Salviano Soares

Bio: Salviano Soares is an academic researcher from University of Trás-os-Montes and Alto Douro. The author has contributed to research in topics: Voice over IP & Interpolation. The author has an hindex of 9, co-authored 74 publications receiving 514 citations.

Classification of Images of Childhood Pneumonia using Convolutional Neural Networks.

Abstract: In this paper we describe a comparative classification of Pneumonia using Convolution Neural Network. The database used was the dataset Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification made available by (Kermany, 2018) with a total of 5863 images, with 2 classes: normal and pneumonia. To evaluate the generalization capacity of the models, cross-validation of k-fold was used. The classification models proved to be efficient compared to the work of (Kermany et al., 2018) which obtained 92.8 % and the present work had an average accuracy of 95.30 %.

Coexistence and Interference Tests on a Bluetooth Low Energy Front-End.

Abstract: Over the last decade, impelled by the industry demand to achieve a technology capable of sending low amount of data payloads, but at the same time with a very low latency and ultra low power consumption, several efforts in wireless network transmission standardization emerged, supporting new applications in health, sports and fitness, medical, sensor networking, and even the automotive industry field. Despite the competition from ANT+, ZigBee, Nike+, NFC and RF4CE, in 2010 the Bluetooth SIG (special interest groups) adopted a new wireless technology named Bluetooth Low Energy (BLE). BLE coexist with Bluetooth in the same chip (called dual mode) therefore assuring this technology a rapid growth among smartphones, iOS, tablets, laptops and PCs. In fact, Bluetooth SIG also announced that it shall be hard to find a smartphone or tablet-PC that does not integrate BLE in the near future. Despite this accelerated growth, BLE shares the same band with Wi-Fi and all other low power technologies, so in order to achieve QoS, a mandatory requirement in many systems, tests for interference and coexistence must be performed. This study analysis the impact of a BLE sensor network on a crowded 2.4GHz room, with multiple Wi-Fi routers, ZigBee sensors and Bluetooth technology.

Models of Learning to Classify X-ray Images for the Detection of Pneumonia using Neural Networks.

Abstract: This article describes a comparison of two neural networks, the multilayer perceptron and Neural Network, for the detection and classification of pneumonia. The database used was the Chest-X-Ray data set provided by (Kermany et al., 2018) with a total of 5840 images, with two classes, normal and with pneumonia. to validate the models used, cross-validation of k-fold was used. The classification models were efficient, resulting in an average accuracy of 92.16% with the Multilayer Perceptron and 94.40% with the Convolution Neural Network.

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Energy harvesting in wireless sensor networks: A comprehensive review

Abstract: Recently, Wireless Sensor Networks (WSNs) have attracted lot of attention due to their pervasive nature and their wide deployment in Internet of Things, Cyber Physical Systems, and other emerging areas. The limited energy associated with WSNs is a major bottleneck of WSN technologies. To overcome this major limitation, the design and development of efficient and high performance energy harvesting systems for WSN environments are being explored. We present a comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs. We also discuss various recently proposed energy prediction models that have the potential to maximize the energy harvested in WSNs. Finally, we identify some of the challenges that still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment.