireless devices to submit data into the BC. The designed architecture can support multiple hardware and software platforms as well as network technologies.
A VLSI architecture design of CAVLC decoder

Variable length code is an integral component of many international standards on image and video compression. Recently, context-based adaptive variable length coding (CAVLC) is adopted by the emerging JVT (also called H.264 in ITU, and AVC in MPEG-4). In this paper, we describe a novel architecture for CAVLC decoder, including a coeff_token decoder, level decoder, total_zeros decoder and run_before decoder. Together with a barrel shifter and controller, the pipeline architecture can decode every syntax element in one clock cycle. Therefore, it is very suitable for video applications that require high throughput.
Conditional Mutual Information based Attribute Weighting on General Bayesian Network Classifier

Bayesian networks are used for building effective classifiers in the field of machine learning. Naive Bayes (NB) classifier which has the simplest structure among Bayesian Network (BN) classifiers assume conditional independence of attributes, given the class. This assumption often fails in real world context. Attribute weighting is one of the major approaches used to alleviate this weakness and is proved to be effective. Unlike the BN classifiers in which class node is treated as a parent to all the attribute nodes, such as NB, BN Augmented Naive Bayes (BAN), Tree Augmented Naive Bayes (TAN), Averaged One Dependence Estimators (AODE), the class node is treated as an ordinary node in General Bayesian Network (GBN). No study had been done on whether attribute weighting will improve GBN classifier.Here, we evaluate the effectiveness of Conditional Mutual Information based attribute weighting on GBN. We experimentally test ICS algorithm for GBN provided by WEKA tool, using 20 UCI datasets and compare it with the attribute weighted approach with regard to ACC which denotes classification accuracy and AUC which measures Area Under ROC Curve. Results of our experiments show that conditional mutual information based attribute weighted GBN significantly outperforms unweighted GBN.
An Outline on Big Data and Big Data Analytics

With the rapid advancements in technological applications have led to the flooding of data from various sources like web, social network data, business data, medical records, etc. over the preceding years. As compared to traditional data, big data reveals a unique characteristic from its three V’s which means big data is unstructured. In this era, the emerging trend requires the involvement of advanced data analysis, acquisition and management techniques to mine and collect appropriate data in a structured way. In this paper, we describe the definitions and the challenges of big data systems. Next, a systematic framework decomposes the architecture of big data systems into four stages like data generation, data acquisition, data storage and data analytics. These stages form the basis of big data value chain. Finally, some solutions are discussed to tackle the challenges of big data and future attention is required for big data systems.
PPG based Respiration Signal Estimation using VMDPCA

The photoplethysmography(PPG) waveform is a pulsatile physiological waveform reflecting the blood volume changes attributed to cardiac synchronous activities. The waveforms components below normal heart beat (less than 0.8Hz) are mainly contributed by respiration, sympatho-adrenal response and body regulatory system. Therefore, PPG waveforms are commonly used as non-invasive approach to extract cardiorespiratory signals such as heartbeat and respiration rate. The existing methods either only estimate the respiration rate or sensitive to noises. Hence we propose a novel method to both recover the respiration signal (RS) and estimate respiration rate (RR) by combining the variational mode decomposition and principal component analysis (VMDPCA) simultaneously. Some 80 PPG samples provided by Physiobank, i.e. the MIMIC database were used to validate the performance of our algorithm. The results were examined with respect to the golden truth respiration signal sampled by capnography. The performance measurement matrix was composed of mean normalized root mean square deviation (NRMSD), magnitude squared coherence (MSC) and Pearson's correlation coefficient (PCC) with values of 0.434, 0.392 and 0.213 respectively. The proposed method had also achieved 6.67 and 3.34 times faster than EEMDPCA and EWTPCA algorithms respectively.
Generation and coherent manipulation of complex quantum states based on integrated frequency combs

The investigation and use of integrated frequency comb sources (i.e. featured by equally-spaced discrete spectral modes) have recently provided a unique framework to address the challenges of generation and coherent manipulation of complex quantum states in on-chip devices. We exploit integrated frequency combs for generating entangled photon pairs, as well as multi-photon states, and high-dimensional (D-level, i.e. quDit) entangled photons. In particular, we manage to coherently manipulate such complex quantum systems by using telecommunications components (standard fiber telecom).
FPL-An End-to-End Face Parts Labeling Framework

Face parts labeling is the process of assigning class labels to each face part. A face parts labeling method FPL which divides a given image into its constitutes parts is proposed in this paper. In most of the previously proposed methods this division is based on three or some time four classes. In the proposed work a given face image is divided into six classes (skin, hair, back, eyes, nose and mouth). A database FaceD consisting of 564 images is labeled with hand and make publically available. A supervised learning model is built through extraction of features from the training data. Testing phase is performed with two semantic segmentation methods i.e., pixel and super-pixel based segmentation. In pixel based segmentation class label is provided to each pixel individually. In super-pixel based method class label is assigned to super-pixels only – as a result same class label is given to all pixels inside a super-pixel. Pixel labeling accuracy reported with pixel and super-pixel based methods is 97.68% and 93.45% respectively.
Super-Resolution of Textual Images using Autoencoders for Text Identification

Textual image identification assumes a particular and sufficient resolution for the Region of Interest (ROI) on the image. The object or letter to be detected on the image must be of specific resolution otherwise most of the system fails to detect properly. Convolutional Neural Networks trained on normal textual images fail to detect characters, which is upscaled by interpolation methods. A deep neural network based model is proposed in this paper for upscaling and detection of textual images that show a significant increase in performance on the standard dataset. The proposed model consists of a customized autoencoder network for 4× upscaling of an image so that an automated system like the convolutional neural network can detect the upscaled images accurately. Results show a significant increase in the Structural Similarity Index (SSIM) and the Peak Signal to Noise Ratio (PSNR) measures for using the proposed model. Most importantly, the proposed approach achieves 96.44% identification accuracy for the Super-Resolution (SR) images.
Application of UAV for Pest, Weeds and Disease Detection using Open Computer Vision

Sustainable agriculture is an important field where not much attention is given though it is highly necessary, so as to monitor the growth of crops for their efficient growth in most nutritious ways. For effective growth of crops, lot of chemicals like fertilizers and pesticides are used, however, excessive usage of them results in damage to land and water resources. The attack of pests is a major criteria which affect crop yield. Various crop monitoring technologies are available which are highly expensive and not all farmers can afford. Moreover in India, farmers are not capable of understanding the operation and handling of such sophisticated technology. In this paper, we propose a system which is cheaper and easy to operate with multiple application. The proposed system uses a technology which utilizes machine learning and ANN algorithms using UAV that helps us to locate regions that are affected by diseases and pesticides so that we can particularly focus on the regions that are affected and apply chemicals only in that particular area, with the entire system being cost effective. For this purpose, we divide the entire area into n x n segments and using image processing, the segmented areas are analysed and processed using Ardupilot and a central operating system to monitor using python and open CV, giving the simplest user interface for monitoring purposes.
Machine learning based Oozie Workflow for Hive Query Schedule mechanism

Apache Oozie is a scheduler framework to run and oversee Hadoop jobs in a distributed environment. It enables various complex jobs, which is to be kept running in a sequential order to accomplish a greater task. In the task, at least two jobs can run parallel to each other without any ambiguity. There are two methods through which Oozie detects Completion of tasks as they are call back and pooling. In Oozie the workflow is a sequence of actions and these actions are arranged in a Directed acyclic graph (DAG) control dependency. These workflow actions can be hive action, pig action, shell action, java action etc, and by using decision trees one can decide how and on which condition a job should run.
Unequal Error Protection Aided Region of Interest Aware Wireless Panoramic Video

Panoramic video with its flawless immersive tele-presence is considered to be the near-future video format of choice since they carry 360 degree coverage of the designated scenes. However, the viewers may focus their specific attention on perfectly lip-synchronized video as part of the panoramic video scene, hence only have a peripheral vision of the remaining parts of a frame. Therefore, it is intuitive to allocate stronger protection to the panoramic video region of interest. As a solution, we propose Region of Interest Aware Unequal Error Protection (ROI-UEP) for wireless transmission of high-efficiency video code (HEVC) sequences. Specifically, the ROI of a panoramic frame may be deemed to be within the 120° angular range of the viewing center, which can be estimated from the viewing trajectory of a head mounted display. Then, the most appropriate unequal forward error correction (FEC) coding rates will be found for the ROI signals by minimizing the expected video distortion. Moreover, the so-called weighted peak signal-to-noise ratio (WPSNR) is proposed for evaluating the quality of the reconstructed panoramic video, where the weights of pixels are taken into account for calculating the distortion caused by the related pixels. Our simulation results show that the ROI based equal error protection (ROI-EEP) scheme substantially outperforms the EEP by a WPSNR of more than 10 dB, while the ROI-UEP scheme further improves its ROI-EEP counterpart by a WPSNR of 9.4 dB at a channel $E_{b}/N_{0}$ of 6 dB.
An Interference-Aware Virtual Machine Placement Strategy for High Performance Computing Applications in Clouds

Cross-interference may happen when applications share a common physical machine, affecting negatively their performances. This problem occurs frequently when high performance applications are executed in clouds. Some papers of the related literature have considered this problem when proposing strategies for Virtual Machine Placement. However, they neither have employed a suitable method for predicting interference nor have considered the minimization of the number of used physical machines and interference at the same time. In this paper, we define the Interference-aware Virtual Machine Placement Problem for HPC applications in Clouds (IVMP) that tackles both problems by minimizing, at the same time, the interference suffered by HPC applications which share common physical machines and the number of physical machines used to allocate them. We propose a mathematical formulation for this problem and a strategy based on the Iterated Local Search framework to solve it. Experiments were conducted in a real scenario by using applications from oil and gas industry and applications from the HPCC benchmark. They showed that our method reduced interference in more than 40%, using the same number of physical machines of the most widely employed heuristics to solve the problem.
Cache Based Side Channel Attack: A Survey

The Cloud Computing (CC) is famous due to shared resources technology. Cloud computing share resources among distrusting customers and provide on demand, cost effective, elasticity services. Due to rapid growth of cloud computing environment, vulnerabilities and their preventions methods are potential increase. We had seen that conventional prevention methods for Side Channel (SC) attack are not suitable for avoidance of cross-VM cashed based SC attacks.In 2016, shared technology issues is a one of top threat consider by cloud security alliance (CSA), which has been published in February 2016 in The Treacherous 12 [1]. This is a under top threat by CSA from last 5 year. In this paper we will discuss multiple method for performing side channel attack and prevention methods. We also discuss the strength of prevention method as well as drawbacks of that method. So that this paper will generate more research scope and new effective idea for prevention of side channel attack, this paper is provide support and background knowledge for new researchers in area of side channel attack in different environments.
An Ecological Adaptive Cruise Control for Mixed Traffic and Its Stabilization Effect

With the rapid development of technologies, the ecological control strategies of connected and autonomous vehicle (CAV) technologies are gaining more and more attention. In this paper, a rule-based ecological cruise control, called the ecological smart driver model (EcoSDM), is proposed to improve the fuel efficiency of an individual vehicle and the traffic flow. By adjusting the spacing between the leading and the following vehicles, EcoSDM provides smoother deceleration and acceleration than the enhanced intelligent driver model (Enhanced-IDM) and the smart driver model (SDM). The linear stability of EcoSDM is analyzed both theoretically and numerically. The numerical results validate the results of theoretical analysis. Moreover, the simulations results show that EcoSDM outperforms the Enhanced-IDM and SDM in terms of stabilization effect on homogeneous traffic flow. In addition, the calibrated VT-Micro model is used to estimate the fuel consumption of CAVs and manually driven vehicles. The result shows that CAVs have better fuel economy than the human-driven vehicles, which is consistent with existing studies. The EcoSDM outperforms Enhanced-IDM and SDM in terms of fuel consumption. For the EcoSDM-equipped CAVs, the fuel saving benefit is greatest when a CAV is at the front of the platoon.
Pseudonym Management Through Blockchain: Cost-Efficient Privacy Preservation on Intelligent Transportation Systems

Research into the established area of the intelligent transportation system is evolving into the Internet of Vehicles, a fast-moving research area, fuelled in part by rapid changes based on cyber-physical systems. It needs to be recognized that existing vehicular communication systems are susceptible to privacy vulnerabilities which require addressing. A practical challenge is that many vehicular communication applications and services make use of basic safety messages that contain the identity of the vehicle, location, and other personal data. A popular way of dealing with this privacy issue is to utilize a pseudonym change scheme to protect the vehicle’s identity and location. However, many such schemes suffer that the cost grows and the certificate management difficulty raises with the number of pseudonyms generated and stored, casting doubt of the economic feasibility of that approach. We propose a decentralized blockchain-based solution for pseudonym management that overcomes these limitations. This scheme consists of pseudonym distribution and a shuffle operation, allowing the reuse of existing pseudonyms to different vehicles. The results reported here, including those from our simulations, demonstrate that the proposed scheme can reuse existing pseudonyms and achieve a better degree of anonymity at a lower cost than existing schemes.
Extending Port Forwarding Concept to IOT

Port forwarding or port mapping is an application of network address translation (NAT) that redirects a communication request from one address and port number combination to another while the packets are traversing a network gateway, such as a router . This technique allows remote computers (for example, computers on the Internet) to connect to a specific computer or service within a private local-area network (LAN). In this paper we have discussed different scenarios for port forwarding.
Inter-Basket and Intra-Basket Adaptive Attention Network for Next Basket Recommendation

Next basket recommendation with consideration of user sequential shopping behaviors plays a significant role in E-commerce to improve the user experience and service quality. Recently, recurrent neural networks (RNNs), especially attention-based RNN, have been widely adopted in the next basket recommendation. However, existing fixed attention mechanisms are not designed to model the dynamic and diverse characteristics of user appetites. In this paper, we propose an inter-basket and intra-basket adaptive attention network (IIAAN) for the next basket recommendation. Specifically, the inter-basket adaptive attention acts on all historical user baskets to model user’s diverse long-term preferences, while the intra-basket adaptive attention is designed to act on item-level in the most recent basket to model user’s dynamic and different short-term preferences. Then, we further integrate inter-basket and intra-basket adaptive attentions together to improve recommendation effectiveness. Finally, we evaluate the proposed model IIAAN using three real-world datasets from various E-commerce platforms. Our experimental results show that our model IIAAN significantly outperforms the state-of-the-art approaches for the next basket recommendation.