Advances in the development of an imaging device for plaque measurement in the area of the carotid artery

This paper describes the advances in the development and subsequent testing of an imaging device for three-dimensional ultrasound measurement of atherosclerotic plaque in the carotid artery. The embolization from the atherosclerotic carotid plaque is one of the most common causes of ischemic stroke and, therefore, we consider the measurement of the plaque as extremely important. The paper describes the proposed hardware for enhancing the standard ultrasonic probe to provide a possibility of accurate probe positioning and synchronization with the cardiac activity, allowing the precise plaque measurements that were impossible with the standard equipment. The synchronization signal is derived from the output signal of the patient monitor (electrocardiogram (ECG)), processed by a microcontroller-based system, generating the control commands for the linear motion moving the probe. The controlling algorithm synchronizes the movement with the ECG waveform to obtain clear images not disturbed by the heart activity.Web of Science28235935

XXIV. mezinárodní kolokvium o regionálních vědách

Title in English: 24th International Colloquium on Regional Sciences: Conference proceedings. The conference proceedings consists of papers presented at the 24rd International Colloquium on Regional Sciences that was organized by Department of Regional Economics and Administration FEA MU. It contains 79 articles arranged by topic. The individual articles deal with e.g. socioeconomic disparities among regions, regional policy, territory attractiveness, tourism or regional public administration

Optimized hand pose estimation CrossInfoNet-based architecture for embedded devices

We present CrossInfoMobileNet, a hand pose estimation convolutional neural network based on CrossInfoNet, specifically tuned to mobile phone processors through the optimization, modification, and replacement of computationally critical CrossInfoNet components. By introducing a state-of-the-art MobileNetV3 network as a feature extractor and refiner, replacing ReLU activation with a better performing H-Swish activation function, we have achieved a network that requires 2.37 times less multiply-add operations and 2.22 times less parameters than the CrossInfoNet network, while maintaining the same error on the state-of-the-art datasets. This reduction of multiply-add operations resulted in an average 1.56 times faster real-world performance on both desktop and mobile devices, making it more suitable for embedded applications. The full source code of CrossInfoMobileNet including the sample dataset and its evaluation is available online through Code Ocean.Web of Science335art. no. 7