Real-Time Implementation and Performance Optimization of Local Derivative Pattern Algorithm on GPUs

Pattern based texture descriptors are widely used in Content Based Image Retrieval (CBIR) for efficient retrieval of matching images. Local Derivative Pattern (LDP), a higher order local pattern operator, originally proposed for face recognition, encodes the distinctive spatial relationships contained in a local region of an image as the feature vector. LDP efficiently extracts finer details and provides efficient retrieval however, it was proposed for images of limited resolution. Over the period of time the development in the digital image sensors had paid way for capturing images at a very high resolution. LDP algorithm though very efficient in content-based image retrieval did not scale well when capturing features from such high-resolution images as it becomes computationally very expensive. This paper proposes how to efficiently extract parallelism from the LDP algorithm and strategies for optimally implementing it by exploiting some inherent General-Purpose Graphics Processing Unit (GPGPU) characteristics. By optimally configuring the GPGPU kernels, image retrieval was performed at a much faster rate. The LDP algorithm was ported on to Compute Unified Device Architecture (CUDA) supported GPGPU and a maximum speed up of around 240x was achieved as compared to its sequential counterpart

Post Event Investigation of Multi-stream Video Data Utilizing Hadoop Cluster

Rapid advancement in technology and in-expensive camera has raised the necessity of monitoring systems for surveillance applications. As a result data acquired from numerous cameras deployed for surveillance is tremendous. When an event is triggered then, manually investigating such a massive data is a complex task. Thus it is essential to explore an approach that, can store massive multi-stream video data as well as, process them to find useful information. To address the challenge of storing and processing multi-stream video data, we have used Hadoop, which has grown into a leading computing model for data intensive applications. In this paper we propose a novel technique for performing post event investigation on stored surveillance video data. Our algorithm stores video data in HDFS in such a way that it efficiently identifies the location of data from HDFS based on the time of occurrence of event and perform further processing. To prove efficiency of our proposed work, we have performed event detection in the video based on the time period provided by the user. In order to estimate the performance of our approach, we evaluated the storage and processing of video data by varying (i) pixel resolution of video frame (ii) size of video data (iii) number of reducers (workers) executing the task (iv) the number of nodes in the cluster. The proposed framework efficiently achieve speed up of 5.9 for large files of 1024X1024 pixel resolution video frames thus makes it appropriate for the feasible practical deployment in any applications

Motion Detection in Low Resolution Grayscale Videos Using Fast Normalized Cross Correrelation on GP-GPU

Motion estimation (ME) has been widely used in many computer vision applications, such as object tracking, object detection, pattern recognition and video compression. The most popular block based similarity measures are the sum of absolute differences (SAD), the sum of squared differences (SSD) and the normalized cross correlation (NCC). Similarity measure obtained using NCC is more robust under varying illumination changes as compared to SAD and SSD. However NCC is computationally expensive and application of NCC using full or exhaustive search method further increases required computational time. Relatively efficient way of calculating the NCC is to pre-compute sum-tables to perform the normalization referred to as fast NCC (FCC). In this paper we propose real time implementation of full search FCC algorithm applied to gray scale videos using NVIDIA’s Compute Unified Device Architecture (CUDA). We present fine-grained optimization techniques for fully exploiting computational capacity of CUDA. Novel parallelization strategies adopted for extracting data parallelism substantially reduce computational time of exhaustive FCC. We show that by efficient utilization of global, shared and texture memories available on CUDA, we can obtain the speedup of the order of 10x as compared to the sequential implementation of FCC

Computer vision based traffic monitoring system for multi-track freeways

Nowadays, development is synonymous with construction of infrastructure. Such road infrastructure needs constant attention in terms of traffic monitoring as even a single disaster on a major artery will disrupt the way of life. Humans cannot be expected to monitor these massive infrastructures over 24/7 and computer vision is increasingly being used to develop automated strategies to notify the human observers of any impending slowdowns and traffic bottlenecks. However, due to extreme costs associated with the current state of the art computer vision based networked monitoring systems, innovative computer vision based systems can be developed which are standalone and efficient in analyzing the traffic flow and tracking vehicles for speed detection. In this article, a traffic monitoring system is suggested that counts vehicles and tracks their speeds in realtime for multi-track freeways in Australia. Proposed algorithm uses Gaussian mixture model for detection of foreground and is capable of tracking the vehicle trajectory and extracts the useful traffic information for vehicle counting. This stationary surveillance system uses a fixed position overhead camera to monitor traffic

Robust Abnormal Event Recognition via Motion and Shape Analysis at ATM Installations

Automated teller machines (ATM) are widely being used to carry out banking transactions and are becoming one of the necessities of everyday life. ATMs facilitate withdrawal, deposit, and transfer of money from one account to another round the clock. However, this convenience is marred by criminal activities like money snatching and attack on customers, which are increasingly affecting the security of bank customers. In this paper, we propose a video based framework that efficiently identifies abnormal activities happening at the ATM installations and generates an alarm during any untoward incidence. The proposed approach makes use of motion history image (MHI) and Hu moments to extract relevant features from video. Principle component analysis has been used to reduce the dimensionality of features and classification has been carried out by using support vector machine. Analysis has been carried out on different video sequences by varying the window size of MHI. The proposed framework is able to distinguish the normal and abnormal activities like money snatching, harm to the customer by virtue of fight, or attack on the customer with an average accuracy of 95.73%

Propagation of Chandipura virus in chick embryos

930-932Stocks of three Indian Chandipura virus (CHPV) isolates; one isolate from an adult febrile case in 1%5 from Chandipura town. Maharashtra, and two isolates from two pediatric encephalitis cases from Andhra Pradesh, 2003 were inoculated in 10-day-old chick embryos by allantoic route. All three virus isolates replicated in chick embryos showing titre of log 1012 to log 1013 EID50. The results demonstrated that chick embryos are susceptible to CHPV and virus grows to high titres in this system. Therefore chick embryos can be used as an alternative host system for cultivation and isolation of CHPV as they are less expensive than laboratory animals and have several other advantages over cell cultures. Also this system can be used for the development of vaccine and diagnostic reagent

Improved block chain system for high secured IoT integrated supply chain

The incredibly complex supply chains in today's world face significant problems in terms of accountability and reliability. Blockchain technology could be able to solve these problems by offering a tamper-proof audit trail of supply chain activities & data on a product lifecycle, but it could resolve the issue of information's inherent lack of reliability. Current Reputation systems offer a practical solution to the confidence issue. Moreover, it is based on a small number of observations, lack granularity or mechanization have considerable overhead, current reputational methods are not suitable for blockchain-based supply chain operations. In this research, we present TrustChain, a three-layered system for managing trust that tracks connections between supply chain actors and automatically calculates trust and reputation scores based on those connections using a consortium blockchain with Internet of Things (IoT). Trustchain would be novel because it supports reputation rankings that distinguish between supply chain participants & goods, allowing the assignment of product-specific reputations for the same participant, (a) the reputation design that assesses the performance of commodities and the trustworthiness of entities based on several observations of supply chain events, (b) the use of smart contracts for transparent, impactful, safe, and automated computation of reputation rating, and (c) reputation scores that differentiate between supply chain members and services

Load Balancing in Cloud Environment: A State-of-the-Art Review

IT services and resources on-demand through Internetwork are offered by Cloud Computing (CC), including the pay-for-you-go aspect. A lot is offered by the CC paradigm, such as Infrastructure related services, computing services, storage, and environments for deployment are also provided. The objective of this study is to survey one of the significant challenges in cloud computing, which is a multi-variant, multi-constraint issue termed Load unbalancing, resulting in the demising of the scalability, efficiency, and performance of the system. Equilibrium in the server workload distribution is still strived for by cloud service providers. The unbalancing issue is resolved by load balancing solutions in two ways: overloading and underloading. An extensive structural literature analysis of Load balancing and its constituent domains with the inclusion of various parameters, such as scalability, make-span, and throughput, are depicted in this research paper to enhance the QoS. A detailed and organized taxonomy of all the Load balancing algorithms based on nature system state, techniques, functionality, and types is also presented. The major focus of the survey is around the Static, dynamic, hybrid, and nature-inspired Load-Balancing algorithms

Direct imaging of pH1N1 2009 influenza virus replication in alveolar pneumocytes in fatal cases by transmission electron microscopy

Human influenza virus pandemics constitute a major global public health issue. Although studies on autopsy specimens from the recent pandemic by the 2009 influenza A (H1N1) virus have revealed a broad spectrum of pathologic findings, direct electron microscopic studies of the lung tissue from influenza fatalities are few. In this study, we examined five well-preserved pulmonary necropsy specimens from fatal cases of laboratory-confirmed pH1N1 from India. The novel observations in comparison with earlier reports included direct imaging of influenza virus budding within dilated cisternae of pneumocytes, cell-free virus emerging from the cell membrane of a pneumocyte in the alveolar lumen, presence of polymorphonuclear cells with red blood cells as inflammatory exudates close to hyaline membranes and extensive cytoplasmic degeneration of epithelial cells of the alveolar lining. These observations are in consistent with the earlier findings and emphasize the possible role of this virus directly infecting cells of the lower respiratory tract as a key event in the rapid pathogenesis of pH1N1 disease process

Allocation of optimal energy from storage systems using solar energy

In order to reduce carbon emissions, a growing reliance on renewable energy sources such as solar energy is required. As a result of their ability to store excess solar electricity that may be used at a later time to reduce waste and increase utility profits, battery energy storage systems (BESSs) have emerged as a factor for power systems that integrates solar power system. BESSs are traditionally put on buses in solar farms, allowing extra electricity via solar to be stored instantaneously and transmission line losses to be kept to an absolute minimum. According to this placement strategy, BESS is exclusively built in the proximity of solar power plants. In this way, deployment of BESS without network topology consideration, and collaboration among BESSs is limited with capacity pooling to store excess electricity from photo voltaic (PV) panels. In this paper, we develop an optimal deployment of BESSs and it is associated with the estimation of the capacity using a multi-objective constraint modelling. The soft margin classifier minimize the curtailment associated with solar energy that considers both the power flow constraint and network topology. The results of entire model shows that the proposed soft margin classifier is efficient in storing the surplus power in the batter devices than other methods