This paper presents a Multiple-Input Multiple-Output (MIMO) propagation model for independent and identically distributed (i.i.d.) channels in the mixture of none-Line-of-Sight (NLOS) and Line-of-Sight (LOS) environments. The derived model enables to evaluate the system statistical characteristics of Signal-to-Noise-Ratio (SNR) for MIMO transmission based on Maximal Ratio Combing (MRC). An application example applying the model in 22 configuration to ITS Inter-Vehicle Communication (IVC) system is introduced. We clarify the effectiveness of the proposed model by comparisons of both computer simulations and measurement results of a field experiment. We also use the model to show the better performance of SNR when applying MIMO to IVC system than SISO and SIMO.

In this paper, we propose a spatially adaptive gradient-projection algorithm for the H.264 video coding standard to remove coding artifacts using local statistics. A hybrid method combining a new weighted constrained least squares (WCLS) approach and the projection onto convex sets (POCS) approach is introduced, where weighting components are determined on the basis of the human visual system (HVS) and projection set is defined by the difference between adjacent pixels and the quantization index (QI). A new visual function is defined to determine the weighting matrices controlling the degree of global smoothness, and a projection set is used to obtain a solution satisfying local smoothing constraints, so that the coding artifacts such as blocking and ringing artifacts can be simultaneously removed. The experimental results show the capability and efficiency of the proposed algorithm.

Various low power technologies have been developed and applied to LSIs from the point of device and circuit design. A lot more CPU cores as well as function IPs are integrated on a single chip LSI today. Therefore, not only the device and circuit low power technologies, but software power control technologies are becoming more important to reduce active power of application systems. This paper overviews the low power technologies and defines power management platform as a combination of hardware functions and software programming interface. This paper discusses importance of the power management platform and direction of its development.

We propose a new majority voting scheme for identifying downlink primary scrambling code, where two voting processes with different coherent correlation intervals (CCIs) are simultaneously performed. A false alarm probability and a threshold adjustment for the proposed scheme are investigated, and it is shown by computer simulations that the proposed scheme can perform well over a wide range of frequency offsets.

Power-aware scheduling of periodic tasks in real-time systems has been extensively studied to save energy while still meeting the performance requirement. Many previous studies use the probability information of tasks' execution cycles to assist the scheduling. However, most of these approaches adopt heuristic algorithms to cope with realistic CPU models with discrete frequencies and cannot achieve the globally optimal solution. Sometimes they even show worse results than non-stochastic DVS schemes. This paper presents an optimal DVS scheme for frame-based real-time systems under realistic power models in which the processor provides only a limited number of speeds and no assumption is made on power/frequency relation. A suboptimal DVS scheme is also presented in this paper to work out a solution near enough to the optimal one with only polynomial time expense. Experiment results show that the proposed algorithm can save at most 40% more energy compared with previous ones.

In this letter, we propose an efficient signal detection method for uplink multiuser systems based on collaborative spatial multiplexing (CSM). The proposed method achieves near-optimal performance and shows only 0.8 dB loss at the target frame error rate (FER) of 10-2. Moreover, the error performance of each user is almost the same in the proposed method, which is an important property in a multiuser MIMO system where each user's error performance must satisfy some fixed error rate criteria.

On uniformly convex real Banach spaces, a fixed point theorem in weak topology for successively recurrent system of fuzzy-set-valued nonlinear mapping equations and its application to ring nonlinear network systems are theoretically discussed in detail. An arbitrarily-level likelihood signal estimation is then established.

This letter presents an experiment for estimating accurate state in distributed power systems. This letter employs a technique that combines a projected Jacobi method with a parallel dual-type method to solve the distributed state estimation with constraints problems. Via numerous tests, this letter demonstrates the efficiency of the proposed method on the IEEE 118-bus with four subsystems in a PC network.

In this paper, we study opacity-enforcing supervisory control of discrete event systems. We consider the case that the secrete behavior of the system is specified by multiple secret languages, and synthesize a maximally permissive supervisor that enforces opacity for all secret languages. We prove that, under a certain assumption on observable and uncontrollable events, a maximally permissive opacity-enforcing supervisor can be synthesized in a modular fashion.

This paper presents a framework of supporting system for a drug formulation. We designed ontology to represent the related knowledge for reusable and sharing purposes. The designed ontology is applied with operation rules to suggest an appropriate generic drug production based on information of original drug. The system also provides a validation module to preliminarily approve a pharmaceutical equivalence of the suggested result. Preliminary testing with four random samples shows potential to reformulate a generic product by returning a satisfactory and acceptable of the system suggestions for all samples.

A new state estimation algorithm is presented for a class of LTI systems that have an input disturbance in polynomial form and a sinusoidal sensor disturbance in the measurement output. Adaptation rules are developed for identifying the unknown magnitude, phase and frequency of the sensor disturbance from the system output measurement. For the application of the identification result to the state estimation problem, the sinusoidal signal with arbitrary initial phase has been considered in this paper. In order to test the performance of the proposed algorithm, comparative computer simulations have been carried out with a robust state observer. Simulation results show the effectiveness of the proposed method.

Improving quality of healthcare for people with chronic conditions requires informed and knowledgeable healthcare providers and patients. Decision support and clinical information system are two of the main components to support improving chronic care. In this paper, we describe an ontology-based information and knowledge management framework that is important for chronic disease care management. Ontology-based knowledge acquisition and modeling based on knowledge engineering approach provides an effective mechanism in capturing expert opinion in form of clinical practice guidelines. The framework focuses on building of healthcare ontology and clinical reminder system that link clinical guideline knowledge with patient registries to support evidenced-based healthcare. We describe implementation and approaches in integrating clinical reminder services to existing healthcare provider environment by focusing on augmenting decision making and improving quality of patient care services.

A critical problem with wireless LAN-based positioning systems is the degradation in position estimation accuracy due to complex variation in the strength of the received signal, which originates in the nature of the underlying wireless channel. Handling such variation effectively is essential for the creation of practical wireless LAN-based positioning systems. In this paper, we propose a collaborative mechanism for correcting position estimation errors. The main objective is to assist users in correcting estimation errors manually by providing access to a shared body of accumulated information on corrections made by many other users. In particular, the mechanism is designed to enable any group of users to collaboratively build upon this body of information. Finally, we evaluate the effectiveness of the proposed mechanism through experiments. The results confirm that the proposed mechanism can provide effective estimation error correction in a realistic environment.

A novel adaptive cyclic prefix (CP) transmission scheme is proposed for the uplink of orthogonal frequency division multiple access (OFDMA) systems to reduce the power consumption of mobile stations (MSs). In the proposed scheme, an MS adaptively changes its CP length in each frame, while the guard interval is maintained at a fixed duration to avoid frame synchronization problem and the interference problem with frames of other users. Using the proposed scheme, MSs can save power by not transmitting signal during the time difference between the guard interval and the duration of the adaptive CP. We numerically analyze the performance of the proposed scheme in terms of achievable capacity, the amount of power saving, and the feedback overhead of CP values. The result shows that the proposed scheme can reduce MS power consumption by about 20% with a small amount of additional feedback overhead.

Flash SSDs are being incorporated in many enterprise storage platforms recently and expected to play a notable role for IO-intensive applications. However, the IO characteristics of flash SSDs are very different from those of hard disks. Since existent storage subsystems are designed on the basis of characteristics of hard disks, the IO performance of flash SSDs may not be obtained as expected. This paper provides an evaluation of flash SSDs in transaction processing systems with TPC-C benchmark. We present performance results with various configurations and describe our observations of the IO behaviors at different levels along the IO path, which helps to understand the performance of flash-based transaction processing systems and provides certain references to build flash-based systems for IO-intensive applications.

With the emergence of Web 2.0, social tagging systems become highly popular in recent years and thus form the so-called folksonomies. Personalized tag recommendation in social tagging systems is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. Many existing tag recommendation approaches assume that users are independent and identically distributed. This assumption ignores the social relations between users, which are increasingly popular nowadays. In this paper, we investigate the role of social relations in the task of tag recommendation and propose a personalized collaborative filtering algorithm. In addition to the social annotations made by collaborative users, we inject the social relations between users and the content similarities between resources into a graph representation of folksonomies. To fully explore the structure of this graph, instead of computing similarities between objects using feature vectors, we exploit the method of random-walk computation of similarities, which furthermore enable us to model a user's tag preferences with the similarities between the user and all the tags. We combine both the collaborative information and the tag preferences to recommend personalized tags to users. We conduct experiments on a dataset collected from a real-world system. The results of comparative experiments show that the proposed algorithm outperforms state-of-the-art tag recommendation algorithms in terms of prediction quality measured by precision, recall and NDCG.

In this paper, we analyze the impact of channel estimation errors for both decode-and-forward (DF) and amplify-and-forward (AF) cooperative communication systems over Nakagami-m fading channels. Firstly, we derive the exact one-integral and the approximate expressions of the symbol error rate (SER) for DF and AF relay systems with different modulations. We also present expressions showing the limitations of SER under channel estimation errors. Secondly, in order to quantify the impact of channel estimation errors, the average signal-to-noise-ratio (SNR) gap ratio is investigated for the two types of cooperative communication systems. Numerical results confirm that our theoretical analysis for SER is very efficient and accurate. Comparison of the average SNR gap ratio shows that DF model is less susceptible to channel estimation errors than AF model.

Shi-Hui et al. proposed a new public key cryptosystem using ergodic binary matrices. The security of the system is derived from some assumed hard problem based on ergodic matrices over GF(2). In this note, we show that breaking this system, with a security parameter n (public key of length 4n2 bits, secret key of length 2n bits and block length of length n2 bits), is equivalent to solving a set of n4 linear equations over GF(2) which renders this system insecure for practical choices of n.

In this paper, we investigate the resource and power allocation schemes of partial block multi-carrier code division multiple access (PB/MC-CDMA) systems. In our proposed scheme, we manage transmit power depending on each user's channel state information (CSI). The objective is to maximize the average bit error ratio (BER) performance with minimal influence from the received signal-to-interference ratio (SIR), both of which are closely related to transmit power. To obtain additional performance improvement, our frequency band rearrangement scheme follows the transmit power control (TPC) process. We evaluate the performance of the proposed scheme using simulations. The results show that the proposed system provides superior performance compared to those of conventional systems.

Background subtraction is widely used in detecting moving objects; however, changing illumination conditions, color similarity, and real-time performance remain important problems. In this paper, we introduce a sequential method for adaptively estimating background components using Kalman filters, and a novel method for detecting objects using margined sign correlation (MSC). By applying MSC to our adaptive background model, the proposed system can perform object detection robustly and accurately. The proposed method is suitable for implementation on a graphics processing unit (GPU) and as such, the system realizes real-time performance efficiently. Experimental results demonstrate the performance of the proposed system.