4,938 research outputs found
Improved reception of in-body signals by means of a wearable multi-antenna system
High data-rate wireless communication for in-body human implants is mainly performed in the 402-405 MHz Medical Implant Communication System band and the 2.45 GHz Industrial, Scientific and Medical band. The latter band offers larger bandwidth, enabling high-resolution live video transmission. Although in-body signal attenuation is larger, at least 29 dB more power may be transmitted in this band and the antenna efficiency for compact antennas at 2.45 GHz is also up to 10 times higher. Moreover, at the receive side, one can exploit the large surface provided by a garment by deploying multiple compact highly efficient wearable antennas, capturing the signals transmitted by the implant directly at the body surface, yielding stronger signals and reducing interference. In this paper, we implement a reliable 3.5 Mbps wearable textile multi-antenna system suitable for integration into a jacket worn by a patient, and evaluate its potential to improve the In-to-Out Body wireless link reliability by means of spatial receive diversity in a standardized measurement setup. We derive the optimal distribution and the minimum number of on-body antennas required to ensure signal levels that are large enough for real-time wireless endoscopy-capsule applications, at varying positions and orientations of the implant in the human body
RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks
Capsule Networks (CapsNets) are able to hierarchically preserve the pose
relationships between multiple objects for image classification tasks. Other
than achieving high accuracy, another relevant factor in deploying CapsNets in
safety-critical applications is the robustness against input transformations
and malicious adversarial attacks.
In this paper, we systematically analyze and evaluate different factors
affecting the robustness of CapsNets, compared to traditional Convolutional
Neural Networks (CNNs). Towards a comprehensive comparison, we test two CapsNet
models and two CNN models on the MNIST, GTSRB, and CIFAR10 datasets, as well as
on the affine-transformed versions of such datasets. With a thorough analysis,
we show which properties of these architectures better contribute to increasing
the robustness and their limitations. Overall, CapsNets achieve better
robustness against adversarial examples and affine transformations, compared to
a traditional CNN with a similar number of parameters. Similar conclusions have
been derived for deeper versions of CapsNets and CNNs. Moreover, our results
unleash a key finding that the dynamic routing does not contribute much to
improving the CapsNets' robustness. Indeed, the main generalization
contribution is due to the hierarchical feature learning through capsules.Comment: To appear at the 2023 International Joint Conference on Neural
Networks (IJCNN), Queensland, Australia, June 202
Challenging capitalism : ethics, exploitation and the sublime in Moon and Source Code
This paper is the first academic article to offer a detailed analysis of both Duncan Jones’ sf films: Moon and Source Code. The readings explore the films’ complex philosophical themes, focusing on ethics, specifically utilitarianism, and the aesthetics of the sublime. Both discourses inform the films’ presentation of technology and labour within futuristic forms of late capitalism. Drawing links between the two films emphasises their shared themes of exploitation, suffering and resistance. This enables an appreciation of the complexities of Moon and provides a new way of reading Source Code, focusing on the interplay between the film’s different realities rather than privileging the virtual space of the train. While the films utilise the aesthetics of the sublime, my readings will trace the ways in which they close down the possibility of transcendence, thereby relocating resistance to the system within different types of replication and repetitio
SciTech News Volume 71, No. 1 (2017)
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