Coronary anomalies and anatomical variants detected by coronary computed tomographic angiography in Kashmir, India

Background: Coronary Artery Anomalies (CAAs) presenting in adulthood are rare and associated with adverse cardiac events, including sudden cardiac death. Coronary artery anomaly is the second most common cause of Sudden Cardiac Death (SCD) in young athletes. Cardiac Computed Tomographic Angiography (CTA) is a readily available non-invasive imaging modality that provides high-resolution anatomical information of the coronary arteries. Multi-detector row CT is superior to conventional angiography in defining the ostial origin and proximal path of anomalous coronary branches.Methods: This was a prospective study included 186 patients who underwent coronary CTA from December 2018 to November 2019 in Government medical College, Srinagar on a 256 slice CT. The indications for coronary CTA were an equivocal, or non-diagnostic stress test, atypical chest pain, suspected anomalous coronary, as well as the evaluation of cardiac cause of syncope.Results: Ramus intermedius was the most common anatomical variant seen in 25 patients (13.4%). The prevalence of coronary anomalies in this study was 5.66% including myocarding bridging. The most common anomaly was high take off of coronary artery from sinotubular junction accounting for 1.6%.Conclusions: Coronary Computed Tomographic angiography is much superior in detecting coronary artery anomalies than invasive coronary angiography because of the absence of soft tissue information like as is needed in myocardial bridging. Proper knowledge of the anomalies and their clinical significance is highly important in planning treatment and easing hardships of cardiologists in dealing with them

Selective subtraction for handheld cameras

© 2013 IEEE. Background subtraction techniques model the background of the scene using the stationarity property and classify the scene into two classes namely foreground and background. In doing so, most moving objects become foreground indiscriminately, except in dynamic scenes (such as those with some waving tree leaves, water ripples, or a water fountain), which are typically \u27learned\u27 as part of the background using a large training set of video data. We introduce a novel concept of background as the objects other than the foreground, which may include moving objects in the scene that cannot be learned from a training set because they occur only irregularly and sporadically, e.g. a walking person. We propose a \u27selective subtraction\u27 method as an alternative to standard background subtraction, and show that a reference plane in a scene viewed by two cameras can be used as the decision boundary between foreground and background. In our definition, the foreground may actually occur behind a moving object. Furthermore, the reference plane can be selected in a very flexible manner, using for example the actual moving objects in the scene, if needed. We extend this idea to allow multiple reference planes resulting in multiple foregrounds or backgrounds. We present diverse set of examples to show that: 1) the technique performs better than standard background subtraction techniques without the need for training, camera calibration, disparity map estimation, or special camera configurations; 2) it is potentially more powerful than standard methods because of its flexibility of making it possible to select in real-time what to filter out as background, regardless of whether the object is moving or not, or whether it is a rare event or a frequent one. Furthermore, we show that this technique is relatively immune to camera motion and performs well for hand-held cameras

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Smart grids and smart homes are getting people\u27s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with day as covariates remained better than the 1, 2, 3, and 4-week scenarios