Fundamentals Of Neural Networks Architectures Algorithms And Applications

The recent changes in climate have increased the importance of environmental monitoring, making it a topical and highly active research area. This field is based on remote sensing and on wireless sensor networks for gathering data about the environment. Recent advancements, such as the vision of the Internet of Things (IoT), the cloud computing model, and cyber-physical systems, provide support for the transmission and management of huge amounts of data regarding the trends observed in environmental parameters. In this context, the current work presents three different IoT-based wireless sensors for environmental and ambient monitoring: one employing User Datagram Protocol (UDP)-based Wi-Fi communication, one communicating through Wi-Fi and Hypertext Transfer Protocol (HTTP), and a third one using Bluetooth Smart. All of the presented systems provide the possibility of recording data at remote locations and of visualizing them from every device with an Internet connection, enabling the monitoring of geographically large areas. The development details of these systems are described, along with the major differences and similarities between them. The feasibility of the three developed systems for implementing monitoring applications, taking into account their energy autonomy, ease of use, solution complexity, and Internet connectivity facility, was analyzed, and revealed that they make good candidates for IoT-based solutions.

Analysis of Three IoT-Based Wireless Sensors for Environmental Monitoring

Condition monitoring can reduce machine breakdown losses, increase productivity and operation safety, and therefore deliver significant benefits to many industries. The emergence of wireless sensor networks (WSNs) with smart processing ability play an ever-growing role in online condition monitoring of machines. WSNs are cost-effective networking systems for machine condition monitoring. It avoids cable usage and eases system deployment in industry, which leads to significant savings. Powering the nodes is one of the major challenges for a true WSN system, especially when positioned at inaccessible or dangerous locations and in harsh environments. Promising energy harvesting technologies have attracted the attention of engineers because they convert microwatt or milliwatt level power from the environment to implement maintenance-free machine condition monitoring systems with WSNs. The motivation of this review is to investigate the energy sources, stimulate the application of energy harvesting based WSNs, and evaluate the improvement of energy harvesting systems for mechanical condition monitoring. This paper overviews the principles of a number of energy harvesting technologies applicable to industrial machines by investigating the power consumption of WSNs and the potential energy sources in mechanical systems. Many models or prototypes with different features are reviewed, especially in the mechanical field. Energy harvesting technologies are evaluated for further development according to the comparison of their advantages and disadvantages. Finally, a discussion of the challenges and potential future research of energy harvesting systems powering WSNs for machine condition monitoring is made.

Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review.

Internet of Things (IoT) constitutes the next step in the field of technology, bringing enormous changes in industry, medicine, environmental care, and urban development. Various challenges are to be met in forming this vision, such as technology interoperability issues, security and data confidentiality requirements, and, last but not least, the development of energy efficient management systems. In this paper, we explore existing networking communication technologies for the IoT, with emphasis on encapsulation and routing protocols. The relation between the IoT network protocols and the emerging IoT applications is also examined. A thorough layer-based protocol taxonomy is provided, while how the network protocols fit and operate for addressing the recent IoT requirements and applications is also illustrated. What is the most special feature of this paper, compared to other survey and tutorial works, is the thorough presentation of the inner schemes and mechanisms of the network protocols subject to IPv6. Compatibility, interoperability, and configuration issues of the existing and the emerging protocols and schemes are discussed based on the recent advanced of IPv6. Moreover, open networking challenges such as security, scalability, mobility, and energy management are presented in relation to their corresponding features. Lastly, the trends of the networking mechanisms in the IoT domain are discussed in detail, highlighting future challenges.

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Network Protocols, Schemes, and Mechanisms for Internet of Things (IoT): Features, Open Challenges, and Trends

Energy harvesting in wireless sensor networks: A taxonomic survey

In image radiation therapy, accurate segmentation of organs at risk (OARs) is a very essential task and has clinical applications in cancer treatment. The segmentation of organs close to lung, breast, or esophageal cancer is a routine and time-consuming process. The automatic segmentation of organs at risk would be an essential part of treatment planning for patients suffering radiotherapy. The position and shape variation, morphology inherent and low soft tissue contrast between neighboring organs across each patient’s scans is the challenging task for automatic segmentation of OARs in Computed Tomography (CT) images. The objective of this paper is to use automatic segmentation of the organs near risk in CT images using deep learning model. The paper proposes a hybrid 3D-ResNet based deep learning model with Atrous spatial pyramid pooling module and Project & Excite (PE)' module for 3D volumetric segmentation using Thoracic Organs at Risk (SegTHOR) dataset. The proposed model produces better results as compared to state-of-the-art deep learning models used in SegTHOR dataset. Proposed 3D volumetric Hybrid deep model could be used for automatic segmentation of OARs in clinical applications and would be helpful to diagnose lung, breast or esophageal cancer in CT images.

Hybrid 3D-ResNet Deep Learning Model for Automatic Segmentation of Thoracic Organs at Risk in CT Images

This paper presents a study of Internal Model Control (IMC) scheme to improve control performance of industrial wireless networked control system subject to measured round trip delays. As compared to the auto-tuned optimized PID controller, IMC controller helps to significantly reduce percentage of overshoot for a first order with dead-time process plant. The paper also highlights the differences in control performance of the process plant with lumped network delay and separate network delays.

Internal model control for industrial wireless plant subject to measured round trip delays

The use of data acquisition system (DAQ) is important in process industry as it enables analog signal from physical system to be converted into a digital signal for processing by computer and vice versa. However, many of the DAQs available in the market today are complex and expensive. This paper presents the development of a simple, low-cost and open source microcontroller-based DAQ system for process automation and control. The proposed DAQ system is built around Arduino Mega 2560 microcontroller. The test experimental results of the developed DAQ system proved its suitability for application in process monitoring and control applications.

Development of low-cost real-time data acquisition system for process automation and control

WirelessHART standard is the first of its kind specifically designed to withstand stringent industrial requirements of safety, stability, reliability and device interoperability. The standard sets out to overcome the problems associated with cabling for monitoring and control applications in the industry. However, the technology is still in the trial process due to lack of adequate infrastructure. Several organizations such as EMERSON process management, AwiaTech Corporation and Linear Technology have currently embarked on the production of Industrial scale as well as laboratory scale gateways and field devices. This paper presents the formation of a WirelessHART network using a laboratory scale network evaluation kit. The effect of distance on the time of joining the network of the field devices is observed. The network will be aimed towards control application.

Implementation of real-time WirelessHART network for control application

Delay has been one of the critical factors affecting control performance of a wireless networked control system. It leads to the instability of the system. Network induced delay awareness is important for investigating the delay effects on the system. There are several direct and indirect ways to measure delays in a network. This paper concentrates on a direct delay measurement approach between WirelessHART controller and its plant. Upstream and downstream delays are measured by calculating the difference at timestamps between two consecutive received messages at the controller and at the plant respectively. The measured roundtrip delay is used to study delay effects on the control system's performance.

Tran Duc Chung

Papers

Hybrid 3D-ResNet Deep Learning Model for Automatic Segmentation of Thoracic Organs at Risk in CT Images

Internal model control for industrial wireless plant subject to measured round trip delays

Development of low-cost real-time data acquisition system for process automation and control

Implementation of real-time WirelessHART network for control application

Effect of network induced delays on WirelessHART control system