Compact and planar active integrated antenna arrays with a high power multi-stage amplifier were developed with effective heat sink mechanism. By attaching an aluminum plate to the backside of the creased amplifier circuit board, effective cooling can be achieved. The nonlinear behavior of the amplifier agrees well with the simulation based on the Angelov model. The high power amplifier circuit consisted of the three-stage amplifier and operated with an output power of 4 W per each element at 5.8 GHz. The 32-element active integrated antenna array stably operated with the output power of 120 W under the effective heat sink design. With a weight of 4 kg, the weight-to-output power ratio and the volume-to-output power ratio of the antenna array are 33.3 g/W and 54.5 cm3/W, respectively. Wireless power transmission was also successfully demonstrated.

A uniform circular array (UCA) is a well-known array configuration which can accomplish estimation of 360 field of view with identical accuracy. However, a UCA cannot estimate coherent signals because we cannot apply the SSP owing to the structure of UCA. Although a variety of studies on UCA in coherent multipath environments have been done, it is impossible to estimate the DOA of coherent signals with different incident polar angles. Then, we have proposed Root-MUSIC algorithm with a cylindrical array. However, the estimation performance is degraded when incident signals arrive with close polar angles. To solve this problem, in the letter, we propose to use SAGE algorithm with a cylindrical array. Here, we adopt a CLA Root-MUSIC for the initial estimation and decompose two-dimensional search to double one-dimensional search to reduce the calculation load. The results show that the proposal achieves high resolution with low complexity.

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.

Exact bit error probabilities (BEP) are derived in closed-form for binary pulsed direct sequence (DS-) and hybrid direct sequence time hopping code division multiple access (DS/TH-CDMA) systems that have potential applications in ultra-wideband (UWB) communications. Flat Nakagami fading channel is considered and the characteristic function (CF) method is adopted. An exact expression of the CF is obtained through a straightforward method, which is simple and good for any arbitrary pulse shape. The CF is then used to obtain the exact BEP that requires less computational complexity than the method based on improved Gaussian approximation (IGA). It is shown under identical operating conditions that the shape of the CF, as well as, the BEP differs considerably for the two systems. While both the systems perform comparably in heavily faded channel, the hybrid system shows better BEP performance in lightly-faded channel. The CF and BEP also strongly depend on chip length and chip-duty that constitute the processing gain (PG). Different combinations of the parameters may result into the same PG and the BEP of a particular system for a constant PG, though remains nearly constant in a highly faded channel, may vary substantially in lightly-faded channel. A comparison of the results from the exact method with those from the standard Gaussian approximation (SGA) reveals that the SGA, though accurate for both the systems in highly-faded channel, becomes extremely optimistic for low-duty systems in lightly-faded channel. The SGA also fails to track several other system trade-offs.

In March, 2007, IEEE802.15.4a was standardized as a low-rate and low-power UWB system for sensor networks. In general, detection of the IEEE802.15.4a signal is considered to be difficult because of its low transmitting power density and low duty cycle. However, if detecting of the IEEE802.15.4a signal is available, it is possible to avoid interference issues both among the IEEE802.15.4a systems and between the 15.4a and other UWB systems. This letter proposes a simple detection method using non-coherent detectors. The possibility of detecting of the IEEE802.15.4a signal by proposal detection method was examined. By conducting experiments with an emulated 15.4a RF signal, the signal detection probability was examined, and 15.4a signal from the range of about 11 meters in the radius could be detected. From this observation, the CSMA/CA method with detecting the signal in 15.4a system may be applied for alternative access method for 15.4a systems.

A method for the exact cost performance analysis of autonomous discrete-time piecewise affine systems is presented. Cost performance refers to the average trajectory cost over the entire region of attraction of the origin. The trajectory cost is based on the infinite-horizon cost. First, a reverse reachability algorithm which constructs the explicit piecewise quadratic trajectory cost function over the entire region of attraction of the origin is presented. Then, an explicit expression for the integral of a quadratic function over a simplex of arbitrary dimension is derived. Thus, the piecewise quadratic trajectory cost function can be integrated exactly and efficiently in order to determine the cost performance of the system as a whole. This alleviates the need to perform a large number of simulations.

The complexity of Finite Impulse Response (FIR) filters is mainly dominated by the number of adders (subtractors) used to implement the coefficient multipliers. It is well known that Common Subexpression Elimination (CSE) method based on Canonic Signed Digit (CSD) representation considerably reduces the number of adders in coefficient multipliers. Recently, a binary-based CSE (BSE) technique was proposed, which produced better reduction of adders compared to the CSD-based CSE. In this paper, we propose a new 4-bit binary representation-based CSE (BCSE-4) method which employs 4-bit Common Subexpressions (CSs) for implementing higher order low-power FIR filters. The proposed BCSE-4 offers better reduction of adders by eliminating the redundant 4-bit CSs that exist in the binary representation of filter coefficients. The reduction of adders is achieved with a small increase in critical path length of filter coefficient multipliers. Design examples show that our BCSE-4 gives an average power consumption reduction of 5.2% and 6.1% over the best known CSE method (BSE, NR-SCSE) respectively, when synthesized with TSMC-0.18 µm technology. We show that our BCSE-4 offers an overall adder reduction of 6.5% compared to BSE without any increase in critical path length of filter coefficient multipliers.

In 3D computer graphics, mesh parameterization is a key technique for digital geometry processings such as morphing, shape blending, texture mapping, re-meshing and so on. Most of the previous approaches made use of an identical primitive domain to parameterize a mesh model. In recent works of mesh parameterization, more flexible and attractive methods that can create direct mappings between two meshes have been reported. These mappings are called "cross-parameterization" and typically preserve semantic feature correspondences between target meshes. This paper proposes a novel approach for parameterizing a mesh into another one directly. The main idea of our method is to combine a competitive learning and a least-square mesh techniques. It is enough to give some semantic feature correspondences between target meshes, even if they are in different shapes or in different poses.

The application of Cumulative Decision Feedback (DF) technique for energy/complexity constrained Wireless Sensor Networks (WSN) is considered. Theoretical bit error probability and average rate of a BPSK modulated DF are derived together with PHY-MAC layers' energy efficiency model for DF and Forward Error Correction (FEC) techniques. Moreover, an empirical optimization, which in turn relies upon a low complexity SNR estimation method also derived in this letter, is applied to the DF technique in order to obtain maximum energy efficiency.

Design by Contract (DBC), originated in the Eiffel programming language, is generally accepted as a practical method for building reliable software. Currently, however, few languages have built-in support for it. In recent years, several methods have been proposed to support DBC in Java. We compare eleven DBC tools for Java by analyzing their impact on the developer's programming activities, which are characterized by seven quality attributes identified in this paper. It is shown that each of the existing tools fails to achieve some of the quality attributes. This motivates us to develop ezContract, an open source DBC tool for Java that achieves all of the seven quality attributes. ezContract achieves streamlined integration with the working environment. Notably, standard Java language is used and advanced IDE features that work for standard Java programs can also work for the contract-enabled programs. Such features include incremental compilation, automatic refactoring, and code assist.

This paper presents a wireless burst modulation/ demodulation method for narrowband ubiquitous communication systems. This method especially suits those systems, whose traffic is especially dominated by short frame bursts. The proposed modulation method provides a preamble-less frame structure with training symbols, which improves both transmission efficiency and burst synchronization performance. Moreover, the proposed demodulation method achieves superior burst synchronization performance in low carrier-to-noise power ratio (CNR) environments by applying a synchronization method that includes symbol timing recovery, slot synchronization, carrier frequency correction and channel tracking. In addition, this paper presents the result of experiments on hardware prototypes of the proposed modulator and demodulator. The basic operation and practical performance of the proposed method is confirmed through testbed studies.

In this paper, we propose a method for selecting n-mode singular vectors in higher-order singular value decomposition. We select the minimum number of n-mode singular vectors, when the upper bound of a least-squares cost function is thresholded. The reduced n-ranks of all modes of a given tensor are determined automatically and the tensor is represented with the minimum number of dimensions. We apply the selection method to simultaneous low rank approximation of matrices. Experimental results show the effectiveness of the n-mode singular vector selection method.

We propose and experimentally demonstrate an all-optical broadband wavelength conversion scheme with simultaneous power amplification based on a pump-modulated fiber optic parametric amplifier (FOPA). All-optical tunable wavelength conversion from one to two wavelengths was achieved with ≥13 dB extinction ratio and <2.7-dB power penalty, accompanied by a high (≥37 dB) and flat ( 3 dB variation) FOPA gain spectrum over 47 nm.

In wireless sensor networks, the sensor nodes collect data, which are routed to a sink node. Most of the existing proposals address the routing problem to maximize network lifetime in the case of a single sink node. In this paper, we extend this problem into the case of multiple sink nodes. To maximize network lifetime, we consider the two problems: (i) how to position multiple sink nodes in the area, and (ii) how to route traffic flows from sensor nodes to sink nodes. In this paper, the solutions to these problems are formulated into a Mixed Integer Linear Programming (MILP) model. However, it is computationally difficult to solve the MILP formulation as the size of sensor network grows because MILP is NP-hard. Thus, we propose a heuristic algorithm, which produces a solution in polynomial time. From our experiments, we found out that the proposed heuristic algorithm provides a near-optimal solution for maximizing network lifetime in dense sensor networks.

We propose a feasible V-BLAST detector based on modified Householder QRD (M-H-QRD) over spatially correlated fading channel, which can almost match the performance of the V-BLAST algorithm with much lower complexity and better numerical stability. Compared to the sorted QRD (S-QRD) detector, the proposed detector requires a smaller minimum word-length to reach the same value of error floor for fixed-point (FP) numerical precision despite no significant performance difference for floating-point machine precision. All these advantages make it attractive when implemented using FP arithmetic.

In this article, we propose a vehicle positioning method that can estimate positions of cars even in areas where the GPS is not available. For the estimation, each car measures the relative distance to a car running in front, communicates the measurements with other cars, and uses the received measurements for estimating its position. In order to estimate the position even if the measurements are received with time-delay, we employed the time-delay tolerant Kalman filtering. For sharing the measurements, it is assumed that a car-to-car communication system is used. Then, the measurements sent from farther cars are received with larger time-delay. It follows that the accuracy of the estimates of farther cars become worse. Hence, the proposed method manages only the states of nearby cars to reduce computing effort. The authors simulated the proposed filtering method and found that the proposed method estimates the positions of nearby cars as accurate as the distributed Kalman filtering.

Transmit beamforming can exploit the spatial diversity afforded by multiple-input multiple-output (MIMO) systems with low complexity. To apply this technique in more practical systems with the constraint of limited feedback, codebook based beamforming and vector quantization technique have been considered in various papers. On the other hand, multi-user scheduling is able to achieve another form of diversity arising from the independence of fading for different users, however, has not been fully taken into account in existing codebook based beamforming schemes. In this letter, a multi-codebook based beamforming and scheduling scheme is proposed, which exploits both spatial diversity and multi-user diversity by switching the codebook for different resource blocks. Meanwhile, the multi-codebook design issue is addressed, the corresponding theoretical analysis is provided, and the performance gain of proposed scheme is simulated. Furthermore, the impacts of related parameters on the performance gain are also investigated.

This paper describes a vision-based and real-time system for detecting road signs from within a moving vehicle. The system architecture which is proposed in this paper consists of two parts, the learning and the detection part of road sign images. The proposed system has the standard architecture with adaboost algorithm. Adaboost is a popular algorithm which used to detect an object in real time. To improve the detection rate of adaboost algorithm, this paper proposes a new combination method of classifiers in every stage. In the case of detecting road signs in real environment, it can be ambiguous to decide to which class input images belong. To overcome this problem, we propose a method that applies fuzzy measure and fuzzy integral which use the importance and the evaluated values of classifiers within one stage. It is called fuzzy-boosting in this paper. Also, to improve the speed of a road sign detection algorithm using adaboost at the detection step, we propose a method which chooses several candidates by using MC generator. In this paper, as the sub-windows of chosen candidates pass classifiers which are made from fuzzy-boosting, we decide whether a road sign is detected or not. Using experiment result, we analyze and compare the detection speed and the classification error rate of the proposed algorithm applied to various environment and condition.

We propose Isochronous-MAC (I-MAC) using the Long-Wave Standard Time Code (so called "wave clock"), and introduce cross-layer design for a low-power wireless sensor node with I-MAC. I-MAC has a periodic wakeup time synchronized with the actual time, and thus we take the wave clock. However, a frequency of a crystal oscillator varies along with temperature, which incurs a time difference among nodes. We present a time correction algorithm to address this problem, and shorten the time difference. Thereby, the preamble length in I-MAC can be minimized, which saves communication power. For further power reduction, a low-power crystal oscillator is also proposed, as a physical-layer design. We implemented I-MAC on an off-the-shelf sensor node to estimate the power saving, and verified that the proposed cross-layer design reduces 81% of the total power, compared to Low Power Listening.

A silicon lateral photodiode is fabricated by standard 0.18 µm CMOS process, and the optical detection property is characterized. The photodiode has interdigital electrode structure with the electrode width of 0.22 µm and the electrode spacing of 0.6 µm. At 830 nm wavelength, the responsivity is 0.12 A/W at low bias voltage, and is increased to 0.6 A/W due to avalanche amplification. The bandwidth is also enhanced from 12 MHz at low bias voltage to 100 MHz at the bias voltage close to the breakdown voltage.