Low Storage Space Research Articles

Introducing WebSocket-Based Real-Time Monitoring System for Remote Intelligent Buildings

Today, wireless sensor networks (WSNs) in electronic engineering are used in the monitoring of remote intelligent buildings, and the need for emerging Web 3.0 is becoming more and more in every aspect of electronic engineering. However, the key challenges of monitoring are the monitoring approaches and storage models of huge historical monitoring data. To address these limitations, we attempt to design a WebSocket-based real-time monitoring system for remote intelligent buildings. On one hand, we utilize the latest HTML5 WebSocket, Canvas and Chart technologies to monitor the sensor data collected in WSNs in the Web browser. The proposed monitoring system supports the latest HTML5 browsers and legacy browsers without native WebSocket capability transparently. On the other hand, we propose a storage model with lifecycle to optimize the NoSQL data warehouse. Finally, we have made the monitoring and storage experiments to illustrate the superiority of our approach. The monitoring experimental results show that the average latency time of our WebSocket monitoring is generally lower than polling, FlashSocket, and Socket solution, and the storage experimental results show that our storage model has low redundancy rate, storage space, and latency.

Global K-Means (GKM) Clustering Algorithm: A Survey

means clustering is a popular clustering algorithm but is having some problems as initial conditions and it will fuse in local minima. A method was proposed to overcome this problem known as Global K-Means clustering algorithm (GKM). This algorithm has excellent skill to reduce the computational load without significantly affecting the solution quality. We studied GKM and its variants and presents a survey with critical analysis. We also proposed a new concept of Faster Global K-means algorithms for Streamed Data sets (FGKM-SD). FGKM-SD improves the efficiency of clustering and will take low time & storage space. the randomly chosen sets. Every run should be initialized using the K final centroid locations from one of the run of 10 subsets. The K center location that we get from this run will be used to initialize the K-means algorithm for the complete data set. The Global K-Means algorithm (The GKM algorithm) (2) is the incremental approach of clustering. We can dynamically add one cluster center at a time using deterministic global search procedure from suitable initial positions. It is consists of N (Where N is the size of the dataset) executions of K- means algorithm. Experimental results of the algorithm show that GKM algorithm considerably out performs the K-means algorithms.

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.

Efficient matching of very complex time series

We propose a novel method (FANSEA) that performs very complex time series matching. The matching here includes comparison and alignment of time series, for diverse needs: diagnosis, clustering, retrieval, mining, etc. The complexity stands in the fact that the method is able to match quasi-periodic time series, that are eventually phase shifted, of different lengths, composed of different number of periods, characterized by local morphological changes and that might be shifted/scaled on the time/magnitude axis. This is the most complex case that can occur in time series matching. The efficiency stands in the fact that the newly developed FANSEA method produces alignments that are comparable to those of the previously published SEA method. However and as a result of data reduction, FANSEA consumes much less time and data; hence, allowing for faster matching and lower storage space. Basically, FANSEA is composed of two main steps: Data reduction by curve simplification of the time series traces and matching through exchange of extracted signatures between the time series under process. Due to the quasi-periodic nature of the electrocardiogram (ECG), the tests were conducted on records selected from the Massachusetts Institute of Technology-Beth Israel Hospital database (MIT-BIH). Numerically, the new method data reduction was up to 80 % and the time reduction was up to 95 %. Accordingly and among many possible applications, the new method is very suitable for searching, querying and mining of large time series databases.

Iris recognition on edge maps

Iris recognition is a potential tool in secure personal identification and authentication in view of properties such as uniqueness, non-invasiveness and stability of human iris patterns. A new approach based on the Hausdorff distance measure is proposed for iris recognition. In contrast to existing approaches that consider grey or colour images, the new approach considers the binary edge maps of irises. Edge maps have advantages in terms of low storage space, fast transmission, fast processing and hardware compatibility. A new measure, called local partial Hausdorff distance, is computed between the binary edge maps of normalised iris images. The proposed dissimilarity measure has been tested on the high-quality UPOL iris images captured in a constrained environment. The recognition performance of the proposed method has been studied for different values of parameters such as block size and partialness. An appropriate choice of these parameters achieves a recognition rate of more than 98%. The results demonstrate the significance of linear features in the iris edge maps in discriminating different irises.

RAID0.5: design and implementation of a low cost disk array data protection method

RAID has long been established as an effective way to provide highly reliable as well as high-performance disk subsystems. However, reliability in RAID systems comes at the cost of extra disks. In this paper, we describe a mechanism that we have termed RAID0.5 that enables striped disks with very high data reliability but low disk cost. We take advantage of the fact that most disk systems use offline backup systems for disaster recovery. With the use of these offline backup systems, the disk system needs to only replicate data since the last backup, thus drastically reducing the storage space requirement. Though RAID0.5 has the same data loss characteristics of traditional mirroring, the lower storage space comes at the cost of lower availability. Thus, RAID0.5 is a tradeoff between lower disk cost and lower availability while still preserving very high data reliability. We present analytical reliability models and experimental results that demonstrate the enhanced reliability and performance of the proposed RAID0.5 system.

Divide, Compress and Conquer: Querying XML via Partitioned Path-Based Compressed Data Blocks

We propose a novel partition path-based (PPB) grouping strategy to store compressed XML data in a stream of blocks. In addition, we employ a minimal indexing scheme called block statistic signature (BSS) on the compressed data, which is a simple but effective technique to support evaluation of selection and aggregate XPath queries of the compressed data. We present a formal analysis and empirical study of these techniques. The BSS indexing is first extended into effective cluster statistic signature (CSS) and multiple-cluster statistic signature (MSS) indexing by establishing more layers of indexes. We analyze how the response time is affected by various parameters involved in our compression strategy such as the data stream block size, the number of cluster layers, and the query selectivity. We also gain further insight about the compression and querying performance by studying the optimal block size in a stream, which leads to the minimum processing cost for queries. The cost model analysis provides a solid foundation for predicting the querying performance. Finally, we demonstrate that our PPB grouping and indexing strategies are not only efficient enough to support path-based selection and aggregate queries of the compressed XML data, but they also require relatively low computation time and storage space when compared with other state-of-the-art compression strategies.

A branch & bound algorithm for the 0-1 mixed integer knapsack problem with linear multiple choice constraints

This paper presents a branch and bound (B&B) algorithm for the 0-1 mixed integer knapsack problem with linear multiple choice constraints. The formulation arose in an application to transportation management for allocating funds to highway improvements. Several model properties are developed and utilized to design a B&B solution algorithm. The algorithm solves at each node of the B&B tree a linear relaxation using an adaptation of an existing algorithm for the linear multiple choice knapsack problem. The special relationship between the parent and children subproblems is exploited by the algorithm. This results in high efficiency and low storage space requirements. The worst case complexity of the algorithm is analyzed and computational results that demonstrate its efficiency in the average case are reported. Scope and purpose statement Optimal resource allocation is one of the most widely studied areas in mathematical programming. We introduce a single resource allocation model that considers both discrete and continuous activities. The model is a natural extension of the knapsack problem with both binary and continuous variables. It has application in transportation management for allocating funds to highway improvements. We explore in depth the special structure of the problem and we present important theory that arises from its study. After identifying the fundamental properties of the problem, we present an efficient solution procedure that outperforms existing commercial software packages.

Simulation study of an automated storage/retrieval system

In this paper we present a simulation study of an automated storage/retrieval system and examine a wide variety of control policies. We compare several storage location assignment policies. For the class-based storage policy, we apply a recent algorithm that enables us to evaluate the trade-off between storage space requirements and travel times. We also study a new storage location policy which combines low storage space requirements with short mean travel times. Furthermore, we study the sequencing of storage and retrieval requests whereby we focus on the trade-off between efficient travel of the S/R machines and response time performance.

Hardware implementation of CMAC neural network with reduced storage requirement

The cerebellar model articulation controller (CMAC) neural network has the advantages of fast convergence speed and low computation complexity. However, it suffers from a low storage space utilization rate on weight memory. In this paper, we propose a direct weight address mapping approach, which can reduce the required weight memory size with a utilization rate near 100%. Based on such an address mapping approach, we developed a pipeline architecture to efficiently perform the addressing operations. The proposed direct weight address mapping approach also speeds up the computation for the generation of weight addresses. Besides, a CMAC hardware prototype used for color calibration has been implemented to confirm the proposed approach and architecture.

Twin grid files: space optimizing access schemes

Storage access schemes for points, supporting spatial searching, usually suffer from an undesirably low storage space utilization. We show how a given set of points can be distributed among two grid files in such a way that storage space utilization is optimal. The optimal twin grid file can be built practically as fast as a standard grid file, i.e., the storage space optimality is obtained at almost no extra cost. We compare the performances of the standard grid file, the optimal static twin grid file, and an efficient dynamic twin grid file, where insertions and deletions trigger the redistribution of points among the two grid files. Twin grid files utilize storage space at roughly 90%, as compared with the 69% of the standard grid file. Typical range queries - the most important spatial search operations - can be answered in twin grid files at least as fast as in the standard grid file.