Smart Sensors and Systems

In this chapter, a new CMOS platform for the real time, multiple molecules detection of the bio molecules will be introduced. The semiconductor channel of the sensor device is composed of the carbon nanotube network (CNN) decorated with the gold nano particles (GNP) as the docking agents for the probe molecules and integrated onto the CMOS chip which receives the modulation of the channel resistance and performs the necessary signal processing. The number of the sensor devices on a chip is in the range of a few thousands and statistical analysis of the multiple sensing is possible in addition to multiple targeting by adding different probe molecules. In addition, the charge screening effects and non selectivity problem, which have been regarded as the show stoppers of the electrical sensing of the bio molecules can be widely avoided by introducing the electrical pulse technique. The architecture of the platform, salient feature of the sensor devices and the behavior of the sensor devices on the electrical pulse agitation will be reviewed.

The transduction of chemical binding event into a mechanical deformationhas been developed as a label free sensing platform with potentially mobile detection setups. In this chapter we will describe the basic and engineering principles of the chemomechanical transduction with key example applications.

For many of future sensor applications, the sensors are required to be of low cost, easy production and to be able to work with multi-physics or bio/chemistry signals, and provide large area, flexible or comfortable surface coverage. All these bring challenges to the current silicon based manufacturing technology. This chapter introduces a hybrid integration concept combining the advantages of both the printed electronics and silicon technologies to address these issues. A fully printable low voltage organic thin-film transistor (OTFT) technology is developed for making the transducer in the hybrid sensor systems. A simple application example for pH sensor tag is demonstrated. This OTFT technology would provide a promising platform for developing general low cost and disposable multi-function integrated sensor systems.

Hyperspectral imaging has become more accessible nowadays as an image-based acquisition tool for physically-meaningful measurements. This technology is now evolving from classical 2D imaging to 3D imaging, allowing us to measure physically-meaningful reflectance on 3D solid objects. This chapter provides a brief overview on the foundations of hyperspectral imaging and introduces advanced applications of hyperspectral 3D imaging. This chapter first surveys the fundamentals of optics and calibration processes of hyperspectral imaging and then studies two typical designs of hyperspectral imaging. In addition to this introduction, this chapter briefly looks over the state-of-the-art applications of hyperspectral 3D imaging to measure hyperspectral intrinsic properties of surfaces on 3D solid objects.

Social CPSs (Cyber-Physical Systems) denote the extended application of the idea of CPSs to the monitoring and control of urban-scale social infrastructure systems. They utilize both cyber data stored in databases and physical data coming from sensor networks in the target physical world for the analysis and optimized control of urban infrastructure systems such as traffic, energy, and water services. This paper focuses on the winter road management in Sapporo where we have the world biggest annual snow fall among the cities with more than 1 million populations. For monitoring the road conditions over the whole city, the use of probe-car data without violating personal data protection is fundamental. This paper first shows that probe car data statistically preprocessed over road links for an urban-scale area still allow us to visualize the dynamic change of the traffic flow in terms of the divergence and flow vector field. These give us sufficient information about the dynamic change of hotspots of traffic, main traffic streams, and route selection preference. The paper also shows more complex and advanced analyses of such data, especially for better winter road management in Sapporo. We extend the well-known multiple coordinated views framework for exploratory visual analytics to multiple coordinated views and analyses by integrating analysis tools with their result visualization views into the same environment. These newly added views may also coordinate with others, and allow users to directly select clusters or mined patterns calculated at runtime to further quantify the underlying database view. Exploratory visual analytics with such an environment enables us to detect road links for effective pinpoint snow removal.

One of the top design priorities for semiconductor chemical sensors is developing simple, low cost, sensitive and reliable sensors to be built in handheld mobile devices. In this chapter, we discuss critical issues for the realization of miniaturized and integrated chemoresistive thin film sensors based on metal oxides and introduce notable recent achievements.

Handheld gas sensing systems have drawn attentions recently for personal use and daily applications. However, commercially available gas detection devices are yet to satisfy the needs due to the challenging issues of system miniaturization, such as insufficient selectivity and sensitivity. In this chapter, we introduce an approach to achieve this goal. Based on an array of surface acoustive wave (SAW) gas sensors, a bio-inspired gas sensing system (also called electronic nose) could be realized to construct a robust system to identify gases. To increase the gas sensitivity, nanocomposites of polymers and ordered mesoporous carbons (OMCs) is introduced. The polymers are directly grown on the carbon material through a radical polymerization process, thus forming interpenetrating and inseparable composite frameworks with carbon. Furthermore, to reduce the system size and power consumption, the integrated circuits (IC) technology is adopted to implement the readout interface circuit to replace bulky instruments, such as frequency counter. Finally, several odor classification algorithms are introduced to perform gas classification.

In recent years, sensors for objective evaluation of quality and quantity of odor substances have shown a wide range of potential applications in many fields. However, the odor quality is difficult to be expressed by quantitative data because the odor sensation is brought about by a variety of volatile compounds, which form a complicated, subjective olfactory sense. Recent progress in molecular biological research of the olfactory system have shown that an odor cluster map produced on the surface of olfactory bulb through olfactory receptors presents essential information for brain to perceive odorants. The clustering perception model provides us with a new concept to design odor sensors with performance equivalent to mammalian olfactory system. The biological-inspired odor sensing based on various molecular recognition technologies, such as partial structure recognized water membrane/Pt electrodes, benzene-patterned self-assembled monolayer (SAM) layers, size and polarity selected molecular sieve materials, and molecularly imprinted polymer (MIP) adsorbents, are introduced to construct an artificial odor map and to evaluate the odor quality. On the other hand, odorants in our living environment can only be perceived by the sense of our olfactory, and odor space is invisible to eyes. The temporal and spatial distribution of odorants in environment is also important information for human and other animals. However, the visualization of odor space by using conventional sensor technologies is a difficult task due to the limited spatiotemporal resolution. Here optical sensing technologies based on fluorescence imaging and localized surface plasmon resonance (LSPR) are developed to visualize the spatiotemporal distribution of odorants in environment. In addition, the application of the developed sensors in the visualization of human body odor and odor release from fragrance encapsulated cyclodextrin inclusion complexes are presented also.

Harvesting energy from the environment is a desirable and increasingly important capability in several emerging applications of smart sensing systems. Due to the low-power characteristics of many smart-sensor systems, their energy harvesting systems (EHS) can achieve high efficiency by emphasizing low overhead in maximum power point tracking (MPPT) and the use of supercapacitors as a promising type of energy storage elements (ESE). Considerations in designing efficient charging circuitry for supercapacitors include leakage, residual energy, topology, energy density, and charge redistribution. This chapter first reviews ambient energy sources and their energy transducers for harvesting, followed by descriptions harvesters with low-overhead efficient charging circuitry and supercapacitor-based storage.

This chapter proposes a novel high-efficiency PV power system for nonvolatile sensor nodes. It demonstrates that the storage-less and converter-less system achieves near 90 % energy efficiency by eliminating energy loss from power converters and storage devices. Furthermore, we propose a dual-channel power supply architecture to improve the quality of service when there are time mismatches between harvested energy and workload. A channel controller dynamically selects either direct channel or indirect one to maximize the energy efficiency under failure rate constraints. Both a simulation platform and a prototype are built to validate the architecture. Finally, we presented an intra-task scheduling algorithm for the storage-less and converter-less channel, which leverages neural network training based on solar profiles and task execution. Compared to the inter-task scheduling, it tracks solar variations more quickly and a much lower deadline missing rate is achieved.

Various brain-imaging techniques, such as CT, fMRI, and EEG, have been introduced with their own strength and weakness. While CT and fMRI systems are anything but portable and thus undermine their use in dynamic conditions, EEG system has poor resolution. NIRS system, however, can be made portable with sufficiently high resolution, enabling its use in dynamic conditions and detecting valuable hemodynamics therefrom. This chapter provides a brief overview of the basic principle on NIRS, the imaging technique of diffuse optical tomography, and the superficial noise reduction method. Then, three different modulations methods for realizing the multi-channel CW NIRS are introduced. Lastly, the implementation of spread-spectrum-code-based CW NIRS is laid out for illustration.

The design concepts of cantilever-based DNA sensors, poly-silicon nanowire-based protein/DNA sensors, a hydrogel-based glucose sensor, an ISFET-based pH sensor, and a bandgap-reference-based temperature sensor are discussed. In addition, the fabrication processes for these MEMS biosensors are presented. Sensor interface readout circuits that can deal with voltage, current, capacitive, resistive sensing signals are introduced. Wireless system-on-chips for DNA/protein/glucose sensing are designed and implemented using 0.35-μm CMOS technology. The experiment procedures are described in detail and complete measurement results are provided in this chapter. The cantilever-based DNA sensing system achieves the detectable DNA concentration lower than 1 pM. The detection limit of 10 fM can be reached by the nanowire-based DNA bio-SoC. In vitro test shows a resolution of 40 mM in glucose detection. The temperature sensor shows great linearity from −20 to 120 °C.

In this chapter, we present two key designs for ultra-low-power electrocardiography sensors, i.e., an event-driven analog-to-digital converter (ADC) and an on-off keying (OOK) transceiver. For the ADC, two QRS detection algorithms, pulse-triggered (PUT) and time-assisted PUT (t-PUT), are proposed based on the level-crossing events generated from the ADC. For the transceiver SoC, we propose a novel supply isolation scheme to avoid the instability induced by such a high receiver gain, use bond wires as inductors to reduce the transmitter power, and utilize near-threshold design (NTD) method for low power digital baseband. Fabricated in 0.13 \(\upmu \mathrm{m}\) CMOS technology, the ADC with QRS detector consumes only 220 nW measured under 300 mV power supply, making it the first nanoWatt compact analog-to-information (A2I) converter with embedded QRS detector. The transceiver SoC is fully integrated with a 10 Mb/s transceiver, digital processing units, an 8051 micro-controlled unit (MCU), a successive approximation (SAR) ADC, and etc. The receiver consumes 0.214 nJ/bit at − 65 dBm sensitivity, and the Tx energy efficiency is 0.285 nJ/bit at an output power of − 5. 4 dBm. In addition, the digital baseband consumes 34.8 pJ/bit with its supply voltage lowered to 0.55 V, indicating its energy per bit is reduced to nearly 1/4 of the super-threshold operation.

As Internet of Things (IoT) and wearable devices become the next wave of emerging markets, it is very necessary to provide a set of sensors related to target applications so that decision-making becomes more reliable from collected data. However multi-sensor approach results in higher data rate and larger power consumption, which may not be accepted by application requirements, e.g. mobile health-care. In this paper, a sensor-fusion approach will be introduced to provide an energy-efficient and data-reliable solution. By exploiting event-driven architecture, energy efficiency can be enhanced; on the other hand, analysis accuracy can be further improved with the support of multi-data sets. As a result, both power consumption and data bandwidth can be minimized with better accuracy to meet those specifications in battery-operated devices. A test vehicle related to mobile health-care applications will also be introduced to demonstrate the feasibility of our proposal.

IoT browser provides a novel way for people to interact with objects through the Internet. Comparing with traditional web browsing environment, IoT browsing environment has some unique features such as the way of interacting with the objects, the importance of spatial-temporal information of objects, and the necessity of resource reuse. In this chapter, we propose a novel IoT browsing system with a learning capability middleware for IoT browser integrated with context-aware services. The system adopts a service-oriented architecture and provides device interoperability, resource reusability and spatial-temporal awareness. More specifically, the system can provide tailored services to meet the preferences of users by using flow-based programming to establish event flows on the IoT browser. Furthermore, learning is introduced to help the users build their event flows by suggesting them the next possible events based on the historical context information of sensors (or smart things). We develop a prototype of the proposed system and demonstrate the applicability of the system in a home browsing scenario. The prototype shows that with the help of the IoT browsing system, heterogeneous devices can cooperate with each other to provide IoT services in accordance with context inference results. In addition, each device can be reused by multiple services with minimum human supervision to reduce hardware and deployment costs.

Cyber physical system (CPS) is a general computation concept, in which “Computers (Cyber world)” and “Real world” are integrated via computer networks. In a cyber physical system, there is a loop structure of “observation,” “processing” and “feedback” in the real world: (i) various kinds of data are acquired from our real world using various sensors; (ii) then those data are transferred to computers, or cyber world, and are processed and analyzed; (iii) the analyzed results are fed back to the real world and the real world are modified according to the feedback. Based on this loop structure, the real world is changed, or adjusted. The concept of cyber physical system is well suited for the framework of various IT-based social services, and, in this chapter, we present our research project applying the CPS to social services, especially to an energy management problem, which is one of the most crucial issues for our future society.

One billion people (15 % of the world population) are unreached in terms of access to quality healthcare services largely as a result of the paucity of healthcare facilities and medical experts in rural areas. We have prototyped “portable health clinic (PHC), a compact telehealth system with diagnostic equipment and GramHealth software for archiving and searching patients’ past health records. The back-end of the system consists of data servers and a medical call center. The front-end has the instances of portable briefcase consisting of medical sensors and measuring equipment operated by healthcare workers living in unreached communities. The front-end data transmission system and Skype telemedicine calls connect with the back-end using mobile network coverage and Internet. Doctors at the medical call center access GramHealth data cloud through the Internet or have a copy of the database in the call center server. Upon receiving a multimedia call from a patient, the doctor can find that patient’s previous EHR record and then create and send an e-Prescription. The healthcare worker’s PHC briefcase is designed to be low cost and portable. It is envisioned as costing less than US$300 (an amount an entrepreneur can borrow from micro-finance institutions such as Grameen Bank in Bangladesh) and light enough to be carried by a female health assistant. The PHC briefcase will be owned and operated by a village health assistant. This will be a sustainable business model as the health assistant can build a professional relationship with her local clientele. We carried out experiments in three remote villages and in two commercial organizations in Bangladesh by collaborating with local organizations to observe the local adoption of the technology. We are looking at the applicability of our PHC system for aging societies in developed countries.