Aassuming that the depolarization-induced noise generated in the dual-polarized channel is AWGN and spreads uniformly over the whole channel, we derive an effective XPD formula that can be used to estimate the depolarization effects for both partially and completely overlapped channels.

We consider a model composed of nonlinear perceptrons and analytically investigate its generalization performance using correlated examples in the framework of on-line learning by a statistical mechanical method. In Hebbian and AdaTron learning, the larger the number of examples used in an update, the slower the learning. In contrast, Perceptron learning does not exhibit such behaviors, and the learning becomes fast in some time region.

We propose a robust and efficient algorithm called ROCKET for clustering large-scale transaction databases. ROCKET is a divisive hierarchical algorithm that makes the most of recent hardware architecture. ROCKET handles the cases with the small and the large number of similar transaction pairs separately and efficiently. Through experiments, we show that ROCKET achieves high-quality clustering with a dramatic performance improvement.

In this paper, we propose a novel feeding structure for a coaxial-excited compact waveguide filter, which is composed of planar resonators called frequency-selective surfaces (FSSs). In our proposed feeding structure, new FSSs located at the input and output ports are directly excited by the coaxial line. By using the FSSs, the transition from the TEM mode to the TE10 mode is realized by the resonance of the FSSs. Therefore, the backshort length from the coaxial probe to the shorted waveguide end can be made much shorter than one-quarter of the guided wavelength. Additionally, the coaxial-excited FSS provides one transmission zero at each stopband. As a design example, a three-stage bandpass filter with 4% bandwidth at the X band is demonstrated. The designed filter has a very compact size of one cavity and has high skirt selectivity with six transmission zeros. The effectiveness of the design is confirmed by the comparison of frequency characteristics obtained by the simulation and measurement.

A circularly-polarized planar array antenna using hexagonal aperture elements is proposed. A 22-element subarray as the basic unit is excited by a corporate-feed circuit located in the lower layer of the double-layered antenna. The hexagonal aperture is designed to achieve a good axial ratio in the boresight. A 1616-element array antenna with uniform element spacing smaller than the free-space wavelength was fabricated by diffusion bonding of laminated thin metal plates for the 60 GHz-band. The high gain of 33.4 dBic is measured with 91.6% antenna efficiency, including losses.

For the deployment of sensor networks, the sensor localization, which finds the position of sensor nodes, is very important. Most previous localization schemes generally use the GPS signal for the sensor localization. However, the GPS signal is unavailable when there is an obstacle between the sensor nodes and satellites. Therefore, in this paper, we propose a new localization scheme which does not use the GPS signal. The proposed scheme localizes the sensors by using three mobile anchors. Because the three mobile anchors collaboratively move by themselves, it is self-localizable and can be adopted even when the sensors are randomly and sparsely deployed in the target field.

This letter has retrained an MQDF classifier on the retraining set, which is constructed by samples locating near classification boundary. The method is evaluated on HCL2000 and HCD Chinese handwriting sets. The results show that the retrained MQDF outperforms MQDF and cascade MQDF on all test sets.

Energy minimization has become one of the primary goals in the embedded real-time domains. Consequently, scratch-pad memory has been employed as partial or entire replacement for cache memory due to its better energy efficiency. However, most previous approaches were not applicable to a preemptive multi-task environment. We propose three methods of partitioning and allocation of scratch-pad memory for fixed-priority-based preemptive multi-task systems. The three methods, i.e., spatial, temporal, and hybrid methods, achieve energy reduction in the instruction memory subsystems. With the spatial method, each task occupies its exclusive space in scratch-pad memory. With the temporal method, the running task uses entire scratch-pad space. The content of scratch-pad memory is swapped out as a task executes or gets preempted. The hybrid method is based on the spatial one but a higher priority task can temporarily use the space of lower priority task. The amount of space is prioritized for higher priority tasks. We formulate each method as an integer programming problem that simultaneously determines (1) partitioning of scratch-pad memory space for the tasks, and (2) allocation of program code to scratch-pad memory space for each task. Our methods not only support the real-time task scheduling but also consider aggressively the periods and priorities of tasks for the energy minimization. Additionally, we implement an RTOS-hardware cooperative support mechanism for runtime code allocation to the scratch-pad memory space. We have made the experiments with the fully functional real-time operating system. The experimental results have demonstrated the effectiveness of our techniques. Up to 73% energy reduction compared to a conventional method was achieved.

In multiple-input multiple-output (MIMO) systems, the multiuser MIMO (MU-MIMO) systems have the potential to provide higher channel capacity owing to multiuser and spatial diversity. Block diagonalization (BD) is one of the techniques to realize MU-MIMO systems, where multiuser interference can be completely cancelled and therefore several users can be supported simultaneously. When the number of multiantenna users is larger than the number of simultaneously receiving users, it is necessary to select the users that maximize the system capacity. However, computation complexity becomes prohibitive, especially when the number of multiantenna users is large. Thus simplified user scheduling algorithms are necessary for reducing the complexity of computation. This paper proposes a simplified capacity-based user scheduling algorithm, based on analysis of the capacity-based user selection criterion. We find a new criterion that is simplified by using the properties of Gram-Schmidt orthogonalization (GSO). In simulation results, the proposed algorithm provides higher sum rate capacity than the conventional simplified norm-based algorithm; and when signal-to-noise power ratio (SNR) is high, it provides performance similar to that of the conventional simplified capacity-based algorithm, which still requires high complexity. Fairness of the users is also taken into account. With the proportionally fair (PF) criterion, the proposed algorithm provides better performance (sum rate capacity or fairness of the users) than the conventional algorithms. Simulation results also shows that the proposed algorithm has lower complexity of computation than the conventional algorithms.

This study develops a fuzzy logic control mechanism in eigenspace-based MLLR speaker adaptation. Specifically, this mechanism can determine hidden Markov model parameters to enhance overall recognition performance despite ordinary or adverse conditions in both training and operating stages. The proposed mechanism regulates the influence of eigenspace-based MLLR adaptation given insufficient training data from a new speaker. This mechanism accounts for the amount of adaptation data available in transformation matrix parameter smoothing, and thus ensures the robustness of eigenspace-based MLLR adaptation against data scarcity. The proposed adaptive learning mechanism is computationally inexpensive. Experimental results show that eigenspace-based MLLR adaptation with fuzzy control outperforms conventional eigenspace-based MLLR, and especially when the adaptation data acquired from a new speaker is insufficient.

In this paper, a novel visual signal reliability (VSR) measure is proposed to consider video degradation at the signal level in audio-visual speaker identification (AVSI). The VSR estimation is formulated using a~ Gaussian fuzzy membership function (GFMF) to measure lighting variations. The variance parameters of GFMF are optimized in order to maximize the performance of the overall AVSI. The experimental results show that the proposed method outperforms the score-based reliability measuring technique.

Histogram-based image features such as HoG, SIFT and histogram of visual words are generally represented as high-dimensional, non-negative vectors. We propose a supervised method of reducing the dimensionality of histogram-based features by using non-negative matrix factorization (NMF). We define a cost function for supervised NMF that consists of two terms. The first term is the generalized divergence term between an input matrix and a product of factorized matrices. The second term is the penalty term that reflects prior knowledge on a training set by assigning predefined constants to cannot-links and must-links in pairs of training data. A multiplicative update rule for minimizing the newly-defined cost function is also proposed. We tested our method on a task of scene classification using histograms of visual words. The experimental results revealed that each of the low-dimensional basis vectors obtained from the proposed method only appeared in a single specific category in most cases. This interesting characteristic not only makes it easy to interpret the meaning of each basis but also improves the power of classification.

Weight perturbation learning was proposed as a learning rule in which perturbation is added to the variable parameters of learning machines. The generalization performance of weight perturbation learning was analyzed by statistical mechanical methods and was found to have the same asymptotic generalization property as perceptron learning. In this paper we consider the difference between perceptron learning and AdaTron learning, both of which are well-known learning rules. By applying this difference to weight perturbation learning, we propose adaptive weight perturbation learning. The generalization performance of the proposed rule is analyzed by statistical mechanical methods, and it is shown that the proposed learning rule has an outstanding asymptotic property equivalent to that of AdaTron learning.

A novel fast algorithm for shape matching using statistical features of shape contexts is presented. By pruning the candidate shapes using the moment-based statistical features of shape contexts, the required number of matching processes is dramatically reduced with negligible performance degradation. Experimental results demonstrate that the proposed algorithm reduces the pruning time up to 1/(r·n) compared with the conventional RSC algorithm while maintaining a similar or better performance, where n is the number of sampled points of a shape and r is the number of randomly selected representative shape contexts for the query shape.

In this letter, a novel design of a switchable microstrip antenna is proposed for circular polarization diversity. The proposed antenna has a simple construction of inner and outer corner-truncated radiating circular patches for switchable circular polarization. By controlling the state of two PIN diodes, left- and right-hand circular polarizations are easily alternated. The results of experiments show excellent switching radiation performances at the resonant frequency and good agreements with the simulated ones.

In this correspondence, a new method to extend the number of quaternary low correlation zone (LCZ) sequence sets is presented. Based on the inverse Gray mapping and a binary sequence with ideal two-level auto-correlation function, numbers of quaternary LCZ sequence sets can be generated by choosing different parameters. There is at most one sequence cyclically equivalent in different LCZ sequence sets. The parameters of LCZ sequence sets are flexible.

The waveguide-penetration method is a method to measure the electrical properties of materials. In this method, a cylindrical object pierces a rectangular waveguide through a pair of holes at the centre of its broad walls. Then, the complex permittivity and permeability of the object are estimated from measured S-parameters after TRL calibration. This paper proposes a new calibration algorithm for the waveguide-penetration method. Reference materials with known electrical properties are fabricated in cylindrical shapes to fit into the holes in the waveguide and are used as calibration standards. The algorithm is formulated using the property of equal traces in similar matrices, and we show that at least two reference materials are needed to calibrate the system. The proposed algorithm yields a simpler means of calibration compared to TRL and is verified using measurements in the S-band. Also, the error sensitivity coefficients are derived. These coefficients give valuable information for the selection of reference materials.

This paper presents a user-calibration-free method for estimating the point of gaze (POG). This method provides a fast and stable solution for realizing user-calibration-free gaze estimation more accurately than the conventional method that uses the optical axis of the eye as an approximation of the visual axis of the eye. The optical axis of the eye can be estimated by using two cameras and two light sources. This estimation is carried out by using a spherical model of the cornea. The point of intersection of the optical axis of the eye with the object that the user gazes at is termed POA. On the basis of an assumption that the visual axes of both eyes intersect on the object, the POG is approximately estimated using the binocular 3D eye model as the midpoint of the line joining the POAs of both eyes. Based on this method, we have developed a prototype system that comprises a 19″ display with two pairs of stereo cameras. We evaluated the system experimentally with 20 subjects who were at a distance of 600 mm from the display. The root-mean-square error (RMSE) of measurement of POG in the display screen coordinate system is 1.58.

Scene-context plays an important role in scene analysis and object recognition. Among various sources of scene-context, we focus on scene-context scale, which means the effective scale of local context to classify an image pixel in a scene. This paper presents random forests based image categorization using the scene-context scale. The proposed method uses random forests, which are ensembles of randomized decision trees. Since the random forests are extremely fast in both training and testing, it is possible to perform classification, clustering and regression in real time. We train multi-scale texton forests which efficiently provide both a hierarchical clustering into semantic textons and local classification in various scale levels. The scene-context scale can be estimated by the entropy of the leaf node in the multi-scale texton forests. For image categorization, we combine the classified category distributions in each scale and the estimated scene-context scale. We evaluate on the MSRC21 segmentation dataset and find that the use of the scene-context scale improves image categorization performance. Our results have outperformed the state-of-the-art in image categorization accuracy.

Since the Content Distribution Network (CDN) and IP multicast have heavy infrastructure requirements, their deployment is quite restricted. In contrast, peer-to-peer (P2P) streaming applications are independent on infrastructures and thus have been widely deployed. Emerging wireless ad-hoc networks are poised to enable a variety of streaming applications. However, many potential problems, that are trivial in wired networks, will emerge when deploying existing P2P streaming applications directly into wireless ad-hoc networks. In this paper, we propose a goodput optimization framework for P2P streaming over wireless ad-hoc networks. A two-level buffer architecture is proposed to reassign the naive streaming systems' data requests. The framework adopts a chunk size-varying transmission algorithm to obtain smooth playback experience and acceptable overhead and utilize limited bandwidth resources efficiently. The distinguishing features of our implementation are as follows: first, the framework works as a middleware and is independent on the streaming service properties; existing P2P streaming application can be deployed in wireless ad-hoc networks with minimum modifications and development cost; second, the proposed algorithm can reduce unnecessary communication overheads compared with traditional algorithms which gain high playback continuity with small chunk size; finally, our scheme can utilize low bandwidth transmission paths rather than discarding them, and thus improve overall performance of the wireless network. We also present a set of experiments to show the effectiveness of the proposed mechanism.