Harsh environments minimally invasive optical sensing technique for extreme temperatures: 1000 degrees C and approaching 2500 degrees C

To the best of our knowledge, for the first time is designed and demonstrated a single crystal Silicon Carbide (SiC)-based minimally invasive smart optical sensor suited for harsh environments and temperatures reaching 2500 °C. The novel sensor design is based on an agile wavelength source, instantaneous single wavelength interferometry, full optical power cycle data acquisition, free-space targeted laser beam, multiple single crystal thick SiC optical frontend chips, and multi-wavelength signal processing for unambiguous temperature measurements to form a fast and distributed smart optical sensor system. Experiments conducted using a 1550 nm eye safe band tunable laser and a 300 micron coating-free thick SiC chip demonstrate temperature sensing from room temperature to 1000 °C with a measured 1.3 °C resolution. Applications for the proposed sensor include use in fossil fuel-based power systems, aerospace/aircraft systems, satellite systems, deep space exploration systems, and drilling and oil mining industries

Smart plug prototype for monitoring electrical appliances in home energy management system

Recently, the technology of Home Energy Management System (HEMS) has expanded for the purpose of reducing energy consumption. This paper presents the development of a smart plug with a wireless Zigbee sensor for measuring power consumption of electrical appliances in the HEMS. Experiments were carried out to evaluate the power consumption of a wireless sensor node in a smart plug using only Zigbee as a microcontroller. Experimental results showed that the smart plug using Zigbee is capable of processing and analyzing the analogue sensor signal with lower power consumption. In addition, the data obtained from the wireless sensor is more accurate and smoother as compared with the data obtained from the oscilloscope. The proposed smart plug has the characteristics of simple design, low cost, low power consumption and easy to control electrical home appliances by switching on/off from the HEMS controller

Mechanical structures for smart-phone enabled sensing

The paper presents a new strategy for sensor design that is made possible by the usage of ubiquitous mobile devices for signal capture, digitization, and data processing. The approach taken is to design simple mechanical sensor elements such that they produce a sensor output that is easily acquired by a mobile smart device such as a phone or tablet computer. To illustrate this concept, two mechanical displacement transducers have been designed and tested. These sensors make use of displacement amplification structures, Moiré pattern gratings and a double-ended-tuning-fork (DETF) resonant structure. The sensors produced either an acoustic or optical signal in response to an input load or displacement, which can then be acquired using the camera or microphone of a mobile device. The computing power and connectivity of mobile devices makes a wide range of processing, visualisation and storage techniques possible at low cost. Using this technique an optical displacement transducer with a range of 150 µm, and a resolution of <5 µm; and an acoustic displacement transducer with a range of 20 µm and a standard error of 0.14 µm, are demonstrated

Ultra-Low Power IoT Smart Visual Sensing Devices for Always-ON Applications

This work presents the design of a Smart Ultra-Low Power visual sensor architecture that couples together an ultra-low power event-based image sensor with a parallel and power-optimized digital architecture for data processing. By means of mixed-signal circuits, the imager generates a stream of address events after the extraction and binarization of spatial gradients. When targeting monitoring applications, the sensing and processing energy costs can be reduced by two orders of magnitude thanks to either the mixed-signal imaging technology, the event-based data compression and the use of event-driven computing approaches. From a system-level point of view, a context-aware power management scheme is enabled by means of a power-optimized sensor peripheral block, that requests the processor activation only when a relevant information is detected within the focal plane of the imager. When targeting a smart visual node for triggering purpose, the event-driven approach brings a 10x power reduction with respect to other presented visual systems, while leading to comparable results in terms of detection accuracy. To further enhance the recognition capabilities of the smart camera system, this work introduces the concept of event-based binarized neural networks. By coupling together the theory of binarized neural networks and focal-plane processing, a 17.8% energy reduction is demonstrated on a real-world data classification with a performance drop of 3% with respect to a baseline system featuring commercial visual sensors and a Binary Neural Network engine. Moreover, if coupling the BNN engine with the event-driven triggering detection flow, the average power consumption can be as low as the sleep power of 0.3mW in case of infrequent events, which is 8x lower than a smart camera system featuring a commercial RGB imager

A sub-mW IoT-endnode for always-on visual monitoring and smart triggering

This work presents a fully-programmable Internet of Things (IoT) visual sensing node that targets sub-mW power consumption in always-on monitoring scenarios. The system features a spatial-contrast $128\mathrm{x}64$ binary pixel imager with focal-plane processing. The sensor, when working at its lowest power mode ($10\mu W$ at 10 fps), provides as output the number of changed pixels. Based on this information, a dedicated camera interface, implemented on a low-power FPGA, wakes up an ultra-low-power parallel processing unit to extract context-aware visual information. We evaluate the smart sensor on three always-on visual triggering application scenarios. Triggering accuracy comparable to RGB image sensors is achieved at nominal lighting conditions, while consuming an average power between $193\mu W$ and $277\mu W$, depending on context activity. The digital sub-system is extremely flexible, thanks to a fully-programmable digital signal processing engine, but still achieves 19x lower power consumption compared to MCU-based cameras with significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa

Smart vest for respiratory rate monitoring of COPD patients based on non-contact capacitive sensing

In this paper, a first approach to the design of a portable device for non-contact monitoring of respiratory rate by capacitive sensing is presented. The sensing system is integrated into a smart vest for an untethered, low-cost and comfortable breathing monitoring of Chronic Obstructive Pulmonary Disease (COPD) patients during the rest period between respiratory rehabilitation exercises at home. To provide an extensible solution to the remote monitoring using this sensor and other devices, the design and preliminary development of an e-Health platform based on the Internet of Medical Things (IoMT) paradigm is also presented. In order to validate the proposed solution, two quasi-experimental studies have been developed, comparing the estimations with respect to the golden standard. In a first study with healthy subjects, the mean value of the respiratory rate error, the standard deviation of the error and the correlation coefficient were 0.01 breaths per minute (bpm), 0.97 bpm and 0.995 (p < 0.00001), respectively. In a second study with COPD patients, the values were -0.14 bpm, 0.28 bpm and 0.9988 (p < 0.0000001), respectively. The results for the rest period show the technical and functional feasibility of the prototype and serve as a preliminary validation of the device for respiratory rate monitoring of patients with COPD.Ministerio de Ciencia e Innovación PI15/00306Ministerio de Ciencia e Innovación DTS15/00195Junta de Andalucía PI-0010-2013Junta de Andalucía PI-0041-2014Junta de Andalucía PIN-0394-201

Form Factor Improvement of Smart-Pixels for Vision Sensors through 3-D Vertically- Integrated Technologies

While conventional CMOS active pixel sensors embed only the circuitry required for photo-detection, pixel addressing and voltage buffering, smart pixels incorporate also circuitry for data processing, data storage and control of data interchange. This additional circuitry enables data processing be realized concurrently with the acquisition of images which is instrumental to reduce the number of data needed to carry to information contained into images. This way, more efficient vision systems can be built at the cost of larger pixel pitch. Vertically-integrated 3D technologies enable to keep the advnatges of smart pixels while improving the form factor of smart pixels.Office of Naval Research N000141110312Ministerio de Ciencia e Innovación IPT-2011-1625-43000

Low-cost autonomous 3-D monitoring systems for hydraulic engineering environments and applications with limited accuracy requirements

The details of developing autonomous 3-D motion monitoring systems based on commercial off-the-shelf (COTS) motion sensors for hydraulic environments are discussed. Possible areas of application, are river bed sediment transport monitoring and monitoring the agitation and other physical parameters inside milk vats with a mechanized agitator. Simplified calculations of inertial navigation systems (INSs) such as Euler angle method, MATLAB programs for further processing, power management systems for autonomous operation including the possibility of inductive power transfer (IPT) and use of microelectromechanical systems (MEMS) technology are discussed. Experimental results for proof of concept systems are highlighted