A novel monitoring system for fall detection in older people

Indexación: Scopus.This work was supported in part by CORFO - CENS 16CTTS-66390 through the National Center on Health Information Systems, in part by the National Commission for Scientific and Technological Research (CONICYT) through the Program STIC-AMSUD 17STIC-03: ‘‘MONITORing for ehealth," FONDEF ID16I10449 ‘‘Sistema inteligente para la gestión y análisis de la dotación de camas en la red asistencial del sector público’’, and in part by MEC80170097 ‘‘Red de colaboración científica entre universidades nacionales e internacionales para la estructuración del doctorado y magister en informática médica en la Universidad de Valparaíso’’. The work of V. H. C. De Albuquerque was supported by the Brazilian National Council for Research and Development (CNPq), under Grant 304315/2017-6.Each year, more than 30% of people over 65 years-old suffer some fall. Unfortunately, this can generate physical and psychological damage, especially if they live alone and they are unable to get help. In this field, several studies have been performed aiming to alert potential falls of the older people by using different types of sensors and algorithms. In this paper, we present a novel non-invasive monitoring system for fall detection in older people who live alone. Our proposal is using very-low-resolution thermal sensors for classifying a fall and then alerting to the care staff. Also, we analyze the performance of three recurrent neural networks for fall detections: Long short-term memory (LSTM), gated recurrent unit, and Bi-LSTM. As many learning algorithms, we have performed a training phase using different test subjects. After several tests, we can observe that the Bi-LSTM approach overcome the others techniques reaching a 93% of accuracy in fall detection. We believe that the bidirectional way of the Bi-LSTM algorithm gives excellent results because the use of their data is influenced by prior and new information, which compares to LSTM and GRU. Information obtained using this system did not compromise the user's privacy, which constitutes an additional advantage of this alternative. © 2013 IEEE.https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=842305

Precipitates Segmentation from Scanning Electron Microscope Images through Machine Learning Techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis

Multi-objective 3D topology optimization of next generation wireless data center network

As one of the next generation network technologies for data centers, wireless data center network has important research significance. Smart architecture optimization and management are very important for wireless data center network. With the ever-increasing demand of data center resources, there are more and more data servers deployed. However, traditional wired links among servers are expensive and inflexible. Benefited from the development of intelligent optimization and other techniques, high speed wireless topology for wireless data center network is studied. Through image processing, a radio propagation model is constructed based on a heat map. The line-of-sight issue and the interference problem are also discussed. By simultaneously considering objectives of coverage, propagation intensity and interference intensity as well as the constraint of connectivity, we formulate the topology optimization problem as a multi-objective optimization problem. To seek for solutions, we employ several state-of-the-art serial MOEAs as well as three parallel MOEAs. For the grouping in distributed parallel algorithms, prior knowledge is referred. Finally, experimental results demonstrate that, the parallel MOEAs perform effectively in optimization results and efficiently in time consumption

Mechanical Properties and Microstructural Characterization of Aged Nickel-based Alloy 625 Weld Metal

The aim of this work was to evaluate the different phases formed during solidification and after thermal aging of the as-welded 625 nickel-based alloy, as well as the influence of microstructural changes on the mechanical properties. The experiments addressed aging temperatures of 650 and 950 A degrees C for 10, 100, and 200 hours. The samples were analyzed by electron microscopy, microanalysis, and X-ray diffraction in order to identify the secondary phases. Mechanical tests such as hardness, microhardness, and Charpy-V impact test were performed. Nondestructive ultrasonic inspection was also conducted to correlate the acquired signals with mechanical and microstructural properties. The results show that the alloy under study experienced microstructural changes when aged at 650 A degrees C. The aging was responsible by the dissolution of the Laves phase formed during the solidification and the appearance of gamma aEuro(3) phase within interdendritic region and fine carbides along the solidification grain boundaries. However, when it was aged at 950 A degrees C, the Laves phase was continuously dissolved and the excess Nb caused the precipitation of the delta-phase (Ni3Nb), which was intensified at 10 hours of aging, with subsequent dissolution for longer periods such as 200 hours. Even when subjected to significant microstructural changes, the mechanical properties, especially toughness, were not sensitive to the dissolution and/or precipitation of the secondary phases

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag

Guest Editorial: Interactive Virtual Environments for Neuroscience

[No abstract available]Scopu

Efficient and Privacy Preserving Video Transmission in 5G-Enabled IoT Surveillance Networks: Current Challenges and Future Directions

Vision sensors in Internet of Things (IoT)-connected smart cities play a vital role in the exponential growth of video data, thereby making its analysis and storage comparatively tough and challenging. Those sensors continuously generate data for 24 hours, which requires huge storage resources, dedicated networks for sharing with data centers, and most importantly, it makes browsing, retrieval, and event searching a difficult and time-consuming job. Video summarization (VS) is a promising direction toward a solution to these problems by analyzing the visual contents acquired from a vision sensor and prioritizing them based on events, saliency, person's appearance, and so on. However, the current VS literature still lacks focus on resource-constrained devices that can summarize data over the edge and upload it to data repositories efficiently for instant analysis. Therefore, in this article, we carry out a survey of functional VS methods to understand their pros and cons for resource-constrained devices, with the ambition to provide a compact tutorial to the community of researchers in the field. Further, we present a novel saliency-aware VS framework, incorporating 5G-enabled IoT devices, which keeps only important data, thereby saving storage resources and providing representative data for immediate exploration. Keeping privacy of data as a second priority, we intelligently encrypt the salient frames over resource-constrained devices before transmission over the 5G network. The reported experimental results show that our proposed framework has additional benefits of faster transmission (1.8~13.77 percent frames of a lengthy video are considered for transmission), reduced bandwidth, and real-time processing compared to state-of-the-art methods in the field

Efficient Video Summarization for Smart Surveillance Systems

In surveillance systems, vast amounts of data are collected from different sources to monitor ongoing video activities. Usually, video data is passively captured by visual sensors and forwarded to the command center, without intelligent Edge functionalities to select essential video information and locally detect abnormal events therein. These shortcomings often seen in practical surveillance scenarios lead to a wastage of storage resources and make data management, retrieval, and informed decision complex and time-consuming. Therefore, endowing visual sensors with video summarization capabilities is of utmost importance for smarter surveillance systems. This study departs from this rationale to propose an efficient neural networks-based video summarization method for surveillance systems. The proposed approach learns how to optimally segment a video by measuring informative features from the data flow, followed by memorability and entropy to maintain the relevance and diversity of the video summary produced on the Edge. Experimental results over benchmark datasets reveal that the proposed scheme outperforms other state-of-the-art counterparts and proves the effectiveness of our method for video summarization in smart cities

Communication Technologies for Edge Learning and Inference: A Novel Framework, Open Issues, and Perspectives

With the continuous advancement of smart devices and their demand for data, the complex computation that was previously exclusive to the cloud server is now moving toward the edge of the network. For numerous reasons (e.g., applications demanding low latencies and data privacy), data-based computation has been brought closer to the originating source, forging the edge computing paradigm. Together with machine learning, edge computing has become a powerful local decision-making tool, fostering the advent of edge learning. However, the latter has become delay-sensitive and resource-thirsty in terms of hardware and networking. New methods have been developed to solve or minimize these issues, as proposed in this study. We first investigated representative communication methods for edge learning and inference (ELI), focusing on data compression, latency, and resource management. Next, we proposed an ELI-based video data prioritization framework that only considers data with events and hence significantly reduces the transmission and storage resources when implemented in surveillance networks. Furthermore, we critically examined various communication aspects related to edge learning by analyzing their issues and highlighting their advantages and disadvantages. Finally, we discuss the challenges and present issues that remain