Network-coding based event diffusion for wireless networks using semi-broadcasting

Publish/subscribe is a well known and powerful distributed programming paradigm with many potential applications. Publish/subscribe content dissemination techniques based on opportunistic networking and network coding-based epidemic routing are key techniques for optimizing network resources, simplifying network architecture, and providing a platform for realizing innovative networking applications and service.In this paper we consider the central problem of any pub/sub implementation, namely the problem of event dissemination, in the case of a wireless mesh network. We propose a new dissemination strategy based on the notion of semi-broadcast. In a semi-broadcast based protocol the actual content is disseminated in two phases. In the first phase only a fraction of the content is broadcasted (pushed) over the network and stored inside any node, whereas in the second phase the missed part is retried (pulled) on demand from other nodes. Thanks to network coding the partial content stored in each node at the end of the first phase is a set of random linear combinations over the whole content. This allows a very efficient recovery strategy as the missed part is found in nearby nodes with a high probability.The benefit of this approach is that only the interested subscribers, which can vary in number and position over time, can engage the pulling phase.We propose several protocols based on non-trivial forwarding mechanisms that employ network coding as a central tool for supporting adaptive event dissemination while exploiting the broadcast nature of wireless transmissions and guided to the semi-broadcast principle. We show a considerable enhancement in term of total flooding costs and full decoding rates by a self parameter control deployment during the dissemination procedure.     

Fast multi-objective optimization of multi-parameter antenna structures based on improved MOEA/D with surrogate-assisted model

For multi-objective design of multi-parameter antenna structures, optimization efficiency and computational cost are two major concerns. In this paper, an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed to improve global optimization capability by diversity detection operation and mixed population update operation. Further, in order to reduce the computational cost, a hybrid optimization strategy integrating a dynamically updatable surrogate-assisted model into the improved MOEA/D is proposed. The numerical results of test functions show that our algorithm outperforms original MOEA/D, modified MOEA/D (M-MOEA/D), and nondominated sorting genetic algorithm II (NGSA-II) in terms of diversity. Experimental validation of Pareto-optimal planar miniaturized multiband antenna designs is also provided, showing excellent convergence and considerable computational savings compared to those previously published approaches.     

A high performance grid-web service framework for the identification of ‘conserved sequence tags’

The continuous increasing of computing power in biological research places a threshold to the single host use and suggests an approach based on distributed computing. An emerging solution is grid technology, which allows organization to make better use of existing computing resources by providing them with a single, transparent, aggregated source of computing power. Equally, bioinformatics analysis often involves many web services, allowing shared access to information and helping the biologist to design, describe, record complex experiments. A new generation of grid infrastructure, where web services are building blocks, allow managent of a web services workflow.This work shows a tool for the identification and functional annotation of ‘Conserved Sequence Tags’ (CSTs) through cross-species genome comparisons, deployed on a Grid System Architecture, based on Web Services concepts and technologies.     

A qualitative thermo-fluid-dynamic analysis of a CO2 solar pipe receiver

In a parabolic trough of a CSP solar plant the solar rays are concentrated on a pipe positioned in the focus of the parabolic reflectors. A flow of carbon dioxide collects the heat from the heated pipe. The thermal gradient between the heated and not-heated part of the metallic pipe leads to receiver destruction while the high temperature in the enlightened part of the pipe can damage the special coating deposed on it. In this paper an extensive qualitative analysis has been made by mean of Ansys Fluent on a standard solar pipe receiver to improve the, convective, heat distribution around the pipe, in order to prevent its failure. Different methods are presented and discussed. In particular a parametric analysis of a pipe equipped with blades/fins is conducted.     

An improved vector quantization method using deep neural network

To address the challenging problem of vector quantization (VQ) for high dimensional vector using large coding bits, this work proposes a novel deep neural network (DNN) based VQ method. This method uses a k-means based vector quantizer as an encoder and a DNN as a decoder. The decoder is initialized by the decoder network of deep auto-encoder, fed with the codes provided by the k-means based vector quantizer, and trained to minimize the coding error of VQ system. Experiments on speech spectrogram coding demonstrate that, compared with the k-means based method and a recently introduced DNN-based method, the proposed method significantly reduces the coding error. Furthermore, in the experiments of coding multi-frame speech spectrogram, the proposed method achieves about 11% relative gain over the k-means based method in terms of segmental signal to noise ratio (SegSNR).     

Hybrid genetic algorithm with fuzzy logic for resource-constrained project scheduling

In this paper, we develop a hybrid genetic algorithm (hGA) with fuzzy logic controller (FLC) to solve the resource-constrained project scheduling problem (rcPSP) which is a well-known NP-hard problem. Our new approach is based on the design of genetic operators with FLC and the initialization with the serial method, which has been shown superior for large-scale rcPSP problems. For solving these rcPSP problems, we firstly demonstrate that our hGA with FLC (flc-hGA) yields better results than several heuristic procedures presented in the literature. Then we evaluate several genetic operators, which include compounded partially mapped crossover (PMX), position-based crossover (PBC), swap mutation (SM), and local search-based mutation (LSM) in order to construct the flc-hGA which have the better optimal makespan and several alternative schedules with optimal makespan.     

Heuristic wavelet shrinkage for denoising

Noise reduction without any prior knowledge of noise or signals is addressed in this study. Compared with conventional filters, wavelet shrinkage can respect this requirement to reduce noise from received signal in wavelet coefficients. However, wavelet threshold depends on an estimate of noise deviation and a weight relating signal's length cannot be applied in every case. This paper uses particle swarm optimization (PSO) to explore a suitable threshold in a complete solution space, named PSOShrink. A general-purpose objective function which is derived from blind signal separation (BSS) theory is further proposed. In simulation, four benchmarks signals and three degrading degrees are testing; meanwhile, three existing algorithm with state-of-the-art are performed for comparison. PSOShrink can not only recovers source signals from a heavy blurred signal but also remains details of a source signal from a light blurred signal; moreover, it performs outstanding denoising in every simulation case.     

An effective pre-store/pre-load method exploiting intra-request idle time of NAND flash-based storage devices

NAND flash-based storage devices (NFSDs) are widely employed owing to their superior characteristics when compared to hard disk drives. However, NAND flash memory (NFM) still exhibits drawbacks, such as a limited lifetime and an erase-before-write requirement. Along with effective software management, the implementation of a cache buffer is one of the most common solutions to overcome these limitations. However, the read/write performance becomes saturated primarily because the eviction overhead caused by limited DRAM capacity significantly impacts overall NFSD performance. This paper therefore proposes a method that hides the eviction overhead and overcomes the saturation of the read/write performance. The proposed method exploits the new intra-request idle time (IRIT) in NFSD and employs a new data management scheme. In addition, the new pre-store eviction scheme stores dirty page data in the cache to NFMs in advance. This reduces the eviction overhead by maintaining a sufficient number of clean pages in the cache. Further, the new pre-load insertion scheme improves the read performance by frequently loading data that needs to be read into the cache in advance. Unlike previous methods with large migration overhead, our scheme does not cause any eviction/insertion overhead because it actually exploits the IRIT to its advantage. We verified the effectiveness of our method, by integrating it into two cache management strategies which were then compared. Our proposed method reduced read latency by 43% in read-intensive traces, reduced write latency by 40% in write-intensive traces, and reduced read/write latency by 21% and 20%, respectively, on average compared to NFSD with a conventional write cache buffer.     

Performance analysis of a micro-combined heating and power system with PEM fuel cell as a prime mover for a typical household in North China

Proton exchange membrane (PEM) fuel cells produce a large amount of waste heat while generating electricity through electrochemical reactions, making them suitable for driving combined heating and power (CHP) systems. According to the hourly thermal and electric loads in a typical North China household, a 2-kW PEM fuel cell-based micro-CHP system with a lithium-ion battery energy storage system is proposed in this paper. The thermal and economic performances of the micro-CHP system with a lithium-ion battery (CHPWB) and a CHP system without a lithium-ion battery (CHPWOB) are comparatively analyzed by developing a thermal and economic performance analysis model on the MATLAB/Simulink platform. The thermal-load-following strategy is adopted during the design and simulation process. The results indicate that the storage capacity of the lithium-ion battery decreases by 6.6% after one cycle. The lithium-ion battery can be charged by the fuel cell stack during off-peak hours or using commercial electricity, and the charging cycle of the system is one week long. The average total efficiency of the CHPWB system can reach 81.24% with considering the energy loss in each conversion process, which is 11.02% higher than that of the CHPWOB system. The daily hydrogen consumption of the CHPWB system can be reduced by 14.47% compared with the CHPWOB system under the same operating conditions, and the average daily costs can be reduced by 8.4% and 9.5% when the lifespan is 10 and 15 years, respectively.