This paper presents a robust object tracking method under pose variation and partial occlusion. In practical environment, the appearance of objects is changed dynamically by pose variation or partial occlusion. Therefore, the robustness to them is required for practical applications. However, it is difficult to be robust to various changes by only one tracking model. Therefore, slight robustness to variations and the easiness of model update are required. For this purpose, Kernel Principal Component Analysis (KPCA) of local parts is used. KPCA of local parts is proposed originally for the purpose of pose independent object recognition. Training of this method is performed by using local parts cropped from only one or two object images. This is good property for tracking because only one target image is given in practical applications. In addition, the model (subspace) of this method can be updated easily by solving a eigen value problem. Performance of the proposed method is evaluated by using the test face sequence captured under pose, partial occlusion, scaling and illumination variations. Effectiveness and robustness of the proposed method are demonstrated by the comparison with template matching based tracker. In addition, adaptive update rule using similarity with current subspace is also proposed. Effectiveness of adaptive update rule is shown by experiment.

This letter proposes a peak power reduction method that optimizes sub-carrier phases of an OFDM signal. The proposed method doesn't require side information transmission and original signal regeneration, which are required in conventional peak power reduction methods with phase optimization, since the optimized phases are distributed as jitter around the original phases before optimization. The iterative PTS (partial transmit sequences) algorithm with a restricted phase control range is used for the jitter injection: the phase optimization process is repeated with widening the control range. A computer simulation is carried out to estimate the proposed method performance. The results show that the proposed method can reduce the peak power by 4 dB when the power penalty caused by phase jitter is only 0.2 dB.

This paper proposes two complexity reduced Maximum Likelihood Detection (MLD) methods for Space Division Multiplexing--OFDM (SDM-OFDM) system to exploit the spatial diversity so as to achieve the improved transmission quality. The proposed methods enable to outperform the other suboptimal detection methods and achieve near MLD performance with a significant reduction in calculation complexity. The various computer simulation results confirm that the proposed methods could realize the above targets and might be promising solution in practical systems.

In this paper we propose a road object extraction technique based on an active contour model (snake) considering inertia and differential features in a movie. Different energy functions can be applicable to snake in order to use information of various objects and various environments. Using many methods for tracking a moving object, snake can be applied to a scene frame by frame. Initial positions of the control points in a frame can refer to the results in the previous frame. We focus on the inertia that works between object shapes in the previous and present frames. In this research inertia is the tendency of a control point to resist its changes in its state of motion in an image space. We introduce an external energy for snake based on inertia of control points. Internal energy functions based on differential features of road geometry are also introduced to extract straight, circular and S-shaped road segments smoothly. The proposed method is applied to extract road geometry from a movie taken by a camera equipped on the flont of a vehicle. Experimental results indicate the availability of the proposed method which is to extract road geometry smoothly and to improve its robustness.

We consider the classification problem to construct a classifier c:{0,1}n{0,1} from a given set of examples (training set), which (approximately) realizes the hidden oracle y:{0,1}n{0,1} describing the phenomenon under consideration. For this problem, a number of approaches are already known in computational learning theory; e.g., decision trees, support vector machines (SVM), and iteratively composed features (ICF). The last one, ICF, was proposed in our previous work (Haraguchi et al., (2004)). A feature, composed of a nonempty subset S of other features (including the original data attributes), is a Boolean function fS:{0,1}S{0,1} and is constructed according to the proposed rule. The ICF algorithm iterates generation and selection processes of features, and finally adopts one of the generated features as the classifier, where the generation process may be considered as embodying the idea of boosting, since new features are generated from the available features. In this paper, we generalize a feature to an extended Boolean function fS:{0,1,*}S{0,1,*} to allow partial knowledge, where * denotes the state of uncertainty. We then propose the algorithm ICF* to generate such generalized features. The selection process of ICF* is also different from that of ICF, in that features are selected so as to cover the entire training set. Our computational experiments indicate that ICF* is better than ICF in terms of both classification performance and computation time. Also, it is competitive with other representative learning algorithms such as decision trees and SVM.

Magnetoencephalography (MEG) is a method to measure a magnetic field generated by electrical neural activity in a brain, and it plays increasingly important role in clinical diagnoses and neurophysiological studies. However, in MEG analysis, the estimation of the brain activity, of the electric current density distribution in a brain which is represented by current dipoles, is problematic. A spatial filter and subsequent reconstruction of the current density distribution estimated by the spatial filter (spatial filtered reconstruction: SFR) are proposed. The spatial filter is designed to be used without prior or temporal information. The proposed spatial filter ensures that it concentrates the current distribution around the activated sources in the conductor. The current distribution estimated by the spatial filter is reconstructed by multiple linear regression. Redundant current dipoles are eliminated, and the current distribution is optimized in the sense of the Mallows Cp statistic. Numerical studies are demonstrated and show successful estimation by SFR in multiple-dipole cases. In single-dipole cases with SNRs of 101 and more, the location of the true dipole was successfully estimated for about 80% of the simulations. The reconstruction with multiple linear regression corrected the location of the maximum current density estimated by the proposed spatial filtering. The dipole on the correct position contributes to more than 70% of the total dipoles in the estimated current distribution in those cases. These results show that the current distribution is effectively localized by SFR. We also investigate the differences among SFR, the LCMV (linearly constrained minimum variance) beamformer and the SAM (synthetic aperture magnetometry), the representatives of spatial filters in MEG analyses. It is indicated that spatial resolution is improved by avoiding dependence on temporal information.

The average performance of a single-user MIMO system under spatially correlated fading and with different types of CSI at the transmitter and with perfect CSI at the receiver was studied in recent work. In contrast to analyzing a single performance metric, e.g. the average mutual information or the average bit error rate, we study an arbitrary representative of the class of matrix-monotone functions. Since the average mutual information as well as the average normalized MSE belong to that class, this universal class of performance functions brings together the information theoretic and signal processing performance metric. We use Lowner's representation of operator monotone functions in order to derive the optimum transmission strategies as well as to characterize the impact of correlation on the average performance. Many recent results derived for average mutual information generalize to arbitrary matrix-monotone performance functions, e.g. the optimal transmit strategy without CSI at the transmitter is equal power allocation. The average performance without CSI is a Schur-concave function with respect to transmit and receive correlation. In addition to this, we derive the optimal transmission strategy with long-term statistics knowledge at the transmitter and propose an efficient iterative algorithm. The beamforming-range is the SNR range in which only one data stream spatially multiplexed achieves the maximum average performance. This range is important since it has a simple receiver structure and well known channel coding. We entirely characterize the beamforming-range. Finally, we derive the generalized water-filling transmit strategy for perfect CSI and characterize its properties under channel correlation.

The first refreshable anonymous token scheme proposed in [1] enables one to provide services in such a way that each of its users is allowed to enjoy only a fixed number of services at the same time. In this paper, we show that the scheme in [1] is insecure and propose a provably secure refreshable partial anonymous token scheme which is a generalization of the previous scheme. The new scheme has an additional ability to control the anonymity level of users. We also propose a formal model and security requirements of the new scheme.

In this paper, we propose a partially-parallel LDPC decoder which achieves a high-efficiency message-passing schedule. The proposed LDPC decoder is characterized as follows: (i) The column operations follow the row operations in a pipelined architecture to ensure that the row and column operations are performed concurrently. (ii) The proposed parallel pipelined bit functional unit enables the column operation module to compute every message in each bit node which is updated by the row operations. These column operations can be performed without extending the single iterative decoding delay when the row and column operations are performed concurrently. Therefore, the proposed decoder performs the column operations more frequently in a single iterative decoding, and achieves a high-efficiency message-passing schedule within the limited decoding delay time. Hardware implementation on an FPGA and simulation results show that the proposed partially-parallel LDPC decoder improves the decoding throughput and bit error performance with a small hardware overhead.

Supporting Quality of Service (QoS) over the Internet is a very important issue and many mechanism have been already devised or are under way towards achieving this goal. One of the most important approaches is the class-based architecture, which provides a scalable mechanism for QoS support in a TCP/IP network. Class-based service differentiation can be realized without resource reservation, admission control and traffic policing. However, the resulting services are only relative. While it is, in principle, not feasible to provision for absolute guarantees without admission control and/or traffic policing, such a service can be reasonably well emulated using adaptive rate allocation at the link scheduler of routers. In this paper, we propose mechanism for link scheduler of router that achieve emulated absolute and other relative guarantees using dynamic weighted fair queueing (DWFQ) combining with class packet dropping. The weights of DWFQ are frequently adjusted to current load conditions and based on prediction of realistic class traffic. These mechanisms can realize many approaches to QoS guarantees and class-based differentiation.

We propose a likelihood detection scheme that utilizes ordering and decision of partial bits in MIMO spatial multiplexing systems. We compute BER performance of the proposed detection scheme under Rayleigh fading channels in a 33 MIMO spatial multiplexing system and compare it with BER performance using MLD only and detection utilizing ZF or MMSE only. In addition, the computational complexity of the proposed detection scheme is compared with that of MLD and detection utilizing ZF or MMSE. The results of our investigation show that the proposed detection is a scheme achieves both good BER performance and low computational complexity.

Network congestion and random errors of wireless link are two well-known noteworthy parameters which degrade the TCP performance over heterogeneous networks. We put forward a novel end-to-end TCP congestion control mechanism, namely TCP BaLDE (Bandwidth and Loss Differentiation Estimate), in which the TCP congestion control categorizes the reason of the packet loss by estimating loss differentiation in order to control the packet transmission rate appropriately. While controlling transmission rate depends on the available bandwidth estimation which is apprehended by the bandwidth estimation algorithm when the sender receives a new ACK with incipient congestion signal, duplicates ACKs or is triggered by retransmission timeout event. Especially, this helps the sender to avoid router queue overflow by opportunely entering the congestion avoidance phase. In simulation, we experimented under numerous different network conditions. The results show that TCP BaLDE can achieve robustness in aspect of stability, accuracy and rapidity of the estimate in comparison with TCP Westwood, and tolerate ACK compression. It can achieve better performance than TCP Reno and TCP Westwood. Moreover, it is fair on bottleneck sharing to multiple TCP flows of the same TCP version, and friendly to existing TCP version.

A differential detection Space-Time Block Code (STBC) is proposed with a high transmission rate, allowing a trade-off between diversity and multiplexing gain with low encoding and decoding complexity. The proposed method offers multiplexing gain by doubling the transmission rate for three and four transmission antennas. Computer simulations demonstrate that the proposed STBC can achieve a 5.8 dB Eb/N0 gain at BER = 10-3 compared with a conventional differential detection STBC for four transmission and two receiving antennas.

We developed two types of practical maximum-likelihood detectors (MLD) for multiple-input multiple-output (MIMO) systems, using a field programmable gate array (FPGA) device. For implementations, we introduced two simplified metrics called a Manhattan metric and a correlation metric. Using the Manhattan metric, the detector needs no multiplication operations, at the cost of a slight performance degradation within 1 dB. Using the correlation metric, the MIMO-MLD can significantly reduce the complexity in both multiplications and additions without any performance degradation. This paper demonstrates the bit-error-rate performance of these MLD prototypes at a 1 Gbps-order real-time processing speed, through the use of an all-digital baseband 44 MIMO testbed integrated on the same FPGA chip.

In this paper we propose a novel spatial fading simulator to evaluate the performance of an array antenna and show its spatial stochastic characteristics by computer simulation based on parameters verified by experimental data. We introduce a cavity-excited circular array (CECA) as a fading simulator that can simulate realistic mobile communication environments. To evaluate the antenna array, two stochastic characteristics are necessary. The first one is the fading phenomenon and the second is the angular spread (AS) of the incident wave. The computer simulation results with respect to fading and AS show that CECA works well as a spatial fading simulator for performance evaluation of an antenna array. We first present the basic structure, features and design methodology of CECA, and then show computer simulation results of the spatial stochastic characteristics. The results convince us that CECA is useful to evaluate performance of antenna arrays.

This paper addresses a speech recognition problem in non-stationary noise environments: the estimation of noise sequences. To solve this problem, we present a particle filter-based sequential noise estimation method for front-end processing of speech recognition in noise. In the proposed method, a noise sequence is estimated in three stages: a sequential importance sampling step, a residual resampling step, and finally a Markov chain Monte Carlo step with Metropolis-Hastings sampling. The estimated noise sequence is used in the MMSE-based clean speech estimation. We also introduce Polyak averaging and feedback into a state transition process for particle filtering. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in the results of non-stationary noise environments a noise compensation method with stationary noise assumptions.

This paper addresses a pipelined partial rolling (PPR) architecture for the AES encryption. The key technique is the PPR architecture. With the proposed architecture on the Altera Stratix FPGA, two PPR implementations achieve 6.45 Gbps throughput and 12.78 Gbps throughput, respectively. Compared with the unrolling implementation that achieves a throughput of 22.75 Gbps on the same FPGA, the two PPR implementations improve the memory efficiency (i.e., throughput divided by the size of memory for core) by 13.4% and 12.3%, respectively, and reduce the amount of the memory by 75% and 50%, respectively. Also, the PPR implementation has a up to 9.83% higher memory efficiency than the fastest previous FPGA implementation known to date. In terms of resource efficiency (i.e., throughput divided by the equivalent logic element or slice), one PPR implementation offers almost the same as the rolling implementation, and the other PPR implementation offers a medium value between the rolling implementation and the unrolling implementation that has the highest resource efficiency. However, the two PPR implementations can be implemented on the minimum-sized Stratix FPGA while the unrolling implementation cannot. The PPR architecture fills the gap between unrolling and rolling architectures and is suitable for small and medium-sized FPGAs.

We present a novel approach for single-channel noise reduction of speech signals contaminated by additive noise. In this approach, the system requires speech samples to be uttered in advance by the same speaker as that of the input signal. Speech samples used in this method must have enough phonetic variety to reconstruct the input signal. In the proposed method, which we refer to as referential reconstruction, we have used a small database created from examples of speech, which will be called reference signals. Referential reconstruction uses an example-based approach, in which the objective is to find the candidate speech frame which is the most similar to the clean input frame without noise, although the input frame is contaminated with noise. When candidate frames are found, they become final outputs without any special processing. In order to find the candidate frames, a correlation coefficient is used as a similarity measure. Through automatic speech recognition experiments, the proposed method was shown to be effective, particularly for low-SNR speech signals corrupted with white noise or noise in high-frequency bands. Since the direct implementation of this method requires infeasible computational cost for searching through reference signals, a coarse-to-fine strategy is introduced in this paper.

In many parallel programs, run-time array redistribution is usually required to enhance data locality and reduce remote memory access on the distributed memory multicomputers. In general, array distribution can be classified into regular distribution and irregular distribution according to the distribution fashion. Many methods for performing regular array redistribution have been presented in the literature. However, for the heterogeneous computation environment, irregular array redistributions can be used to adjust data assignment at run-time. In this paper, an Essential Cycle Calculation method for unequal block sizes array redistribution is presented. In the ECC method, a processor first computes the source/destination processor/data sets of array elements in the first essential cycle of the local array it owns. From the source/destination processor/data sets of array elements in the first essential cycle, we can construct packing/unpacking pattern tables. Since each essential cycle has the same communication pattern, based on the packing/unpacking pattern tables, a processor can pack/unpack array elements efficiently. To evaluate the performance of the ECC method, we have implemented this method on an IBM SP2 parallel machine and compare it with the Sequence method. The cost models for these methods are also presented. The experimental results show that the ECC method greatly outperforms the Sequence method for all test samples.

We propose the concept of an opinion model for interactive multimodal services and apply it to an audiovisual communication service. First, psychological factors of an audiovisual communication service were extracted by using the semantic differential (SD) technique and factor analysis. Forty subjects participated in subjective tests and performed point-to-point conversational tasks on a PC-based video phone that exhibited various network qualities. The subjects assessed those qualities on the basis of 25 pairs of adjectives. Two psychological factors, i.e., an aesthetic feeling and a feeling of activity, were extracted from the results. Then, quality impairment factors affecting these two psychological factors were analyzed. We found that the aesthetic feeling was affected by IP packet loss and video coding bit rate, and the feeling of activity depended on delay time, video packet loss, video coding bit rate, and video frame rate. Using this result, we formulated an opinion model derived from the relationships among quality impairment factors, psychological factors, and overall quality. The validation test results indicated that the estimation error of our model was almost equivalent to the statistical reliability of the subjective score.