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Cognition® Corporation Announces Compass® PRO: A Flexible SaaS Solution Purpose-Built for Medical Device Product Development

Cognition® Corporation Announces Compass® PRO: A Flexible SaaS Solution Purpose-Built for Medical Device Product Development
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Ben Higgitt , Gerald Wesel , Cognition Corporation , Media Contactkristen Callahandirector Of Marketingcognition Corporation , See Campaign , Product Line Manager , Use Specification , Task Analysis , Use Scenario Analysis , Correct Use Analysis , Use Error , Design History Files , Contactkristen Callahandirector , Google News , Nexis Newswire ,

"On Virtualizing Targets Coverage in Energy Harvesting IoT Systems" by Longji Zhang, Kwan Wu Chin et al.

This paper considers targets coverage in energy harvesting Internet of Things (IoT) networks. Specifically, solar-powered sensor devices employ network virtualization technology to partition their resources, such as energy, memory, and computation workload, in order to serve requests with different coverage requirements. Our objective is to maximize the revenue from completing requests. To this end, we outline a mixed integer linear program (MILP) to optimize the start time of each request and the set of nodes that serve a request. We also propose a heuristic, called energy harvesting aware request placement (EHARP), to determine requests to be deployed in each time slot based on energy harvesting conditions and the resource state of sensor nodes. Furthermore, we propose two model predictive control (MPC) approaches, called MPC-MILP and MPC-EHARP, respectively, which deploy requests based on energy arrival at devices over a given time window as predicted by a Gaussian mixture model (GM ....

Energy Harvesting , Nternet Of Things , Mathematical Optimization , Receding Horizon Control , Soft Sensors , Task Analysis , Task Assignment , Virtual Network Function , Wireless Sensor Networks ,

"Higher Order Polynomial Transformer for Fine-Grained Freezing of Gait " by Renfei Sun, Kun Hu et al.

Freezing of Gait (FoG) is a common symptom of Parkinson’s disease (PD), manifesting as a brief, episodic absence, or marked reduction in walking, despite a patient’s intention to move. Clinical assessment of FoG events from manual observations by experts is both time-consuming and highly subjective. Therefore, machine learning-based FoG identification methods would be desirable. In this article, we address this task as a fine-grained human action recognition problem based on vision inputs. A novel deep learning architecture, namely, higher order polynomial transformer (HP-Transformer), is proposed to incorporate pose and appearance feature sequences to formulate fine-grained FoG patterns. In particular, a higher order self-attention mechanism is proposed based on higher order polynomials. To this end, linear, bilinear, and trilinear transformers are formulated in pursuit of discriminative fine-grained representations. These representations are treated as multiple streams and furthe ....

Deep Learning , Feature Extraction , Igher Order Attention , Olynomial Transformation , Self Attention , Patiotemporal Phenomena , Task Analysis ,

"Low-dose CT Image Synthesis for Domain Adaptation Imaging Using a Gene" by Ming Li, Jiping Wang et al.

Deep learning (DL) based image processing methods have been successfully applied to low-dose x-ray images based on the assumption that the feature distribution of the training data is consistent with that of the test data. However, low-dose computed tomography (LDCT) images from different commercial scanners may contain different amounts and types of image noise, violating this assumption. Moreover, in the application of DL based image processing methods to LDCT, the feature distributions of LDCT images from simulation and clinical CT examination can be quite different. Therefore, the network models trained with simulated image data or LDCT images from one specific scanner may not work well for another CT scanner and image processing task. To solve such domain adaptation problem, in this study, a novel generative adversarial network (GAN) with noise encoding transfer learning (NETL), or GAN-NETL, is proposed to generate a paired dataset with a different noise style. Specifically, we pr ....

Biomedical Imaging , Computed Tomography , Deep Learning , Domain Adaptation , Generative Adversarial Networks , Mage Coding , Image Synthesis , Task Analysis , Transfer Learning ,

"Novel Task Scheduling Approaches in Energy Sharing Solar-Powered IoT N" by Yuhan Cui, Kwan Wu Chin et al.

This paper considers task scheduling in solarpowered Internet of things (IoT) networks where devices are capable of sharing energy wirelessly. Our aim is to minimize the completion time of all tasks. We outline a novel mixed integer linear program (MILP) to schedule tasks and determine whether devices share their harvested energy via radio frequency (RF) in each time slot. The MILP considers the coupling between the energy level at devices across time slots. It also considers the dependency of tasks, whereby each task must be executed on a given set of devices in a specific order. Further, we propose a heuristic algorithm called minimum time first with energy sharing (MinTime-ES) for large scale networks. Our results show that with energy sharing, MILP and MinTime-ES achieve 28.86 and 7.83% reduction in task completion time as compared to competing algorithms that do not consider energy sharing between devices. ....

Energy Harvesting , Nternet Of Things , Jobs Flow , Logic Gates , Pop Hard , Radio Frequency , Resource Management , Task Analysis , Wireless Charging ,