Domain Adaptation for Car Accident Detection in Videos

© 2019 IEEE. In this paper, we implement a deep learning model for car accident detection using synthetic videos while adapting the model, using domain adaptation (DA), to real videos from CCTV traffic cameras. The synthetic data are rendered using a video game. The reason to use such data is the lack of real videos of car crashes from CCTV. Though a video game may allow us to generate car crashes in a variety of scenarios, the distinction in synthetic and real videos can negatively affect the model\u27s performance. Accordingly, our aim is three-fold: render numerous synthetic videos having significant variations, train a 3D CNN based deep model on the collected videos, and use DA to adapt the model from synthetic to real videos. Our experimental results, obtained under a variety of experimental setups, demonstrate the feasibility of using our approach for car accident detection in real videos

Quantification and Characterization of the Motion and Shape of a Moving Cell

The main function of a blood cell's surface is to receive information the environment. Recently, experiments have indicated that the cell membrane plays a vital role in the life, development, and regulation of cells. However, there is no existing method to quantify the observable changes in membrane shape that occur in locomotion. To achieve this objective using automatic techniques of digital image processing, the main goal of this research is to develop an image interpretation system capable of analyzing the structural changes in the morphology of a non-rigid moving object from a sequence of pictures. […]La principale fonction de la membrane d'un globule est de recevoir de l’information de son environnement. Recemment, des expériences ont démontré que la membrane joue un role primordial dans la vie, le développement et la régulation des globules. Toutefois, il n'existe pas de méthode permettant de quantifier les changements observables de la forme de la membrane au cours de la locomotion. Afin d'atteindre cet objectif tout en utilisant des techniques automatiques de traitement des images digitales, le but principal de cette recherche est de concevoir un système d'interprétation d'images capable d'analyser les changements structuraux de la morphologie d'un objet non-rigide en mouvement à partir d'une séquence d'images. […

Mansoura conventional electrophysiological study and ablation registry at time between march 2020 to march 2021

Background: Catheter ablation has been described as a standard therapy for cardiac tachyarrhythmias. Although multiple registries have been reported from different geographical regions, like Europe and the USA, little is known about the criteria and outcomes of such patients in the Egyptian setting. Herein, we report the distribution of cardiac arrhythmias, success rate, and complications of catheter ablation in our tertiary care cardiac setting. Methods: This prospective cross-sectional study included 50 patients who underwent catheter ablation for cardiac tachyarrhythmias. Results: Most patients were older than 40 years (62%). Our study revealed the following types of arrhythmias; atrioventricular reciprocating tachycardia (AVRT) (30%), atrioventricular nodal reentry tachycardia (AVNRT) (48%), atrial tachycardia (4%), Wolff-Parkinson-White syndrome (12%), and atrial flutter (6%). Decremental retrograde conduction was noted in 62% of patients, while non-decremental conduction was present in 38% of them. A slow pathway was ablated in most patients (48%), while other ablated areas included the upper and lower crista terminalis (4%), posterior septum (10%), lateral annulus either right or left one (26%), anterior septum (2%), mid septum (4%), and cavotricuspid isthmus (6%). Our general success rate was 98%. AVNRT was significantly associated with older age, as 67.7% of their patients were older than 40 years.&nbsp

Triplet Loss Network for Unsupervised Domain Adaptation

Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain. Many methods have been proposed to resolve this problem, using techniques such as generative adversarial networks (GAN), but the complexity of such methods makes it hard to use them in different problems, as fine-tuning such networks is usually a time-consuming task. In this paper, we propose a method for unsupervised domain adaptation that is both simple and effective. Our model (referred to as TripNet) harnesses the idea of a discriminator and Linear Discriminant Analysis (LDA) to push the encoder to generate domain-invariant features that are category-informative. At the same time, pseudo-labelling is used for the target data to train the classifier and to bring the same classes from both domains together. We evaluate TripNet against several existing, state-of-the-art methods on three image classification tasks: Digit classification (MNIST, SVHN, and USPC datasets), object recognition (Office31 dataset), and traffic sign recognition (GTSRB and Synthetic Signs datasets). Our experimental results demonstrate that (i) TripNet beats almost all existing methods (having a similar simple model like it) on all of these tasks; and (ii) for models that are significantly more complex (or hard to train) than TripNet, it even beats their performance in some cases. Hence, the results confirm the effectiveness of using TripNet for unsupervised domain adaptation in image classification