Posterior Cramér–Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM

This paper describes Needles, an edge based stereo algorithm designed to take advantage of the smoothness of many textured surfaces. The correspondence problem is not addressed explicitly. Rather, a simple two stage process extracts surface position and orientation directly. Firstly local disparity histograms over a large range are constructed. Maxima in the histograms correspond to the possible surface depths. A Hough transform is used to fit a plane to the ambiguous disparity points close to the histogram maxima. This confirms and makes more precise the estimates of disparity obtained from the histograms. Local surface disparity and orientation are calculated from the best planar fit after all the histogram maxima (above a threshold) have been tried. This is an extension of an algorithm described in (Pollard 1985) which uses a Hough transform to find local surface orientation without explicit matching. In his algorithm pairs of possible matches vote for the disparity gradient between them. When all pairs have voted the winning disparity gradient (and hence, surface orientation) has the highest Hough accumulator value.

The present work investigates the effects of long‐duration geomagnetic storms on VLF signal during ionospheric sunrise time, commonly known as D Layer Preparation Time (DLPT) depth. The VLF signal at 19.8 kHz transmitted from Northwest Cape, Australia, and received at a low‐latitude station, Tripura, India, is used for the present analysis. The data for the analysis are selected from November 2008 to October 2011. In the active period of the geomagnetic storms, the average DLPT depth is found to have a negative correlation coefficient of 0.91 with geomagnetic Ap index. It is also found that with each 10 unit increase of Ap index, the DLPT depth (the day and night asymmetry level) changes by 1.25 dB. The results are supported with modeled International Reference Ionosphere (IRI) electron density data and DLPT depth at 71 km height for the three positions, namely, receiver position, signal hop position, and the transmitter position along the total Great Circle Path. It is found that the receiver position electron density is the main controlling factor for DLPT depth. The correlation between IRI electron density and DLPT depth increases from −0.13 at transmitter position to −0.33 at the first hop position, to −0.46 at the receiver position, respectively. The percentage change of post storm electron density, at 71 km height, is found to increase by more than 100% at the receiver position. The results are discussed on the basis of the electron density changes over the signal propagation path, mainly caused by the geomagnetic storms.

In a wireless body area network, the position of the sensor nodes are fixed, while the receiver position can be changed. In this paper, we evaluate three popular receiver positions based on the bit error rate obtained from ultra wideband (UWB) based sensor nodes and a UWB receiver hardware. The three receiver positions evaluated are waist, chest and arm. It is shown that by selecting the optimum receiver position, it is possible to transmit up to 20 dB less power, while maintaining the required bit error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> . Evaluation of different combination of sensor nodes shows that chest position is preferred location for the receiver.

A unified differential geometric framework for estimation of local surface shape and orientation from projective distortion is proposed, based on a differential version of the stationarity assumption introduced by Malik and Rosenholtz. This framework allows the information content of the gradient of any descriptor defined in a local coordinate frame to be characterized in a very compact form. The analysis encompasses both full affine descriptors and the classical texture gradients. For estimation of local surface orientation and curvature from uncertain observations of affine distortion, the proposed framework allows the dimensionality of the search space to be reduced from five to one. >

Presents a 100-slide tutorial on wireless multihop networks. Wireless mesh networks, mobile ad-hoc networks and wireless sensor networks are some of the names related to a more general concept that can be described as wireless multihop networks. Recently, the research community has devoted a lot of attention to this topic since now it may become applied in a general manner leaving the niche market it has had since its origins. Sometimes, the research community is overoptimistic about the application of this technology, and in fact, in the technology itself. This enthusiasm brings to unrealistic research scenarios and, so, to unrealistic models and conclusions. The topic of the tutorial is motivated for this feeling of lack of connection between reality and theory in the study of this kind of networks. The main ideas presented are based on practical experiences in using this technology and in some well known papers that cover the same issues. The tutorial starts with an introduction to some applications and the technologies available. From this point, different key aspects such as mobility, range, interference, metrics, routing or power consumption are presented considering different degrees of complexity and realism. The tutorial presents measurements from real systems (e.g. WLAN mesh networks and wireless sensor networks) related to range, packet losses, reconfiguration times and the effects of mobility. The tutorial is intended to focus the research in the area, avoiding too general scenarios that provide no clear solutions and to provide some reference behaviors and values to validate simulation or theoretical results.

In wireless networks, the broadcast nature of wireless transmission enables cooperation by sharing the same transmissions with nearby receivers and thus can help improve spatial reuse and boost network throughput along a multi-hop routing. The performance of wireless networks can be further improved if prior information available at the receivers can be utilized to achieve perfect interference subtraction. In this paper, we investigate performance gain on network throughput for wireless cooperative networks by using a simple MUD scheme, called overlapped transmission, in which multiple transmissions are allowed only when the information in the interfering signal is known at the receiver. It is shown that the scheme of cooperative transmission with overlapping increases network throughput by 24% compared to that of direct transmission with overlapping. We then propose a new cooperation scheme called supplementary cooperation, which improves the performance gain of direct transmission with overlapping by 42%. Analytical results are developed to show that in a general network scenario, supplementary cooperation achieves bit error rate (BER) reduction of 34.87%, compared with the conventional cooperative transmission. Furthermore, we proposed a criterion for finding the best cooperative route to achieve maximum network throughput in a general network.

Efficient phase shift migration for ultrasonic full-matrix imaging of multilayer composite structures

On-body UWB signal propagation is analyzed using two different types of UWB antennas. For the study, measurements of the frequency response by means of a VNA (vector network analyzer) are performed at 2001 discrete frequency points in the 3 to 6 GHz range. Separated measures are taken for four different transmitter positions on the head and six receiver positions on the body. Channel estimation parameters, such as mean excess delay, delay spread and path loss are obtained. Results are similar for both types of antennas regarding path loss exponent and average power delay profiles.

Humans maintain good memory and recognition capability of previous environments when they are learning about new ones. Thus humans are able to continually learn and increase their experience. It is also obvious importance for autonomous mobile robot. The simultaneous localization and mapping system plays an important role in localization and navigation of robot. The loop-closure detection method is an indispensable part of the relocation and map construction, which is critical to correct mappoint errors of simultaneous localization and mapping. Existing visual loop-closure detection methods based on deep learning are not capable of continual learning in terms of cross-scene environment, which bring a great limitation to the application scope. In this article, we propose a novel end-to-end loop-closure detection method based on continual learning, which can effectively suppress the decline of the memory capability of simultaneous localization and mapping system by introducing firstly the orthogonal projection operator into the loop-closure detection to overcome the catastrophic forgetting problem of mobile robot in large-scale and multi-scene environments. Based on the three scenes from public data sets, the experimental results show that the proposed method has a strong capability of continual learning in the cross-scene environment where existing state-of-the-art methods fail.

In this work, we built an accurate mathematical model of the 1-bit metasurface based on the time-reversal theory. Based on this theory we Figure out how the number and the position of transmitters and receivers influence the communication capacity in different areas. We give out the EM manipulation ability difference between the ideal metasurface and the 1-bit quantization metasurface. Meanwhile, we introduce a fast coding optimization algorithm to get the pattern needed for the programable metasurface to establish the stale, efficient, secure connection between the transmitter and the receiver. Theoretical analysis shows that the EM manipulation ability of the 1-bit metasurface can keep pace with the ideal metasurface and it is sufficient to implement passive wireless communication and sensing especially in the inpatient ward, old people’s home, classroom, and other indoor living spaces.

This paper presents the estimation of received power signal based on Support Vector Regression (SVR). The simulated datasets are used, which contain the positions of transmitter (Tx) and receiver (Rx), the distance of TX and RX, and corresponding path loss, and the carrier frequencies. SVR presents the accuracy estimation of simulated datasets computing which shows Mean Square Error (MSE) as an average value of estimation errors that are squared, Root Mean Square Error (RMSE) as another parameter for measuring the accuracy of a estimation as a root value of MSE Average Root also R² as the coefficient of determination tool for measuring the ability of a model in explaining dependent variable variations. If the value of R² approaches one, it means that predictive results can follow variable patterns or variations well dependent. Cross Validation is a performance measurement. The aim is to find the best hyper-parameter combination so that machine learning can predict data accurately and prevent over-fitting problems. Optimal parameter values are determined by using the Grid Search Method, where machine learning will do modeling using the range C ɤ and ɛ given. Therefore, SVR Hyper-Parameter shows the most optimized parameter with C which affects the penalty given when there is an error in classification, Gamma that affects the pace of learning process, Epsilon indicates the error limit than can be ignored. The parameter values that produce the highest accuracy or the smallest error will be chosen as the best parameter.

This paper deals with multi-target localization in statistical MIMO radar. An improved target locating algorithm is proposed which combines Kalman filtering with fuzzy C clustering. The Kalman filter is utilized to acquire the information of target location and fuzzy C clustering is used for data fusion as there are multiple receivers in radar. For target locating in MIMO radar, we first utilize the maximum likelihood estimation algorithm to estimate the parameters of targets. To eliminate the influence of noise on the parameter estimation, we take advantage of the gliding property of Kalman filter to process the result of parameter estimation. All these processing data from different receivers is fused by fuzzy C cluster to obtain the parameters estimation of all targets. We give scenarios including MIMO radar and targets to analyze the performance of this target location algorithm. With considering the effects of noise, the position of receivers and transmitters and the moving of targets, the analysis is carried out by evaluating the location accuracy of the algorithm. The simulation result shows that the proposed method can locate multiply targets effectively and improves the location accuracy.

Underground Mining (UM) is a hostile industry that generally requires a wireless communication system as a cross-cutting axis for its optimal operation. Therefore, in the last five years, it has been shown that, in addition to radio-frequency-based communication links, wireless optical communications, such as Visible Light Communication (VLC), can be applied to UM environments. The application of VLC systems in underground mines, known as UM-VLC, must take into account the unique physical features of underground mines. Among the physical phenomena found in underground mines, the most important ones are the positioning of optical transmitters and receivers, irregular walls, shadowing, and a typical phenomenon found in tunnels known as scattering, which is caused by the atmosphere and dust particles. Consequently, it is necessary to use proper dust particle distribution models consistent with these scenarios to describe the scattering phenomenon in a coherent way in order to design realistic UM-VLC systems with better performance. Therefore, in this article, we present an in-depth study of the interaction of optical links with dust particles suspended in the UM environment and the atmosphere. In addition, we analytically derived a hemispherical 3D dust particle distribution model, along with its main statistical parameters. This analysis allows to develop a more realistic scattering channel component and presents an enhanced UM-VLC channel model. The performance of the proposed UM-VLC system is evaluated using computational numerical simulations following the IEEE 802.1.5.7 standard in terms of Channel Impulse Response (CIR), received power, Signal-to-Noise-Ratio (SNR), Root Mean Square (RMS) delay spread, and Bit Error Rate (BER). The results demonstrate that the hemispherical dust particle distribution model is more accurate and realistic in terms of the metrics evaluated compared to other models found in the literature. Furthermore, the performance of the UM-VLC system is negatively affected when the number of dust particles suspended in the environment increases.

Distilled representation using patch-based local-to-global similarity strategy for visual place recognition

This research was designed to compare three types of image shading: One generated with a Lambertian BRDF and homogeneous illumination such that image intensity was determined entirely by local surface orientation irrespective of position; one that was textured with a linear intensity gradient, such that image intensity was determined entirely by local surface position irrespective of orientation; and another that was generated with a Lambertian BRDF and inhomogeneous illumination such that image intensity was influenced by both position and orientation. A gauge figure adjustment task was used to measure observers' perceptions of local surface orientation on the depicted surfaces, and the probe points included 60 pairs of regions that both had the same orientation. The results show clearly that observers' perceptions of these three types of stimuli were remarkably similar, and that probe regions with similar apparent orientations could have large differences in image intensity. This latter finding is incompatible with any process for computing shape from shading that assumes any plausible reflectance function combined with any possible homogeneous illumination. Meeting abstract presented at VSS 2014