Required Storage Space Research Articles

Online Balancing Two Independent Criteria upon Placements and Deletions

We study the online bicriteria load balancing problem in this paper. We choose a system of distributed homogeneous file servers located in a cluster as the scenario and propose an online approximate solution for balancing their loads and required storage spaces upon placements and deletions. By placement (resp. deletion), we mean to insert a document into (resp. remove a document from) a server system. The main technique is to keep two global quantities large enough. To the best of our knowledge, the technique is novel, and the result is the first one, in the literature. Our result works for any sequences of document placements and deletions. For each deletion, a limited number of documents are reallocated. The load and storage space bounds are 1.5 to 4 times those in the best existing result for sole placements. We refer sole placements to those placement algorithms that do not allow any reallocation and replication. The time complexity, for each operation, is O(logMN), where M is the number of servers, and N is the number of existing documents in the servers, plus the reallocation cost for document deletion. The price for handling document deletion is almost totally reflected by the reallocation cost, and the higher bounds of load and storage spaces, while the O(logN) additive term in the time complexity serves as the remainder.

Unified split‐step precise integration time‐domain method for dispersive media

Based on the use of auxiliary differential equations, a unified split-step precise integration time-domain (SS-PITD) method is proposed for modelling wave propagation in linear dispersive media. The unification of this method is achieved by using a general expression to represent the Debye, Lorentz and Drude dielectric medium models. Moreover, due to the introduction of the SS scheme, the original two-dimensional (2D) or 3D electromagnetic problem reduces to a set of 1D sub-problems which can be efficiently solved by using the conventional PITD algorithm. It leads to a great reduction in the requirement of computation time and storage space. The proposed method is verified with a numerical example and compared with the finite-difference time-domain method.

Redesigning the UK Emergency Ambulance

Redesigning the UK Emergency Ambulance

Demonstration of a frozen sample aliquotter to prepare plasma and serum aliquots without thawing frozen parent samples.

Human biospecimens represent invaluable resources to advance molecular medicine, epidemiology, and biomarker discovery/validation, among other biomedical research. Biobanks typically cryopreserve biospecimens to safeguard their biochemical composition. However, exposing specimens repeatedly to freeze/thaw cycles can degrade their integrity in unforeseen ways. Those biobanks storing liquid samples, thus, regularly make a fundamental compromise at collection time between freezing samples in many small volumes (e.g., 0.5 mL or smaller) or in fewer, larger volumes (e.g., 1.8 mL). The former eliminates the need to expose samples to repeated freeze/thaw cycling, although increasing up-front labor costs, consumables used, and cold storage space requirements. The latter decreases up-front labor costs, consumables, and cold storage requirements, yet exposes samples repeatedly to damaging freeze/thaw cycles when smaller aliquots are needed for analysis. The Rhode Island BioBank at Brown University (RIBB) thoroughly evaluated the performance of an original technology that minimizes a sample's exposure to freeze/thaw cycling by enabling the automated extraction of frozen aliquots from one single frozen parent sample without thawing it. A technology that eliminates unnecessary sample exposures to freeze/thaw cycles could help protect sample integrity, extend its useful life, and effectively rectify and eliminate the aforementioned need to compromise. This report presents the results of the evaluation, and conclusively demonstrates the technology's ability to extract multiple uniform frozen aliquots from a single cryotube of never-thawed frozen human plasma, which faithfully represent the parent sample when analyzed for typical biochemical analytes, showing a coefficient of variability lower than 5.5%.

Finger Vein Representation by Modified Binary Tree Model

Finger vein recognition has high identification accuracy and strong security performance, which can be used in banks, offices, factories, etc. Although image representation is not a necessary process for finger vein recognition, a proper representation method can help to explore distribution regularities and structure differences of finger veins, and provides instructive information for finger vein recognition. It is very difficult to represent finger veins because of their irregular structure. Therefore, four principles (caliber uniformity, node replication, loop splitting, and virtual connection) are proposed in this paper, first to simplify the finger vein structure as a binary tree structure. Then a modified binary tree model is proposed based on the binary tree structure. The new model uses the binary tree to describe the relationships between different vein branches and uses a B-spline function to describe the spatial structure of vein branches. Experiments show that this model can quantitatively describe the relationships between, and the spatial structure of, vein branches with little representation error and low storage space requirements.

Novel distributed key revocation scheme for wireless sensor networks

ABSTRACTKey revocation plays an essential role in ensuring the confidentiality of wireless sensor networks following the compromise of one or more of the network nodes. The distributed key revocation protocol presented by Chan et al. has a rapid reaction time and avoids the single‐point failure problem inherent in centralized schemes. However, the scheme is computationally complex, requires a large storage space, and incurs high communication overheads. Accordingly, this paper proposes a novel distributed, scalable, and cost‐effective key revocation scheme based on Blom's t‐secure property. In the proposed approach, any node identified as having a malicious behavior by t or more sensor nodes is categorized as a compromised node, and its keys are immediately revoked from the entire network. The proposed scheme not only satisfies the rigorous definition of a distributed key revocation protocol but also yields a significant improvement over Chan's revocation scheme in terms of a lower storage space requirement, a reduced communication overhead, and a lower computational cost. Copyright © 2013 John Wiley & Sons, Ltd.

Storage Space Optimisation for Green Data Center

The characteristic that determines a green data center is low amount of carbon footprint produced by its physical data storages. Increasing demand of data volumes in many data intensive applications call for additional physical data storages that are not only impractically large to maintain, but also contribute to the amount of carbon footprint produced. It is argued in this paper that, if storage space can be optimised to gain free spaces, the storage space requirement can be reduced. In this paper, a model to optimize database storage space by mining functional dependencies that are present among data sets is proposed. Sample data sets from the microbial domain have been used where data of large data volume raises storage space concern. The initial results of the implementation of the model that is necessary in designing a complete space optimization algorithm are presented.

Performance comparison of data compression algorithms for environmental monitoring wireless sensor networks

Wireless sensor networks (WSNs) have serious resource limitations ranging from finite power supply, limited bandwidth for communication, limited processing speed, to limited memory and storage space. Data compression can help reduce memory and storage space requirements on sensor node. In WSNs, radio communication is the major consumer of energy. Therefore, applying data compression before transmission will significantly and directly help in reducing total power consumption of a sensor node thereby extending the network lifetime. In this article, we propose a simple lossless data compression algorithm designed specifically to be used by environmental monitoring sensor nodes for the compression of environmental data which are characterised by significant fluctuations in entropy. To verify the effectiveness of our proposed algorithm, we compare its compression performance with two existing WSNs compression algorithms using real-world environmental datasets. We show that our algorithm outperforms the other two algorithms when the entropy of the dataset is large.

Agent Based Health Monitoring of Elderly People in Indoor Environments Using Wireless Sensor Networks

This paper presents a design of a health care monitoring system based on wireless sensor networks (WSN) which is capable of collecting, retrieving, storing and analysing the vital signs of the patient. Vital signs such as body temperature, blood pressure, pulse rate and respiratory rate are monitored. To manage these sensors and for collecting and storing data in a database, a Multi Agent System (MAS) is used. The dynamic nature and mobility of the agents make them suitable for maintaining these sensors in the WSN. The proposed MAS consists of four agents namely Admin agent, Control agent, Query agent and Data agent. Admin agent plays the role of invoking and terminating other agents. Control agent is responsible for storing the data sensed by the sensors into the database. Data agent performs data reduction which is achieved using Epsilon approximation. The use of Data agent in the proposed scheme reduces data traffic and the requirement of secondary storage space. Query agent is responsible for providing a GUI for the doctor to view the patient's vital sign details. The proposed agent based system has been implemented using Java language in JADE environment and the results are validated.

Advances in Voice Enabled Human Machine Communication

inherent advantage of speech communication due to its variability, convenience and speed along with our increasing requirements to communicate with machines has driven the attention of researchers towards mechanical recognition of speech. Technological advancements and improvements in the fundamental approaches have shown a successful transition from small vocabulary isolated word recognition to large vocabulary continuous speech recognition. Even after years of research and development the accuracy of automatic speech recognition remains one of the major challenges. Design of speech recognition system requires careful selection of feature extraction technique and modeling approach to cover the challenges faced due to variability of speech-speaker characteristic, storage space and processing speed requirements. In this paper an effort has been made to highlight the progress made so far for mechanizing the recognition of speech along with the major challenges in this field. Authors have also presented a brief description of voice enabled service for common people. The objective of this paper is to summarize some of the well known methods used at various stages of speech recognition system along with their benefits and limitations.

A Monte Carlo approach to estimating the spectrum of large positive semidefinite matrices.

The method presented in this paper aims at furnishing a series of numerical values to approximate the matrix eigenvalues. The method is based on a statistical model that involves the first three central moments of the eigenvalue distribution, and it relies on the solution of a nonlinear system of equations that implements the moment-matching method and on a subsequent procedure of Monte Carlo simulations. The method is only applicable to real positive semidefinite matrices (PSD), and it is especially useful when other techniques lead to computational problems, e.g., when the matrices become too large to be processed or the required storage space sets heavy limits to the computational process.

Online Bounds on Balancing Two Independent Criteria with Replication and Reallocation

We study the online bicriteria load balancing problem in this paper. We choose a system of distributed homogeneous file servers located in a cluster as the scenario and propose three online approximate solutions for balancing their loads and required storage spaces upon placements. We first revisit the best existing solution for simple placement (i.e., without replication and reallocation), and rewrite it in our first algorithm by imposing some flexibilities. Our second algorithm is to apply document replication. The upper bound of load is significantly reduced, without sacrificing that of the storage space. This upper bound contains at least one special case which can never be outperformed by any online simple placement algorithms. Lastly, we show that there exists an online algorithm which allows very little document reallocation, but gives an upper bound result on the load and storage space, which is never reachable by any online algorithms for simple placement. The time complexities of the first two algorithms are in O(log M), and the last algorithm runs in O(log MN) time, where M is the number of servers, and N is the number of existing documents.

A Cost Effectiveness and Capacity Analysis for the Introduction of Universal Rotavirus Vaccination in Kenya: Comparison between Rotarix and RotaTeq Vaccines

BackgroundDiarrhoea is an important cause of death in the developing world, and rotavirus is the single most important cause of diarrhoea associated mortality. Two vaccines (Rotarix and RotaTeq) are available to prevent rotavirus disease. This analysis was undertaken to aid the decision in Kenya as to which vaccine to choose when introducing rotavirus vaccination.MethodsCost-effectiveness modelling, using national and sentinel surveillance data, and an impact assessment on the cold chain.ResultsThe median estimated incidence of rotavirus disease in Kenya was 3015 outpatient visits, 279 hospitalisations and 65 deaths per 100,000 children under five years of age per year. Cumulated over the first five years of life vaccination was predicted to prevent 34% of the outpatient visits, 31% of the hospitalizations and 42% of the deaths. The estimated prevented costs accumulated over five years totalled US$1,782,761 (direct and indirect costs) with an associated 48,585 DALYs. From a societal perspective Rotarix had a cost-effectiveness ratio of US$142 per DALY (US$5 for the full course of two doses) and RotaTeq US$288 per DALY ($10.5 for the full course of three doses). RotaTeq will have a bigger impact on the cold chain compared to Rotarix.ConclusionVaccination against rotavirus disease is cost-effective for Kenya irrespective of the vaccine. Of the two vaccines Rotarix was the preferred choice due to a better cost-effectiveness ratio, the presence of a vaccine vial monitor, the requirement of fewer doses and less storage space, and proven thermo-stability.

CANNED VEGETABLE PRODUCTS FOR SOCIAL NUTRITION

The developed technology, regulatory and technical documentation allowed to produce 3-5-kg packages of sterilized vegetables: potatoes, beets, carrots and cabbage, which are in great demand in the social nutrition (the army, schools, hospitals). The use of packaging for products of composite materials allows to reduce the amount of required storage space.

Polynomial level-set method for attractor estimation

In this study, we present a polynomial level-set method for attractor estimation. This method uses the sub-level representation of sets. The problem of flowing these sets under the advection map of a dynamic system is converted to a semi-definite program, which is used to compute the coefficients of the polynomials. The required storage space for describing the result is much less than the mesh-based methods. The characteristics of attractors are used in the algorithm formulations so that the associated numerical error can be reduced. We further address the related problems of constraining the degree of the polynomials. Various numerical examples are used to show the effectiveness of the advection approach.

Modified DWT Based Medical Image Transmission Using Reduced Storage Space

With the rapid development in communications technology, various methods of medical image transmission are investigated. A comprehensive system integrating various applications within a common infrastructure is usually necessary for this purpose. This infrastructure includes the physical facilities and equipments which are used to capture, transmit, store, process, and display medical data and images. To effectively reduce the cost of realizing the infrastructure, data compression is usually desired so that the bandwidth and storage requirements for delivering and storing medical data are lowered. In this research work, initially the images are enhanced using local adaptive filter and the Pixels of Interest (POI) is selected to reduce the size of the medical image to be transferred. The second step is where a modified Discrete Wavelet Transform (MDWT) is applied to compress the image data with minimum storage space, and then the image is transmitted over the Internet. At the receiving side the POI image is reconstructed. The objectives of this research are to design and implement Modified DWT compression technique using a uniform quantizer and reduced Sub band Coding to split up the frequency band of a image and then to code each sub band using a coder. Dimension reduction technique is used to reduce the storage space. The developed algorithm involves dimension reduction technique by using intermediate co-efficient storage to reduce the storage space in the DWT architecture. Hence, at the receiver the side, retrieval of intermediate co-efficient is obtained by decoding each sub band using decoder for the frequency band of the images. By using the IDWT, the original image is retrieved without any loss of information in the image. To compare the measure of compression MSE, PSNR and SNR values, ROI of the CT images are compressed by using standard JPEG and DWT methods. Image compression using JPEG, DWT and Modified DWT has been developed to find optimum method for transmitting medical images through the Internet with less storage space and low bandwidth requirements. The present method offered better results and is found to be the optimum method with high PSNR, low MSE and less storage space. As a future work, the effectiveness of Modified DWT will be compared with other emerging Imaging techniques by taking image as a whole. In medical image transmission, instead of still medical images which used imaging techniques, moving images may also be transmitted using modified DWT.

Optimizations for Elastodynamic Simulation Analysis with FMM-DRBEM and CUDA

In this study, we propose a novel method to accelerate the process of elastodynamic analysis in 3D problems with BEM (boundary element method). With applying the DRBEM (dual reciprocity boundary element method) to form new integral equations for reducing complexity;the modified FMM (fast multipole method)is introduced to simplify the computation process and save storage space by avoiding intermediate coefficientmatrices. At the same time, FMM-DRBEM is reprogrammed in parallel byapplying GPU with CUDA (Compute Unified Device Architecture)for improving efficiency further.The main features in this paper are: (1)with respect to defects of classical method for elastodynamic, modified FMM- DRBEM algorithm is introduced; (2)optimal algorithms of translationsin FMM are discussed in parallel for improving the effect of GPU with CUDA;(4) PLE (Posi- tion Location Equation) and GHST (Global Hierarchy Substitution Transferring) methods are proposedfor improving efficiency, saving storage space requirement and optimizing data transforming process between host and device, respectively. The effect has been tested by numerical examples in the last section, and signifi- cantoptimal results both in efficiency and accuracy have been observed.

Comparing Different Sparse Matrix Storage Structures as Index Structure for Arabic Text Collection

In the authors’ study they evaluate and compare the storage efficiency of different sparse matrix storage structures as index structure for Arabic text collection and their corresponding sparse matrix-vector multiplication algorithms to perform query processing in any Information Retrieval (IR) system. The study covers six sparse matrix storage structures including the Coordinate Storage (COO), Compressed Sparse Row (CSR), Compressed Sparse Column (CSC), Block Coordinate (BCO), Block Sparse Row (BSR), and Block Sparse Column (BSC). Evaluation depends on the storage space requirements for each storage structure and the efficiency of the query processing algorithm. The experimental results demonstrate that CSR is more efficient in terms of storage space requirements and query processing time than the other sparse matrix storage structures. The results also show that CSR requires the least amount of disk space and performs the best in terms of query processing time compared with the other point entry storage structures (COO, CSC). The results demonstrate that BSR requires the least amount of disk space and performs the best in terms of query processing time compared with the other block entry storage structures (BCO, BSC).

Dynamic proxy-assisted scalable broadcasting of videos for heterogeneous environments

Periodic broadcasting (PB) is a scalable technique for providing video-on-demand services. It significantly reduces server I/O and backbone network bandwidth requirements at the expense of high storage space and high network bandwidth requirements for clients. Traditional protocols assume homogeneous clients with identical resources. Unfortunately, in practice clients have very different bandwidths, and these are usually insufficient to provide video-on-demand (VoD) service from a PB server. Existing work on heterogeneous clients has focused on devising broadcast schedules to cater to low-bandwidth clients, which inevitably requires an extra backbone network bandwidth between the server and the clients. In our previous work, we proposed to use proxies residing at the edge of backbone network to accommodate low bandwidth clients for PB-based VoD services. The server broadcasts a video using a PB protocol while the proxy receives and stores the data in its local buffer and broadcasts the stored data to the clients in its local network. It significantly reduces the waiting time of low-bandwidth clients without requiring any extra backbone bandwidth by using a proxy buffer and channels. However, although lots of PB protocols have been proposed, the scheme can be applied only to some old PB protocols based on a pyramid protocol. In this paper, we propose a proxy-assisted PB system that can be generally applied to almost all the existing PB protocols, by dynamically managing buffer space and channels in proxy servers. Thus, with our proposed system, PB VoD system can be optimized in terms of the resource usages in backbone networks, proxy servers, and clients, by adopting more suitable PB protocols.

F-Fractional Bit Minwise Hashing

In information retrieval, minwise hashing algorithm is often used to estimate similarities among documents. b -bit minwise hashing is capable of gaining substantial advantages in terms of computational efficiency and storage space by only storing the lowest b bits of each (minwise) hashed value (e.g., b =1 or 2). In this paper, we propose a fractional bit hashing method, which extends the existing b -bit Minwise hashing. It is shown theoretically that the fractional bit hashing has a wider range of selectivity for accuracy and storage space requirements. Theoretical analysis and experimental results demonstrate the effectiveness of this method.