Sensitive optical biosensors for unlabeled targets: a review.

Hydrogen sensors are of increasing importance in connection with the development and expanded use of hydrogen gas as an energy carrier and as a chemical reactant. There are an immense number of sensors reported in the literature for hydrogen detection and in this work these sensors are classified into eight different operating principles. Characteristic performance parameters of these sensor types, such as measuring range, sensitivity, selectivity and response time are reviewed and the latest technology developments are reported. Testing and validation of sensor performance are described in relation to standardisation and use in potentially explosive atmospheres so as to identify the requirements on hydrogen sensors for practical applications.

Hydrogen Sensors - A review

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people’s lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of “providing artificial intelligence for every person and every organization at everywhere”. Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence , aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge , this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge , i.e., Edge DL.

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

We report a miniaturized fiber inline Fabry-Perot interferometer (FPI), with an open micro-notch cavity fabricated by one-step fs laser micromachining, for highly sensitive refractive index measurement. The device was tested for measurement of the refractive indices of various liquids including isopropanol, acetone and methanol at room temperature, as well as the temperature-dependent refractive index of deionized water from 3 to 90 degrees C. The sensitivity for measurement of refractive index change of water was 1163 nm/RIU at the wavelength of 1550 nm. The temperature cross-sensitivity of the device was about 1.1x10(-6) RIU/degrees C. The small size, all-fiber structure, small temperature dependence, linear response and high sensitivity, make the device attractive for chemical and biological sensing.

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Temperature-insensitive miniaturized fiber inline Fabry-Perot interferometer for highly sensitive refractive index measurement

Data intensive analysis is the major challenge in smart cities because of the ubiquitous deployment of various kinds of sensors. The natural characteristic of geodistribution requires a new computing paradigm to offer location-awareness and latency-sensitive monitoring and intelligent control. Fog Computing that extends the computing to the edge of network, fits this need. In this paper, we introduce a hierarchical distributed Fog Computing architecture to support the integration of massive number of infrastructure components and services in future smart cities. To secure future communities, it is necessary to integrate intelligence in our Fog Computing architecture, e.g., to perform data representation and feature extraction, to identify anomalous and hazardous events, and to offer optimal responses and controls. We analyze case studies using a smart pipeline monitoring system based on fiber optic sensors and sequential learning algorithms to detect events threatening pipeline safety. A working prototype was constructed to experimentally evaluate event detection performance of the recognition of 12 distinct events. These experimental results demonstrate the feasibility of the system's city-wide implementation in the future.

Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities

The ubiquitous deployment of various kinds of sensors in smart cities requires a new computing paradigm to support Internet of Things (IoT) services and applications, and big data analysis Fog Computing, which extends Cloud Computing to the edge of network, fits this need In this paper, we present a hierarchical distributed Fog Computing architecture to support the integration of massive number of infrastructure components and services in future smart cities To secure future communities, it is necessary to build large-scale, geospatial sensing networks, perform big data analysis, identify anomalous and hazardous events, and offer optimal responses in real-time We analyze case studies using a smart pipeline monitoring system based on fiber optic sensors and sequential learning algorithms to detect events threatening pipeline safety A working prototype was constructed to experimentally evaluate event detection performance of the recognition of 12 distinct events These experimental results demonstrate the feasibility of the system's city-wide implementation in the future

A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities

We report a miniaturized inline Fabry-Perot interferometer directly fabricated on a single-mode optical fiber with a femtosecond laser. The device had a loss of 16 dB and an interference visibility exceeding 14 dB. The device was tested and survived in high temperatures up to 1100°C. With an accessible cavity and all-glass structure, the new device is attractive for sensing applications in high-temperature harsh environments.

Miniaturized fiber inline Fabry-Perot interferometer fabricated with a femtosecond laser.

A coaxial cable sensor device with periodic impedance discontinuities along the length of its cable. The cable comprises an inner conductor, insulating material disposed around the length of the inner conductor, and an outer conductor disposed around the insulating material. The periodic impedance discontinuities are created by physical deformations or material alterations to at least one of the inner conductor, the outer conductor, and the insulating material. The sensor device may be used to measure temperature, pressure, strain, and acoustic waves in building structures, and is well suited for down-hole or underwater applications.

Tao Wei

Papers

Temperature-insensitive miniaturized fiber inline Fabry-Perot interferometer for highly sensitive refractive index measurement

Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities

A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities

Miniaturized fiber inline Fabry-Perot interferometer fabricated with a femtosecond laser.

Coaxial cable bragg grating sensor