This paper proposes an integrated approach to simultaneous detection and localization of multiple object categories using both generative and discriminative models. Our approach consists of first generating a set of hypotheses for each object category using a generative model (pLSA) with a bag of visual words representing each object. Based on the variation of objects within a category, the pLSA model automatically fits to an optimal number of topics. Then, the discriminative part verifies each hypothesis using a multi-class SVM classifier with merging features that combines spatial shape and appearance of an object. In the post-processing stage, environmental context information along with the probabilistic output of the SVM classifier is used to improve the overall performance of the system. Our integrated approach with merging features and context information allows reliable detection and localization of various object categories in the same image. The performance of the proposed framework is evaluated on the various standards (MIT-CSAIL, UIUC, TUD etc.) and the authors' own datasets. In experiments we achieved superior results to some state of the art methods over a number of standard datasets. An extensive experimental evaluation on up to ten diverse object categories over thousands of images demonstrates that our system works for detecting and localizing multiple objects within an image in the presence of cluttered background, substantial occlusion, and significant scale changes.

For integrated WLAN/cellular networks, we propose an energy-efficient vertical handover mechanism that both improves the energy efficiency of the mobile nodes and reduces the WLAN frame overhead.

Generalized quasi-cyclic (GQC) codes form a wide and useful class of linear codes that includes thoroughly quasi-cyclic codes, finite geometry (FG) low density parity check (LDPC) codes, and Hermitian codes. Although it is known that the systematic encoding of GQC codes is equivalent to the division algorithm in the theory of Grobner basis of modules, there has been no algorithm that computes Grobner basis for all types of GQC codes. In this paper, we propose two algorithms to compute Grobner basis for GQC codes from their parity check matrices; we call them echelon canonical form algorithm and transpose algorithm. Both algorithms require sufficiently small number of finite-field operations with the order of the third power of code-length. Each algorithm has its own characteristic. The first algorithm is composed of elementary methods and is appropriate for low-rate codes. The second algorithm is based on a novel formula and has smaller computational complexity than the first one for high-rate codes with the number of orbits (cyclic parts) less than half of the code length. Moreover, we show that a serial-in serial-out encoder architecture for FG LDPC codes is composed of linear feedback shift registers with the size of the linear order of code-length; to encode a binary codeword of length n, it takes less than 2n adder and 2n memory elements.

In this paper, we focus on resource allocation schemes for minimizing the energy consumption of subscriber stations (SSs) in uplink flows of the IEEE 802.16 OFDMA systems. The resource allocation schemes assign subcarriers, powers, and data rates to each SS based on the measured signal to noise ratio (SNR) of the uplink channel and predefined modulation and coding scheme as system parameters. Previous research efforts to optimize resource allocation focus on the rate and throughput maximizations, and develop suboptimal heuristic algorithms. However, this paper intends to reduce the energy consumption of SSs by considering the relationship between energy efficiency and resource allocation. In order to clearly formulate the relationship, we use the Multiple Choice Knapsack (MCK) problem, which is proved to be an NP-hard problem. We propose two heuristic schemes to solve the NP-hard problem, which adaptively use the modulation and coding scheme, defined in the IEEE 802.16 OFDMA systems to minimize the required transmission power of each SS. Our simulation results show that the proposed schemes can reduce the energy consumption by up to 53% compared to the channel state information (CSI) scheme, which determines the modulation and coding level only considering the channel state information.

The Dst index is the most popular measure of a scale of magnetic storms, and it is widely used as a monitor of the conditions of the Earth's magnetosphere. Since the Dst index contains contributions from multiple magnetospheric phenomena, it is important to distinguish each of the contributions in order to obtain meaningful information about the conditions of the magnetosphere. There have been several efforts which modeled temporal evolution of the Dst index empirically, and these empirical models considers some contributions separately. However, they take only short-term varations into accout, and contributions from phenomena which show long-term variations are neglected. In the present study, we have developed a technique for estimating the component of long-term variations of the Dst index using solar wind data and a nonlinear empirical model. The newly-developed technique adopts an algorithm which is similar to the particle filter. This algorithm allows an on-line processing of a long sequence of Dst data, which would enable a real-time estimation of system variables in a nonlinear system model. The estimates of the long-term variations can be used for accurate estimation of other contributions to the Dst index, which would provide credible information about the conditions of the magnetosphere. The framework proposed in the present study could be applied for the purpose of continuous real-time monitoring of the environment of the magnetosphere.

In this letter, we analyze the convergence speed of layered decoding of block-type low-density parity-check codes and verify that the layered decoding gives faster convergence speed than the sequential decoding with randomly selected check node subsets. Also, it is shown that using more subsets than the maximum variable node degree does not improve the convergence speed.

A critical problem after a natural/manmade disaster is to provide immediate post-disaster communication links between the disaster victims and some overlay networks. This paper proposes a novel scheme that uses the surviving Mobile handSets (MS) as sensing nodes to form an auto-configured Hierarchical Cognitive Radio Network (H-CRN). The implementation of this H-CRN is explained through detailed problem scenario statement and step-by-step implementation of automatic identification of emergency situation by the MS nodes. An overview of the cross-layer framework used by the MS nodes is also presented. This novel scheme is tested through some hypothesis along with probability calculations for successful identification of emergency situation, formation of ad hoc group and Emergency Beacon Message (EBM) transmission.

Two adaptive energy detectors are proposed for cognitive radio systems to detect the primary users. Unlike the conventional energy detector (CED) where a decision is made after receiving all samples, our detectors make a decision with the sequential arrival of samples. Hence, the sample size of the proposed detectors is adaptive. Simulation results show that for a desired performance, the average sample size of the proposed detectors is much less than that of the CED. Therefore, they are more agile than the CED.

Multiple access based on energy spreading transform (EST) in [1] has been shown to effectively separate multiuser signals in an iterative manner. In this paper, an optimum hard-decision detector for the EST-based multiple access is proposed. The proposed scheme employs minimum mean square error (MMSE) processing at each iteration to enhance the performance of the original scheme. Analysis and simulation results show the significant performance improvement of the proposed scheme over the original method.

A classification method is presented for differentiating honeycombed High Resolution Computed Tomographic (HRCT) images from normal HRCT images. For successful classification of honeycombed HRCT images, a complete set of methods and algorithms is described from segmentation to extraction to feature selection to classification. Wavelet energy is selected as a feature for classification using K-means clustering. Test data of 20 patients are used to validate the method.

In spectrum-sharing systems where the secondary user (SU) opportunistically accesses the primary user (PU)'s licensed channel, the SU should satisfy both the transmit power constraint of the SU transmitter and the received power constraint at the PU receiver. This letter studies the ergodic capacity of spectrum-sharing systems in fading channels. The ergodic capacity expression along with the optimal power allocation scheme is derived considering both the average transmit and received power constraints. The capacity function in terms of the two power constraints is found to be divided into transmit power limited region, received power limited region and dual limited region. Numerical results in Rayleigh fading channels are presented to verify our analysis.

The performance of symmetric and asymmetric cryptography algorithms in small devices is presented. Both temporal and energy costs are measured and compared with the basic functional costs of a device. We demonstrate that cryptographic power costs are not a limiting factor of the autonomy of a device and explain how processing delays can be conveniently managed to minimize their impact.

Interaction between emergency medical technicians (EMTs) and doctors is essential in emergency medical care. Doctors require diverse information related to a patient to provide efficient aid. In 2005, we started the Ikoma119 project and have developed a ubiquitous communication platform for emergency medical care called Mobile ER. Our platform, which is based on wireless internet technology, has such desirable properties as low-cost, location-independent service, and ease of service introduction. We provide an overview of our platform and describe the services that we have developed. We also discuss the remaining issues to realize our platform's actual operation.

Nonnegative matrix factorization (NMF) and its extensions such as Nonnegative Tensor Factorization (NTF) have become prominent techniques for blind sources separation (BSS), analysis of image databases, data mining and other information retrieval and clustering applications. In this paper we propose a family of efficient algorithms for NMF/NTF, as well as sparse nonnegative coding and representation, that has many potential applications in computational neuroscience, multi-sensory processing, compressed sensing and multidimensional data analysis. We have developed a class of optimized local algorithms which are referred to as Hierarchical Alternating Least Squares (HALS) algorithms. For these purposes, we have performed sequential constrained minimization on a set of squared Euclidean distances. We then extend this approach to robust cost functions using the alpha and beta divergences and derive flexible update rules. Our algorithms are locally stable and work well for NMF-based blind source separation (BSS) not only for the over-determined case but also for an under-determined (over-complete) case (i.e., for a system which has less sensors than sources) if data are sufficiently sparse. The NMF learning rules are extended and generalized for N-th order nonnegative tensor factorization (NTF). Moreover, these algorithms can be tuned to different noise statistics by adjusting a single parameter. Extensive experimental results confirm the accuracy and computational performance of the developed algorithms, especially, with usage of multi-layer hierarchical NMF approach [3].

The Transition Edge Sensor (TES)-Energy Dispersive Spectrometer (EDS) is an X-ray detector with high-energy resolution (12.8 eV). The TES can be mounted to a scanning electron microscope (SEM). The TES-EDS is based on a cryogen-free dilution refrigerator. The high-energy resolution enables analysis of the distribution of various elements in samples under low acceleration voltage (typically under 5 keV) by using K-lines of light elements and M lines of heavy elements. For example, the energy of the arsenic L line differs from the magnesium K line by 28 eV. When used to analyze the spore of the Pteris vittata L plant, the TES-EDS clearly reveals a different distribution of As and Mg in the micro region of the plant. The TES-EDS with SEM yields detailed information about the distribution of multi-elements in a sample.

With the rapid progress of electronic and information technology, an expectation for the realization of body area network (BAN) by means of ultra wide band (UWB) techniques has risen. Although the signal from a single UWB device is very low, the energy absorption may increase significantly when many UWB devices are simultaneously adorned to a human body. An analysis method is therefore required from the point of view of biological safety evaluation. In this study, two approaches, one is in the time domain and the other is in the frequency domain, are proposed for the specific energy absorption (SA) and the specific absorption rate (SAR) calculation. It is shown that the two approaches have the same accuracy but the time-domain approach is more straightforward in the numerical analysis. By using the time-domain approach, SA and SAR calculation results are given for multiple UWB pulse exposure to an anatomical human body model under the Federal Communications Commission (FCC) UWB limit.

Wireless sensor networks (WSNs) have attracted a significant amount of interest from many researchers because they have great potential as a means of obtaining information of various environments remotely. WSNs have a wide range of applications, such as natural environmental monitoring in forest regions and environmental control in office buildings. In WSNs, hundreds or thousands of micro-sensor nodes with such resource limitations as battery capacity, memory, CPU, and communication capacity are deployed without control in a region and used to monitor and gather sensor information of environments. Therefore, a scalable and efficient network control and/or data gathering scheme for saving energy consumption of each sensor node is needed to prolong WSN lifetime. In this paper, assuming that sensor nodes synchronize to intermittently communicate with each other only when they are active for realizing the long-term employment of WSNs, we propose a new synchronization scheme for gathering sensor information using chaotic pulse-coupled neural networks (CPCNN). We evaluate the proposed scheme using computer simulations and discuss its development potential. In simulation experiments, the proposed scheme is compared with a previous synchronization scheme based on a pulse-coupled oscillator model to verify its effectiveness.

Clustering the sensor nodes is one of the most popular and effective approaches for applications that must support hundreds or thousands of nodes. The conventional algorithms consider various parameters to evenly distribute the energy load. However, energy consumption problem of the cluster head still remains. In this paper, we propose a novel clustering approach that periodically elects cluster heads with assistant nodes. The assistant nodes substitute for each cluster head to transmit sensor readings to the base station. Performance evaluations show that our proposed clustering algorithm achieves about 10-40% better performance than the existing clustering algorithms in terms of lifetime.

The physical meaning of the energy velocity in lossy Lorentz media is clarified. First, two expressions for the energy velocity, one by Brillouin and another by Diener, are examined. We show that, while Diener's is disqualified, Brillouin's is acceptable as energy velocity. Secondly, we show that the signal velocity defined by Brillouin and Baerwald is exactly identical with the Brillouin's energy velocity. Thirdly, by using triangle-modulated harmonic wave, we show that the superluminal group velocity plays its role as a revelator only after the arrival of the signal traveling at the subluminal energy velocity. In short, nothing moves at the group velocity, and every frequency component of a signal propagates at its own energy velocity.

This paper investigates noise reduction performance and performs convergence analysis of a Variable Error Data Normalized Step-Size Least Mean Square (VEDNSS LMS) algorithm. Adopting VEDNSS LMS provides fast convergence at early stages of adaptation while ensuring small final misadjustment. An analysis of convergence and steady-state performance for zero-mean Gaussian inputs is provided. Simulation results comparing the proposed algorithm to existing algorithms indicate its superior performance under various noise and frequency environments.