Rockfall detection from LIDAR point clouds: A clustering approach using R

The analysis of rockfall characteristics and spatial distribution is fundamental to understand and model the main factors that predispose to failure. In our study we analysed LiDAR point clouds aiming to: (1) detect and characterise single rockfalls; (2) investigate their spatial distribution. To this end, different cluster algorithms were applied: 1a) Nearest Neighbour Clutter Removal (NNCR) in combination with the Expectation?Maximization (EM) in order to separate feature points from clutter; 1b) a density based algorithm (DBSCAN) was applied to isolate the single clusters (i.e. the rockfall events); 2) finally we computed the Ripley's K-function to investigate the global spatial pattern of the extracted rockfalls. The method allowed proper identification and characterization of more than 600 rockfalls occurred on a cliff located in Puigcercos (Catalonia, Spain) during a time span of six months. The spatial distribution of these events proved that rockfall were clustered distributed at a welldefined distance-range. Computations were carried out using R free software for statistical computing and graphics. The understanding of the spatial distribution of precursory rockfalls may shed light on the forecasting of future failures.

Clustering and international competitiveness of information technology industry in the Saint Petersburg area

The main objective of this study is to assess the potential of the information technology industry in the Saint Petersburg area to become one of the new key industries in the Russian economy. To achieve this objective, the study analyzes especially the international competitiveness of the industry and the conditions for clustering. Russia is currently heavily dependent on its natural resources, which are the main source of its recent economic growth. In order to achieve good long-term economic performance, Russia needs diversification in its well-performing industries in addition to the ones operating in the field of natural resources. The Russian government has acknowledged this and started special initiatives to promote such other industries as information technology and nanotechnology. An interesting industry that is basically less than 20 years old and fast growing in Russia, is information technology. Information technology activities and markets are mainly concentrated in Russia’s two biggest cities, Moscow and Saint Petersburg, and areas around them. The information technology industry in the Saint Petersburg area, although smaller than Moscow, is especially dynamic and is gaining increasing foreign company presence. However, the industry is not yet internationally competitive as it lacks substantial and sustainable competitive advantages. The industry is also merely a potential global information technology cluster, as it lacks the competitive edge and a wide supplier and manufacturing base and other related parts of the whole information technology value system. Alone, the industry will not become a key industry in Russia, but it will, on the other hand, have an important supporting role for the development of other industries. The information technology market in the Saint Petersburg area is already large and if more tightly integrated to Moscow, they will together form a huge and still growing market sufficient for most companies operating in Russia currently and in the future. Therefore, the potential of information technology inside Russia is immense.

Managing manufacturing data and improving its accuracy

There are two main objects in this study: First, to prove the importance of data accuracy to the business success, and second, create a tool for observing and improving the accuracy of ERP systems production master data. Sub-objective is to explain the need for new tool in client company and the meaning of it for the company. In the theoretical part of this thesis the focus is in stating the importance of data accuracy in decision making and it's implications on business success. Also basics of manufacturing planning are introduced in order to explain the key vocabulary. In the empirical part the client company and its need for this study is introduced. New master data report is introduced, and finally, analysing the report and actions based on the results of analysis are explained. The main results of this thesis are finding the interdependence between data accuracy and business success, and providing a report for continuous master data improvement in the client company's ERP system.

Ultra-high Field (7 T) MRI and a Novel Robust Segmentation of Thalamic Nuclei on DWI for Gamma Knife Surgery Purposes: The Targeting of the Ventrointermediate Nucleus

Introduction: Gamma Knife surgery (GKS) is a noninvasive neurosurgical stereotactic procedure, increasingly used as an alternative to open functional procedures. This includes the targeting of the ventrointermediate nucleus of the thalamus (e.g., Vim) for tremor. Objective: To enhance anatomic imaging for Vim GKS using high-field (7 T) MRI and Diffusion Weighted Imaging (DWI). Methods: Five young healthy subjects and two patients were scanned both on 3 and 7 T MRI. The protocol was the same in all cases, and included: T1-weighted (T1w) and DWI at 3T; susceptibility weighted images (SWI) at 7T for the visualization of thalamic subparts. SWI was further integrated into the Gamma Plan Software® (LGP, Elekta Instruments, AB, Sweden) and co-registered with 3T images. A simulation of targeting of the Vim was done using the quadrilatere of Guyot. Furthermore, a correlation with the position of the found target on SWI and also on DWI (after clustering of the different thalamic nuclei) was performed. Results: For the 5 healthy subjects, there was a good correlation between the position of the Vim on SWI, DWI and the GKS targeting. For the patients, on the pretherapeutic acquisitions, SWI helped in positioning the target. For posttherapeutic sequences, SWI supposed position of the Vim matched the corresponding contrast enhancement seen at follow-up MRI. Additionally, on the patient's follow-up T1w images, we could observe a small area of contrast-enhancement corresponding to the target used in GKS (e.g., Vim), which belongs to the Ventral-Lateral-Ventral (VLV) nuclei group. Our clustering method resulted in seven thalamic groups. Conclusion: The use of SWI provided us with a superior resolution and an improved image contrast within the central gray matter, enabling us to directly visualize the Vim. We additionally propose a novel robust method for segmenting the thalamus in seven anatomical groups based on DWI. The localization of the GKS target on the follow-up T1w images, as well as the position of the Vim on 7 T, have been used as a gold standard for the validation of VLV cluster's emplacement. The contrast enhancement corresponding to the targeted area was always localized inside the expected cluster, providing strong evidence of the VLV segmentation accuracy. The anatomical correlation between the direct visualization on 7T and the current targeting methods on 3T (e.g., quadrilatere of Guyot, histological atlases, DWI) seems to show a very good anatomical matching.

On evaluating the accuracy and biological plausibility of diffusion MRI tractograms

In diffusion MRI, traditional tractography algorithms do not recover truly quantitative tractograms and the structural connectivity has to be estimated indirectly by counting the number of fiber tracts or averaging scalar maps along them. Recently, global and efficient methods have emerged to estimate more quantitative tractograms by combining tractography with local models for the diffusion signal, like the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) framework. In this abstract, we show the importance of using both (i) proper multi-compartment diffusion models and (ii) adequate multi-shell acquisitions, in order to evaluate the accuracy and the biological plausibility of the tractograms.

Accuracy of cognitive MRI-targeted biopsy in hitting prostate cancer-positive regions of interest.

PURPOSE: Prostate cancer (PCa) diagnosis relies on clinical suspicion leading to systematic transrectal ultrasound-guided biopsy (TRUSGB). Multiparametric magnetic resonance imaging (mpMRI) allows for targeted biopsy of suspicious areas of the prostate instead of random 12-core biopsy. This method has been shown to be more accurate in detecting significant PCa. However, the precise spatial accuracy of cognitive targeting is unknown. METHODS: Consecutive patients undergoing mpMRI-targeted TRUSGB with cognitive registration (MRTB-COG) followed by robot-assisted radical prostatectomy were included in the present analysis. The regions of interest (ROIs) involved by the index lesion reported on mpMRI were subsequently targeted by two experienced urologists using the cognitive approach. The 27 ROIs were used as spatial reference. Mapping on radical prostatectomy specimen was used as reference to determine true-positive mpMRI findings. Per core correlation analysis was performed. RESULTS: Forty patients were included. Overall, 40 index lesions involving 137 ROIs (mean ROIs per index lesion 3.43) were identified on MRI. After correlating these findings with final pathology, 117 ROIs (85 %) were considered as true-positive lesions. A total of 102 biopsy cores directed toward such true-positive ROIs were available for final analysis. Cognitive targeted biopsy hit the target in 82 % of the cases (84/102). The only identified risk factor for missing the target was an anterior situated ROI (p = 0.01). CONCLUSION: In experienced hands, cognitive MRTB-COG allows for an accuracy of 82 % in hitting the correct target, given that it is a true-positive lesion. Anterior tumors are less likely to be successfully targeted.

Symptoms Have Modest Accuracy in Detecting Endoscopic and Histologic Remission in Adults With Eosinophilic Esophagitis.

BACKGROUND & AIMS: It is not clear whether symptoms alone can be used to estimate the biologic activity of eosinophilic esophagitis (EoE). We aimed to evaluate whether symptoms can be used to identify patients with endoscopic and histologic features of remission. METHODS: Between April 2011 and June 2014, we performed a prospective, observational study and recruited 269 consecutive adults with EoE (67% male; median age, 39 years old) in Switzerland and the United States. Patients first completed the validated symptom-based EoE activity index patient-reported outcome instrument and then underwent esophagogastroduodenoscopy with esophageal biopsy collection. Endoscopic and histologic findings were evaluated with a validated grading system and standardized instrument, respectively. Clinical remission was defined as symptom score <20 (range, 0-100); histologic remission was defined as a peak count of <20 eosinophils/mm(2) in a high-power field (corresponds to approximately <5 eosinophils/median high-power field); and endoscopic remission as absence of white exudates, moderate or severe rings, strictures, or combination of furrows and edema. We used receiver operating characteristic analysis to determine the best symptom score cutoff values for detection of remission. RESULTS: Of the study subjects, 111 were in clinical remission (41.3%), 79 were in endoscopic remission (29.7%), and 75 were in histologic remission (27.9%). When the symptom score was used as a continuous variable, patients in endoscopic, histologic, and combined (endoscopic and histologic remission) remission were detected with area under the curve values of 0.67, 0.60, and 0.67, respectively. A symptom score of 20 identified patients in endoscopic remission with 65.1% accuracy and histologic remission with 62.1% accuracy; a symptom score of 15 identified patients with both types of remission with 67.7% accuracy. CONCLUSIONS: In patients with EoE, endoscopic or histologic remission can be identified with only modest accuracy based on symptoms alone. At any given time, physicians cannot rely on lack of symptoms to make assumptions about lack of biologic disease activity in adults with EoE. ClinicalTrials.gov, Number: NCT00939263.

Local behavior and lymph node metastases of Wilms' tumor: accuracy of computed tomography

Objective To evaluate the accuracy of computed tomography for local and lymph node staging of Wilms' tumor. Materials and Methods Each case of Wilms' tumor was evaluated for the presence of abdominal lymph nodes by a radiologist. Signs of capsule and adjacent organ invasion were analyzed. Surgical and histopathological results were taken as the gold standard. Results Sensitivity was 100% for both mesenteric and retroperitoneal lymph nodes detection, and specificity was, respectively, 12% and 33%, with positive predictive value of 8% and 11% and negative predictive value of 100%. Signs of capsular invasion presented sensitivity of 87%, specificity of 77%, positive predictive value of 63% and negative predictive value of 93%. Signs of adjacent organ invasion presented sensitivity of 100%, specificity of 78%, positive predictive value of 37% and negative predictive value of 100%. Conclusion Computed tomography tumor showed low specificity and low positive predictive value in the detection of lymph node dissemination. The absence of detectable lymph nodes makes their presence unlikely, and likewise regarding the evaluation of local behavior of tumors.

Accuracy of computer-aided ultrasound as compared with magnetic resonance imaging in the evaluation of nonalcoholic fatty liver disease in obese and eutrophic adolescents

AbstractObjective:To compare the accuracy of computer-aided ultrasound (US) and magnetic resonance imaging (MRI) by means of hepatorenal gradient analysis in the evaluation of nonalcoholic fatty liver disease (NAFLD) in adolescents.Materials and Methods:This prospective, cross-sectional study evaluated 50 adolescents (aged 11–17 years), including 24 obese and 26 eutrophic individuals. All adolescents underwent computer-aided US, MRI, laboratory tests, and anthropometric evaluation. Sensitivity, specificity, positive and negative predictive values and accuracy were evaluated for both imaging methods, with subsequent generation of the receiver operating characteristic (ROC) curve and calculation of the area under the ROC curve to determine the most appropriate cutoff point for the hepatorenal gradient in order to predict the degree of steatosis, utilizing MRI results as the gold-standard.Results:The obese group included 29.2% girls and 70.8% boys, and the eutrophic group, 69.2% girls and 30.8% boys. The prevalence of NAFLD corresponded to 19.2% for the eutrophic group and 83% for the obese group. The ROC curve generated for the hepatorenal gradient with a cutoff point of 13 presented 100% sensitivity and 100% specificity. As the same cutoff point was considered for the eutrophic group, false-positive results were observed in 9.5% of cases (90.5% specificity) and false-negative results in 0% (100% sensitivity).Conclusion:Computer-aided US with hepatorenal gradient calculation is a simple and noninvasive technique for semiquantitative evaluation of hepatic echogenicity and could be useful in the follow-up of adolescents with NAFLD, population screening for this disease as well as for clinical studies.

Solitary pulmonary nodule and 18F-FDG PET/CT. Part 2: accuracy, cost-effectiveness, and current recommendations

Abstract A solitary pulmonary nodule is a common, often incidental, radiographic finding. The investigation and differential diagnosis of solitary pulmonary nodules remain complex, because there are overlaps between the characteristics of benign and malignant processes. There are currently many strategies for evaluating solitary pulmonary nodules. The main objective is to identify benign lesions, in order to avoid exposing patients to the risks of invasive methods, and to detect cases of lung cancer accurately, in order to avoid delaying potentially curative treatment. The focus of this study was to review the evaluation of solitary pulmonary nodules, to discuss the current role of 18F-fluorodeoxyglucose positron-emission tomography, addressing its accuracy and cost-effectiveness, and to detail the current recommendations for the examination in this scenario.

Anonymizing Graphs: Measuring Quality for Clustering

Peer-reviewed

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Quantitative market and sales forecast accuracy analysis

The main objective of this master’s thesis was to quantitatively study the reliability of market and sales forecasts of a certain company by measuring bias, precision and accuracy of these forecasts by comparing forecasts against actual values. Secondly, the differences of bias, precision and accuracy between markets were explained by various macroeconomic variables and market characteristics. Accuracy and precision of the forecasts seems to vary significantly depending on the market that is being forecasted, the variable that is being forecasted, the estimation period, the length of the estimated period, the forecast horizon and the granularity of the data. High inflation, low income level and high year-on-year market volatility seems to be related with higher annual market forecast uncertainty and high year-on-year sales volatility with higher sales forecast uncertainty. When quarterly market size is forecasted, correlation between macroeconomic variables and forecast errors reduces. Uncertainty of the sales forecasts cannot be explained with macroeconomic variables. Longer forecasts are more uncertain, shorter estimated period leads to higher uncertainty, and usually more recent market forecasts are less uncertain. Sales forecasts seem to be more uncertain than market forecasts, because they incorporate both market size and market share risks. When lead time is more than one year, forecast risk seems to grow as a function of root forecast horizon. When lead time is less than year, sequential error terms are typically correlated, and therefore forecast errors are trending or mean-reverting. The bias of forecasts seems to change in cycles, and therefore the future forecasts cannot be systematically adjusted with it. The MASE cannot be used to measure whether the forecast can anticipate year-on-year volatility. Instead, we constructed a new relative accuracy measure to cope with this particular situation.

Error Modeling and Accuracy Analysis of a Novel Mobile Hybrid Parallel Robot

Over the last decades, calibration techniques have been widely used to improve the accuracy of robots and machine tools since they only involve software modification instead of changing the design and manufacture of the hardware. Traditionally, there are four steps are required for a calibration, i.e. error modeling, measurement, parameter identification and compensation. The objective of this thesis is to propose a method for the kinematics analysis and error modeling of a newly developed hybrid redundant robot IWR (Intersector Welding Robot), which possesses ten degrees of freedom (DOF) where 6-DOF in parallel and additional 4-DOF in serial. In this article, the problem of kinematics modeling and error modeling of the proposed IWR robot are discussed. Based on the vector arithmetic method, the kinematics model and the sensitivity model of the end-effector subject to the structure parameters is derived and analyzed. The relations between the pose (position and orientation) accuracy and manufacturing tolerances, actuation errors, and connection errors are formulated. Computer simulation is performed to examine the validity and effectiveness of the proposed method.

A Method for Sky-Condition Classification from Ground-Based Solar Radiation Measurements

Identification of clouds from satellite images is now a routine task. Observation of clouds from the ground, however, is still needed to acquire a complete description of cloud conditions. Among the standard meteorologicalvariables, solar radiation is the most affected by cloud cover. In this note, a method for using global and diffuse solar radiation data to classify sky conditions into several classes is suggested. A classical maximum-likelihood method is applied for clustering data. The method is applied to a series of four years of solar radiation data and human cloud observations at a site in Catalonia, Spain. With these data, the accuracy of the solar radiation method as compared with human observations is 45% when nine classes of sky conditions are to be distinguished, and it grows significantly to almost 60% when samples are classified in only five different classes. Most errors are explained by limitations in the database; therefore, further work is under way with a more suitable database