We have proposed and successfully demonstrated a two step method for localizing defects on an LSI chip. The first step is the same as a conventional laser-SQUID (L-SQUID) imaging where a SQUID and a laser beam are fixed during LSI chip scanning. The second step is a new L-SQUID imaging where a laser beam is stayed at the point, located in the first step results, during SQUID scanning. In the second step, a SQUID size (Aeff) and the distance between the SQUID and the LSI chip (ΔZ) are key factors limiting spatial resolution. In order to improve the spatial resolution, we have developed a micro-SQUID and the vacuum chamber housing both the micro-SQUID and the LSI chip. The Aeff of the micro-SQUID is a thousand of that of a conventional SQUID. The minimum value of ΔZ was successfully reduced to 25 µm by setting both the micro-SQUID and an LSI chip in the same vacuum chamber. The spatial resolution in the second step was shown to be 53 µm. Demonstration of actual complicated defects localization was succeeded, and this result suggests that the two step localization method is useful for LSI failure analysis.

This letter presents the variation of localization error to network parameters, the number of range estimation results from anchor nodes (ANs) and average distance between ANs in centralized Wireless Sensor Network (WSN). In sensor network, ANs estimate the relative range to Target Node (TN) using Time-Of-Arrival (TOA) information of Ultra WideBand (UWB) radio and a fusion center determines the final localization of TN based on estimation results reported. From simulation results, the variation of localization error, which is defined as the difference between localization result of TN and its actual location, is represented as the function of number of estimation results to average distance between ANs. The distribution of localization error is matched to the Rician distribution whose K-factor value is given by the proposed formula as well. Finally, the normalized error function for the efficient localization network design is characterized.

The frequency domain binaural model (FDBM) has been previously proposed to localize multiple sound sources. Since the method requires only two input signals and uses interaural phase and level differences caused by the diffraction generated by the head, flexibility in application is very high when the head is considered as an object. When an object is symmetric with respect to the two microphones, the performance of sound source localization is degraded, as a human being has front-back confusion due to the symmetry in a median plane. This paper proposes to reduce the degradation of performance on sound source localization by a combination of the microphone pair outputs using the FDBM. The proposed method is evaluated by applying to a security camera system, and the results showed performance improvement in sound source localization because of reducing the number of cones of confusion.

This letter proposes a UWB signaling localization scheme for indoor multipath channel. It demonstrates that the proposed method does not require LOS path (LP) and is suitable for severe non line-of-sight (NLOS) condition. A low-complexity TOA estimation algorithm, the strongest path (SP) detection by convolution, is designed, which is easier to implement than the LP detection since it dispenses with the process of threshold setting. Experiments under NLOS channels in IEEE.802.15.4a are conducted and the localization influences due to the algorithm parameters are discussed. The results prove the feasibility of the proposed localization scheme under the indoor multipath NLOS environment.

Sensor node localization is an important issue in wireless sensor networks (WSNs) due to the dynamic nature of sensor deployment. Generally, in wireless sensor network localization, the absolute positions of certain anchor nodes are required based on the use of global positioning systems, then all the other nodes are approximately localized using various algorithms based on a coordinate system of the anchors. This paper proposes a neighbor position-based localization algorithm (NPLA) that can greatly enhance the positioning accuracy when compared with current overlapping connectivity localization algorithms that attempt to use the observation of connectivity to a set of anchors to determine a node's position. The proposed method localizes the sensor nodes using both the anchors' positions and neighbor node information. However, unlike existing overlapping-based methods, the proposed NPLA does not assume the same radio transmission range. A simulation study is used to demonstrate the positioning accuracy of the proposed method with different anchor and sensor node densities.

In this paper, we propose a new localization algorithm and improve the DV-Hop algorithm by using a differential error correction scheme that is designed to reduce the location error accumulated over multiple hops. This scheme needs no additional hardware support and can be implemented in a distributed way. The proposed method can improve location accuracy without increasing communication traffic and computing complexity. Simulation results show the performance of the proposed algorithm is superior to that of the DV-Hop algorithm.

In this paper, a robust sound source localization approach is proposed. The approach retains good performance even when model errors exist. Compared with previous work in this field, the contributions of this paper are as follows. First, an improved broad-band and near-field array model is proposed. It takes array gain, phase perturbations into account and is based on the actual positions of the elements. It can be used in arbitrary planar geometry arrays. Second, a subspace model errors estimation algorithm and a Weighted 2-Dimension Multiple Signal Classification (W2D-MUSIC) algorithm are proposed. The subspace model errors estimation algorithm estimates unknown parameters of the array model, i.e., gain, phase perturbations, and positions of the elements, with high accuracy. The performance of this algorithm is improved with the increasing of SNR or number of snapshots. The W2D-MUSIC algorithm based on the improved array model is implemented to locate sound sources. These two algorithms compose the robust sound source approach. The more accurate steering vectors can be provided for further processing such as adaptive beamforming algorithm. Numerical examples confirm effectiveness of this proposed approach.

A neighbor-aided authentication watermarking based on a chaotic system with feedback is proposed in this paper. This algorithm can not only detect malicious manipulations but reveal block substitutions when the VQ attack occurs. An image is partitioned into non-overlapped blocks. The pixels in one block and its neighboring block are combined to produce an authentication watermark based on a chaotic system with feedback, which is sensitive to the initial value. The produced watermark is embedded into the current block. During detection, the detector extracts the watermark firstly, then generates a reference sequence and compares it with the extracted watermark to authenticate the integrity of the image and locate the tampered regions. Experimental results prove the effectiveness of our method.

The RFID (Radio Frequency IDentification) tag technology is expected as a tool of localization. By the localization of RFID tags, a mobile robot which installs in RFID readers can recognize surrounding environments. In addition, RFID tags can be applied to a navigation system for walkers. In this paper, we propose an adaptive likelihood distribution scheme for the localization of RFID tags. This method adjusts the likelihood distribution depending on the signal intensity from RFID tags. We carry out the performance evaluation of estimated position error by both computer simulations and implemental experiments. We show that the proposed system is more effective than the conventional system.

Although it is very important to conduct listening tests when constructing a practical sound field reproduction system based on wave field synthesis, listening tests are very expensive. A localization model of synthesized sound images that predicts the results of listening tests is proposed. This model reduces the costs of constructing a reproduction system because it makes it possible to omit the listening tests. The proposed model uses the precedence effect and predicts the direction of synthesized sound images based on the inter-aural time difference. A comparison of the results predicted by the proposed model and the localized results of listening tests shows that the model accurately predicts the localized results.

Today, localization of nodes in Wireless Sensor Networks (WSNs) is a challenging problem. Especially, it is almost impossible to guarantee that one algorithm giving optimal results for one topology will give optimal results for any other random topology. In this study, we propose a centralized, range- and anchor-based, hybrid algorithm called RH+ that aims to combine the powerful features of two orthogonal techniques: Classical Multi-Dimensional Scaling (CMDS) and Particle Spring Optimization (PSO). As a result, we find that our hybrid approach gives a fast-converging solution which is resilient to range-errors and very robust to topology changes. Across all topologies we studied, the average estimation error is less than 0.5 m. when the average node density is 10 and only 2.5% of the nodes are beacons.

This paper proposes a robust omnidirectional audio-visual (AV) talker localizer for AV applications. The proposed localizer consists of two innovations. One of them is robust omnidirectional audio and visual features. The direction of arrival (DOA) estimation using an equilateral triangular microphone array, and human position estimation using an omnidirectional video camera extract the AV features. The other is a dynamic fusion of the AV features. The validity criterion, called the audio- or visual-localization counter, validates each audio- or visual-feature. The reliability criterion, called the speech arriving evaluator, acts as a dynamic weight to eliminate any prior statistical properties from its fusion procedure. The proposed localizer can compatibly achieve talker localization in a speech activity and user localization in a non-speech activity under the identical fusion rule. Talker localization experiments were conducted in an actual room to evaluate the effectiveness of the proposed localizer. The results confirmed that the talker localization performance of the proposed AV localizer using the validity and reliability criteria is superior to that of conventional localizers.

A sound localization method in the proximal region is proposed, which is based on a low-cost 3D sound localization algorithm with the use of head-related transfer functions (HRTFs). The auditory parallax model is applied to the current algorithm so that more accurate HRTFs can be used for sound localization in the proximal region. In addition, head-shadowing effects based on rigid-sphere model are reproduced in the proximal region by means of a second-order IIR filter. A subjective listening test demonstrates the effectiveness of the proposed method. Embedded system implementation of the proposed method is also described claiming that the proposed method improves sound effects in the proximal region only with 5.1% increase of memory capacity and 8.3% of computational costs.

We previously proposed a method of sound source and microphone localization. The method estimates the locations of sound sources and microphones from only time differences of arrival between signals picked up by microphones even if all their locations are unknown. However, there is a problem that some estimation results converge to local minimum solutions because this method estimates locations iteratively and the error function has multiple minima. In this paper, we present a new iterative method to solve the local minimum problem. This method achieves accurate estimation by selecting effective initial locations from many random initial locations. The computer simulation and experimental results demonstrate that the presented method eliminates most local minimum solutions. Furthermore, the computational complexity of the presented method is similar to that of the previous method.

We propose an accurate, distributed localization method that uses the connectivity measure to localize nodes in a wireless sensor network. The proposed method is based on a self-organizing isometric embedding algorithm that adaptively emphasizes the most accurate range of measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors and updates its estimate of position by minimizing a local cost function and then passes this update to the neighboring sensors. Simulations demonstrate that the proposed method is more robust to measurement error than previous methods and it can achieve comparable results using much fewer anchor nodes than previous methods.

This work is concerned with the problem of robot localization using standard RFID tags as landmarks and an RFID reader as a landmark sensor. A main advantage of such an RFID-based localization system is the availability of landmark ID measurement, which trivially solves the data association problem. While the main drawback of an RFID system is its low spatial accuracy. The result in this paper is an improvement of the localization accuracy for a standard short-range RFID sensor. One of the main contributions is a proposal of a machine learning approach in which multiple classifiers are trained to distinguish RFID-signal features of each location. Another contribution is a design tool for tag arrangement by which the tag configuration needs not be manually designed by the user, but can be automatically recommended by the system. The effectiveness of the proposed technique is evaluated experimentally with a real mobile robot and an RFID system.

This paper overviews the high-speed power-line communication (PLC) technology, and the related standardization and regulatory activities are described. PLC modems of 200 Mbps class become a practical use stage in the West, and the standardization activity is active now. The discussion for deregulation is being continued in also Japan, and regulation values have been proposed. Another topic in this paper is a sensor network application of PLC, which is an indoor fine-grained localization system by acoustic Direct-Sequence Code Division Multiplexed (DS-CDM) signals. The obtained average accuracy of the localization in a 4 m2 plane was 1 cm if there was no obstacle. To realize the localization system, some novel ideas, such as PLC speakers, a synchronization method based on the zero-crossing timing of the mains frequency, and integrated wired/wireless PLC, are introduced.

The electroencephalogram (EEG) has become a widely used tool for investigating brain function. Brain signal source localization is a process of inverse calculation from sensor information (electric potentials for EEG) to the identification of multiple brain sources to obtain the locations and orientation parameters. In this paper, we describe a combination of the backpropagation neural network (BPNN) with the nonlinear least-square (NLS) method to localize two dipoles with reasonable accuracy and speed from EEG data computerized by two dipoles randomly positioned in the brain. The trained BPNN, obtains the initial values for the two dipoles through fast calculation and also avoids the influence of noise. Then the NLS method (Powell algorithm) is used to accurately estimate the two dipole parameters. In this study, we also obtain the minimum distance between the assumed dipole pair, 0.8 cm, in order to localize two sources from a smaller limited distance between the dipole pair. The present simulation results demonstrate that the combined method can allow us to localize two dipoles with high speed and accuracy, that is, in 20 seconds and with the position error of around 6.5%, and to reduce the influence of noise.

Localization of a vehicle is a key component for driving assistance or autonomous navigation. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches, which rely on some predefined known landmarks or a priori information about the environment, no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3-D information of the scene from image sequences. Relative positions of the camera motion are then estimated by registering the 3-D feature points from two consecutive image frames. Localization of the mobile platform is finally given by the reference to its initial position.

In this paper, we propose a new localization scheme for wireless sensor networks consisting of a huge number of sensor nodes equipped with simple wireless communication devices such as wireless LAN and Bluetooth. The proposed scheme is based on the Point-In-Triangle (PIT) test proposed by He et al. The scheme is actually implemented by using Bluetooth devices of Class 2 standard, and the performance of the scheme is evaluated in an actual environment. The result of experiments indicates that the proposed scheme could realize a localization with an error of less than 2 m.