An efficient approach for multi-user multi-cloud service composition in human–land sustainable computational systems

The Journal of Supercomputing - The increasing social problems on population, resources and environment enable the interaction between nature and humanity to become one of the most active research...     

Distributed deep learning platform for pedestrian detection on IT convergence environment

The Journal of Supercomputing - IT technology and traditional industries have been combined recently, resulting in IT convergence technology in various fields. Through convergence with the...     

SOSP: a stepwise optimal sparsity pursuit algorithm for practical compressed sensing

Compressed Sensing (CS), as a promising paradigm for acquiring signals, is playing an increasing important role in many real-world applications. One of the major components of CS is sparse signal recovery in which greedy algorithm is well-known for its speed and performance. Unfortunately, in many classic greedy algorithms, such as OMP and CoSaMP, the real sparsity is a key prior information, but it is blind. In another words, the true sparsity is not available for many practical applications. Due to this disadvantage, the performance of these algorithms are significantly reduced. In order to avoid too much dependence of classic greedy algorithms on the true sparsity, this paper proposed an efficient reconstruction greedy algorithm for practical Compressed Sensing, termed stepwise optimal sparsity pursuit (SOSP). Differs from the existing algorithms, the unique feature of SOSP algorithm is that the assumption of sparsity is needed instead of the true sparsity. Hence, the limitations of sparsity in practical application can be tackled. Based on an arbitrary initial sparsity satisfying certain conditions, the SOSP algorithm employs two variable step sizes to hunt for the optimal sparsity step by step by comparing the final reconstruction residues. Since the proposed SOSP algorithm preserves the ideas of original algorithms and innovates the prior information of sparsity, thus it is applicable to any effective algorithm requiring known sparsity. Extensive experiments are conducted in order to demonstrate that the SOSP algorithm offers a superior reconstruction performance in terms of discarding the true sparsity.

     

Anonymous proximity mobile payment (APMP)

The growth of wireless networks and the increasing popularity of mobile devices present an significant opportunity to empower them as a payment device. Unfortunately, several problems hinder the widespread acceptance of mobile payments, for example, privacy protection and user anonymity. Measures to ensure anonymity in payment systems must be considered as an important factor in privacy and system acceptance. We propose a new measure to enhance the level of anonymity in mobile payments where users can customize their anonymity, according to their personal preferences. We rely on IPAS (Implicit Password Authentication System) (Almuairfi et al. 2011) for dispute resolution to support our proposed idea.     

An efficient strategy of nonuniform sensor deployment in cyber physical systems

In this paper, we study the sensor deployment pattern problem in cyber physical systems. When designing the sensor deployment pattern, the network lifetime maximization while covering the given area/targets and forwarding sensor data to a sink node is an important issue. In order to prolong the network lifetime by balancing energy depletion across all sensors, we propose a novel nonuniform sensor distribution strategy. Since sensors located closer to the sink are more involved in data forwarding, sensor densities in different areas should be varied according to the distance to the sink. Based on the nonuniform sensor distribution, we propose sensor deployment patterns to satisfy the coverage and connectivity requirements and prolong the network lifetime. A numerical computation is performed to validate and compare the effectiveness of the proposed deployment patterns.     

Decimation of human face model for real-time animation in intelligent multimedia systems

Animating a complex human face model in real-time is not a trivial task in intelligent multimedia systems for next generation environments. This paper proposes a generation scheme of a simplified model for real-time human face animation in intelligent multimedia systems. Previous work mainly focused on the geometric features when generating a simplified human face model. Such methods may lose the critical feature points for animating human faces. The proposed method can find those important feature points and can generate the feature-preserved low-level models busing our new quadrics. The new quadrics consist of basic error metrics and feature edge quadrics. The quality of facial animation with a lower-level model is as good as that of a computationally expansive original model. In this paper, we prove that our decimated facial model is effective in facial animation using a well-known expression-retargeting technique.     

Lifetime maximization considering target coverage and connectivity in directional image/video sensor networks

A directional sensor network consists of a large number of directional sensors (e.g., image/video sensors), which have a limited angle of sensing range due to technical constraints or cost considerations. In such directional sensor networks, the power saving issue is a challenging problem. In this paper, we address the Directional Cover and Transmission (DCT) problem of organizing the directional sensors into a group of non-disjoint subsets to extend the network lifetime. One subset in which the directional sensors cover all the targets and forward the sensed data to the sink is activated at one time, while the others sleep to conserve their energy. For the DCT problem proven to be the NP-complete problem, we present a heuristic algorithm called the Shortest Path from Target to Sink (SPTS)-greedy algorithm. To verify and evaluate the proposed algorithm, we conduct extensive simulations and show that it can contribute to extending the network lifetime to a reasonable extent.     

Efficient journaling writeback schemes for reliable and high-performance storage systems

We propose a efficient writeback scheme that enables guaranteeing throughput in high-performance storage systems. The proposed scheme, called de-fragmented writeback (DFW), reduces positioning time of storage devices in writing workloads, and thus enables fast writeback in storage systems. We consider both of storage media in designing DFW scheme; traditional rotating disk and emerging solid-state disks. First, sorting and filling holes methods are used for rotating disk media for the higher throughput. The scheme converts fragmented data blocks into sequential ones so that it reduces the number of write requests and unnecessary disk-head movements. Second, flash block aware clustering-based writeback scheme is used for solid-state disks considering the characteristics of flash memory. The experimental results show that our schemes guarantee system’s high throughput while guaranteeing data reliability.