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Smart farming is the integration of advanced technologies such as sensors, wireless communication, Internet of Things (IoT), artificial intelligence (AI), and robots, with traditional farming practices. The goal is to increase the yield and quality of the crops or other agricultural products, as well as to reduce the cost and improve efficiency. Sensors play a critical role in smart farming as they provide the most important information related to varied aspects of agriculture, namely soil conditions, plant growth, and environmental conditions. With the emergence of printed electronics technologies, printed sensors have gained much attention in recent years. Printed sensors are fabricated through the additive manufacturing and are extremely versatile. They can be made on any solid substrate and be conformable, flexible, stretchable, low-cost, and/or bio-degradable. These unique features have made the printed sensors attractive for a variety of applications. This article aims to review the potential applications of printed sensors in smart farming. First, the state of the art of printed sensor fabrication is presented with a discussion on their advantages and disadvantages. Then, the measurement and monitoring applications of printed sensors in the key aspects of smart farming, are reviewed, including measurement of chemicals, soil monitoring, and microclimate monitoring in greenhouses. The article also pinpoints the limitations and potentials of printed sensors for farming applications. This article will benefit the researchers and practitioners in the field to advance the printed sensor technologies to mitigate the current issues in smart farming. [ABSTRACT FROM AUTHOR]

In recent years, the wireless sensor node (WSN) has been used in condition monitoring of the railway axle box bearing to guarantee the safety of the high-speed train. This article proposes a variable reluctance energy harvesting system to power the WSN for extending the service life of the WSN integrated within the smart bearing. The variable reluctance energy harvesting system consists of a variable reluctance energy harvester (VREH) and a power management circuit (PMC), which is able to convert the rotation motion energy into electrical energy efficiently. The key geometrical parameters of the VREH were investigated to obtain the optimal energy harvesting performance by the theoretical modeling and the finite-element analysis. The PMC was designed in the LTspice to achieve the purposes of impedance matching, voltage boosting, rectification, and regulation. The energy harvester was prototyped and used for powering a WSN embedded in the bearing. The powering capability of the energy harvesting system was experimentally verified under the rotation speed range of 400–1200 r/min. Under the rotational speed of 1200 r/min and optimal impedance matching, the generated power of the prototype of harvester reaches 76.05 mW and the charging efficiency of PMC is 52.6%. It is proven that the designed energy harvesting system is capable of powering the WSN for detecting the rotation speed, vibration, strain, and temperature information of the bearing. The proposed smart bearing system has a great potential for improving the reliability of the high-speed train. [ABSTRACT FROM AUTHOR]

Rotating machines are widely used in industries, manufacturing, and oil and gas plants as a critical component for process availability. The inadvertent failure of these rotating machines causes significant process downtime and incurs higher repair costs and loss of revenue. These failures can belong to electrical, thermal, or mechanical fault categories. Early detection of all these faults is very much critical for avoiding complete failure of these machines and requires continuous 24/7 online monitoring using sensors or intelligent electronic devices (IEDs). However, an affordable low-cost and efficient online monitoring is desired to practice specifically for medium-voltage (MV) machines which have a larger install base. Electrical signature analysis (ESA) technology offers such flexibility and requires measuring just current and/or voltage at a motor control panel for machine health diagnosis, unlike vibration analysis (VA) requiring installation of sensors and its wiring on the machine. Furthermore, an intelligent, self-reliable, and autonomous ESA procedure is required for monitoring the machines due to the lack of ESA standards in practice unlike for VA. This article proposes a new autonomous electrical signature analysis (AESA)-based measurement technique implemented in IED, that is, Protection Relay, offering 24/7 online monitoring. The proposed technique implemented in Relay not only avoids dependence on standalone ESA hardware but also provides earlier detection of failures using peak and energy magnitudes computation approach at fault frequencies. To validate the proposed method, various tests were performed on the actual 1000- and 300-HP motors with/without mechanical faults in a machine repair shop and the results are discussed. The performance of the proposed method is also compared with the commercially available third-party ESA device results proving the efficacy. [ABSTRACT FROM AUTHOR]

Over the last decade, the rapid development in health care industry and security has driven innovations in the field of wearable electronics and smart sensors. This report demonstrates the fabrication of molybdenum diselenide (MoSe2) by a facile, low-cost and scalable hydrothermal synthesis method followed by a vacuum filter deposition on cellulose paper. The fabricated device is utilized as a pressure and strain sensor. The fabricated device is passivated using polydimethylsiloxane (PDMS), and also, PDMS film enhances the flexibility of the device. The calculated sensitivity of pressure sensor is ~0.3 kPa−1, which is comparable and relatively better than the pressure sensor fabricated using sophisticated techniques. Furthermore, the device exhibited significant of merit for strain sensing with a gauge factor of ~6.84. The fabricated device showed excellent resilience in 500 cycle bending tests, which promises the robustness of the device. As an application, the fabricated sensor is used to develop a wireless keyboard by integrating the sensor on fingertips of an individual and a real-time human motion monitoring application in which the data are transmitted wirelessly to a smartphone with a dedicated android application. The successful fabrication of MoSe2/paper-based cost-effective, flexible, and biodegradable pressure-strain sensor shows tremendous applications in future intelligent user interface systems, human security, and personal health care monitoring. [ABSTRACT FROM AUTHOR]

In this article, the real-time nonintrusive load-monitoring (NILM) problem with limited measurements, i.e., low sampling rate data, is investigated. NILM is a technique to identify the various types of appliances by analyzing the voltage and current features collected by sensors, such as smart outlets. It is one of the most important topics in smart grid management and optimization. Although recent NILM methods developed several general models that can be used for various scenarios, these algorithms either require high-frequency measurement devices or have been limited to use for some specific appliances. A real-time intelligent NILM algorithm that is able to be transferred among different appliances with low-frequency data is still lacking and desperately needed. In this article, a novel online learning-based intelligent NILM algorithm has been developed that can, online, infer a variety of appliances using a transferred model with limited measurements. Especially, the developed algorithm integrates the emerging transfer learning technique along with deep neural networks. Two neural networks are used in two different stages: 1) the long short-term memory (LSTM) neural network to extract the lower level spatial and temporal features from the gray-scale image generated by the measurements and 2) the probabilistic neural network (PNN) to classify the appliance type as well as transfer between appliances. Finally, the algorithm is implemented into practical smart outlet hardware to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

In this article, a compact ultralow-power smart temperature sensor realized in 130-nm standard CMOS technology, provided by STMicroelectronics, is presented. The sensor is designed with the aim of measuring local temperature variations in the range of −20 °C–85 °C in temperature drift compensation applications. It relies on a simple circuit topology that employs two current-controlled oscillators—a divider and a 15-bit counter—which allows temperature-to-digital conversion. Experimental results show an inaccuracy of ±0.6 °C ($3\sigma$) and a resolution of 0.012 °C with power consumption as low as $6~\mu \text{W}$ at 1.3-V power supply with a total die area of only 0.00916 mm2. [ABSTRACT FROM AUTHOR]

The selective detection of pollutants in water in a laboratory scenario has been presented by authors exploiting low-cost optical biosensors based on plastic optical fibers (POFs) and biological or biomimetic receptors. So, for instance, the detection in water of naphthalene, perfluoroalkyl substances (PFAs) and polyfluoroalkyl substances have been investigated with interesting detection limits when compared to those obtained by using different expensive traditional approaches (e.g., liquid chromatography-mass spectrometry with high performances). In this article, we have developed and tested a novel approach used in a smart measuring system to use POF sensors in situ for the remote measures of pollutants in water for smart cities applications. More specifically, we have used different water–glycerin solutions to test the novel sensor system based on a Raspberry Pi connected to the Internet and to a spectrometer, a light source, a POF sensor, and two computers connected to Internet used as client and server. [ABSTRACT FROM AUTHOR]

Smartphones are an indispensable tool in modern day-to-day life. Their widespread use has spawned numerous applications targeting diverse domains, such as biomedical, environment sensing, and infrastructure monitoring. In such applications, the accuracy of the sensors at the core of the system is still questionable since these devices are not originally designed for high-accuracy sensing purposes. In this article, we investigate the accuracy limits of one of the commonly used sensors, namely, smartphone accelerometer. We focus on the efficacy of a smartphone as an acceleration measuring device, rather than focusing only on the accuracy of its internal accelerometer chip. This holistic approach includes additional errors that arise from the device operating system, such as sampling time uncertainty. Hence, we propose a novel smart device accelerometer error model that includes the traditional additive noise as well as sampling time uncertainty errors represented by a white Gaussian process. The model is validated experimentally using shake table experiments, and the maximum likelihood estimation (MLE) is used to estimate the sampling time uncertainty standard deviation. Moreover, we derive the Cramer–Rao lower bound (CRLB) of acceleration estimation based on the proposed model. [ABSTRACT FROM AUTHOR]

In this article, we propose a cuffless blood pressure (BP) estimation technique based on deep learning for smart wristwatches. Photoplethysmography (PPG) and electrocardiography (ECG) signals are first collected from the sensors installed at a smart wristwatch. Ground-truth systolic BP (SBP) and diastolic BP (DBP) measurements are then obtained by a reference device, a mercury sphygmomanometer. In order to estimate the SBP and DBP, we extract feature vectors and reconstruct them through a feature selection process. Next, we design a two-stage system of stacked deep neural network (DNN)-based SBP and DBP estimation models and compare our results with those obtained using estimation techniques in the previously reported algorithms such as the polynomial regression (PR), support vector machine (SVM), artificial neural network (ANN), and deep belief network (DBN)-DNN. In order to verify the proposed algorithm against the conventional algorithms, we quantitatively compare the results in terms of mean error (ME) with standard deviation, mean absolute error (MAE) with standard deviation, Pearson correlation, box plot, and Bland–Altman plot. For this, 110 subjects contributed to the database (DB), each of which is collected three times for 20 s. The quantitative errors turn out to be lower than that of the existing methods, which shows the superiority of our approach. To enhance the BP estimation performance for each individual user further, we devise the personal adaptation algorithm for the BP estimation algorithm that yields better BP estimates. [ABSTRACT FROM AUTHOR]

With the manufacturing industry stepping into the emerging new era of big data and intelligence, the amount of data collected from perception and monitoring systems with multiple smart sensors has increased tremendously. Such huge amount of multisensory data may not only power many aspects of fault diagnosis, but also bring great opportunities and challenges in modern manufacturing industry. In addition, with respect to intelligent fault diagnosis for machinery, few researches have been focused on the compound fault diagnosis under big-data circumstance. Therefore, a novel, intelligent, compound, fault decoupling method based on deep capsule network (CN) and ensemble learning is developed for compound fault decoupling and diagnosis using multisensory data. First, a decoupling CN (DCN) is constructed as the basic model. Second, taking the full advantage of multisensory data, the DCN model can be pretrained with multiple sensor data, which can obtain various pretrained DCN models. Finally, combining with ensemble learning skill, the pretrained DCN models are integrated by a combination strategy to obtain the deep ensemble CN (DECN) model for intelligent compound fault decoupling and diagnosis. The performance of the DECN model is validated on an automobile transmission (AT) data set with two compound faults, and the experimental results illustrate that the DECN model obtains higher diagnosis accuracy and decouples the compound fault correctly. [ABSTRACT FROM AUTHOR]

The main motivation of paraglider pilots is to stay in the air for as long as possible. Therefore, paraglider pilots are always searching for thermal upwind that allow them to gain altitude. These thermal lifts are difficult to detect. Therefore, sensors and devices that indicate the vertical speed (so-called variometers) are widely used among paraglider pilots. This article presents the design and the implementation of an ultralow-power, self-sustaining, high-precision, wrist-worn variometer with a minimal form factor but infinite lifetime, which can visually and acoustically indicate the vertical velocity. This article demonstrates the benefits of combining multisource energy harvesting (EH) for wearable devices with low power design, exploiting a novel near-threshold ARM Cortex-M4F microcontroller, the Ambiq Apollo2, for the onboard processing. The experimental results show a power consumption of only $17.12~\mu \text{W}$ in sleep mode and $1937.21~\mu \text{W}$ in the worst case scenario when processing the data and outputting an audio feedback. Measurements confirmed that combining both thermal and solar EH makes the designed electronics self-sustaining. Without EH, the system will be operational for up to 372 h in always-on mode (worst case scenario) supplied by a 200-mAh lithium-ion battery. [ABSTRACT FROM AUTHOR]

This paper introduces sparse low-power sensor networks using passive electric potential sensors for crowd-aware smart buildings. The proposed networks can detect human presence, track their positions, and estimate room occupancy by measuring the electric field disturbances caused by human movement. In addition, the proposed passive sensing system can remotely monitor human physiological signals of nearby stationary occupants (standoff ~1.5 m) when the ambient 60-Hz fields are filtered out. Based on an experimentally calibrated source range model, we further propose an improved multiple-occupant near-field indoor localization method using a maximum likelihood algorithm combined with dividing rectangular search. The tradeoff between search efficiency and localization accuracy is also discussed. Experimental results in two different rooms for 1 week duration show the effectiveness of our proposed sensing system: human identification with an accuracy of 98.3% on six participants, occupant localization methods with room occupancy estimation accuracy of 89.03%, and average localization error less than 0.3 m. These results open up new possibilities in security, indoor navigation or positioning systems, human–machine interfaces, and tele-health applications. [ABSTRACT FROM AUTHOR]

Reading out from large-scale silicon photomultiplier (SiPM) arrays is a fundamental technical obstacle blocking the application of revolutionary SiPM technologies in nuclear imaging systems. Typically, it requires using dedicated application-specific integrated circuits (ASICs) that need a long iterative process, special expertise, and tools to develop. The pico-positron emission tomography (Pico-PET) electronics system is an advanced 100-channel readout system based on 1-bit sigma–delta modulation and a field-programmable gate array (FPGA). It is compact ($6\times 6\times0.8$ cm3 in size), consumes little power (less than 3W), and is constructed with off-the-shelf low-cost components. In experimental studies, the Pico-PET system demonstrates excellent and consistent performance. In addition, it has some unique features that are essential for nuclear imaging systems, such as its ability to measure V–I curves, breakdown voltages, and the dark currents of 100 SiPMs accurately, simultaneously, and in real time. The flexibility afforded by FPGAs allows multiple-channel clustering and intelligent triggering for different detector designs. These highly sought-after features are not offered by any other ASICs and electronics systems developed for nuclear imaging. We conclude that the Pico-PET electronics system provides a practical solution to the long-standing bottleneck problem that has limited the development of potentially advanced nuclear imaging technology using SiPMs. [ABSTRACT FROM AUTHOR]

Minimizing the power consumption of always-on sensors is crucial for extending the lifetime of battery-operated devices that are required to monitor events continuously and for long periods. This paper proposes a novel programmable $\mu \text{W}$ event-driven acoustic detector featuring “always-on” audio pattern recognition. The event-driven detector detects up to eight programmable spectral–temporal features extracted with a low-power single-channel analog circuit and classifies the features by an onboard microcontroller. The event-driven detector is combined with novel microbial fuel cells (MFCs) to achieve self-sustainability in an underwater scenario. Experimental results demonstrate that the power consumption of the detector is only $26.89~\mu \text{W}$ during always-on mode, achieving up to 59-dB sound pressure level of sensitivity. High detection accuracy of up to 95.89% in recognizing acoustic patterns has been experimentally verified. Accurate measurements with commercial MFCs demonstrate the capability to achieve self-sustainability in always-on monitoring. [ABSTRACT FROM AUTHOR]

Sensors are used to determine a variety of health and security parameters. Pressure mats and camera analysis are frequently used to determine a person’s chair posture. Measurement of parameters can be derived from a variety of different sensor types, which may already be present in an environment. This paper presents the use of the concept of “sensory substitution,” where a sensor designed to measure Quantity X is used to measure Quantity Y. The concept is used to enable alternative sensing techniques for medical and security applications, such as determining chair occupancy, postural shift times, and even current postural state, without using cameras or pressure sensors. Specifically, the postural state of a subject is determined in a laboratory study using various arrays of both temperature sensors and acoustic sensors. This method can be used standalone, can augment other sensors, or can validate data from pressure or visual-based systems. These alternative sensors could also be used for other aspects of smart infrastructure. The system was tested with different types and styles of furniture, including chairs, armrests, cushions, and beds. A least mean square algorithm was deployed to remove noises enabling acoustic posture detection. Using a thresholding algorithm, postural changes are then identified using audio. In a controlled environment, over 90% of postural change events were detected. Thus, the paper shows two alternative instrumentation and measurement methods to determine occupancy, postural change timings, and even posture states of a person in a chair. [ABSTRACT FROM AUTHOR]

A smart structure using intensity-based optical fiber sensors has the potential to be a simple and low-cost system. In this paper, a novel fiber-optic smart structure system using a complementary metal–oxide–semiconductor (CMOS) is demonstrated that simultaneously monitors a number of hetero-core optical fiber bending sensors. The proposed system can integrate a number of optical fiber transmission lines. Therefore, the system does not require a number of photodetectors, which is different from conventional techniques. To simultaneously measure optical intensity, we coupled a number of hetero-core optical fiber sensors to a CMOS camera. The optical fiber sensor can detect macrobending to the optical light intensity. The output images comprise luminance spots distribution directly representing the sensor arrangements in the smart structure. A template matching technique based on the sum of absolute differences was introduced to distinguish between the overall behaviors of structures. A recognition experiment for object shapes was performed with a smart pressure sheet in which hetero-core optical fiber sensors are embedded as a simple case. In addition, a real-time human body posture recognition application was implemented using a fiber-optic smart clothing to apply the proposed system. Recognition results show that all images comprising luminance spots distribution were matched with the correct template images, with an error rate of 0% for 15 trials. [ABSTRACT FROM PUBLISHER]

In the last few years, several wearables appeared in the market, for fitness and healthcare applications. Such smart devices have been proposed as a possible solution for lowering the costs of healthcare, leading to the mHealth revolution. In the typical scenario, each wearable, embedding sensors, processing units and communication modules, adopts a smartphone for data collection, data displaying, and remote communication. In this paper, authors modify this paradigm simplifying the wearables (e.g., relying only on simple analog front ends and communication interfaces) and exploiting the (relatively large) computational capability of the smartphone, not only for implementing gateway features but also for processing raw biosignals as well. Several experiments verify the feasibility of the proposed approach and demonstrate that “local” biosensor virtualization is possible, expanding possibilities of mHealth. In particular, tests have been carried out to evaluate the performance of hearth rate computation and respiratory rate virtual sensor, starting from a single-lead electrocardiogram signal. [ABSTRACT FROM PUBLISHER]

The interest of industry and academia in medical measurements continues to increase, due to technology advancements that allow new sensors, measurement systems, and methodologies, supporting a wide number of medical applications. [ABSTRACT FROM PUBLISHER]

This paper presents two compact frequency-output temperature sensors based on a multivibrator current-to-frequency converter to improve sensor linearity over a wide temperature range featuring low-voltage low-power operation. Both sensors have been fabricated in a low-cost 0.18- \mu \textm CMOS technology with a single 1.2 V supply voltage. They show high linearity over a temperature range of −40 °C to +120 °C, achieving an inaccuracy of ±1 °C, with sensitivities of 335 Hz/°C and 460 Hz/°C, a power consumption below 3 \mu \textW , and an area below 0.025 mm ^2 . These features make them a highly suitable solution in the case of portable applications. [ABSTRACT FROM PUBLISHER]

Health monitoring of infrastructure and other critical components composed of complex composite materials is an important ongoing concern. The embedded modulated scatterer technique (MST) has shown potential for evaluating electrical (i.e., complex dielectric) properties of bulk materials. This paper investigates its potential utility for evaluating properties of layered composite structures. The approach is based on irradiating an MST probe with an electromagnetic wave. This incident wave induces a current along the length of the thin dipole probe as a function of its load impedance and the material surrounding it, including boundaries within a layered structure. Consequently, the MST sensor may be placed at a specific location or boundary such that the probe response can be monitored over time for critical changes in the geometrical or the material property of the structure. In order to use MST for evaluating important characteristics of layered structures, this application must be fully investigated, and its limitations established. This paper presents an inclusive study of the application of MST for the evaluation of layered materials through pertinent electromagnetic simulations as well as experimental corroboration of the simulation results. The experimental results show that embedded MST is capable of detecting boundaries between layers within a layered structure and is also sensitive to the distance to the boundary. [ABSTRACT FROM AUTHOR]

Measurement errors such as gain, offset, nonlinearity, hysteresis, and cross-sensitivity degrade sensor performance, meaning that self-compensation becomes an important aspect in the maintenance of smart sensors. The progressive polynomial calibration (PPC) method is a step-by-step compensation algorithm that can fix some of the aforementioned errors by using microprocessors. In this paper, a modified-PPC (M-PPC) method is introduced. It is then shown that the number of calibration points, their selection, and permutation mechanisms can affect the M-PPC method. Therefore, an intelligent algorithm is presented to select appropriate calibration points from the input–output data. Intelligent selection of calibration points during the M-PPC method makes the calibration process simple, accurate, less time consuming, and low on computational load. A numerical example is provided to show the advantages of the proposed method in comparison with previously published ones. Finally, a test bench based on a thermistor with a nonlinearity characteristic is employed to examine the experimental results of the proposed method. [ABSTRACT FROM PUBLISHER]

Modern monitoring systems require high-performance instrumentation, low-cost primary sensors, online signal processing, portability, and versatility. Smart sensor utilization is an approach to comply with these features. From an industrial point of view, voltage, electric current, and vibrations are signals commonly analyzed during system monitoring, and wavelet and wavelet packet transforms are popular techniques for extracting time-frequency information from these signals. Here, a system-on-chip-based smart sensor with reconfigurable architecture is proposed for time-frequency analysis applying either wavelet or wavelet packet transform. The proposed smart sensor uses a proprietary hardware processing unit and a custom embedded microprocessor. It can connect to different primary sensors without additional instrumentation and shows obtained results in a 2-D or 3-D view on a video graphics array screen or liquid crystal display. Obtained results from three different study cases demonstrate that the proposed smart sensor is able to show online and in real-time the frequency evolution effects in time-varying signals. [ABSTRACT FROM PUBLISHER]

Detection and evaluation of corrosion in reinforcing steel bars (rebars) commonly used in cement-based structures is an important ongoing concern. Embedded sensor technologies hold promise for this purpose. In recent years, modulated scatterer technique (MST) has shown great potential as an embedded sensor technology. The first step in evaluating the potential of embedded MST for rebar corrosion detection is to evaluate its potential for detecting the presence of subtle corrosion in a rebar. This short paper investigates the measurement potential for a dual-loaded differential MST approach for detecting relatively subtle corrosion in a steel rebar. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a prototype of a stator-free revolutions-per-minute (RPM) sensor based on two microelectromechanical-system uniaxial accelerometers. This paper first introduces the operating principle of the stator-free RPM sensor. It then discusses the associated architecture and design issues of this new sensing method. It then describes the detail of the prototype sensor hardware and software design of the common-mode rejection method and its signal processing. Experiments using the prototype sensor have been also conducted to further verify and strengthen the arguments put forward in the previous discussion. All experiments in this paper took place on a lathe machine in a laboratory. Sensor calibration under a MATLAB environment is also described. Experimental results confirm the interesting property of this sensor, namely, that it provides higher precision at higher RPM. The conclusion summarizes the design considerations, the experimental results, and the motivation in relation to future works for this stator-free RPM sensing method. [ABSTRACT FROM PUBLISHER]

This paper presents a front-end circuit with pulsewidth modulation (PWM) output for a capacitive sensor interface. A single-MOSFET-based capacitance-ratio-modulated current (CRMC) circuit is proposed to transform the sensed capacitance into a current proportional to a capacitance ratio. The proposed single-MOSFET-based CRMC circuit not only saves a lot of chip area but also lowers power consumption. Then, a dual-slope integration circuit transforms further the modulated current into PWM output. A prototype chip is designed and fabricated with a 0.35- \mu\m complementary metal–oxide–semiconductor process and has a core area of 0.09 \mm^2. The measurement results of the prototype chip demonstrate an accuracy value of up to 9.3 bit with 54- \mu\W power consumption. The microwatt power consumption and the small chip area of the proposed front-end circuit make it very suitable for a capacitive sensor interface in smart sensor chips. [ABSTRACT FROM PUBLISHER]

Unobtrusive pressure sensors can be used for biological monitoring and long-term health assessment in smart homes. The challenge in detecting events from smart home data is that people have different mattresses, unlike in hospitals where bedding is standardized. This paper proposes to model central apneas using an under-mattress pressure sensor as a measuring instrument. The model uses three parameters, namely, a relative threshold and two time lengths, applied to a moving variance signal. The use of a relative threshold allows apneas to be detected under a variety of different conditions and improves results compared to hard-coded thresholds. The algorithm developed herein was applied to simulated apneas collected from pressure sensors placed under nine different mattresses. The parameters determined from the training set were applied to the test set and produced classification results of 0.78 positive predictive value (PPV) if the bed occupant's position is known and 0.75 PPV if the position is unknown. The use of the relative threshold approach overcomes the variability in mattress types found in smart homes. [ABSTRACT FROM PUBLISHER]

Today, wireless sensor technology is based on monolithic transceivers that optimize cost but have a rigid hardware architecture. In this paper, a new architecture for wireless sensors is presented. It is based on a software-defined radio concept and shows impressive adaptability to external conditions. The proposed architecture, which is called the wireless ultrasmart sensor (WUSS), enables the use of a software programmable transceiver, which can simultaneously manage multiple radio communication channels or standards. In addition, the WUSS architecture can mix the flexible communication part of the smart device with the sensor conditioning part. This combination represents a new generation of general-purpose sensors for microwave measurements in the gigahertz range (e.g., displacement and position sensing) with enhanced versatility since the same radio-frequency front end is both used to communicate and make measurements. Experimental results demonstrate the feasibility of the proposed architecture. A prototype shows the following abilities: managing multiple protocols using a single antenna, interfacing standard sensors, making delay and phase measurements with a standard deviation of 3 ns, and assigning the time of arrival to incoming radio signals with an error on the order of 20 ns. [ABSTRACT FROM AUTHOR]

A reconfigurable sensor-networking architecture is proposed, which is constructed from lightweight functional elements running on small virtual machines. The proposed architecture provides a flexible measurement framework that is easily adaptable to changing system requirements in run-time. The system is based on Communicating Sequential Processes and thus provides the possibility of verification and checking of system correctness in order to produce provably robust and reliable measurement systems. The structural deadlock-free property of the network is formally proven. [ABSTRACT FROM AUTHOR]

Recently, the network management of sensor nodes has gained interest as more sensors are being networked with Internet technologies as opposed to traditional instrumentation and measurement networks. Sensor networking standardization efforts lead a more interoperable way of identification and communication among sensors, and networking technologies provide multiple-access mechanisms to a high number of sensors. The convergence of sensor networking toward internetworking provides flexibility in protocol development for network management. This paper presents a novel demonstration of such a convergence by utilizing a common network management protocol, i.e., Simple Network Management Protocol (SNMP), and a sensor networking standardization effort, i.e., Transducer Electronic Data Sheet (TEDS). The TEDS information for two types of sensors has been transferred to the SNMP domain through a novel management information base design, and the network management system has been implemented on an actual testbed. [ABSTRACT FROM PUBLISHER]

Sensor networks have proven their usefulness in various acoustic localization applications. Recently, a consistency-function-based algorithm has been proposed, which can provide accurate solutions even if a large number of independent outliers are present in a measurement set. In certain practical cases, e.g., in non-line-of-sight reverberant areas, however, sensors may have cooperative and consistent errors, resulting in bad estimates. In this paper, an adaptive consistency-function-based solution is proposed, which can compensate for cooperative and systematic measurement errors and thus provides accurate results even if the original consistency-function-based algorithm fails. Stochastic initialization is also proposed, which is able to accelerate execution of the algorithm by several orders of magnitude while the global optimum is still provided with arbitrarily high probability. [ABSTRACT FROM AUTHOR]

Valves play a critical role in rocket-engine test stands because they are essential for the cryogen transport mechanisms that are vital to test operations. Sensors that are placed on valves monitor the pressure, temperature, flow rate, valve position, and any other features that are required for diagnosing their functionality. Integrated system-health management (ISHM) algorithms have been used to identify and evaluate anomalous operating conditions of systems and subsystems (e.g., valves and valve components) on complex structures, such as rocket test stands. In order for such algorithms to be useful, there is a need to develop realistic models for the most common and problem-prone elements. Furthermore, the user needs to be provided with efficient tools to explore the nature of the anomaly and its possible effects on the element, as well as its relationship to the overall system state. This paper presents the development of an intelligent-valve framework that is capable of tracking and visualizing events of the large linear actuator valve (LLAV) in order to detect anomalous conditions. The framework employs a combination of technologies, including a dynamic data exchange data-transfer protocol, autoassociative neural networks, empirical and physical models, and virtual-reality environments. The diagnostic procedure that is developed has the ability to be integrated into existing ISHM systems and can be used for assessing the integrity of rocket-engine test-stand components. [ABSTRACT FROM PUBLISHER]

Due to recent advancements in social networks, many people can consume diversified services on a daily basis and have developed an association with different communities of interest (COIs) via these services. However, a person only accesses a subset of these services at a given time either to consume certain services or to share information with a COI. This paper tries to answer two important research questions: 1) “how to dynamically capture user context from heterogeneous sources” and 2) “which services and COI are related to any given context.” To address these two challenges, we propose a framework called SenseFace, which provides user context from two sources: 1) a body sensor network (BSN) and 2) multimedia information contained the within social network space. We present the detailed design and implementation of the framework and share our preliminary test results. [ABSTRACT FROM AUTHOR]

In future smart environments, mobile applications will find a dynamically varying number of networked sensors that offer their measurement data. This additional information supports mobile applications in operating faster, with a higher precision and enhanced safety. The potentially increased redundancy obtained in such scenarios, however, is seriously affected by additional uncertainties as well. First, the dependence on wireless communication introduces new latencies and faults, as well as errors, and second, sensors of the environment may be disturbed, of low quality, or even faulty. The quality of the collected data therefore has to be dynamically assessed. Our work aims to provide a generic programming abstraction for fault-tolerant sensors and fusion nodes that cope with varying quality of measurements and communication. [ABSTRACT FROM AUTHOR]

The architecture and operation of a wireless instrumentation bus based on the Bluetooth standard are presented together with a detailed characterization. The bus, via suitable wireless interface boards, allows measurement instruments equipped with standard RS232, IEEE488, universal serial bus, and local area network interface ports to be simply connected within the wireless area without any firmware modification. In addition, a comparison with traditional wired interface buses is carried out in terms of communication reliability and throughput with reference to some basic operating modes. Finally, the results of distance tests and a detailed evaluation of power consumption for each wireless interface module are reported. This will enable measurement system designers to evaluate the convenience of the solution for their application requirements. [ABSTRACT FROM PUBLISHER]

This paper studies the problem of data communication protocols for multiprocessor smart sensors and embedded applications with hard real-time (HRT) or critical requirements. We propose a time-triggered communication interface and set of protocols, called Predictable ARchitecture for Sensor Communication Systems (PARSECS), specifically designed to sustain, at low costs and complexity, the predictable operation of such HRT systems. The general interface architecture, data format, and communication protocols are discussed, along with a case study—the implementation of PARSECS on the full-duplex serial peripheral interface for the COllaborative Robotic Environment—the Timisoara eXperiment (CORE-TX) smart sensors platform. Its predictability, timeliness, and overall performance evaluation and validation are presented in detail based on experimental results and measurements. A comparative study with some of the most prominent systems in the field is also provided. [ABSTRACT FROM PUBLISHER]

In this paper, we present a framework that bridges body sensor networks (BSNs) and social networks by mapping a subgroup of members of one's social network with each sensory data feed of his BSN. We leverage the open stack of the Internet by creating an overlay on top of existing social networks. Thus, any sensor triggering sensory data push from a BSN is directly forwarded to the interested subgroup of one's social network only. In response to event-based spontaneous sensory data push from a BSN to members of social networks, the framework also provides a downstream RESTful access to each of the sensory data by the authorized members of social networks. We present the detailed design and implementation of the framework and share our preliminary test results. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are nowadays a reality. The wide consensus gained by the IEEE 802.15.4 standard has facilitated the adoption of wireless links not only in the consumer world but also as a valid replacement of traditional wired industrial networks. In particular, the physical layer specs of IEEE 802.15.4 comply with the critical requirements of industrial applications as low cost and low power. The most remarkable example of this strategy is Wireless HART (WH), which is the first wireless fieldbus based on an open standard and specifically designed for process measurement and control applications. It uses traditional IEEE 802.15.4 radios but adopts a synchronized time-division medium- access strategy and relies on HART protocol for the upper layers. Its mesh topology allows for efficient and reliable coverage of large areas. It was presented in September 2007, but test specifications have officially been released just a few months ago, together with the announcement of the "Wireless Test System," which is only available to HART consortium members. In this paper, the authors deeply discuss the design and performance of a new and innovative packet analyzer purposely designed for the WH protocol. To the authors' knowledge, it is the first instrument that pursues a distributed approach so that it can be used both for laboratory tests and for on-the-field measurements during the plant commissioning. It is capable to simultaneously scan all available RF channels and to furnish packet timestamping with accuracy on the order of microsecond, as verified by an extensive measurement campaign. In addition, a dissector for the well-known WireShark software has also been implemented and described, providing a very simple and intuitive analysis. Moreover, the proposed instrument has been interfaced with a preliminary version of the analysis software supplied by the HART consortium. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of rigid-body jointangle measurement based on microelectromechanical-system (MEMS) biaxial accelerometers and uniaxial gyroscopes. In comparison to conventional magnetic and optical joint angular sensors, this new inertial sensing principle has the advantages of flexible installation and true contactless sensing. This paper focuses on the comparison of four different inertial-sensor combination methods that are reported in reference papers and utilizes the theory of rigid-body kinematics to explain and analyze their advantages and weaknesses. Experiments have also been conducted to further verify and strengthen the arguments put forward in the analysis. All experiments in this paper took place on a custom-built rigid-body robot arm model that can be manipulated by hand. Sensor calibration and accelerometer alignment issues are also described, and their details are discussed. The experiment results presented in this paper show significant differences with reference to the achieved angular accuracy for various situations when using the four different sensor combination methods. In some cases, the angular error based on one method is more than 0.04 rad, while that from another method is within ±0.005 rad. The noise levels of angular readings from different methods are also experimentally compared and analyzed. The conclusion drawn serves to guide readers toward a suitable method for their particular application. [ABSTRACT FROM AUTHOR]

Energy conservation techniques for wireless sensor networks generally assume that data acquisition and processing have energy consumption that is significantly lower than that of communication. Unfortunately, this assumption does not hold in a number of practical applications, where sensors may consume even more energy than the radio. In this context, effective energy management should include policies for an efficient utilization of the sensors, which become one of the main components that affect the network lifetime. In this paper, we propose an adaptive sampling algorithm that estimates online the optimal sampling frequencies for sensors. This approach, which requires the design of adaptive measurement systems, minimizes the energy consumption of the sensors and, incidentally, that of the radio while maintaining a very high accuracy of collected data. As a case study, we considered a sensor for snow-monitoring applications. Simulation experiments have shown that the suggested adaptive algorithm can reduce the number of acquired samples up to 79% with respect to a traditional fixed-rate approach. We have also found that it can perform similar to a fixed-rate scheme where the sampling frequency is known in advance. [ABSTRACT FROM AUTHOR]

This paper concerns the Fourier analysis of measurements taken at times that are not at all uniformly spaced yet are accurately known. Such can be the case when measurements are obtained by networked sensors with accurate or synchronized clocks using common Internet Protocol networks or other networks that do not guarantee timely data delivery. Such can also be the case when the object being measured is only sporadically available for measurement. A method for computing a complex spectrum from a minimal number of samples taken at nonuniform time intervals is described. The novelty of this paper lies in the method's ability to minimize the number of samples required to ensure a desired accuracy. A practical application of the method lies in doing spectral analysis of signals obtained from networked sensors where acquiring each sample is expensive in time, power, or money, and so, the number of samples taken is to be minimized. The method has been tested on two rather different sets of actual measurements: one taken via smart sensors connected to the Internet and the other taken by a number of observational astronomers using a variety of apparatus over decades. [ABSTRACT FROM AUTHOR]

This paper concerns the filtering of measurements that are taken by networked sensors at nonuniform intervals but that are accurately time stamped. Traditional digital filtering methods are difficult or impossible to use due to nonuniform sampling. Two filtering methods are described. Both are based on making an assumption about the signal behavior between measurements, such as the signal being constant between measurements. In the first method, a filter is formulated as an ordinary differential equation that is incrementally solved as measurements arrive. Such filtering is general; nonlinear and nontime invariant filters may be constructed. In the second method, signal convolution with a continuous-time finite impulse response filter is efficiently performed using a spline representation for the filter response. Such filters are "FIR like" in the sense that they have frequency-domain performance similar to FIR filters and have only slightly worse asymptotic computation time and memory requirements compared to FIR filters, yet have the advantage of being able to deal with nonuniformly sampled measurements. Examples of the operation of both sorts of filters are shown on actual measured data. [ABSTRACT FROM AUTHOR]

Commercial sensors have generally, due to their own characteristics, some undesirable influences on the measured quantity and its precision. In particular, the dynamic characteristics can be reflected on to the measured quantity and lead to false or delayed interpretation of the underlying physical process. The quality and, therefore, the cost of the sensor are often tied to the dynamic performance of these instruments. Intelligent sensors are able to adapt to changing environments, calibrate themselves, and predict the pattern of the future signal. This paper presents algorithms to improve the dynamic performance of sensors, to identify the dynamic characteristics of the sensor, and to predict the future pattern of the measured quantity. In particular, two inverse filters are proposed for the improvement of the sensor's dynamic performance. One filter incorporates an optimal constant feedback gain that reduces the computational cost and increases the accuracy. A system identification method is used to identify the sensor's dynamic properties and allows for adaptation of the inverse filter's parameters. This identification algorithm computes the optimum input to the system, i.e., the sensor. The optimization is based on the inverse covariance matrix of the information matrix. A genetic algorithm is used to perform both optimizations for the computation of the optimal input and for the optimal constant feedback gain. In addition, a predictive filter formulation is given that is based on the identified system. Simulation results indicate that both inverse filters are capable of recovering the original or true signal. The second filter shows superiority in terms of convergence, lower computational cost, and lower error due to its optimized parameters. The predictive filter indicates good working accuracy for the signal prediction. [ABSTRACT FROM AUTHOR]

In this paper, a Bluetooth-based sensor network is presented. Based on a Piconet, the proposed network supports Web-server capabilities, allowing the wireless sensors to be accessed by commercial browsers through the Internet. An experimental setup has been realized using three temperature sensors, and related information has been viewed by PC clients. Measurements about power dissipation, area coverage, and response time confirm the proposed network feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

A portable parameter monitoring and analysis system for physiological studies and for assisting patient-centric health care management is developed. The system uses the network approach to acquire the data from sensors and transmit them to a server through wireless propagation means. The system automates the acquisition and monitoring of physiological parameters by continuous display on the monitor screen. Programming is done using industry strength software to display data and trends in real time on a standard PC. The measured data is accurate and lives up to the standards of the industry. [ABSTRACT FROM AUTHOR]

A prototype of a quartz-crystal microbalance-based transducer is proposed for in-situ measurements of very low chloroform activity in a gaseous environment. The sensing element consists of an innovative semicrystalline form of syndiotactic poly styrene sprayed as a thin film on the piezoelectric quartz electrode of the microbalance. Chloroform sorption and desorption in the film give rise to a mass variation, implying a related shift in the resonance frequency of the quartz. Results of static and dynamic metrological characterization of the transducer prototype at varying chloroform concentration and temperature in a gaseous environment are reported. Satisfactory sensitivity, repeatability, and dynamic hysteresis are highlighted. [ABSTRACT FROM AUTHOR]

In this paper, we present a switched-capacitor sigma--delta (Σ--Δ) modulator for high resolution applications. In particular, this Σ--Δ modulator is well suited for distributed sensor networks. The circuit, implemented in a double-poly, double-metal 0.6 μm CMOS technology, is based on a fourth-order single-loop architecture with a sampling frequency of 256 kHz. The chip consumes 50 mW from a single 5-V supply and achieves a signal-to-noise ratio of 104.9 dB over a bandwidth of 400 Hz, corresponding to a resolution of 17.1 bits. [ABSTRACT FROM AUTHOR]

This paper introduces a CMOS-image-sensor-performance evaluation system that can compute parameters including pixelwise dynamic range, dynamic linearity, signal-to-noise ratio, and chip-level dynamic uniformity with a single measurement. The proposed system offers a simpler and significantly more integrated approach than the available systems by employing an embedded processing system and a large-data-processing method with multithreading capability. This paper also presents a new optimization technique to overcome the memory-size limitations of embedded systems for larger data-processing applications. The proposed system is designed and implemented to target small business applications and laboratories conducting research on CMOS quality sensors. [ABSTRACT FROM AUTHOR]