The release of IEEE 802.15.4e specification significantly develops IEEE 802.15.4. The most inspiring improvement is the enhancement for medium access control (MAC) sublayer. To study the performance of IEEE 802.15.4e MAC, in this paper we first present an overview of IEEE 802.15.4e and introduce three MAC mechanisms in IEEE 802.15.4e. And the major concern here is the Time Slotted Channel Hopping (TSCH) mode that provides deterministic access and increases network capacity. Then a detailed analytical Markov chain model for TSCH carrier sense multiple access with collision avoidance (CSMA-CA) is presented. Expressions which cover most of the crucial issues in performance analysis such as the packet loss rate, energy consumption, normalized throughput, and average access delay are presented. Finally the performance evaluation for the TSCH mode is given and we make a comprehensive comparison with unslotted CSMA-CA in non-beacon enabled mode of IEEE 802.15.4. It can validate IEEE 802.15.4e network can provide low energy consumption, deterministic access and increase network capacity.

Content distribution to a large number of concurrent clients stresses both server and network. While the server limitation can be circumvented by deploying server clusters, the network limitation is far less easy to cope with, due to the difficulty in measuring and balancing network load. In this paper, we use two useful network load metrics, the worst link stress (WLS) and the degree of interference (DOI), and formulate the problem as partitioning the clients into disjoint subsets subject to the server capacity constraint so that the WLS and the DOI are reduced for each session and also well balanced across the sessions. We present a network load-aware partition algorithm, which is practicable and effective in achieving the design goals. Through experiments on PlanetLab, we show that the proposed scheme has the remarkable advantages over existing schemes in reducing and balancing the network load. We expect the algorithm and performance metrics can be easily applied to various Internet applications, such as media streaming, multicast group member selection.

Wireless network systems provide fast data transmission rates and various services to users of mobile devices such as smartphones and smart pads. Because many people use high-performance mobile devices, the use of real-time multimedia services is increasing rapidly. However, the preoccupation of resources by real-time traffic users is causing harm to other services-for example, frequent call interference, lowered service quality, and poor network performance. This paper suggests a resource allocation algorithm for effective traffic service support in a hybrid network. The main objective is to obtain an optimum value of data rates by comparing user requirements with the amount of resources that can be allocated. A new mechanism based on Adaptive-Quality of Service (QoS) and a monitoring system based on Queue-Aware are proposed. Adaptive-QoS supports effective resource control according to the type of traffic service, and the monitoring system based on Queue-Aware measures the amount of resources in order to calculate the maximum that can be allocated. We apply our algorithm to a test system and use Qualnet 4.5.1 to evaluate its performance.

Because H.264/SVC can meet the needs of different networks and user terminals, it has become more and more popular. In this paper, we focus on the spatial resolution scalability of H.264/SVC and propose a blind video watermarking algorithm for the copyright protection of H.264/SVC coded video. The watermark embedding occurs before the H.264/SVC encoding, and only the original enhancement layer sequence is watermarked. However, because the watermark is embedded into the average matrix of each macro block, it can be detected in both the enhancement layer and base layer after downsampling, video encoding, and video decoding. The proposed algorithm is examined using JSVM, and experiment results show that is robust to H.264/SVC coding and has little influence on video quality.

Lung cancer is considered to be the leading cause of cancer death worldwide. A technique commonly used consists of analyzing sputum images for detecting lung cancer cells. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid errors. The manual screening of sputum samples has to be improved by using image processing techniques. In this paper we present a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer based on the analysis of the sputum color image with the aim to attain a high accuracy rate and to reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we present a framework for segmentation and extraction of sputum cells in sputum images using respectively, a Bayesian classification method followed by region detection and feature extraction techniques to determine the shape of the nuclei inside the sputum cells. The final results will be used for a (CAD) system for early detection of lung cancer. We analyzed the performance of a Bayesian classification with respect to the color space representation and quantification. Our methods were validated via a series of experimentation conducted with a data set of 100 images. Our evaluation criteria were based on sensitivity, specificity and accuracy.

Since Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) were introduced, many revised or extended topic models have appeared. Due to the intractable likelihood of these models, training any topic model requires to use some approximation algorithm such as variational approximation, Laplace approximation, or Markov chain Monte Carlo (MCMC). Although these approximation algorithms perform well, training a topic model is still computationally expensive given the large amount of data it requires. In this paper, we propose a new method, called non-simultaneous sampling deactivation, for efficient approximation of parameters in a topic model. While each random variable is normally sampled or obtained by a single predefined burn-in period in the traditional approximation algorithms, our new method is based on the observation that the random variable nodes in one topic model have all different periods of convergence. During the iterative approximation process, the proposed method allows each random variable node to be terminated or deactivated when it is converged. Therefore, compared to the traditional approximation ways in which usually every node is deactivated concurrently, the proposed method achieves the inference efficiency in terms of time and memory. We do not propose a new approximation algorithm, but a new process applicable to the existing approximation algorithms. Through experiments, we show the time and memory efficiency of the method, and discuss about the tradeoff between the efficiency of the approximation process and the parameter consistency.

Electrocardiograms (ECGs) are widely used by clinicians to identify the functional status of the heart. Thus, there is considerable interest in automated systems for real-time monitoring of arrhythmia. However, intra- and inter-patient variability as well as the computational limits of real-time monitoring poses significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is therefore a promising new intelligent diagnostic tool.

With the explosive growth of computer networks, many remote service providing servers and multi-server network architecture are provided and it is extremely inconvenient for users to remember numerous different identities and passwords. Therefore, it is important to provide a mechanism for a remote user to use single identity and password to access multi-server network architecture without repetitive registration and various multi-server authentication schemes have been proposed in recent years. Recently, Tsaur et al. proposed an efficient and secure smart card based user authentication and key agreement scheme for multi-server environments. They claimed that their scheme satisfies all of the requirements needed for achieving secure password authentication in multi-server environments and gives the formal proof on the execution of the proposed authenticated key agreement scheme. However, we find that Tsaur et al.'s scheme is still vulnerable to impersonation attack and many logged-in users' attack. We propose an extended scheme that not only removes the aforementioned weaknesses on their scheme but also achieves user anonymity for hiding login user's real identity. Compared with other previous related schemes, our proposed scheme keeps the efficiency and security and is more suitable for the practical applications.

Via the Internet, the information infrastructure of modern health care has already established medical information systems to share electronic health records among patients and health care providers. Data hiding plays an important role to protect medical images. Because modern medical devices have improved, high resolutions of medical images are provided to detect early diseases. The high quality medical images are used to recognize complicated anatomical structures such as soft tissues, muscles, and internal organs to support diagnosis of diseases. For instance, 16-bit depth medical images will provide 65,536 discrete levels to show more details of anatomical structures. In general, the feature of low utilization rate of intensity in 16-bit depth will be utilized to handle overflow/underflow problem. Nowadays, most of data hiding algorithms are still experimenting on 8-bit depth medical images. We proposed a novel reversible data hiding scheme testing on 16-bit depth CT and MRI medical image. And the peak point and zero point of a histogram are applied to embed secret message k bits without salt-and-pepper.

Current group key agreement protocols, which are often tree-based, have unnecessary delays that are caused when members with low-performance computer systems join a group key computation process. These delays are caused by the computations necessary to balance a key tree after membership changes. An alternate approach to group key generation that reduces delays is the dynamic prioritizing mechanism of queue-based group key generation. We propose an efficient group key agreement protocol and present the results of performance evaluation tests of this protocol. The queue-based approach that we propose is scalable and requires less computational overhead than conventional tree-based protocols.

It requires a lot of costs and manpower to manage an election. The electronic voting scheme can make the election system economic and trustful. The widespread use of smart phones causes mobile voting to be a major issue. The smart phone can be used as a mobile voting platform since it can carry out many services in addition to basic telephone service. To make mobile voting practical and trustful, we analyzed two subjects of study. Firstly, the way to make a biometric based mobile ID, which has legal binding forces. In mobile voting, user identification is accomplished on line since the voter should be able to vote wherever they go. The digital ID conducts a similar role to the need for a resident card. The user's identity is bound to the resident card legally. To bind the user's identity to the smart phone, we use USIM. Biometric recognition is also needed to authenticate the user, since the user cannot prove him or her on line face-to-face. The proposed mobile ID can be reissued by means of introducing a random secret value. Secondly, the mobile voting scheme is proposed where candidates can accept election results without doubt. The goal of an election is to select a leader among two or more candidates. Existing electronic voting schemes mainly focus on the study of ballot verification accomplished by voters. These approaches are not safe against collusion attacks where candidates and the election administration center are able to collude to fabricate election results. Therefore, a new type of voting and counting method is needed where candidates can directly take part in voting and counting stages. The biometric based multi-signature scheme is used to make the undeniable multi-signed ballot. The ballot cannot be verified without the help of all candidates. If candidates accept election results without a doubt, the fairness of the election is satisfied.