This article presents an indoor positioning and communication platform, using fluorescent lights. We set up a practical implementation of a VLC (Visible Light Communication) system in a University building. To finalize this work, it is important that we analyze the properties of the reception signal, especially the length of the data string that can be received at different walking speed. In this paper, we present a model and a series of formulae for analyzing the relationship between positioning signal availability and other important parameters, such as sensor angle, walking speed, data transmission rate, etc. We report a series of real-life experiments using VLC system and compare the results with those generated by the formula. The outcome is an improved design for determination of the reception area with more than 97% accurate signals, and an optimal transmission data length, and transmission rate.

Amidst conflicting views about whether soft cooperative energy detection scheme (SCEDS) outperforms hard cooperative energy detection scheme (HCEDS) greatly in cognitive radio, we establish the bridge that mathematically connects SCEDS and HCEDS by closed approximations. Through this bridge, it is demonstrate that, if the number of detectors of HCEDS is 1.6 times as that of SCEDS, they have nearly the same performance which is confirmed by numerical simulations, enabling a quantitative evaluation of the relation between them and a resolution of the conflicting views.

In this paper, we propose a continuous range query processing method over moving objects. To efficiently process continuous range queries, we design a main-memory-based query index that uses smaller storage and significantly reduces the query processing time. We show through performance evaluation that the proposed method outperforms the existing methods.

In this paper, we propose a new algorithm to detect the presence of narrow band interference signals on the band of an Ultra Wide-Band (UWB) system when the UWB spectrum overlaps the bands of other narrow band wireless services. In our proposed algorithm for an UWB Multi-Band Orthogonal Frequency Division Multiplexing (MB-OFDM) system, an appropriate model is selected from the assumed interference models based on the Akaike Information Criterion (AIC) which is an explicit theoretic criterion and a measure of fit of the model. The proposed algorithm does not need a priori information on the interference signals except that we can reduce a computational complexity to implement the algorithm if we have knowledge of the bands of the interference signals. Furthermore, we introduce the Expectation Maximization (EM) algorithm to our algorithm in order to estimate the transmitted signals and the interference signals simultaneously. The proposed algorithm may not require the pilot symbols in the assumed UWB system to detect the presence of other systems. By computer simulations, we show that the proposed algorithm validly detects the presence of interference signals on the UWB band.

The ability to estimate a target location is essential in many applications of wireless sensor networks. Received signal strength indicator (RSSI)-based maximum likelihood (ML) method in a wireless sensor network usually requires a pre-determined statistical model on the variation of RSSI in a sensing area and uses it as an ML function when estimating the location of a target in the sensing area. However, when estimating the location of a target, due to several reasons, we often measure the RSSIs which do not follow the statistical model, in other words, which are outlier on the statistical model. As the result, the effect of the outlier RSSI data worsens the estimation accuracy. If the wireless sensor network has a lot of sensor nodes, we can improve the estimation accuracy intentionally rejecting such outlier RSSIs. In this paper, we propose a simple outlier RSSI data rejection algorithm for an ML location estimation. The proposed algorithm iteratively eliminates the anchor nodes which measure outlier RSSIs. As compared with the location estimation methods with previously proposed outlier RSSI data rejection algorithms, our proposed method performs better with much less computational complexity.

In P2P content distribution systems, there are many cases in which the content can be classified into hierarchically organized categories. In this paper, we propose a hybrid overlay network design suitable for such content called Pastry/NSHCC (Pastry for Non-Strictly Hierarchically Categorized Content). The semantic information of classification hierarchies of the content can be utilized regardless of whether they are in a strict tree structure or not. By doing so, the search scope can be restrained to any granularity, and the number of query messages also decreases while maintaining keyword searching availability. Through simulation, we showed that the proposed method provides better performance and lower overhead than unstructured overlays exploiting the same semantic information.

Wavelet-based features with simplicity and high efficacy have been used in many pattern recognition (PR) applications. These features are usually generated from the wavelet coefficients of coarse levels (i.e., high octaves) in the discrete periodized wavelet transform (DPWT). In this paper, a new 1-D non-recursive DPWT (NRDPWT) is presented for real-time high octave decomposition. The new 1-D NRDPWT referred to as the 1-D RRO-NRDPWT can overcome the word-length-growth (WLG) effect based on two strategies, resisting error propagation and applying a reversible round-off linear transformation (RROLT) theorem. Finite precision performance analysis is also taken to study the word length suppression efficiency and the feature efficacy in breast lesion classification on ultrasonic images. For the realization of high octave decomposition, a segment accumulation algorithm (SAA) is also presented. The SAA is a new folding technique that can reduce multipliers and adders dramatically without the cost of increasing latency.

In this paper, we study performance evaluation of workflow-based information systems. Because of state space explosion, analysis by stochastic models, such as stochastic Petri nets and queuing models, is not suitable for workflow systems in which a large number of flow instances run concurrently. We use fluid-flow approximation technique to overcome this difficulty. In the proposed method, GSPN (Generalized Stochastic Petri Nets) models representing workflows are approximated by a class of timed continuous Petri nets, called routing timed continuous Petri nets (RTCPN). In RTCPN models, each discrete set is approximated by a continuous region on a real-valued vector space, and variance in probability distribution is replaced with a real-valued interval. Next we derive piecewise linear systems from RTCPN models, and use interval methods to compute guaranteed enclosures for state variables. As a case study, we solve an optimal resource assignment problem for a paper review process.

To guarantee the timely provisioning of QoS to real-time oriented multiparty and distributed computing applications (e.g., video conferencing and grid computing) that require the utilization of time and quantity-related resources, the resource should be reserved in advance. However, this new type of reservation (i.e., advance reservation) may collide with legacy (i.e., immediate) reservations that do not specify their session duration. In this paper, to suggest a balanced solution between sharing pools of resources and the managing of collisions between reservation calls, a revenue-based resource sharing scheme (focusing on the bandwidth) is proposed. The proposed scheme attempts to avoid possible collisions by employing a virtual profile of resources reserved in advance in an effort to achieve enhanced resource utilization. It is shown through NS-2 [22] -based network simulations that the proposed scheme can achieve balanced performance when compared with other schemes, including static resource partitioning and complete resource sharing.

A simple transmit diversity scheme based on cyclic-shift pseudo noise (PN) sequences for the time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) system is proposed and the corresponding channel estimation algorithms are also investigated. Simulation results are presented to verify the effectiveness of the proposed method.

In a sensor network, a certain area or environment are observed by a lot of distributed sensor nodes and a sink collects data observed by sensor nodes. The observed data sequences that sensor nodes generate may have space- and/or time-dependent correlation. This correlation is regarded as redundant information and can be used for channel error correction by joint decoder using correlation. In this paper, we propose the joint decoding scheme using the time-dependent correlation consisting of the consecutive data sequences generated by a sensor node. We also propose a power control scheme using the time-dependent correlation for reduction in energy consumption. We evaluate the packet error rate and the energy consumption ratio, and clarify the effect of our proposed schemes.

In this paper, an efficient node-level target classification scheme in wireless sensor networks (WSNs) is proposed. It uses acoustic and seismic information, and its performance is verified by the classification accuracy of vehicles in a WSN. Because of the hard limitation in resources, parametric classifiers should be more preferable than non-parametric ones in WSN systems. As a parametric classifier, the Gaussian mixture model (GMM) algorithm not only shows good performances to classify targets in WSNs, but it also requires very few resources suitable to a sensor node. In addition, our sensor fusion method uses a decision tree, generated by the classification and regression tree (CART) algorithm, to improve the accuracy, so that the algorithm drives a considerable increase of the classification rate using less resources. Experimental results using a real dataset of WSN show that the proposed scheme shows a 94.10% classification rate and outperforms the k-nearest neighbors and the support vector machine.

Packet delivery in Proxy Mobile IPv6 (PMIPv6) relies on an anchor node called LMA. All packets sent by a source node reach a receiver node via LMA, even though the two nodes attach to the same MAG. In some scenarios, PMIPv6 results in high delivery latency and processing costs due to this unnecessary detour. To address this issue, several PMIPv6 route optimization schemes have been proposed. However, high signaling costs and excessive delays remain when handover is performed. For this reason, we propose an enhanced PMIPv6 route optimization (EPRO) scheme. In addition, we analyze the performance of the EPRO. Analytical results indicate that the EPRO outperforms previous schemes in terms of signaling overhead and handover latency.

Conveying information about who, what, when and where is a primary purpose of some genres of documents, typically news articles. Statistical models that capture dependencies between named entities and topics can play an important role in handling such information. Although some relationships between who and where should be mentioned in such a document, no statistical topic models explicitly address the textual interactions between a who-entity and a where-entity. This paper presents a statistical model that directly captures the dependencies between an arbitrary number of word types, such as who-entities, where-entities and topics, mentioned in each document. We show that this multitype topic model performs better at making predictions on entity networks, in which each vertex represents an entity and each edge weight represents how a pair of entities at the incident vertices is closely related, through our experiments on predictions of who-entities and links between them. We also demonstrate the scale-free property in the weighted networks of entities extracted from written mentions.

MIMO (Multiple Input Multiple Output) communications systems equipped with array antennas at both the transmitter and receiver sides are a promising scheme to realize higher rate and/or reliable data transmission. In this paper, capacity analysis of MIMO Rayleigh channel with spatial correlation at the receiver of multipath taken into account is presented. In general, a model configuration of local scattering around a mobile station in MIMO environment is carried out by simulation to examine spatial correlation coefficients. Based on statistical properties of the eigenvalues of correlated complex random Wishart matrices, the exact closed-form expressions of distribution of the eigenvalues are investigated. Then, the general closed-form evaluation of integral form is proposed based on Meijer's G-function. The results demonstrate that the ergodic capacities are improved by increasing the number of the antennas and the SNR's. Compared with i.i.d. (independent identically distributed) Rayleigh channel, the incremental improvement of correlated Rayleigh channel is reduced by spatial fading correlation. The analytical results validated by Monte-Carlo simulations show a good agreement.

Point Pattern Matching (PPM) is an essential problem in many image analysis and computer vision tasks. This paper presents a two-stage algorithm for PPM problem using ellipse fitting and dual Hilbert scans. In the first matching stage, transformation parameters are coarsely estimated by using four node points of ellipses which are fitted by Weighted Least Square Fitting (WLSF). Then, Hilbert scans are used in two aspects of the second matching stage: it is applied to the similarity measure and it is also used for search space reduction. The similarity measure named Hilbert Scanning Distance (HSD) can be computed fast by converting the 2-D coordinates of 2-D points into 1-D space information using Hilbert scan. On the other hand, the N-D search space can be converted to a 1-D search space sequence by N-D Hilbert Scan and an efficient search strategy is proposed on the 1-D search space sequence. In the experiments, we use both simulated point set data and real fingerprint images to evaluate the performance of our algorithm, and our algorithm gives satisfying results both in accuracy and efficiency.

This paper proposes a novel channel estimation method for iterative equalization in MIMO systems. The proposed method incorporates co-channel interference (CCI) cancellation in the channel estimator and the channel estimation is successively performed with respect to each stream. Accuracy of channel estimation holds the key to be successfully converged the iterative equalization and decoding process. Although the channel estimates can be re-estimated by means of LS (Least Square) channel estimation using tentative decisions obtained in the iterative process, its performance is severely limited in a MIMO system because of erroneous decisions and ill-conditioned channel estimation matrix. The proposed method can suppress the above effects by means of CCI cancellation and successive channel estimation. Computer simulation confirms that the proposed channel estimation method can accurately estimate the channel, and the receiver with iterative equalization and the proposed method achieves excellent decoding performance in a MIMO-SM system.

Gamma correction is an essential function and is time consuming task in every display device such as CRT and LCD. And gray scale CCT reproduction in most LCD are quite different from those of standard CRT. An automated fast and accurate display adjusment method and system for gamma correction and for constant gray scale CCT calibration of mobile phone LCD is presented in this paper. We develop the test pattern disply and register control program in mobile phone and devleop automatic measure program in computer using spectroradimeter. The proposed system is maintain given gamma values and CCT values accuratly. In addition, This system is possible to fast mobile phone LCD adjusment within one hour.

We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

Focusing on time correlation of real communication channels, a channel quantization algorithm based on finite state vector quantization (FSVQ) is proposed. Firstly channels are partitioned into finite states, then codebooks corresponding to each state are constructed, which are used to quantize channels transferred from corresponding states. Further, the state transition function is designed to ensure the synchronization between transmitter and receiver. The proposed algorithm can achieve improved performance with the same feedback load compared with classical memoryless channel quantizer without consideration of the influence of time correlation. Simulation results verify the effectiveness of the proposed algorithm.