Pedestrian Flow Identification and Occupancy Prediction for Indoor Areas.

Indoor localization is used to locate objects and people within buildings where outdoor tracking tools and technologies cannot provide precise results. This paper aims to improve analytics research, focusing on data collected through indoor localization methods. Smart devices recurrently broadcast automatic connectivity requests. These packets are known as Wi-Fi probe requests and can encapsulate various types of spatiotemporal information from the device carrier. In addition, in this paper, we perform a comparison between the Prophet model and our implementation of the autoregressive moving average (ARMA) model. The Prophet model is an additive model that requires no manual effort and can easily detect and handle outliers or missing data. In contrast, the ARMA model may require more effort and deep statistical analysis but allows the user to tune it and reach a more personalized result. Second, we attempted to understand human behaviour. We used historical data from a live store in Dubai to forecast the use of two different models, which we conclude by comparing. Subsequently, we mapped each probe request to the section of our place of interest where it was captured. Finally, we performed pedestrian flow analysis by identifying the most common paths followed inside our place of interest.

Pedestrian Augmented Reality Navigator.

Navigation is often regarded as one of the most-exciting use cases for Augmented Reality (AR). Current AR Head-Mounted Displays (HMDs) are rather bulky and cumbersome to use and, therefore, do not offer a satisfactory user experience for the mass market yet. However, the latest-generation smartphones offer AR capabilities out of the box, with sometimes even pre-installed apps. Apple's framework ARKit is available on iOS devices, free to use for developers. Android similarly features a counterpart, ARCore. Both systems work well for small spatially confined applications, but lack global positional awareness. This is a direct result of one limitation in current mobile technology. Global Navigation Satellite Systems (GNSSs) are relatively inaccurate and often cannot work indoors due to the restriction of the signal to penetrate through solid objects, such as walls. In this paper, we present the Pedestrian Augmented Reality Navigator (PAReNt) iOS app as a solution to this problem. The app implements a data fusion technique to increase accuracy in global positioning and showcases AR navigation as one use case for the improved data. ARKit provides data about the smartphone's motion, which is fused with GNSS data and a Bluetooth indoor positioning system via a Kalman Filter (KF). Four different KFs with different underlying models have been implemented and independently evaluated to find the best filter. The evaluation measures the app's accuracy against a ground truth under controlled circumstances. Two main testing methods were introduced and applied to determine which KF works best. Depending on the evaluation method, this novel approach improved the accuracy by 57% (when GPS and AR were used) or 32% (when Bluetooth and AR were used) over the raw sensor data.

Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation.

BACKGROUND: Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. OBJECTIVE: Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. METHODS: We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. RESULTS: The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. CONCLUSIONS: Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation.

Spontaneous perforation of common bile duct in a young female: An intra-operative surprise.

INTRODUCTION: Spontaneous CBD perforation is one of the rare causes of acute abdomen in infants and extremely rare in adults. It is rarely suspected and correctly diagnosed preoperatively. PRESENTATION OF CASE: A 17year old female presented to Emergency Department with sudden onset of pain and distention of abdomen, associated with vomiting and non-passage of flatus and stool for 3days and features of generalized peritonitis. On exploration, a perforation of size 0.5cm in diameter was present on the antero-lateral surface of supraduodenal part of common bile duct (CBD) below the junction of cystic duct and common hepatic duct. Cholecystectomy done and the CBD repaired over a T-tube. DISCUSSION: Spontaneous perforation of bile duct should ideally manage with T-tube drainage of the CBD along with cholecystectomy. In case with distal obstruction of the CBD, a biliary enteric bypass should be done. CONCLUSION: Due to the paucity of cases, the index of suspicion for this diagnosis is low. But bilious peritoneal tap, features of generalized peritonitis and absence of free gas under diaphragm in abdominal x-ray may be considered as clues for suspicion. Accordingly, Surgery remains the mainstay of treatment.

Gastric GIST or gastric schwannoma-A diagnostic dilemma in a young female.

INTRODUCTION: Gastrointestinal stromal tumor (GIST) is the commonest mesenchymal tumor of GI tract and 60-70% of it seen in the stomach, whereas Gastric schwannoma is a benign, slow growing and one of the rare neoplasms of stomach. Age distribution, clinical, radiological features and gross appearance of both tumors are similar. PRESENTATION OF CASE: We report a rare case of gastric schwannoma in a 20-year-old girl, who underwent subtotal gastrectomy with the suspicion of a GIST preoperatively but later confirmed to be gastric schwannoma postoperatively after immunohistochemical study. DISCUSSION: Accordingly, the differential diagnosis for gastric submucosal mass should be gastric schwannoma. Furthermore, Gastric schwannoma is a benign neoplasm with excellent prognosis after surgical resection, whereas 10-30% of GIST has malignant behavior. Therefore, it is important to distinguish between gastric schwannoma and GIST so as to make an accurate diagnosis for optimally guide treatment options. CONCLUSION: Due to the paucity of gastric schwannoma, the index of suspicion for this diagnosis is low. So it is important to include gastric schwannoma in the differential diagnosis when preoperative imaging studies reveal submucosal exophytic gastric mass and after resection of the tumor with a negative margin, it should be sent for immunohistochemical study for confirmation of diagnosis.