A technique to reduce noise transfer functions (NTF) of switched-capacitor (SC) integrators without changing their signal transfer functions (STF) is proposed. The proposed technique based on a simple reconnection scheme of multiple sampling capacitors. It can be implemented into any SC integrators as long as they have a transfer delay. A design strategy is also given to reduce the effect of parasitic capacitors. An SC integrator with a small total capacitance and a low noise transfer gain based on the proposed technique is also proposed. For a given design example, the total capacitance and the simulated noise transfer gain of the proposed SC integrator are 37% and 90% less than the conventional one.

This paper considers the discrete model of the cart-pendulum system modeled by discrete mechanics, which is known as a good discretizing method for mechanical systems and has not been really applied to control theory. We first sum up basic concepts on discrete mechanics and discuss the explicitness of the linear approximation of the discrete Euler-Lagrange Equations. Next, the discrete cart-pendulum system is derived and analyzed from the viewpoint of solvability of implicit nonlinear control systems. We then show a control algorithm to stabilize the discrete cart-pendulum based on the discrete-time optimal regulator theory. Finally, some simulations are shown to demonstrate the effectiveness of the proposed algorithm.

In this letter, an area-efficient architecture for the hardware implementation of the real-time prime factor Fourier transform (PFFT) is presented. In the proposed architecture, a prime length DFT module with the one-point-per-cycle (OPPC) property is implemented by the parallel distributed arithmetic (DA), and a cyclic convolution feature is exploited to simplify the structure of the DA cells. Based on the proposed architecture, a real-time 65-point PFFT processor is designed, and the synthesis results show that it saves over 8% gates compared to the existing real-time 64-point DFT designs.

In this letter, we propose an adaptive sensing/transmission scheduling policy in which the secondary user senses the spectrum when its channel condition is poor for transmission. The adaptive sensing/transmission scheduling is modeled as a Markov process and a near-optimal algorithm is proposed to determine the sensing/transmission policy. Simulation results verify our analysis and demonstrate the superiority of the proposed algorithm.

In order to implement multidimensional scaling (MDS) efficiently, we propose a new method named “global mapping analysis” (GMA), which applies stochastic approximation to minimizing MDS criteria. GMA can solve MDS more efficiently in both the linear case (classical MDS) and non-linear one (e.g., ALSCAL) if only the MDS criteria are polynomial. GMA separates the polynomial criteria into the local factors and the global ones. Because the global factors need to be calculated only once in each iteration, GMA is of linear order in the number of objects. Numerical experiments on artificial data verify the efficiency of GMA. It is also shown that GMA can find out various interesting structures from massive document collections.

The explosive growth of Internet usage has caused problems for the current Internet in terms of traffic congestion within networks and performance degradation of end-to-end flows. Therefore, a reconsideration of the current Internet has begun and is being actively discussed worldwide with the goals of enabling efficient share of limited network resources (i.e., the link bandwidth) and improved performance. To directly address the inefficiency of TCP's congestion mitigation solely on the end-to-end basis, in this paper we propose an adaptive split connection scheme on advanced relay nodes; this scheme dynamically splits end-to-end TCP connections on the basis of congestion status in output links. Through simulation evaluations, we examine the effectiveness and potential of the proposed scheme.

The peer-assisted streaming paradigm has been widely employed to distribute live video data on the internet recently. In general, the mesh-based pull approach is more robust and efficient than the tree-based push approach. However, pull protocol brings about longer streaming delay, which is caused by the handshaking process of advertising buffer map message, sending request message and scheduling of the data block. In this paper, we propose a new approach, mesh-push, to address this issue. Different from the traditional pull approach, mesh-push implements block scheduling algorithm at sender side, where the block transmission is initiated by the sender rather than by the receiver. We first formulate the optimal upload bandwidth utilization problem, then present the mesh-push approach, in which a token protocol is designed to avoid block redundancy; a min-cost flow model is employed to derive the optimal scheduling for the push peer; and a push peer selection algorithm is introduced to reduce control overhead. Finally, we evaluate mesh-push through simulation, the results of which show mesh-push outperforms the pull scheduling in streaming delay, and achieves comparable delivery ratio at the same time.

Substrate noise coupling has been seriously concerned in the design of advanced analog and radio frequency (RF) integrated circuits (ICs). This paper reviews recent advancements in the modeling, analysis, and evaluation of substrate noise coupling at IC chip level. Noise generation from digital circuits and propagation to the area of analog circuits are clearly visualized both by full-chip simulation as well as by on-chip measurements, for silicon test vehicles. The impacts of substrate noise coupling are also in-depth discussed at device, circuit, as well as system levels. Overall understanding of substrate noise coupling will then provide the basics for highly reliable design of analog and RF ICs.

We developed advanced techniques for the growth of self-assembled quantum dots (QDs) for fabricating a broadband light source that can be applied to optical coherence tomography (OCT). Four QD ensembles and strain reducing layers (SRLs) were grown in selective areas on a wafer by the use of a 90° rotational metal mask. The SRL thickness was varied to achieve appropriate shifts in the peak wavelength of the QD emission spectrum of up to 120 nm. The four-color QD ensembles were expected to have a broad bandwidth of more than 160 nm due to the combination of excited state emissions when introduced in a current-induced broadband light source such as a superluminescent diode (SLD). Furthermore, a desired shape of the SLD spectrum can be obtained by controlling the injection current applied to each QD ensemble. The broadband and spectrum shape controlled light source is promising for high-resolution and low-noise OCT systems.

In this paper, we consider a stabilization problem for the cart-pendulum system based on discrete mechanics, which is known as a good discretizing method for mechanical systems and has not been really applied to control theory. First, the continuous and discrete cart-pendulum systems are explained. We next propose a transformation method that converts a discrete-time input derived from the discrete-time optimal regulator theory into a continuous-time zero-order hold input, and carry out some simulations on stabilization of the cart-pendulum system by the transformation method. Then, we apply not only our proposed method but also existing methods to an experimental laboratory of the cart-pendulum system and perform some experiments in order to verify the availability of the proposed method.

In this paper, we have reported the development of a snowblower support system which can safely navigate snowblowers, even during a whiteout, with the combination of a very accurate GPS system, so called RTK-GPS, and a unique and highly accurate map of roadsides and obstacles on roads. Particularly emphasized new techniques in this paper are ways to detect accurate geographical positions of roadsides and obstacles by utilizing and analyzing 3D laser scanned data, whose data has become available in recent days. The experiment has shown that the map created by the methods and RTK-GPS can sufficiently navigate snowblowers, whereby a secure and pleasant social environment can be archived in snow areas of Japan. In addition, proposed methods are expected to be useful for other systems such as a quick development of a highly accurate road map, a safely navigation of a wheeled chair, and so on.

This paper presents a novel method for robust object tracking in video sequences using a hybrid feature-based observation model in a particle filtering framework. An ideal observation model should have both high ability to accurately distinguish objects from the background and high reliability to identify the detected objects. Traditional features are better at solving the former problem but weak in solving the latter one. To overcome that, we adopt a robust and dynamic feature called Grayscale Arranging Pairs (GAP), which has high discriminative ability even under conditions of severe illumination variation and dynamic background elements. Together with the GAP feature, we also adopt the color histogram feature in order to take advantage of traditional features in resolving the first problem. At the same time, an efficient and simple integration method is used to combine the GAP feature with color information. Comparative experiments demonstrate that object tracking with our integrated features performs well even when objects go across complex backgrounds.

The main benefit of HECC is that it has much smaller parameter sizes and offers equivalent security as ECC and RSA. However, there are still more researches on ECC than on HECC. One of the reasons is that the computation of scalar multiplication cannot catch up. The Kummer surface can speed up the scalar multiplication in genus two curves. In this paper, we find that the scalar multiplication formulas of Duquesne in characteristic p > 3 on the Kummer surface are not correct. We verify and revise the formulas with mathematical software. The operation counts become 29M + 2S for new pseudo-addition formulas and 30M + 10S for doubling ones. And then we speed up the scalar multiplication on the Kummer surface with Euclidean addition chains.

In the past few years, discriminant analysis and manifold learning have been widely used in feature extraction. Recently, the sparse representation technique has advanced the development of pattern recognition. In this paper, we combine both discriminant analysis and manifold learning with sparse representation technique and propose a novel feature extraction approach named sparsity preserving embedding with manifold learning and discriminant analysis. It seeks an embedded space, where not only the sparse reconstructive relations among original samples are preserved, but also the manifold and discriminant information of both original sample set and the corresponding reconstructed sample set is maintained. Experimental results on the public AR and FERET face databases show that our approach outperforms relevant methods in recognition performance.

Beamforming with sparse constraint has shown significant performance improvement. In this letter, a least squares constant modulus blind adaptive beamforming with sparse constraint is proposed. Simulation results indicate that the proposed approach exhibits better performance than the well-known least squares constant modulus algorithm (LSCMA).

The Kobayashi potential in electromagnetic theory is reviewed. As an illustration we consider two problems, diffraction of plane wave by disk and rectangular plate of perfect conductor. Some numerical results are compared with approximated and experimental results when they are available to verify the validity of the present method. We think the present method can be used as reference solutions of the related problems.

In this letter we perform an evaluation procedure of the Multi-Cluster Gaussian Scatterer Distribution Channel model. We present analytical expressions that allow to calculate the Angle of Arrival and Time of Arrival statistics directly and derive an expression to calculate the Angle Spread. The use of these expressions allows channel evaluation without the need for multiple ray simulation, thus reducing computational burden.

Multihop ad-hoc networks have a dynamic topology. Retrieving a route towards a remote peer requires the execution of a recipient lookup, which can publicly reveal sensitive information about him. Within this context, we propose an efficient, practical and scalable solution to guarantee the anonymity of recipients' nodes in ad-hoc networks.

In this paper, the substitutability of the indifferentiability framework with non-sequential scheduling is examined by reformulating the framework through applying the Task-PIOA framework, which provides non-sequential activation with oblivious task sequences. First, the indifferentiability framework with non-sequential scheduling is shown to be able to retain the substitutability. Thus, the substitutability can be applied in another situation that processes of the systems may behave non-sequentially. Next, this framework is shown to be closely related to reducibility of systems. Reducibility is useful to discuss about the construction of a system from a weaker system. Finally, two modelings with respectively sequential scheduling and non-sequential scheduling are shown to be mutually independent. We find examples of systems which are indifferentiable under one model but differentiable under the other. Thus, the importance of scheduling in the indifferentiability framework is clarified.

This paper presents the iterative progressive numerical methods (IPNMs) based on the induced dimension reduction (IDR) theorem. The IDR theorem is mainly utilized for the development of new nonstationary linear iterative solvers. On the other hand, the use of the IDR theorem enables to revise the classical linear iterative solvers like the Jacobi, the Gauss-Seidel (GS), the relaxed Jacobi, the successive overrelaxation (SOR), and the symmetric SOR (SSOR) methods. The new IPNMs are based on the revised solvers because the original one is similar to the Jacobi method. In the new IPNMs, namely the IDR-based IPNMs, we repeatedly solve linear systems of equations by using a nonstationary linear iterative solver. An initial guess and a stopping criterion are discussed in order to realize a fast computation. We treat electromagnetic wave scattering from 27 perfectly electric conducting spheres and reports comparatively the performance of the IDR-based IPNMs. However, the IDR-based SOR- and the IDR-based SSOR-type IPNMs are not subject to the above numerical test in this paper because of the problem with an optimal relaxation parameter. The performance evaluation reveals that the IDR-based IPNMs are better than the conventional ones in terms of the net computation time and the application range for the distance between objects. The IDR-based GS-type IPNM is the best among the conventional and the IDR-based IPNMs and converges 5 times faster than a standard computation by way of the boundary element method.