Redundancy Ratio Research Articles

Redundant data minimisation using hexagonal scalar premier neighbourhood for optimum camera actuation scheme

This study reports an efficient scheme inspired by scalar premier (SP) selection for actuation of optimum number of cameras so as to minimise the amount of redundant data transmission. The proposed algorithm passes through three major steps. Initially, all the sensors are randomly deployed and messages are exchanged among themselves to know the location information of each other. In the second step, the entire monitored region is divided into number of virtual sub-regions, in which the scalars present at each sub-region select six SPs in a hexagonal manner in such a way that the cameras to be activated due to them cover distinct portions of geographic regions. Finally, when event takes place, these SPs communicate their respective cameras regarding its occurrence. Consequently, the cameras exchange messages among each other for deciding which among them are to be actuated. The less camera actuation, improved coverage ratio, reduced event-loss ratio, least redundancy ratio along with minimised energy and power consumption obtained from the experimental evaluation reveal the effectiveness of the proposed algorithm as compared with two other recently devised approaches in the literature.

Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based constraint splitting augmented Lagrangian shrinkage algorithm

Recently, dictionary learning (DL) based methods have been introduced to compressed sensing magnetic resonance imaging (CS-MRI), which outperforms pre-defined analytic sparse priors. However, single-scale trained dictionary directly from image patches is incapable of representing image features from multi-scale, multi-directional perspective, which influences the reconstruction performance. In this paper, incorporating the superior multi-scale properties of uniform discrete curvelet transform (UDCT) with the data matching adaptability of trained dictionaries, we propose a flexible sparsity framework to allow sparser representation and prominent hierarchical essential features capture for magnetic resonance (MR) images. Multi-scale decomposition is implemented by using UDCT due to its prominent properties of lower redundancy ratio, hierarchical data structure, and ease of implementation. Each sub-dictionary of different sub-bands is trained independently to form the multi-scale dictionaries. Corresponding to this brand-new sparsity model, we modify the constraint splitting augmented Lagrangian shrinkage algorithm (C-SALSA) as patch-based C-SALSA (PB C-SALSA) to solve the constraint optimization problem of regularized image reconstruction. Experimental results demonstrate that the trained sub-dictionaries at different scales, enforcing sparsity at multiple scales, can then be efficiently used for MRI reconstruction to obtain satisfactory results with further reduced undersampling rate. Multi-scale UDCT dictionaries potentially outperform both single-scale trained dictionaries and multi-scale analytic transforms. Our proposed sparsity model achieves sparser representation for reconstructed data, which results in fast convergence of reconstruction exploiting PB C-SALSA. Simulation results demonstrate that the proposed method outperforms conventional CS-MRI methods in maintaining intrinsic properties, eliminating aliasing, reducing unexpected artifacts, and removing noise. It can achieve comparable performance of reconstruction with the state-of-the-art methods even under substantially high undersampling factors.

Centralised cum sub‐centralised scheme for multi‐event coverage and optimum camera activation in wireless multimedia sensor networks

The major challenge in sensor networks is how to have a perfect coverage of the region to be monitored. This study reports a novel algorithm inspired by centralised cum sub-centralised scheme that works efficiently for handling multiple events that occur simultaneously, while reducing the number of unnecessarily activated cameras so as to cover the occurring event region effectively. When any event takes place, the sensing of event occurs up to certain extent outside the exact event boundary. Hence, the cameras present beyond the boundary of event are activated unnecessarily. Since the proposed methodology avoids undesirable activation of cameras, the additional energy and power expenditure due to those cameras as well as the redundant data transmission prevailing due to them are eliminated. Results obtained from experimental evaluation reveal that the proposed methodology significantly reduces the number of unnecessarily activated cameras while providing the adequate coverage of event region for multi-event occurrence. Further more, the reduced camera actuation with increased coverage ratio, minimised redundancy ratio, improved field of view utilisation, less energy as well as power expenditure required for camera activation obtained from the investigation justifies the effectiveness of the proposed algorithm as compared with two other approaches recently proposed in the literature.

New Medical Image Fusion Approach with Coding Based on SCD in Wireless Sensor Network

The technical development and practical applications of big-data for health is one hot topic under the banner of big-data. Big-data medical image fusion is one of key problems. A new fusion approach with coding based on Spherical Coordinate Domain (SCD) in Wireless Sensor Network (WSN) for big-data medical image is proposed in this paper. In this approach, the three high-frequency coefficients in wavelet domain of medical image are pre-processed. This pre-processing strategy can reduce the redundant ratio of big-data medical image. Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on the multi-scale edge of medical image, it can be fused and reconstructed. Experimental results indicate the novel approach is effective and very useful for transmission of big-data medical image(especially, in the wireless environment).

Fault diagnosis of chemical processes with incomplete observations: A comparative study

An important problem to be addressed by diagnostic systems in industrial applications is the estimation of faults with incomplete observations. This work discusses different approaches for handling missing data, and performance of data-driven fault diagnosis schemes. An exploiting classifier and combined methods were assessed in Tennessee–Eastman process, for which diverse incomplete observations were produced. The use of several indicators revealed the trade-off between performances of the different schemes. Support vector machines (SVM) and C4.5, combined with k-nearest neighbourhood (kNN), produce the highest robustness and accuracy, respectively. Bayesian networks (BN) and centroid appear as inappropriate options in terms of accuracy, while Gaussian naïve Bayes (GNB) is sensitive to imputation values. In addition, feature selection was explored for further performance enhancement, and the proposed contribution index showed promising results. Finally, an industrial case was studied to assess informative level of incomplete data in terms of the redundancy ratio and generalize the discussion.

Shared MPR Sets for Moderately Dense Wireless Multihop Networks

Multipoint relays (MPRs) are used for flooding topology control messages and finding the shortest paths for unicast communications in the optimized link state routing protocol (OLSR). In this paper, we propose a method for achieving more efficient MPR selection in moderately dense wireless multihop networks (including sensor networks) than the conventional MPR selection. First, we analyze moderately dense networks to show that a node close to the two-hop border has little probability of being a two-hop neighbor. Second, we explain that there is a chance of the node's MPRs being shared with its neighbors. To maximize this chance, we propose using shared MPR sets. These sets minimize the MPR ratio, which is defined as the number of nodes selected as MPRs by at least one neighbor divided by the total number of nodes in the network. Simulations are used to confirm the efficiency of using shared MPR sets. A centralized heuristic algorithm shows an MPR ratio redundancy in moderately dense networks that is about 10% of that obtained through conventional MPR selection.

Color image segmentation using PDTDFB domain hidden Markov tree model

The pyramidal dual-tree directional filter bank (PDTDFB) transform is a new image decomposition, which has many advantages, such as multiscale and multidirectional transform, efficient implementation, high angular resolution, low redundant ratio, and shiftable subbands. In this paper, we present a new color image segmentation algorithm based on PDTDFB domain hidden Markov tree (HMT) model. Firstly, the joint statistics and mutual information of the PDTDFB coefficients are studied. Then, the PDTDFB coefficients are modeled using a HMT model with Gaussian mixtures, which can effectively capture the intra-scale, inter-scale, and inter-direction dependencies. Finally, a color image segmentation using PDTDFB domain HMT model is developed, in which expectation–maximization (EM) parameter estimation, Bayesian multiscale raw segmentation, context based multiscale fusion, and majority-vote based color component fusion are used. Experimental evidence shows that the proposed color image segmentation algorithm has very effective segmentation results in comparison with the state-of-the-art segmentation methods recently proposed in the literature.

Sequencing and comparative genomics analysis in Senecio scandens Buch.-Ham. Ex D. Don, based on full-length cDNA library

Senecio scandens Buch.-Ham. ex D. Don, an important antibacterial source of Chinese traditional medicine, has a widespread distribution in a few ecological habitats of China. We generated a full-length complementary DNA (cDNA) library from a sample of elite individuals with superior antibacterial properties, with satisfactory parameters such as library storage (4.30 × 106 CFU), efficiency of titre (1.30 × 106 CFU/mL), transformation efficiency (96.35%), full-length ratio (64.00%) and redundancy ratio (3.28%). The BLASTN search revealed the facile formation of counterparts between the experimental sample and Arabidopsis thaliana in view of high-homology cDNA sequence (90.79%) with e-values <1e – 50. Sequence similarities to known proteins indicate that the entire sequences of the full-length cDNA clones consist of the major of functional genes identified by a large set of microarray data from the present experimental material. For other Compositae species, a large set of full-length cDNA clones reported in the present article will serve as a useful resource to facilitate further research on the transferability of expressed sequence tag-derived simple sequence repeats (EST-SSR) development, comparative genomics and novel transcript profiles.

DRS: Dynamic Region Scheduling for Solar-powered Wireless Sensor Networks

Energy restriction is crucial for large-scale application of Wireless Sensor Networks (WSNs). To achieve sustainable operations, the wireless sensor nodes need to harvest energy from the surrounding environment. This paper proposed the Dynamic Region Scheduling (DRS) for solar-powered WSNs. In DRS, all the wireless sensor nodes are allocated into different regions with different transmitting priorities and the collected data could only be delivered from lower priority region to higher or the same priority regions. Each sensor node would renew its region ID dynamically by changing its Mask-identifier (MI) and thus would help the data delivered directly to the sink without any redundancies. Compared with another two routing algorithm SEHRP and EAHC, the results shows that DRS has achieved a significant performance improvement of a higher data delivery rate, a lower energy consumptions as well as lower data loss ratio and data redundancy ratio, which would be greatly helpful to prolong the lifetime of WSN.

Joint on-the-fly network coding/video quality adaptation for real-time delivery

This paper introduces a redundancy adaptation algorithm based on an on-the-fly erasure network coding scheme named Tetrys in the context of real-time video transmission. The algorithm exploits the relationship between the redundancy ratio used by Tetrys and the gain or loss in encoding bit rate from changing a video quality parameter called the Quantization Parameter (QP). Our evaluations show that with equal or less bandwidth occupation, the video protected by Tetrys with redundancy adaptation algorithm obtains a PSNR gain up to or more than 4dB compared to the video without Tetrys protection. We demonstrate that the Tetrys redundancy adaptation algorithm performs well with the variations of both loss pattern and delay induced by the networks. We also show that Tetrys with the redundancy adaptation algorithm outperforms traditional block-based FEC codes with and without redundancy adaptation.

Novel and efficient tag SNPs selection algorithms

SNPs are the most abundant forms of genetic variations amongst species; the association studies between complex diseases and SNPs or haplotypes have received great attention. However, these studies are restricted by the cost of genotyping all SNPs; thus, it is necessary to find smaller subsets, or tag SNPs, representing the rest of the SNPs. In fact, the existing tag SNP selection algorithms are notoriously time-consuming. An efficient algorithm for tag SNP selection was presented, which was applied to analyze the HapMap YRI data. The experimental results show that the proposed algorithm can achieve better performance than the existing tag SNP selection algorithms; in most cases, this proposed algorithm is at least ten times faster than the existing methods. In many cases, when the redundant ratio of the block is high, the proposed algorithm can even be thousands times faster than the previously known methods. Tools and web services for haplotype block analysis integrated by hadoop MapReduce framework are also developed using the proposed algorithm as computation kernels.

Design of Oversampled Interleaved DFT Modulated Filter Bank Using 2Block Gauss-Seidel Method

In this paper, we present several new properties of the recently introduced interleaved DFT modulated filter bank and an efficient algorithm for designing the filter bank. The periodicity and symmetry properties of the overall transfer function and aliasing transfer functions are stated. Then the design of the filter bank is formulated into a constrained optimization problem that jointly minimizes the overall distortion and aliasing distortion subject to fixed bounds on the stopband energy, transition-band energy, and passband flatness of the prototype filters. The constrained optimization problem is solved by the 2block Gauss-Seidel method, which alternatively optimizes the analysis PF pair and the synthesis PF pair. Since the overall distortion and aliasing distortion are jointly minimized, the proposed algorithm can lead to filter banks with small reconstruction error, even when the filter banks behave with a low redundancy ratio and short PFs. The convergence of the proposed algorithm is proved. Numerical examples and comparisons with the existing method are included to demonstrate the performance of the proposed algorithm.

Resources searching method for unstructured peer-to-peer networks based on shortcuts between subtrees

Through studying resources searching methods of unstructured Peer-to-Peer(P2P) network,a new P2P overlay network was proposed.Using a multi-way balanced tree topology,the network created shortcuts between the added node and each subtree of the root when a new node inserted,which forwarded first-hop of a query message only,and then query messages could be forwarded in parallel in each subtree of the root without relying on the root.Compared with the performance of Gnutella and Random Walks network in simulation,the proposed network had lower messages redundancy ratio,higher resource searching success ratio and lower average searching time complexity.The analytical results show that the proposed network is efficient and feasible for searching resources.

Spatiotemporal Differences in Relative Efficiency of Regional Industrial Water Utilization in Xinjiang

DEA was used to estimate the relative efficiency of industrial water in Xinjiang. Using data from 2005 to 2010 for fifteen prefectures in Xinjiang, this research selected productive water usage, employed persons, land area and fixed asset investment as the inputs, and chose gross regional product as the output. The scale redundancy ratio and technical redundancy ratio were also calculated. On the basis, the water utilization relative efficiency was calculated for every prefecture and the spatial-temporal variation of regional industrial water distribution were discussed. The spatial analyses show that the relative efficiency of industrial water utilization is distinctly different among the fifteen prefectures. Agriculture-oriented areas have relatively low water use efficiency, mainly due to the large proportion of agricultural water and seriously wasted water. Every prefecture should adjust the industrial structure and strengthen the agricultural water's conservation according to local condition.

Reduction of reliability testings of data-measuring system due to its functional redundancy

The article considers one of the ways of improvement of efficiency of testings of reliability of data-measuring system in the form of reduction of the total procedure due to its functional redundancy. The research is required because the bulk of the total expenditure falls on tests, and this significantly affects the amount of funding when creating complex engineering systems. To solve this problem we have calculated the ratio of functional redundancy of the system and the probability of failure-free operation for different values of influencing factors. It is shown that a quantitative measure of performance of a certain condition for the reliability of the system is the possibility of its implementation. We presented the criterion of its performance from the perspective of systematic review. We considered the questions of the risk to reduce the reliability testing procedures. We highlighted the limit requirements for the operation of data-measuring system, which is checked in conditions of reduction of testings of its reliability due to functional redundancy.

A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback

Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select novel pseudo-relevant documents based on Lavrenko’s relevance model approach. The main idea is to use overlapping clusters to find dominant documents for the initial retrieval set, and to repeatedly use these documents to emphasize the core topics of a query.The proposed resampling method can skip some documents in the initial high-ranked documents and deterministically construct overlapping clusters as sampling units. The hypothesis behind using overlapping clusters is that a good representative document for a query may have several nearest neighbors with high similarities, participating in several different clusters. Experimental results on large-scale web TREC collections show significant improvements over the baseline relevance model.To justify the proposed approach, we examine the relevance density and redundancy ratio of feedback documents. A higher relevance density will result in greater retrieval accuracy, ultimately approaching true relevance feedback. The resampling approach shows higher relevance density than the baseline relevance model on all collections, resulting in better retrieval accuracy in pseudo-relevance feedback.

Design and Dimensioning of Logical Survivable Topologies Against Multiple Failures

In IP-over-WDM networks, protection can be offered at the optical layer or at the IP layer. Today, it is well acknowledged that synergies need to be developed between the IP and optical layers in order to optimize the resource utilization and to reduce the costs and the energy consumption of future networks. In this paper, we study the design of logical survivable topologies for service recovery against multiple failures, including SRLG-shared risk link group-failures in IP-over-WDM networks. We propose a new optimization model, called SURLOG_CGILP, based on a column generation path formulation. It is highly scalable and allows the exact solution of several benchmark instances, which have only been solved with the help of heuristics so far. In the numerical experiments, we investigate the dimensioning of the physical links assuming IP restoration against multiple-link failures. We observe that the redundancy ratios (recovery over primary ratios for the bandwidth requirements) that are obtained are similar to the redundancy ratios reported for optical protection.

A novel shared-path protection algorithm with correlated risk against multiple failures in flexible bandwidth optical networks

In this paper, to decrease the traffic loss caused by multiple link failures, we consider the correlated risk among different connection requests when both the primary and backup paths are routed and assigned spectrum. Therefore, a novel shared-path protection algorithm is developed, named shared-path protection algorithm with correlated risk (SPP_CR), in flexible bandwidth optical networks. Based on the correlated risk, the routing can be diverse and the sharing in backup spectral resource will be restricted by SPP_CR algorithm, then the dropped traffic caused by simultaneous multiple failures between primary and backup path can be efficiently decreased. Simulation results show that, SPP_CR algorithm (i) achieves the higher successful service ratio (SSR) than traditional shared-path protection (SPP), shared-path protection with dynamic load balancing (SPP_DLB) and dedicated path protection (DPP); (ii) makes a better tradeoff in blocking probability, protection ratio (PR), average frequency slots consumed (AFSC) and redundancy ratio (RR) than SPP, SPP_DLB and DPP algorithms.

ASM: Adaptive Voice Stream Multicast over Low-Power Wireless Networks

Low-power Wireless Networks (LWNs) have become increasingly available for mission-critical applications such as security surveillance and disaster response. In particular, emerging low-power wireless audio platforms provide an economical solution for ad hoc voice communication in emergency scenarios. In this paper, we develop a system called Adaptive Stream Multicast (ASM) for voice communication over multihop LWNs. ASM is composed of several novel components specially designed to deliver robust voice quality for multiple sinks in dynamic environments: 1) an empirical model to automatically evaluate the voice quality perceived at sinks based on current network condition; 2) a feedback-based Forward Error Correction (FEC) scheme where the source can adapt its coding redundancy ratio dynamically in response to the voice quality variation at sinks; 3) a Tree-based Opportunistic Routing (TOR) protocol that fully exploits the broadcast opportunities on a tree based on novel forwarder selection and coordination rules; and 4) a distributed admission control algorithm that ensures the voice quality guarantees when admitting new voice streams. ASM has been implemented on a low-power hardware platform and extensively evaluated through experiments on a test bed of 18 nodes. The experiment results show that ASM can achieve satisfactory multicast voice quality in dynamic environments while incurring low-communication overhead.

A novel multi-link fault-tolerant algorithm for survivability in multi-domain optical networks

With the large-scale deployment of optical network equipments, the problems of separated domains management and the multi-domain-based survivability have become the primary challenge in new generation intelligent optical networks. Aimed at resisting multi-link failures in multi-domain optical networks (MDON), a heuristic multi-link fault-tolerant (MLFT) algorithm for survivability in MDON is proposed in this article, which applied Hamiltonian cycle protection and segment-shared protection to establish a novel survivability strategy for either intra-domain's or inter-domain's multiple links, respectively. Furthermore, a new virtual-link mapping scheme and link-cost formulas are presented to encourage the appropriate routing selection and load balancing, which can also contributed to better resource utilization ratio and blocking ratio. Simulation results show that MLFT realizes the multi-link fault-tolerant survivability at a lower cost with better performances in redundancy ratio, blocking ratio, and computation complexity.