OS-Guard: on-site signature based framework for multimedia surveillance data management

This paper presents OS-Guard(On-Site Guard), a novel on-site signature based framework for multimedia surveillance data management. One of the major concerns in widespread deployment of multimedia surveillance systems is the enormous amount of data collected from multiple media streams that need to be communicated, observed and stored for crime alerts and forensic analysis. This necessitates investigating efficient data management techniques to solve this problem. This work aims to tackle this problem, motivated by the following observation, more data does not mean more information. OS-Guard is a novel framework that attempts to collect informative data and filter out non-informative data on-site, thus taking a step towards solving the data management problem. In the framework, both audio and video cues are utilized by extracting features from the incoming data stream and the resultant real valued feature data is binarized for efficient storage and processing. A feature selection process based on association rule mining selects discriminant features. A short representative sample of the whole database is generated using a novel reservoir sampling algorithm that is stored onsite and used with an support vector machine to classify an important event. Initial experiments for a Bank ATM monitoring scenario demonstrates promising results.     

NodeWiz: Fault-tolerant grid information service

Large scale grid computing systems may provide multitudinous services, from different providers, whose quality of service will vary. Moreover, services are deployed and undeployed in the grid with no central coordination. Thus, to find out the most suitable service to fulfill their needs, or to find the most suitable set of resources on which to deploy their services, grid users must resort to a Grid Information Service (GIS). This service allows users to submit rich queries that are normally composed of multiple attributes and range operations. The ability to efficiently execute complex searches in a scalable and reliable way is a key challenge for current GIS designs. Scalability issues are normally dealt with by using peer-to-peer technologies. However, the more reliable peer-to-peer approaches do not cater for rich queries in a natural way. On the other hand, approaches that can easily support these rich queries are less robust in the presence of failures. In this paper we present the design of NodeWiz, a GIS that allows multi-attribute range queries to be performed efficiently in a distributed manner, while maintaining load balance and resilience to failures.     

THERMOHYDRAULICS OF LAMINAR FLOW THROUGH A CIRCULAR TUBE HAVING INTEGRAL HELICAL CORRUGATIONS AND FITTED WITH HELICAL SCREW-TAPE INSERT

The experimental friction factor and Nusselt number data for laminar flow through a circular duct having integral helical corrugations and fitted with a helical screw-tape insert are presented. Predictive friction factor and Nusselt number correlations are also presented. The thermohydraulic performance was evaluated. The major findings of this experimental investigation are that the helical screw-tape insert in combination with integral helical corrugations performs significantly better than the individual enhancement technique acting alone for laminar flow through a circular duct up to a certain value of the fin parameter. This research finding is useful in designing tubes carrying solar thermal mass of viscous oil in a parabolic trough solar collector used in environmentally sound and increasingly cost-effective solar thermal electric power plants. The result is also useful in designing heat exchangers used in process industries.     

An Unsupervised Writer Identification Based on Generating ClusterableEmbeddings

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupervised learning. However, supervised methods require a large amount of annotation data, which is impossible in most scenarios. On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted. This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features. A pairwise architecture-based Autoembedder was applied to generate clusterable embeddings for handwritten text images. Furthermore, the trained baseline architecture generates the embedding of the data image, and the K-means algorithm is used to distinguish the embedding of individual writers. The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks. In addition, traditional evaluation metrics are used in the proposed model. Finally, the proposed model is compared with a few unsupervised models, and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.     

Design of efficient lightweight strategies to combat DoS attack in delay tolerant network routing

Delay tolerant networks (DTNs) are characterized by delay and intermittent connectivity. Satisfactory network functioning in a DTN relies heavily on co-ordination among participating nodes. However, in practice, such co-ordination cannot be taken for granted due to possible misbehaviour by relay nodes. Routing in a DTN is, therefore, vulnerable to various attacks, which adversely affect network performance. Several strategies have been proposed in the literature to alleviate such vulnerabilities—they vary widely in terms of throughput, detection time, overhead etc. One key challenge is to arrive at a tradeoff between detection time and overhead. We observe that the existing table-based reactive strategies to combat Denial-of-service (DoS) attacks in DTN suffer from two major drawbacks: high overhead and slow detection. In this paper, we propose three secure, light-weight and time-efficient routing algorithms for detecting DoS attacks (Blackhole and Grey-hole attacks) in the Spray & Focus routing protocol. The proposed algorithms are based on use of a small fraction of privileged (trusted) nodes. The first strategy, called TN, outperforms the existing table-based strategy with 20–30 % lesser detection time, 20–25 % higher malicious node detection and negligible overhead. The other two strategies, CTN_MI and CTN_RF explore the novel idea that trusted nodes are able to utilize each others’ information/experience using their long range connectivity as and when available. Simulations performed using an enhanced ONE simulator reveals that investing in enabling connectivity among trusted nodes (as in CTN_RF) can have significant performance benefits.     

Potentiality of Indian pineapple leaf fiber for apparels

The use of textile-grade fiber extracted from the Indian pineapple leaf is an unexplored area. Pineapple leaf fiber (PALF) is fine, soft, and moderately strong and is much acceptable for fashion garment. An attempt has been made to extract and soften Indian PALF from the pineapple leaves by decortication and subsequent water retting. Decorticated cum retted fiber has shown better physicomechanical and surface appearance properties than decorticated fiber. Fine yarn (38 tex) was spun in a suitable spinning system, and the property performance was found to be suitable to make eco-sustainable novelty fabric. Fabric was developed by using cotton as warp yarn and PALF-based yarn as weft in a handloom. The developed fabric shows very good physical and mechanical properties, desired for apparels. This inferred that Indian PALF may be successfully used to make eco-niche apparel quality fabric as well as novelty textiles.     

A study on the adsorption mechanism of mercury on Aspergillus versicolor biomass

The adsorption behavior of mercury on Aspergillus versicolor biomass (AVB) has been investigated in aqueous solution to understand the physicochemical process involved and to explore the potentiality of AVB in pollution control management. This biomass has been successfully used for reducing the mercury concentration level in the effluent of chloralkali and battery industries to a permissible limit. The results establish that 75.6 mg of mercury is adsorbed per gram of biomass. The adsorption process is found to be a function of pH of the solution, with the optimum range being pH 5.0-6.0. The process obeys the Langmuir-Freundlich isotherm model. Scanning electron microscopic analysis demonstrates a conspicuous surface morphology change of the mercury-adsorbed biomass. A nearly uniform distribution of metal ions on the mycelial surface excepting a few aggregation points is revealed by X-ray elemental mapping profiles. The results of zeta potential measurement, Fourier transform infrared (FTIR) spectroscopy, and blocking of the functional groups by chemical modification reflect the binding of mercury on the biomass occurs through electrostatic and complexation reactions. The accumulation of mercury on the cell wall associated with negligible diffusion and or transportation into cytoplasm finds support from the results of adsorption kinetics and transmission electron micrographs. Mercury adsorption on biomass also leads to elongation of cells and cytoplasmic aggregation of spheroplast/protoplasts, indicating that the cell wall acts as a permeation barrier against this toxic metal.     

Projecting water withdrawal and supply for future decades in the U.S. under climate change scenarios

The sustainability of water resources in future decades is likely to be affected by increases in water demand due to population growth, increases in power generation, and climate change. This study presents water withdrawal projections in the United States (U.S.) in 2050 as a result of projected population increases and power generation at the county level as well as the availability of local renewable water supplies. The growth scenario assumes the per capita water use rate for municipal withdrawals to remain at 2005 levels and the water use rates for new thermoelectric plants at levels in modern closed-loop cooling systems. In projecting renewable water supply in future years, median projected monthly precipitation and temperature by sixteen climate models were used to derive available precipitation in 2050 (averaged over 2040-2059). Withdrawals and available precipitation were compared to identify regions that use a large fraction of their renewable local water supply. A water supply sustainability risk index that takes into account additional attributes such as susceptibility to drought, growth in water withdrawal, increased need for storage, and groundwater use was developed to evaluate areas at greater risk. Based on the ranking by the index, high risk areas can be assessed in more mechanistic detail in future work.