1,720,978 research outputs found
Arduino Nano-Based System for Tennis Shot Classification
No full textWearable technology has gained significant attention in research and commercial applications, including sports, where data collection and analysis play a crucial role in improving skills. This study focuses on tennis and the real-time classification of main shots, such as forehand, backhand, and serve. While previous studies have utilized machine learning methods for classification, they often relied on cloud or desktop processing. This paper proposes a novel neural architecture for real-time shot classification using an embedded device directly attached to a tennis racket, specifically the Arduino Nano 33 BLE Sense. The system processes six-axis time-series data collected from the IMU sensor, and the goal is to develop a lightweight model that can operate within the computational and memory limitations of edge devices. A 1-D convolutional neural network (CNN) is proposed for shot classification, which can effectively process 1-D time series. The experimental results demonstrate the successful classification of forehand, backhand, and serve shots using the trained model. This work highlights the potential of time series analysis in sports activities and emphasizes the importance of leveraging low-power embedded devices for efficient real-time analysis in the field
Neural Architecture for Tennis Shot Classification on Embedded System
No full textData analysis has become a common practice in professional and amateur sport activities, to monitor the player state and enhance performance. In tennis, performance analysis requires detecting and recognizing the different types of shots. With the advances in microcontrollers and machine learning algorithms, this topic becomes ever more considerable. We propose a 1-D convolutional neural network (CNN) model and an embedded system based on Arduino-Nano system for real-time shot classification. The network is trained through a dataset composed of three different tennis shot types, with 6 features recorded by an inertial device placed on the racket. Results demonstrate that the proposed model is able to discriminate the tennis shots with high accuracy, also generalizing to different users. The network has been deployed on a low-cost Arduino nano 33 IoT model, with an inference time of 65 ms
Benchmarking Microcontrollers with Ultra-Low Resolution Images Classification
No full textThe advancement of edge devices equipped with specialized hardware accelerators, data caches, and microcontroller units (MCUs) has brought the deployment and execution of Deep Neural Network (DNN) models closer to users and real-world sensor systems. This paper explores the potential of various specialized MCUs across four real-world applications (waste classification, presence detection, miniature robot detection, and sign language interpretation). We evaluate three well-known MCU (STM32H7, Arduino, and MAX78000) comparing their inference time and power/energy consumption on four ultra-low resolution image-classification datasets with varying input and task complexities. Our findings indicate that all MCUs deliver excellent performance in terms of inference time and energy consumption for real-time applications, with the MAX78000 outperforming the others in all the metrics considered, thanks to the use of a DNN accelerator
Quantization of MobileNetV2 for Resource-Constrained Microcontrollers
No full textThe proliferation of IoT and edge computing devices demands for efficient deployment strategies due to their limited computational capabilities and energy constraints. This paper investigates the application of deep learning models in such resource-constrained environments, focusing on the quantization of the MobileNetV2 architecture. We evaluate three primary quantization techniques: dynamic quantization, static quantization, and quantization-aware training. Using a subset of the Vehicle Make and Model Recognition dataset, specifically the Most Stolen Vehicles in the US in 2017, we compare our results with a previous study, highlighting the advancements achieved through MobileNetV2 and its quantization process. Deployment on two STM32 boards, L4 and H7 series, demonstrates the effectiveness of the quantized MobileNetV2 model in achieving efficient, low-power, and low-latency execution on low-power MCUs
Catastrophic hemodynamic changes in a patient with undiagnosed pheochromocytoma undergoing abdominal hysterectomy [36]
No full text[No abstract available]O'Riordan JA, 1997, INT ANESTHESIOL CLIN, V35, P99, DOI 10.1097-00004311-199703540-00008; SELLEVOLD OFM, 1985, ACTA ANAESTH SCAND, V29, P474; Tarant NS, 2006, ANESTH ANALG, V102, P642, DOI 10.1213-01.ane.0000184827.79120.4373
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Assessment of Recurrent Spiking Neural Networks on Neuromorphic Accelerators for Naturalistic Texture Classification
No full textThis paper presents the implementation of a Recurrent Spiking Neural Network (RSNN) using surrogate gradient descent for naturalistic textures classification. The implementation choices for the RSNN are limited to hardware-friendly models since it is intended to be integrated into an electronic skin system. Hence, a comparison between the von-Neumman and neuromorphic computing approaches has been assessed in terms of hardware efficiency. The energy consumption per inference of the proposed model is estimated using the Keras-Spiking tool built-in NengoDL framework, on three different devices namely: GPU, Intel Loihi, and SpiNNaker. The obtained results indicate that the aforementioned neuromorphic devices achieve several orders of magnitude gains in energy over von-Neumman hardware. Moreover, the proposed RSNN model overcomes similar state-of-the-art solutions in terms of classification accuracy and hardware complexity making it a promising candidate for embedded electronic skin applications
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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