One of promising application offered by implant body area networks (BANs) is a capsule endoscope localization system. To begin with, this paper performs finite-difference time-domain (FDTD) simulations on implant BAN propagation with a numerical human model, and investigates the propagation characteristics of implant BAN signals at 400 MHz medical implant communication service (MICS) band. Then, the paper presents a capsule endoscope localization system which utilizes only received signal strength indicator (RSSI) and two estimation methods, such as a maximum likelihood (ML) estimation method and a least squares (LS) method. Furthermore, we evaluate the two localization methods by two computer simulation scenarios. Our computer simulation results demonstrate that the ML localization can improve the location estimation accuracy as compared with the LS localization, that is, our performance comparison reveals that a careful consideration the propagation characteristics of implant BANs signals is efficient in terms of estimation performance improvement in capsule endoscope localization.

At the stage of software debugging, the effective interaction between software debugging engineers and fault localization techniques can greatly improve fault localization performance. However, most fault localization approaches usually ignore this interaction and merely utilize the information from testing. Due to different goals of testing and fault localization, the lack of interaction may lead to the issue of information inadequacy, which can substantially degrade fault localization performance. In addition, human work is costly and error-prone. It is vital to study and simulate the pattern of debugging engineers as they apply their knowledge and experience to this interaction to promote fault localization effectiveness and reduce their workload. Thus this paper proposes an effective fault localization approach to simulate this interaction via feedback. Based on results obtained from fault localization techniques, this approach utilizes test data generation techniques to automatically produce feedback for interacting with these fault localization techniques, and then iterate this process to improve fault localization performance until a specific stopping condition is satisfied. Experiments on two standard benchmarks demonstrate the significant improvement of our approach over a promising fault localization technique, namely the spectrum-based fault localization technique.

As deployment of wireless ad hoc networks for location-based services increases, accurate localization of mobile nodes is becoming more important. Localization of a mobile node is achieved by estimating its distances from a group of anchor nodes. If some anchors are malicious and colluding, localization accuracy cannot be guaranteed. In this article, we present the security conditions for exact localization in the presence of colluding malicious anchors. We first derive the minimum number of truthful anchors that are required for exact localization in 2-D Euclidean space where some anchors may be collinear. Second, we extend our security condition to 3-D localization where some anchors may be coplanar.

The RFID tag system has received a lot of attention for ubiquitous computing. An RFID tag is attached to an object. With the unique ID of the RFID tag, a user identifies the object provided with the RFID tag and derives appropriate information about the object. One important application in the RFID technology is localizing RFID tags, which can be very useful in acquiring the position information concerning the RFID tags. It can be applied to navigation systems and positional detection systems for mobile robots. This paper proposes a new adaptive multi-range-sensing method for 3D localization of passive RFID tags by using a probabilistic approach. In this method, a mobile object (human, robot, etc.) with an RFID reader estimates the positions of RFID tags with multiple communication ranges dynamically. The effectiveness of the proposed method was demonstrated in experiments.

Radiation integral areas are localized and reduced based upon the locality of scattering phenomena. In the high frequency, the scattering field is given by the currents, not the entire region, but on the local areas near the scattering centers, such as the stationary phase points and edge diffraction points, due to the cancelling effect of integrand in the radiation integral. The numerical calculation which this locality is implemented into has been proposed for 2-dimensional problems. The scattering field can be approximated by integrating the currents weighted by the adequate function in the local areas whose size and position are determined appropriately. Fresnel zone was previously introduced as the good criterion to determine the local areas, but the determination method was slightly different, depending on the type of scattering centers. The objective of this paper is to advance the Fresnel zone criteria in a 2-dimensional case to the next stage with enhanced generality and applicability. The Fresnel zone number is applied not directly to the actual surface but to the virtual one associated with the modified surface-normal vector satisfying the reflection law. At the same time, the argument in the weighting function is newly defined by the Fresnel zone number instead of the actual distance from the scattering centers. These two revisions bring about the following three advantages; the uniform treatment of various types scattering centers, the smallest area in the localization and applicability to 3-dimensional problems.

This paper describes a method for estimating Direction Of Arrival (DOA) of multiple sound sources in full azimuth with three microphones. Estimating DOA with paired microphone arrays creates imaginary sound sources because of time delay of arrival (TDOA) being identical between real and imaginary sources. Imaginary sound sources can create chronic problems in multiple Sound Source Localization (SSL), because they can be localized as real sound sources. Our proposed approach is based on the observation that each microphone array creates imaginary sound sources, but the DOA of imaginary sources may be different depending on the orientation of the paired microphone array. With the fact that a real source would always be localized in the same direction regardless of the array orientation, we can suppress the imaginary sound sources by minimum filtering based on Steered Response Power – Phase Transform (SRP-PHAT) method. A set of experiments conducted in a real noisy environment showed that the proposed method was accurate in localizing multiple sound sources.

Radar networks show an interesting potential for safety and comfortable applications such as short-range automotive monitoring system or indoor monitoring. This paper presents our novel estimation algorithm of a target position. Especially we evaluate the performance about estimation accuracy and resistance to ghost targets under multipath environment. In above applications, the robust estimation is needed because the receivers tend to output corrupted measurement data. The corrupted data are mostly generated by multipath, sensitivity of receivers. As a result of computer simulations, our algorithm has fine accuracy and robust detections compared with a popular trilateration algorithm.

We consider the position estimation system for targets which exist in near wide area. The system has multiple sensors and estimates the position with multiple receivers. In the past, if receivers were arranged on a straight line, the large position error in the same direction of the line is generated. In order to reduce the error, we propose a novel estimation algorithm using transmitter's directivity information. Our system use directional emission made by an array of antennas in a transmitter. In this paper, the error characteristic which should be solved is introduced firstly. After that, our algorithm is presented. Finally the performance of the error reduction is shown by computer simulations. And we also confirm the reduction by experimental trials. The results indicate good reduction of the error.

Wireless sensor networks are comprised of several sensor nodes that communicate via wireless technology. Locating the sensor nodes is a fundamental problem in developing applications for wireless sensor networks. In this paper, we introduce a distributed localization scheme, called the Rectangle Overlapping Approach (ROA), using a mobile beacon with GPS and a directional antenna. The node locations are computed by performing simple operations that rely on the rotation angle and position of the mobile beacon. Simulation results show that the proposed scheme is very efficient and that the node positions can be determined accurately when the beacon follows a random waypoint movement model.

WSNs (Wireless Sensor Networks) are becoming more widely used in various fields, and localization is a crucial and essential issue for sensor network applications. In this letter, we propose a low-complexity localization mechanism for WSNs that operate in 3D (three-dimensional) space. The basic idea is to use aerial vehicles that are deliberately equipped with anchor nodes. These anchors periodically broadcast beacon signals containing their current locations, and unknown nodes receive these signals as soon as the anchors enter their communication range. We estimate the locations of the unknown nodes based on the proposed scheme that transforms the 3D problem into 2D computations to reduce the complexity of 3D localization. Simulated results show that our approach is an effective scheme for 3D self-positioning in WSNs.

A novel algorithm is presented for estimating the 3-D location (azimuth angle, elevation angle, and range) of multiple sources with a uniform circular array (UCA). Based on its centrosymmetric property, a UCA is divided into two subarrays. The steering vectors for these subarrays then yield a 2-D direction of arrival (DOA)-related rotational invariance property in the signal subspace, which enables 2-D DOA estimations using a generalized-ESPRIT algorithm. Based on the estimated 2-D DOAs, a range estimation can then be obtained for each source by defining the 1-D MUSIC spectrum. Despite its low computational complexity, the proposed algorithm can almost match the performance of the benchmark estimator 3-D MUSIC.

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 paper addresses the problem of ordering the color SIFT descriptors in the independent component analysis for image classification. Component ordering is of great importance for image classification, since it is the foundation of feature selection. To select distinctive and compact independent components (IC) of the color SIFT descriptors, we propose two ordering approaches based on local variation, named as the localization-based IC ordering and the sparseness-based IC ordering. We evaluate the performance of proposed methods, the conventional IC selection method (global variation based components selection) and original color SIFT descriptors on object and scene databases, and obtain the following two main results. First, the proposed methods are able to obtain acceptable classification results in comparison with original color SIFT descriptors. Second, the highest classification rate can be obtained by using the global selection method in the scene database, while the local ordering methods give the best performance for the object database.

These days, cheap and intelligent sensors, networked through wireless links and deployed in large numbers, provide unprecedented opportunities for monitoring and controlling homes, cities and the environment. Networked sensors also offer a broad range of applications. Localization capability is essential in most wireless sensor networks applications; for instance in environmental monitoring applications such as animal habitat monitoring, bush fire surveillance, water quality monitoring and precision agriculture, the measurement data are meaningless without accurate knowledge of where they are obtained. Localization techniques are used to determine location information by estimating the location of each sensor node. Distance measurement errors are commonly known to affect the accuracy of the estimated location; resulting in errors that may be due to inherent or environmental factors. Trilateration [1] is a well-known method for localizing nodes by using the distances to three anchor nodes; yet it performs poorly when they are many distance measurement errors. Therefore, we propose the LRD (Localization with Ratio-Distance) algorithm, which performs strongly even in the presence of many measurement errors associated with the estimated distance to anchor nodes. Simulations using the OPNET Modeler show that LRD is more accurate than trilateration.

DV-Hop algorithm produces errors in location estimations due to inaccurate hop size. We propose a novel localization scheme based on DV-Hop to improve positioning accuracy with least error hop sizes of anchors and average hop sizes of unknowns. The least error hop size of an anchor minimizes its location error, but it may be far small or large. To cope with this inconsistent hop size, each unknown node calculates its average hop size with hop sizes from anchors. Simulation results show that the proposed algorithm outperforms the DV-Hop algorithm in location estimations.

The detection of object categories with large variations in appearance is a fundamental problem in computer vision. The appearance of object categories can change due to intra-class variations, background clutter, and changes in viewpoint and illumination. For object categories with large appearance changes, some kind of sub-categorization based approach is necessary. This paper proposes a sub-category optimization approach that automatically divides an object category into an appropriate number of sub-categories based on appearance variations. Instead of using predefined intra-category sub-categorization based on domain knowledge or validation datasets, we divide the sample space by unsupervised clustering using discriminative image features. We then use a cluster performance analysis (CPA) algorithm to verify the performance of the unsupervised approach. The CPA algorithm uses two performance metrics to determine the optimal number of sub-categories per object category. Furthermore, we employ the optimal sub-category representation as the basis and a supervised multi-category detection system with χ2 merging kernel function to efficiently detect and localize object categories within an image. Extensive experimental results are shown using a standard and the authors' own databases. The comparison results reveal that our approach outperforms the state-of-the-art methods.

We propose an improved TDoA (Time Difference of Arrival) localization scheme based on PSO (Particle Swarm Optimization) in UWB (Ultra Wide Band) systems. The proposed scheme is composed of two steps: the re-estimation of TDoA parameters and the re-localization of tag position. In both steps, the PSO algorithm is employed to improve the performance. In the first step, the proposed scheme re-estimates the TDoA parameters obtained by traditional TDoA localization to reduce the TDoA estimation error. In the second step, the proposed scheme with the TDoA parameters estimated in the first step, re-localizes the tag to minimize the location error. Simulation results show that the proposed scheme achieves better location performance than the traditional TDoA localization in various channel environments.

A robust method is presented for 3D face landmarking with facial pose and expression variations. This method is based on Multi-level Partition of Unity (MPU) Implicits without relying on texture, pose, orientation and expression information. The MPU Implicits reconstruct 3D face surface in a hierarchical way. From lower to higher reconstruction levels, the local shapes can be reconstructed gradually according to their significance. For 3D faces, three landmarks, nose, left eyehole and right eyehole, can be detected uniquely with the analysis of curvature features at lower levels. Experimental results on GavabDB database show that this method is invariant to pose, holes, noise and expression. The overall performance of 98.59% is achieved under pose and expression variations.

In this paper, we propose a method for robot self-position identification by active sound localization. This method can be used for autonomous security robots working in room environments. A system using an AIBO robot equipped with two microphones and a wireless network is constructed and used for position identification experiments. Differences in arrival time to the robot's microphones are used as localization cues. To overcome the ambiguity of front-back confusion, a three-head-position measurement method is proposed. The position of robot can be identified by the intersection of circles restricted using the azimuth differences among different sound beacon pairs. By localizing three or four loudspeakers as sound beacons positioned at known locations, the robot can identify its position with an average error of 7 cm in a 2.53.0 m2 working space in the horizontal plane. We propose adjusting the arrival time differences (ATDs) to reduce the errors caused when the sound beacons are high mounted. A robot navigation experiment was conducted to demonstrate the effectiveness of the proposed position-identification system.

Iris recognition is an important biometric method for personal identification. The accuracy of an iris recognition system highly depends on the success of an iris segmentation step. In this paper, a robust and accurate iris segmentation algorithm for closed-up NIR eye images is developed. The proposed method addressed problems of different characteristics of iris databases using local image properties. A precise pupil boundary is located with an adaptive thresholding combined with a gradient-based refinement approach. A new criteria, called a local signal-to-noise ratio (LSNR) of an edge map of an eye image is proposed for localization of the iris's outer boundary. The boundary is modeled with a weighted circular integral of LSNR optimization technique. The proposed method is experimented with multiple iris databases. The obtained results demonstrated that the proposed iris segmentation method is robust and desirable. The proposed method accurately segments iris region, excluding eyelids, eyelashes and light reflections against multiple iris databases without parameter tunings. The proposed iris segmentation method reduced false negative rate of the iris recognition system by half, compared to results obtained using Masek's method.