Investigation on the evolution of an indoor robotic localization system based on wireless networks

This work addresses the evolution of an artificial neural network (ANN) to assist in the problem of indoor robotic localization. We investigate the design and building of an autonomous localization system based on information gathered from wireless networks (WN). The article focuses on the evolved ANN, which provides the position of a robot in a space, as in a Cartesian coordinate system, corroborating with the evolutionary robotic research area and showing its practical viability. The proposed system was tested in several experiments, evaluating not only the impact of different evolutionary computation parameters but also the role of the transfer functions on the evolution of the ANN. Results show that slight variations in the parameters lead to significant differences on the evolution process and, therefore, in the accuracy of the robot position.

TDOA-Based Localization System with Narrow-band Signals

This paper gives a general overview of the challenges that arise in using narrow-band signals, such as GSM, for localisation based on the time properties of the signal. Specifically, synchronisation and retrieving of time information are addressed. We pursue two contributions, namely, analysis of achievable synchronisation precision and processing of narrowband signals that can enable localization down to a meter. Keywords-localization, narrow band signals, TOA, TDOA I.

SDR-based Passive Indoor Localization System for GSM

This study deals with indoor positioning using GSM radio, which has the distinct advantage of wide coverage over other wireless technologies. In particular, we focus on passive localization systems that are able to achieve high localization accuracy without any prior knowledge of the indoor environment or the tracking device radio settings. In order to overcome these challenges, newly proposed localization algorithms based on the exploitation of the received signal strength (RSS) are proposed. We explore the effects of non-line-of-sight communication links, opening and closing of doors, and human mobility on RSS measurements and localization accuracy. We have implemented the proposed algorithms on top of software defined radio systems and carried out detailed empirical indoor experiments. The performance results show that the proposed solutions are accurate with average localization errors between 2.4 and 3.2 meters.

A Time-based Passive Source Localization System for Narrow-band Signal

Time-based indoor localization has been investigated for several years but the accuracy of existing solutions is limited by several factors, e.g., imperfect synchronization, signal bandwidth and indoor environment. In this paper, we compare two time-based localization algorithms for narrow-band signals, i.e., multilateration and fingerprinting. First, we develop a new Linear Least Square (LLS) algorithm for Differential Time Difference Of Arrival (DTDOA). Second, fingerprinting is among the most successful approaches used for indoor localization and typically relies on the collection of measurements on signal strength over the area of interest. We propose an alternative by constructing fingerprints of fine-grained time information of the radio signal. We offer comprehensive analytical discussions on the feasibility of the approaches, which are backed up by evaluations in a software defined radio based IEEE 802.15.4 testbed. Our work contributes to research on localization with narrow-band signals. The results show that our proposed DTDOA-based LLS algorithm obviously improves the localization accuracy compared to traditional TDOA-based LLS algorithm but the accuracy is still limited because of the complex indoor environment. Furthermore, we show that time-based fingerprinting is a promising alternative to power-based fingerprinting.

A Passive Source Localization System for IEEE 802.15.4 Signal

In this work, we provide a passive location monitoring system for IEEE 802.15.4 signal emitters. The system adopts software defined radio techniques to passively overhear IEEE 802.15.4 packets and to extract power information from baseband signals. In our system, we provide a new model based on the nonlinear regression for ranging. After obtaining distance information, a Weighted Centroid (WC) algorithm is adopted to locate users. In WC, each weight is inversely proportional to the nth power of propagation distance, and the degree n is obtained from some initial measurements. We evaluate our system in a 16m-18m area with complex indoor propagation conditions. We are able to achieve a median error of 2:1m with only 4 anchor nodes.

A Passive WiFi Source Localization System based on Fine-grained Power-based Trilateration

Indoor localization systems become more interesting for researchers because of the attractiveness of business cases in various application fields. A WiFi-based passive localization system can provide user location information to third-party providers of positioning services. However, indoor localization techniques are prone to multipath and Non-Line Of Sight (NLOS) propagation, which lead to significant performance degradation. To overcome these problems, we provide a passive localization system for WiFi targets with several improved algorithms for localization. Through Software Defined Radio (SDR) techniques, we extract Channel Impulse Response (CIR) information at the physical layer. CIR is later adopted to mitigate the multipath fading problem. We propose to use a Nonlinear Regression (NLR) method to relate the filtered power information to propagation distances, which significantly improves the ranging accuracy compared to the commonly used log-distance path loss model. To mitigate the influence of ranging errors, a new trilateration algorithm is designed as well by combining Weighted Centroid and Constrained Weighted Least Square (WC-CWLS) algorithms. Experiment results show that our algorithm is robust against ranging errors and outperforms the linear least square algorithm and weighted centroid algorithm.

Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.

Intraoperative localization of tantalum markers for proton beam radiation of choroidal melanoma by an opto-electronic navigation system: a novel technique

External beam proton radiation therapy has been used since 1975 to treat choroidal melanoma. For tumor location determination during proton radiation treatment, surgical tantalum clips are registered with image data. This report introduces the intraoperative application of an opto-electronic navigation system to determine with high precision the position of the tantalum markers and their spatial relationship to the tumor and anatomical landmarks. The application of the technique in the first 4 patients is described.

Intraneuronal angiotensinergic system in rat and human dorsal root ganglia

To elucidate the local formation of angiotensin II (Ang II) in the neurons of sensory dorsal root ganglia (DRG), we studied the expression of angiotensinogen (Ang-N)-, renin-, angiotensin converting enzyme (ACE)- and cathepsin D-mRNA, and the presence of protein renin, Ang II, Substance P and calcitonin gene-related peptide (CGRP) in the rat and human thoracic DRG. Quantitative real time PCR (qRT-PCR) studies revealed that rat DRG expressed substantial amounts of Ang-N- and ACE mRNA, while renin mRNA as well as the protein renin were untraceable. Cathepsin D-mRNA and cathepsin D-protein were detected in the rat DRG indicating the possibility of existence of pathways alternative to renin for Ang I formation. Angiotensin peptides were successfully detected with high performance liquid chromatography and radioimmunoassay in human DRG extracts. In situ hybridization in rat DRG confirmed additionally expression of Ang-N mRNA in the cytoplasm of numerous neurons. Intracellular Ang II staining could be shown in number of neurons and their processes in both the rat and human DRG. Interestingly we observed neuronal processes with angiotensinergic synapses en passant, colocalized with synaptophysin, within the DRG. In the DRG, we also identified by qRT-PCR, expression of Ang II receptor AT(1A) and AT(2)-mRNA while AT(1B)-mRNA was not traceable. In some neurons Substance P and CGRP were found colocalized with Ang II. The intracellular localization and colocalization of Ang II with Substance P and CGRP in the DRG neurons may indicate a participation and function of Ang II in the regulation of nociception. In conclusion, these results suggest that Ang II may be produced locally in the neurons of rat and human DRG and act as a neurotransmitter.

Fluorescent-magnetic hybrid nanoparticles induce a dose-dependent increase in proinflammatory response in lung cells in vitro correlated with intracellular localization

Iron-platinum nanoparticles embedded in a poly(methacrylic acid) (PMA) polymer shell and fluorescently labeled with the dye ATTO 590 (FePt-PMA-ATTO-2%) are investigated in terms of their intracellular localization in lung cells and potential to induce a proinflammatory response dependent on concentration and incubation time. A gold core coated with the same polymer shell (Au-PMA-ATTO-2%) is also included. Using laser scanning and electron microscopy techniques, it is shown that the FePt-PMA-ATTO-2% particles penetrate all three types of cell investigated but to a higher extent in macrophages and dendritic cells than epithelial cells. In both cell types of the defense system but not in epithelial cells, a particle-dose-dependent increase of the cytokine tumor necrosis factor alpha (TNFalpha) is found. By comparing the different nanoparticles and the mere polymer shell, it is shown that the cores combined with the shells are responsible for the induction of proinflammatory effects and not the shells alone. It is concluded that the uptake behavior and the proinflammatory response upon particle exposure are dependent on the time, cell type, and cell culture.

Immunohistochemical localization of osteoclastogenic cell mediators in feline tooth resorption and healthy teeth

Tooth resorption is among the most common and most challenging problems in feline dentistry It is a progressive disease eventually leading to tooth loss and often root replacement. The etiology of moth resorption remains obscure and to date no effective therapeutic approach is known. The present study is aimed at assessing the reliability of radiographic imaging and addressing the possible involvement of receptor activator of NF kappa B (RANK), its ligand (RANKL), and osteoprotegerin (OPG) in the process of tooth resorption. Teeth from 8 cats were investigated by means of radiographs and paraffin sections followed by immunolabeling. Six cats were diagnosed with tooth resorption based on histopathologic and radiographic findings. Samples were classified according to a four-stage diagnostic system. Radiologic assessment of tooth resorption correlated very strongly with histopathologic findings. Tooth resorption was accompanied by a strong staining with all three antibodies used, especially with anti-RANK and anti-RANKL antibodies. The presence of OPG and RANKL at the resorption site is indicative of repair attempts by fibroblasts and stromal cells. These findings should be extended by further investigations in order to elucidate the pathophysiologic processes underlying tooth resorption that might lead to prophylactic and/or therapeutic measures. J Vet Dent 27(2); 75 - 83, 2010

A navigation system for open liver surgery: design, workflow and first clinical applications

The surgical treatment of liver tumours relies on precise localization of the lesions and detailed knowledge of the patient-specific vascular and biliary anatomy. Detailed three-dimensional (3D) anatomical information facilitates complete tumour removal while preserving a sufficient amount of functional liver tissue.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.

Design techniques and analysis of high-resolution neural recording systems targeting epilepsy focus localization

The design of a high-density neural recording system targeting epilepsy monitoring is presented. Circuit challenges and techniques are discussed to optimize the amplifier topology and the included OTA. A new platform supporting active recording devices targeting wireless and high-resolution focus localization in epilepsy diagnosis is also proposed. The post-layout simulation results of an amplifier dedicated to this application are presented. The amplifier is designed in a UMC 0.18µm CMOS technology, has an NEF of 2.19 and occupies a silicon area of 0.038 mm(2), while consuming 5.8 µW from a 1.8-V supply.