16 resultados para Localization accuracy metrics

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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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.

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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.

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Many location-based services target users in indoor environments. Similar to the case of dense urban areas where many obstacles exist, indoor localization techniques suffer from outlying measurements caused by severe multipath propaga??tion and non-line-of-sight (NLOS) reception. Obstructions in the signal path caused by static or mobile objects downgrade localization accuracy. We use robust multipath mitigation techniques to detect and filter out outlying measurements in indoor environments. We validate our approach using a power-based lo??calization system with GSM. We conducted experiments without any prior knowledge of the tracked device's radio settings or the indoor radio environment. We obtained localization errors in the range of 3m even if the sensors had NLOS links to the target device.

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The application of image-guided systems with or without support by surgical robots relies on the accuracy of the navigation process, including patient-to-image registration. The surgeon must carry out the procedure based on the information provided by the navigation system, usually without being able to verify its correctness beyond visual inspection. Misleading surrogate parameters such as the fiducial registration error are often used to describe the success of the registration process, while a lack of methods describing the effects of navigation errors, such as those caused by tracking or calibration, may prevent the application of image guidance in certain accuracy-critical interventions. During minimally invasive mastoidectomy for cochlear implantation, a direct tunnel is drilled from the outside of the mastoid to a target on the cochlea based on registration using landmarks solely on the surface of the skull. Using this methodology, it is impossible to detect if the drill is advancing in the correct direction and that injury of the facial nerve will be avoided. To overcome this problem, a tool localization method based on drilling process information is proposed. The algorithm estimates the pose of a robot-guided surgical tool during a drilling task based on the correlation of the observed axial drilling force and the heterogeneous bone density in the mastoid extracted from 3-D image data. We present here one possible implementation of this method tested on ten tunnels drilled into three human cadaver specimens where an average tool localization accuracy of 0.29 mm was observed.

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Time-based localization techniques such as multilateration are favoured for positioning to wide-band signals. Applying the same techniques with narrow-band signals such as GSM is not so trivial. The process is challenged by the needs of synchronization accuracy and timestamp resolution both in the nanoseconds range. We propose approaches to deal with both challenges. On the one hand, we introduce a method to eliminate the negative effect of synchronization offset on time measurements. On the other hand, we propose timestamps with nanoseconds accuracy by using timing information from the signal processing chain. For a set of experiments, ranging from sub-urban to indoor environments, we show that our proposed approaches are able to improve the localization accuracy of TDOA approaches by several factors. We are even able to demonstrate errors as small as 10 meters for outdoor settings with narrow-band signals.

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OBJECTIVE To evaluate the accuracy of neurologic examination versus magnetic resonance imaging (MRI) in localization of cervical disk herniation and evaluate the usefulness of withdrawal reflex testing in dogs. DESIGN Retrospective case series. ANIMALS 35 client-owned dogs with a single-level cervical disk herniation as determined via MRI. PROCEDURES 1 of 2 board-certified neurologists performed a complete neurologic examination in each dog. Clinical signs of a cervical lesion included evidence of neck pain and tetraparesis. The withdrawal reflex was used for neuroanatomic localization (C1-C5 or C6-T2). Agreement between results of neurologic and MRI examinations was determined. RESULTS Agreement between neurologic and MRI diagnoses was 65.8%. In 11 dogs in which the lesion was clinically localized to the C6-T2 segment on the basis of a decreased withdrawal reflex in the forelimbs, MRI revealed an isolated C1-C5 disk lesion. In 1 dog, in which the lesion was suspected to be at the C1-C5 level, MRI revealed a C6-T2 lesion. Cranial cervical lesions were significantly associated with an incorrect neurologic diagnosis regarding site of the lesion. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that the withdrawal reflex in dogs with cervical disk herniation is not reliable for determining the affected site and that a decreased withdrawal reflex does not always indicate a lesion from C6 to T2.

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Cardiogoniometry (CGM), a spatiotemporal electrocardiologic 5-lead method with automated analysis, may be useful in primary healthcare for detecting coronary artery disease (CAD) at rest. Our aim was to systematically develop a stenosis-specific parameter set for global CAD detection. In 793 consecutively admitted patients with presumed non-acute CAD, CGM data were collected prior to elective coronary angiography and analyzed retrospectively. 658 patients fulfilled the inclusion criteria, 405 had CAD verified by coronary angiography; the 253 patients with normal coronary angiograms served as the non-CAD controls. Study patients--matched for age, BMI, and gender--were angiographically assigned to 8 stenosis-specific CAD categories or to the controls. One CGM parameter possessing significance (P < .05) and the best diagnostic accuracy was matched to one CAD category. The area under the ROC curve was .80 (global CAD versus controls). A set containing 8 stenosis-specific CGM parameters described variability of R vectors and R-T angles, spatial position and potential distribution of R/T vectors, and ST/T segment alterations. Our parameter set systematically combines CAD categories into an algorithm that detects CAD globally. Prospective validation in clinical studies is ongoing.

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Image-guided microsurgery requires accuracies an order of magnitude higher than today's navigation systems provide. A critical step toward the achievement of such low-error requirements is a highly accurate and verified patient-to-image registration. With the aim of reducing target registration error to a level that would facilitate the use of image-guided robotic microsurgery on the rigid anatomy of the head, we have developed a semiautomatic fiducial detection technique. Automatic force-controlled localization of fiducials on the patient is achieved through the implementation of a robotic-controlled tactile search within the head of a standard surgical screw. Precise detection of the corresponding fiducials in the image data is realized using an automated model-based matching algorithm on high-resolution, isometric cone beam CT images. Verification of the registration technique on phantoms demonstrated that through the elimination of user variability, clinically relevant target registration errors of approximately 0.1 mm could be achieved.

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OBJECTIVE: In ictal scalp electroencephalogram (EEG) the presence of artefacts and the wide ranging patterns of discharges are hurdles to good diagnostic accuracy. Quantitative EEG aids the lateralization and/or localization process of epileptiform activity. METHODS: Twelve patients achieving Engel Class I/IIa outcome following temporal lobe surgery (1 year) were selected with approximately 1-3 ictal EEGs analyzed/patient. The EEG signals were denoised with discrete wavelet transform (DWT), followed by computing the normalized absolute slopes and spatial interpolation of scalp topography associated to detection of local maxima. For localization, the region with the highest normalized absolute slopes at the time when epileptiform activities were registered (>2.5 times standard deviation) was designated as the region of onset. For lateralization, the cerebral hemisphere registering the first appearance of normalized absolute slopes >2.5 times the standard deviation was designated as the side of onset. As comparison, all the EEG episodes were reviewed by two neurologists blinded to clinical information to determine the localization and lateralization of seizure onset by visual analysis. RESULTS: 16/25 seizures (64%) were correctly localized by the visual method and 21/25 seizures (84%) by the quantitative EEG method. 12/25 seizures (48%) were correctly lateralized by the visual method and 23/25 seizures (92%) by the quantitative EEG method. The McNemar test showed p=0.15 for localization and p=0.0026 for lateralization when comparing the two methods. CONCLUSIONS: The quantitative EEG method yielded significantly more seizure episodes that were correctly lateralized and there was a trend towards more correctly localized seizures. SIGNIFICANCE: Coupling DWT with the absolute slope method helps clinicians achieve a better EEG diagnostic accuracy.

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BACKGROUND Neuronavigation has become an intrinsic part of preoperative surgical planning and surgical procedures. However, many surgeons have the impression that accuracy decreases during surgery. OBJECTIVE To quantify the decrease of neuronavigation accuracy and identify possible origins, we performed a retrospective quality-control study. METHODS Between April and July 2011, a neuronavigation system was used in conjunction with a specially prepared head holder in 55 consecutive patients. Two different neuronavigation systems were investigated separately. Coregistration was performed with laser-surface matching, paired-point matching using skin fiducials, anatomic landmarks, or bone screws. The initial target registration error (TRE1) was measured using the nasion as the anatomic landmark. Then, after draping and during surgery, the accuracy was checked at predefined procedural landmark steps (Mayfield measurement point and bone measurement point), and deviations were recorded. RESULTS After initial coregistration, the mean (SD) TRE1 was 2.9 (3.3) mm. The TRE1 was significantly dependent on patient positioning, lesion localization, type of neuroimaging, and coregistration method. The following procedures decreased neuronavigation accuracy: attachment of surgical drapes (DTRE2 = 2.7 [1.7] mm), skin retractor attachment (DTRE3 = 1.2 [1.0] mm), craniotomy (DTRE3 = 1.0 [1.4] mm), and Halo ring installation (DTRE3 = 0.5 [0.5] mm). Surgery duration was a significant factor also; the overall DTRE was 1.3 [1.5] mm after 30 minutes and increased to 4.4 [1.8] mm after 5.5 hours of surgery. CONCLUSION After registration, there is an ongoing loss of neuronavigation accuracy. The major factors were draping, attachment of skin retractors, and duration of surgery. Surgeons should be aware of this silent loss of accuracy when using neuronavigation.

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Clock synchronization is critical for the operation of a distributed wireless network system. In this paper we investigate on a method able to evaluate in real time the synchronization offset between devices down to nanoseconds (as needed for positioning). The method is inspired by signal processing algorithms and relies on fine-grain time information obtained during the reconstruction of the signal at the receiver. Applying the method to a GPS-synchronized system show that GPS-based synchronization has high accuracy potential but still suffers from short-term clock drift, which limits the achievable localization error.

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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.

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The delineation of shifting cultivation landscapes using remote sensing in mountainous regions is challenging. On the one hand, there are difficulties related to the distinction of forest and fallow forest classes as occurring in a shifting cultivation landscape in mountainous regions. On the other hand, the dynamic nature of the shifting cultivation system poses problems to the delineation of landscapes where shifting cultivation occurs. We present a two-step approach based on an object-oriented classification of Advanced Land Observing Satellite, Advanced Visible and Near-Infrared Spectrometer (ALOS AVNIR) and Panchromatic Remote-sensing Instrument for Stereo Mapping (ALOS PRISM) data and landscape metrics. When including texture measures in the object-oriented classification, the accuracy of forest and fallow forest classes could be increased substantially. Based on such a classification, landscape metrics in the form of land cover class ratios enabled the identification of crop-fallow rotation characteristics of the shifting cultivation land use practice. By classifying and combining these landscape metrics, shifting cultivation landscapes could be delineated using a single land cover dataset.

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BACKGROUND: Accurate projection of implanted subdural electrode contacts in presurgical evaluation of pharmacoresistant epilepsy cases by invasive EEG is highly relevant. Linear fusion of CT and MRI images may display the contacts in the wrong position due to brain shift effects. OBJECTIVE: A retrospective study in five patients with pharmacoresistant epilepsy was performed to evaluate whether an elastic image fusion algorithm can provide a more accurate projection of the electrode contacts on the pre-implantation MRI as compared to linear fusion. METHODS: An automated elastic image fusion algorithm (AEF), a guided elastic image fusion algorithm (GEF), and a standard linear fusion algorithm (LF) were used on preoperative MRI and post-implantation CT scans. Vertical correction of virtual contact positions, total virtual contact shift, corrections of midline shift and brain shifts due to pneumencephalus were measured. RESULTS: Both AEF and GEF worked well with all 5 cases. An average midline shift of 1.7mm (SD 1.25) was corrected to 0.4mm (SD 0.8) after AEF and to 0.0mm (SD 0) after GEF. Median virtual distances between contacts and cortical surface were corrected by a significant amount, from 2.3mm after LF to 0.0mm after AEF and GEF (p<.001). Mean total relative corrections of 3.1 mm (SD 1.85) after AEF and 3.0mm (SD 1.77) after GEF were achieved. The tested version of GEF did not achieve a satisfying virtual correction of pneumencephalus. CONCLUSION: The technique provided a clear improvement in fusion of pre- and post-implantation scans, although the accuracy is difficult to evaluate.

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Clock synchronization in the order of nanoseconds is one of the critical factors for time-based localization. Currently used time synchronization methods are developed for the more relaxed needs of network operation. Their usability for positioning should be carefully evaluated. In this paper, we are particularly interested in GPS-based time synchronization. To judge its usability for localization we need a method that can evaluate the achieved time synchronization with nanosecond accuracy. Our method to evaluate the synchronization accuracy is inspired by signal processing algorithms and relies on fine grain time information. The method is able to calculate the clock offset and skew between devices with nanosecond accuracy in real time. It was implemented using software defined radio technology. We demonstrate that GPS-based synchronization suffers from remaining clock offset in the range of a few hundred of nanoseconds but the clock skew is negligible. Finally, we determine a corresponding lower bound on the expected positioning error.