Localization Error Rate Research Articles

Diagnostic utility of staging abdominal computerized tomography and repeat endoscopy in detecting localization errors at initial endoscopy in colorectal cancer.

Colonoscopy has a reported localization error rate as high as 21% in detecting colorectal neoplasms. Preoperative repeat endoscopy has been shown to be protective against localization errors. There is a paucity of literature assessing the utility of staging computerized tomography (CT) and repeat endoscopy as diagnostic tools for detecting localization errors following initial endoscopy. The objective of this study is to determine the diagnostic characteristics of staging CT and repeat endoscopy in correcting localization errors at initial endoscopy. A retrospective cohort study was conducted at a large tertiary academic center between January 2006 and August 2014. All patients undergoing surgical resection for CRC were identified. Group comparisons were conducted between (1) patients that underwent only staging CT (staging CT group), and (2) patients that underwent staging CT and repeat endoscopy (repeat endoscopy group). The primary outcome was localization error correction rate for errors at initial endoscopy. 594 patients were identified, 196 (33.0%) in the repeat endoscopy group, and 398 (77.0%) patients in the staging CT group. Error rates for each modality were as follows: initial endoscopy 8.8% (95% CI 6.5-11.0), staging CT 9.3% (95% CI 6.5-11.0), and repeat endoscopy 2.6% (95% CI 0.3-4.7); p < 0.01. Repeat endoscopy was superior to staging CT in correcting localization errors for left-sided / rectal lesions (81.2% vs. 33.3%; p < 0.01), right-sided lesions (80.0% vs. 54.5%; p = 0.21), and overall lesions (80.8% vs. 42.3%; p < 0.01). Repeat endoscopy compared to staging CT demonstrated relative risk reduction of 66.7% (95% CI 22-86%), absolute risk reduction of 38.5% (95% CI 14.2-62.8%), and odds ratio of 0.18 (95% CI 0.05-0.61) for correcting errors at initial endoscopy. Repeat endoscopy in colorectal cancer is superior to staging CT as a diagnostic tool for correcting localization-based errors at initial endoscopy.

Swarm Intelligence Optimization Techniques for Obstacle-Avoidance Mobility-Assisted Localization in Wireless Sensor Networks

In many applications of wireless sensor networks (WSNs), node location is required to locate the monitored event once occurs. Mobility-assisted localization has emerged as an efficient technique for node localization. It works on optimizing a path planning of a location-aware mobile node, called mobile anchor (MA). The task of the MA is to traverse the area of interest (network) in a way that minimizes the localization error while maximizing the number of successful localized nodes. For simplicity, many path planning models assume that the MA has a sufficient source of energy and time, and the network area is obstacle-free. However, in many real-life applications such assumptions are rare. When the network area includes many obstacles, which need to be avoided, and the MA itself has a limited movement distance that cannot be exceeded, a dynamic movement approach is needed. In this paper, we propose two novel dynamic movement techniques that offer obstacle-avoidance path planning for mobility-assisted localization in WSNs. The movement planning is designed in a real-time using two swarm intelligence based algorithms, namely grey wolf optimizer and whale optimization algorithm. Both of our proposed models, grey wolf optimizer-based path planning and whale optimization algorithm-based path planning, provide superior outcomes in comparison to other existing works in several metrics including both localization ratio and localization error rate.

Preoperative segmental localization of colorectal carcinoma: CT colonography vs. optical colonoscopy

BackgroundAdequate preoperative segmental localization of colorectal cancer is important to indicate the right surgical treatment. Preoperative localization has become more important in the era of minimally invasive surgery. The aim of this study was to compare optical colonoscopy (OC) and CT colonography (CTC) with respect to the error rates in the segmental localization of colorectal carcinoma. MethodsA total of 420 patients with histopathologically proven colorectal carcinoma underwent CTC between December 2006 and February 2017. 284 Of these patients underwent surgical resection and had their carcinomas located on CTC report as well as OC report and surgical report. The segmental localization error rates of OC and CTC were compared using surgery as golden standard. McNemar's test was used to evaluate the differences in error rate. Results284 Patients with a total of 296 colorectal carcinomas were evaluated. The segmental localization error rate of CTC (39/296, 13.2%) was found to be lower than the segmental localization error rate of OC (64/296, 21.6%) (p < 0.001). Per segment analysis showed that OC had a significantly higher error rate for carcinomas located in the descending colon (60.6% vs. 21.2% [p < 0.001] and cecum(60.0% vs. 23.3% [p = 0.001]). In 9.2% of the patients (26/284), localization based on CTC would lead to a change in surgical plan. ConclusionCTC has a lower localization error rate than OC, which is most relevant for tumors located in the descending colon. If there is a doubtful localization on OC, particularly in the left-sided colon, an additional CTC should be performed to choose the best surgical treatment.

A New Range-Free and Storage-Efficient Localization Algorithm Using Neural Networks in Wireless Sensor Networks

Wireless Sensor Network is one of the new technologies that have gotten more attention in the past few years. The localization problem is one of the most important topics in these types of the networks. The traditional positioning techniques cannot be used in these networks due to the hardware restrictions of the sensor nodes. Lately, some positioning methods which use soft computing approaches such as neural networks, are proposed for solving the localization problem. In this paper, we propose a new range-free localization algorithm which uses the neural networks for this purpose. This method utilizes Particle swarm optimization (PSO) algorithm to optimize the number of neurons of hidden layers of neural networks. The objective function considers both localization accuracy and storage overhead, simultaneously. The proposed algorithm is implemented and simulated in isotropic networks with and without coverage hole, and anisotropic networks. The obtained result show, in the different environmental conditions, the proposed algorithm has a less localization error rate and less storage requirement than the analogous methods.

HTCRL: A Range-Free Location Algorithm Based on Homothetic Triangle Cyclic Refinement in Wireless Sensor Networks

Wireless sensor networks (WSN) have become a significant technology in recent years. They can be widely used in many applications. WSNs consist of a large number of sensor nodes and each of them is energy-constrained and low-power dissipation. Most of the sensor nodes are tiny sensors with small memories and do not acquire their own locations. This means determining the locations of the unknown sensor nodes is one of the key issues in WSN. In this paper, an improved APIT algorithm HTCRL (Homothetic Triangle Cyclic Refinement Location) is proposed, which is based on the principle of the homothetic triangle. It adopts perpendicular median surface cutting to narrow down target area in order to decrease the average localization error rate. It reduces the probability of misjudgment by adding the conditions of judgment. It can get a relatively high accuracy compared with the typical APIT algorithm without any additional hardware equipment or increasing the communication overhead.

Surface code error correction on a defective lattice

The yield of physical qubits fabricated in the laboratory is much lower than that of classical transistors in production semiconductor fabrication. Actual implementations of quantum computers will be susceptible to loss in the form of physically faulty qubits. Though these physical faults must negatively affect the computation, we can deal with them by adapting error-correction schemes. In this paper we have simulated statically placed single-fault lattices and lattices with randomly placed faults at functional qubit yields of 80%, 90%, and 95%, showing practical performance of a defective surface code by employing actual circuit constructions and realistic errors on every gate, including identity gates. We extend Stace et al's superplaquettes solution against dynamic losses for the surface code to handle static losses such as physically faulty qubits []. The single-fault analysis shows that a static loss at the periphery of the lattice has less negative effect than a static loss at the center. The randomly faulty analysis shows that 95% yield is good enough to build a large-scale quantum computer. The local gate error rate threshold is , and a code distance of seven suppresses the residual error rate below the original error rate at . 90% yield is also good enough when we discard badly fabricated quantum computation chips, while 80% yield does not show enough error suppression even when discarding 90% of the chips. We evaluated several metrics for predicting chip performance, and found that the average of the product of the number of data qubits and the cycle time of a stabilizer measurement of stabilizers gave the strongest correlation with logical error rates. Our analysis will help with selecting usable quantum computation chips from among the pool of all fabricated chips.

Bluetooth-Tracing RSSI Sampling Method as Basic Technology of Indoor Localization for Smart Homes

In recent years, smart homes have become the center of interest for IT companies and construction companies and various types of smart homes have been made currently available on the market. Yet, these equipment are costly and it is not easy to convert existing equipment for smart home application as they may require additional resources which could also inflict much costs. The extra costs involving the remodeling of existing housing structure and installment of new equipments can be avoided by using advanced wireless technologies. As an example, this paper proposes an indoor localization system that adopts Bluetooth technology and uses RSSI values for localization. Researchers have configured a system where the central control device will recognize all other devices or equipments in the system, communicate with each other, and respond to the commands or the information provided. However, despite the efforts of many researchers, existing RSSI-based indoor localization systems do not show a satisfactory level of accuracy such that we have devised a system that traces the trend in the RSSI samples. The RSSI sampling algorithm uses Delta values obtained from the Delta sampling process to improve system accuracy and to lower the costs. The analysis results led us to believe that our algorithm has a reduced localization error rate by 12%-point compared to the algorithm that used raw sampling method.

Preoperative localization of colorectal cancer: a systematic review and meta-analysis.

Preoperative colorectal tumor localization is crucial for appropriate resection and treatment planning. As the localization accuracy of conventional colonoscopy is considered to be low, several localization techniques have been developed. We systematically reviewed the tumor localization error rates of several preoperative endoscopic techniques and synthesized information on risk factors for localization errors and procedure-related adverse events. MEDLINE, EMBASE, the Cochrane Library, and the grey literature were searched. Studies were included if they reported tumor localization errors in patients with colorectal cancer undergoing resection with curative intent. Using random-effects models, pooled incidence of tumor localization errors were derived for conventional colonoscopy and colonoscopic tattooing. Due to the lack of comparative studies, a direct comparison of the pooled estimates was performed. Procedure-related adverse events, risk factors for localization errors, and the localization outcomes of other techniques such as colonoscopic clip placement, radioguided occult colonic lesion identification, and the use of magnetic endoscope imaging were also synthesized. A total of 38 non-randomized controlled and observational studies were included in this review (2578 patients underwent conventional colonoscopy and 643 colonoscopic tattooing). The pooled incidence of localization errors with conventional colonoscopy was 15.4% (95% CI 12.0-18.7), whereas that of colonoscopic tattooing was 9.5% (95% CI 5.7-13.3), mean difference 5.9% (95% CI 0.65-11.14, p=0.03). Adverse events secondary to tattooing were infrequent, and most were cases of ink spillage. Limited information was available for other localization techniques. Conventional colonoscopy has a higher incidence of localization error compared to colonoscopic tattooing for localization of colorectal cancer. Colonoscopic tattooing is safe and leads to fewer tumor localization errors. Given the widespread adoption of laparoscopic resections for colorectal cancer, routine colonoscopic tattooing should be adopted. However, studies directly comparing different localization techniques are needed.

Interoperability in encoded quantum repeater networks

The future of quantum repeater networking will require interoperability between various error correcting codes. A few specific code conversions and even a generalized method are known, however, no detailed analysis of these techniques in the context of quantum networking has been performed. In this paper, we analyze a generalized procedure to create Bell pairs encoded heterogeneously between two separate codes used often in error corrected quantum repeater network designs. We begin with a physical Bell pair, then encode each qubit in a different error correcting code, using entanglement purification to increase the fidelity. We investigate three separate protocols for preparing the purified encoded Bell pair. We calculate the error probability of those schemes between the Steane [[7,1,3]] code, a distance three surface code and single physical qubits by Monte Carlo simulation under a standard Pauli error model, and estimate the resource efficiency of the procedures. A local gate error rate of $10^{-3}$ allows us to create high-fidelity logical Bell pairs between any of our chosen codes. We find that a postselected model, where any detected parity flips in code stabilizers result in a restart of the protocol, performs the best.

Vision-Based Coordinated Localization for Mobile Sensor Networks

In this paper, we propose a coordinated localization algorithm for mobile sensor networks with camera sensors to operate under Global Positioning System (GPS) denied areas or indoor environments. Mobile robots are partitioned into two groups. One group moves within the field of views of remaining stationary robots. The moving robots are tracked by stationary robots and their trajectories are used as spatiotemporal features. From these spatiotemporal features, relative poses of robots are computed using multiview geometry and a group of robots is localized with respect to the reference coordinate based on the proposed multirobot localization. Once poses of all robots are recovered, a group of robots moves from one location to another while maintaining the formation of robots for coordinated localization under the proposed multirobot navigation strategy. By taking the advantage of a multiagent system, we can reliably localize robots over time as they perform a group task. In experiment, we demonstrate that the proposed method consistently achieves a localization error rate of 0.37% or less for trajectories of length between $715~{\rm cm}$ and $890~{\rm cm}$ using an inexpensive off-the-shelf robotic platform.

Fingerprint-Based Device-Free Localization Performance in Changing Environments

Device-free localization (DFL) systems locate a person in an environment by measuring the changes in received signals on links in a wireless network. A fingerprint-based DFL method collects a training database of measurement fingerprints and uses a machine learning classifier to determine a person's location from a new fingerprint. However, as the environment changes over time due to furniture or other objects being moved, the fingerprints diverge from those in the database. This paper addresses, for DFL methods that use received signal strength as measurements, the degradation caused as a result of environmental changes. We perform experiments to quantify how changes in an environment affect accuracy, through a repetitive process of randomly moving an item in a residential home and then conducting a localization experiment, and then repeating. We quantify the degradation and consider ways to be more robust to environmental change. We find that the localization error rate doubles, on average, for every six random changes in the environment. We find that the random forests classifier has the lowest error rate among four tested. We present a correlation method for selecting channels, which decreases the localization error rate from 4.8% to 1.6%.

A KD curvature based corner detector

Due to the low efficiency of curvature calculation in corner detection algorithms, we propose a new corner detection technique for discrete curvature estimation based on KD curvature. Firstly, KD curvature is redefined and computed for each point on the curve after edge extraction and smoothing. Then, non-maximum suppression is used for obtaining candidate corner sets. Finally, the refined corner sets are retained with false and unstable corners removed. In addition, we introduce corner strength, as a new concept, for controlling detection precision. Our experimental results show that the proposed method outperforms existing detectors in both computational efficiency and flexibility of corner detection. Moreover, the average repeatability rate and local error rate are raised about 20 percent respectively.

A Scalable Uniform Localization Performance Evaluated Model and Performance Analysis of Range-free Algorithms in WSN

In this paper, we investigated and solved two issues in the research of Wireless Sensor Network (WSN). Firstly, we proposed a Scalable Uniform Localization Performance evaluated model (SULP evaluated model). There are two parts of this model, modules and data structures, both of them can be customized and extended by users as they need. By using this model researchers can save more time because they pay more attention to the algorithm itself but not to the evaluate environment and parameters. Secondly, we investigated the impacts of communications radius and anchor node ratio on localization algorithms. Four classical algorithms have been evaluated under the scalable model. Experimental results show that the performance including localization error rate, localization cover rate and energy consumption have difierent trends when communication radius and anchor node ratio changing in a reasonable range. Finally we concluded the regulars of the trends to provide instructions to industrial applications and other researchers.

Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields

Selecting between different dependency structures of hidden Markov random field can be very challenging, due to the intractable normalizing constant in the likelihood. We answer this question with approximate Bayesian computation (ABC) which provides a model choice method in the Bayesian paradigm. This comes after the work of Grelaud et al. (2009) who exhibited sufficient statistics on directly observed Gibbs random fields. But when the random field is latent, the sufficiency falls and we complement the set with geometric summary statistics. The general approach to construct these intuitive statistics relies on a clustering analysis of the sites based on the observed colors and plausible latent graphs. The efficiency of ABC model choice based on these statistics is evaluated via a local error rate which may be of independent interest. As a byproduct we derived an ABC algorithm that adapts the dimension of the summary statistics to the dataset without distorting the model selection.

When emotions disturb the localization of road elements: Effects of anger and sadness

While driving the activation of particular schemata induces an attentional focus where road users and road elements can be expected. Moreover, negative emotions can interfere with the information processing and the management of attentional resources leading to inattention.We compared two negative emotions according to the arousal dimension (exciting versus calming) rather than according to the hedonic value. Three mood states (anger, sadness and neutral) have been induced to reveal their effects on the localization of road elements. We used a modified version of the jumble scenes paradigm to provide evidence for the existence of driving-related schemata which appeared to be guiding visual search. The results reported here revealed that the three groups take more or less advantage of the use of their visual schemata to localize road elements. Sadness increased the localization error rate. Participants induced in anger, were slower to locate road elements than participants induced in sadness and in neutral mood. These results are congruent with the fact that drivers exposed to anger-could be slower to detect atypical hazards. Future studies should go deeper in the understanding of how emotional states regulate attentional capacities.

A New Spatio-temporal Event Model based on Multi-tuple for Cyber-Physical Systems

Aiming to solve the problem of no well reflecting the spatial-temporal characteristic of CPS in current CPS event model, a new spatial-temporal event model based on multi-tuple for CPS is proposed in this paper.The basic idea of the new method is that we use the five tuples (object, time, space, event and property) to express a CPS event. As a result, our supposed model not only can more comprehensively reflect the spatial-temporal characteristic of CPS event, but also can reflect the dynamic change feature of external environment.The simulation results show that our proposed model scheme has a good performance in terms of reducing event average localization error rate and lowering event average timing error rate compared with other general event model methods.

인지무선시스템을 위한 전송 손실 지수 추정 기반의 기 사용자 위치 검출 기법

In cognitive radio networks, acquirement of position information of primary user is very important to secondary network since localization information of primary users can be utilized for improving the spectrum efficiency of secondary network and for avoiding harmful interference to primary users by using proper power control. Among various location methods, Received Signal Strength (RSS)-based localization has been widely used for distance measurements in the location detection process despite its inherent inaccuracy because it can be easily implemented without any additional hardware cost. In the RSS-based localization, the distance is measured by the received signal strength, and distance error can be caused by many factors such as fading, shadowing and obstacle between two nodes. In the paper, therefore we propose a localization scheme based on estimation of path-loss exponent to localize the location of primary users more accurately. Through simulations, it is shown that the proposed scheme can provide less localization error and interference rate to primary users than other schemes.

The Application Research of Multi-hop 3D AOA with Space-based Angle Transmission Localization Algorithm in Terrain Modeling

Node’s localization is a key technology in Wireless Sensor Networks. There are only quite a few studies on 3D localization which is suffered in slow progress, actually, is one of the main difficulties in WSN localization. Based on the study of the application of terrain modeling, localization algorithm for sensor nodes in 3D condition has been focus on as well as the application of terrain model. The paper proposed a new algorithm named Multi-hop 3D AOA With Space-based Angle Transmission, and then combined the new algorithm with Delaunay triangulation algorithm for terrain modeling. The results of simulation have shown that as the ratio of beacon nodes and their communication radius changes, the algorithm’s localization error rate range from 7% to 27%, and has a good terrain modeling accuracy, range from 59% to 98%.

Topological quantum computing with a very noisy network and local error rates approaching one percent

A scalable quantum computer could be built by networking together many simple processor cells, thus avoiding the need to create a single complex structure. The difficulty is that realistic quantum links are very error prone. A solution is for cells to repeatedly communicate with each other and so purify any imperfections; however prior studies suggest that the cells themselves must then have prohibitively low internal error rates. Here we describe a method by which even error-prone cells can perform purification: groups of cells generate shared resource states, which then enable stabilization of topologically encoded data. Given a realistically noisy network (≥10% error rate) we find that our protocol can succeed provided that intra-cell error rates for initialisation, state manipulation and measurement are below 0.82%. This level of fidelity is already achievable in several laboratory systems.

상대좌표 추정 알고리즘을 이용한 고정노드 저감기법

Abstract: Recently, with the rapid development of factory automation and logistics system, a few workers were able to manage the broad workplace such as large vessels and warehouse. To estimate the exact location of these work-ers in the conventional wireless indoor localization systems, three or more fixed nodes are generally used to recog-nize the location of a mobile node consisting of a single node. However, these methods are inefficient in terms of node deployment because the broad workplace requires a lot of fixed nodes compared to workers(mobile nodes). Therefore, to efficiently deploy fixed nodes in these environments that need a few workers, this paper presents a novel estimation algorithm which can reduce the number of fixed nodes by efficiently recognizing the relative coor-dinates of two fixed nodes through a mobile node composed of three nodes. Also, to minimize the distance errors between mobile node and fixed node, rounding estimation(RE) technique is proposed. Experimental results show that the error rate of localization is improved, by using proposed RE technique, 90.9% compared to conventional trilat-eration in the free space. In addition, despite the number of fixed nodes can be reduced by up to 50% in the in-door free space, the proposed estimation algorithm recognizes precise location which has average error of 0.15m.