The principle and performance of optical stealth communication technique is summarized and its applications to smart sensor networks are proposed. Optical stealth communication, which is also known as optical steganography, hides information in wide band analog noise. The wide band and fast changing noise protects the stealth information from being detected and recorded by an eavesdropper. If the matching condition cannot be achieved when receiving the stealth information, the information is permanently lost and cannot be recovered by post-processing techniques. The optical stealth communication technique is proposed to protect the sensing information in smart sensor networks. Private information in both the fronthaul connections and the communications between the sensor nodes and the wireless stations can be hidden in the optical stealth communication channels.
The Mechanical Expansion Amplifier1 (MEA) is a sensing and amplifying concept unlike anything before it. This all-mechanical device, not limited by semiconductor noise, produces record-setting sensitivities in the applications studied thus far: radiometers, gravitational wave detectors, magnetometers, and terahertz receivers. All instruments operate at ambient temperature without sensitivity loss. Truly new concepts, such as the MEA, start a flurry of academic and industrial activity. Scientific pioneers have many unexplored areas to choose from: New noise models. New optimized materials. New interpretations of physical limits. New industrial, commercial, and military applications. New careers and industries. A 3-piece mechanical model explains the new amplification concept. Any material that expands/contracts in response to an environmental stimulus can serve as the amplifier input. This introduction offers an intuitive understanding for several unusual MEA features. Footnotes reference in-depth papers available on the web. The features in this short introduction include gain production, noise sources, and gain control.
Electric wheelchairs restore mobility and independence to those with injuries limiting their movement. However, individuals suffering from arthritis or motor neuron degenerative diseases e.g. amyotrophic lateral sclerosis (ALS) lack the fine motor control to fluently control electric wheelchairs. These individuals are left dependent on others, drastically reducing their independence and quality of life. This study seeks to mitigate these problems by presenting a wheelchair capable of autonomous navigation, with minimal directive from the user. The wheelchair uses the robot operating system (ROS), microcontrollers, rotary encoders, and a LIDAR unit to navigate. The LI-DAR unit provides vital measurements of the environment which are used to construct a map of the surroundings. ROS receives input from the LIDAR sensor along with the rotary encoders to determine a navigable path to a user-defined destination. ROS sends movement commands to a microcontroller to move the wheelchair along the designated path. The navigation system is designed to adapt to changes in the environment and reroute the wheelchair if new obstacles emerge. During the autonomous system testing, involving ten trials, the wheelchair successfully navigated to the expected destination in 100% of the trials. Based on these preliminary results, the autonomous wheelchair has the potential to restore independent indoor mobility. Further development could provide autonomous navigation capabilities in outdoor environments.
The MEA (magnetic expansion amplifier) magnetometer design achieves 25e-18 Tesla/√Hz sensitivity under favorable environmental conditions. The earth's magnetic field is 200 billion times larger. The MEA-based magnetometer operates at ocean temperatures, whereas the most sensitive predecessors require molten metal or cryogenics to get a fraction of the sensitivity. The passive, low-power MEA design detects a frogman pushing a few cubic meters of contraband through a harbor entrance. Small military submarines are detectable even if they use magnetic anomaly countermeasures. Underwater objects seldom match the diamagnetism of water exactly, causing them to bend the earth's magnetic field very slightly. Modeling the perturbation with a single magnetic moment (current loop) gives sufficient accuracy at distances far from the object. Other applications include mineral prospecting, locating buried or sunken objects, earthquake prediction, material studies, and passive detection of land mines.
Real-time detection of transient weak magnetic signals is a common problem in many areas, such as tracking of in-pipe robots by the transmitting and receiving of extremely low frequency (ELF) magnetic signals, which is a critical issue related to the safety of pipelines and attracts special attention. Owing to the limit of transmitting power and interruption in open air as well as the mobility of the robot, the received ELF magnetic signals are relatively weak and transient, and are even as low as 10 pico-tesla, and the related SNR is as low as -3 dB.In this paper, a novel transient weak ELF magnetic signal detection method is proposed based on orthogonal search coil sensors. First, a Morlet wavelet is employed to model received orthogonal transient signals in which the envelope decay rate represents moving velocity. Second, a least square criterion is used to construct orthogonal signal energy statistic, then a one-to-one relationship between the envelope decay rate and the moving velocity is established. Third, the ternary decision tree method is developed to maximize orthogonal signal energy statistic and realize optimal real-time signal detection under the Newman-Pearson criterion.The simulation and the experimental results both show that the detection performance is quite close to the theoretical upper boundary, and the developed instruments based on the orthogonal search coil sensors are effective in real-time tracking.
State-of-the-art wearable systems are typically performance-constrained, battery-based devices which can, at most, reach self-sustainability using energy harvesting and aggressive duty-cycling. In this work, we present a wearable vision sensor node which can reliably execute computationally-intensive computer-vision algorithms in an energy-opportunistic fashion. By leveraging a burst-generation scheme, the proposed system can efficiently provide the energy guarantees required for tasks with temporal dependencies, even under highly variable harvesting conditions. By mounting the node on a user's glasses, the node is able to acquire a sequence of images and determine the user's walking speed, requiring only a small solar panel and capacitor. Both hardware and software have been fully optimized for ultra-low power consumption and high performance. Extensive experimental results show the energy node's energy proportionality and the accuracy of its walking speed estimation.
A vision based tracking algorithm was developed for active load damping on a 1/9 th scale extending boom crane using the Microsoft Xbox 360 Kinect RGB-D camera. The damping performance was compared to the IMU approach based on the Microstrain Inertia Link sensor. With the IMU solution, the radial damping ratio was improved by a factor of 2.2 and an order of magnitude in the tangential direction. The vision solution achieved a damping ratio improvement by a factor 2.0 and 4.7 in the radial and tangential directions respectively. The settling times for both sensors were with a few seconds at a total duration of 25 and 15 seconds for the radial and tangential directions when active damping was applied. The cranes response was a limiting factor in the system's damping performance as it would be with a full scale crane.
An Internet of Things (IOT) interface is introduced in this paper with the goal of filling the compatibility gap between currently available industrial analog sensors (legacy sensors) and network protocols. It is intended to be compatible with conventional analog sensor outputs with current loop, voltage and pulse outputs. Connection to the Internet is via Ethernet. To this end, IEEE 21451 family of standards is employed as a reference to come up with an interoperable interface. This family of standards provide an infrastructure which allows auto configuration (plug and play) and interoperability without the need for operator intervention. Transducer electronic data sheets (TEDS) play a key role in this process and they supply the system with the operating characteristics that are needed to use the transducer modules. The TEDS-only version of this family of standards (IEEE 21451-4) has received significant attention and is being used in this paper. The developed interface consists of transducer interface module (TIM) and network capable application processor (NCAP). Any type of industrial analog sensor can be connected to this interface and the interface will convert the input signal from analog to digital, format it in engineering units and then ref-format it in Internet format. The primary format of IEEE 21451-4 is XMPP which emphasizes on interoperability and security of messages in a network with machine to machine (M2M) communications that can be sensors, actuators and other type of devices.
In this letter, we propose and implement a shadow approach for real-time hand gesture recognition using a light-dependent resistor array, which can detect analog signals from hand shadows. The main advantage of the shadow approach is real-time recognition. Instead of a three-dimensional (3D) hand gesture, the shadow approach detects two-dimensional hand shadows and then measures the intensity of the signal from each element of a sensor array to recognize the 3D gesture. The recognition accuracy was higher than 90 %.
In this paper, we present a real-time drone detection and monitoring system, that users can easily utilize in daily life to detect drones using sound data. This system performs FFT on the sampled real-time data and performs drone detection using the transformed data through two different methods, Plotted Image Machine Learning (PIL) and K Nearest Neighbors (KNN). The PIL uses image data from the visualized FFT graph to detect robust points, and compares the average image similarity with a reference FFT template associated with a target of interest. Whereas, the KNN uses FFT-format csv files to compare the average distance similarity. Experiments were performed with the two methods. As a result, the accuracy rate of 83% and 61% was shown in each of PIL and KNN. The major deliverables of this work are a software package framework one may use to experiment with various sound samples and classifiers via modifiable classifier modules and initial testing of two classifiers. Future work, enabled by the software framework developed, can employ more capable classifiers.
Most pollination of commercial blueberries is carried out by honeybees from hundreds of hives. An activity sensor for monitoring the health and productivity of beehives is presented. Honeybees flying near the entrance of a beehive were observed with a low-powered 5.8 GHz Doppler radar. Spectral moments, entropy, diversity, and root-mean-square (rms) power were evaluated to identify foraging bees and to quantify the level of foraging activity. Theoretical and experimental results are presented to demonstrate that entropy, diversity, and rms power are correlated and are equally valid indicators of bee activity. In particular, the rms power of the Doppler signal can be measured without a coherent receiver and without signal processing. This type of measurement lends itself to a second, considerably less expensive, implementation of a bee activity sensor.
Handheld metal detectors are ubiquitous devices in humanitarian demining. They are sensitive to extremely small metallic objects as in low-metallic content landmines. However, this leads to enormous false alarm rates (1 to 1000). Recent research in electromagnetic induction techniques for discrimination of hazardous metallic targets and clutter gives rise to a hope of faster, smarter and safer mine detection. These techniques require that position of the metal detector's sensor head be known to sub-centimeter accuracy. To this aim, we present and evaluate an ultra-wide bandwidth positioning embedded system. We evaluate its precision and accuracy, and show that it can be used for tracking of the sensor head with at least 40 Hz positon refresh rate. The achieved measurement uncertainty is better than 10 cm. We discuss the further improvements that can be made in such a localization system.
Deployment of sensor systems for smart, Internet of Things (IoT) environments may be subject to high cost, physical limitations, building modification regulations, or lengthy processes. Flexibility of sensor type choice can lead to overcoming various constraints. Very low cost, easily deployable sensors can provide data other than that for which it was designed. In this paper, we use temperature sensors, rather than the customarily used job-specific sensors, to measure the mechanical events of opening of a fridge door and the physical event of water flow in a pipe. A given sensor that measures a particular parameter can instead use a different, alternative low cost sensor to answer the same end question: the means by which the answer is derived differs, and thus the sensor's modality shifts. We show results of replacing flow meters in pipes and mechanical switches in refrigerator doors with temperature sensors, thereby shifting the modality of the temperature sensor from measuring the room temperature to measuring other physical parameters.
We have developed a floor based personnel detection system to extract gait parameters including walking speed, stride length and stride time, we call it smart carpet. These parameters are validated with a GAITRite Electronic mat. The smart carpet is laid over the GAITRite mat, and subjects walked across the mat for 9 trials each. The data acquisition system of the smart carpet recorded the location of the active sensors, which were later used to extract the gait parameters. An excellent agreement for walking speed, stride length and stride time between the two systems is achieved. The mean percentage error difference for walking speed is 1.43%, stride length is -4.32% and stride time is -5.73%. For walking speed, the standard deviation is 4.39, which emphasizes that 68% of the error is within a 5%. We compared our work to the work done by a research group who used a vision based Kinect and web cameras system with excellent agreement.
This work describes how needle shape silicon probes with integrated magnetoresistive sensing elements can be used in neurosciences. The fabricated probes allow simultaneous electric and magnetic recording with a micrometric spatial resolution, therefore offering great advantages for the detection of neural magnetic fields. In this paper we present results obtained upon insertion of high sensitivity magnetoresistive sensors in a mouse hippocampus brain slice submerged by Krebs solution. To perform these in-vitro experiments, an electric pulsed stimulus was applied to the CA3 region of the hippocampus while the needle shape probe was placed at CA1 to measure the field created by the ionic currents. The magnetoresistive sensor presented an output signal with amplitude of 130 nV, recorded 9 ms after the stimulation. Considering the signal has a pure magnetic nature, the detected magnetic field would be of 33 nT. However, cumulative parasitic effects arising from capacitive couplings can induce an electric component in the recorded signal. In this case, and considering a minimum recording frequency of 5 Hz (detectivity of 54 nT/Hz1/2), a capacitance ∼ 10 nF was estimated through the probe's oxide passivation layer.
Recent progress in low power electronics, sensors and transmitters, as well as suitable electronics for power harvesting, makes autonomous devices a concrete possibility. Environmental and human motion mechanical vibration sources have gained much attention as potential sources of energy. In this paper preliminary results from investigation of a simple mixed inductive-piezoelectric energy harvester from human walking are presented. The paper mainly focuses on the architecture of the harvester and on the analysis of the voltage signals generated by the inductive harvester. Preliminary tests aimed at investigating the proposed mechanism were performed by using the harvester device worn by different users to collect energy during human motion at different speed.
This paper presents magnetic and anisotropic magnetoresistance (AMR) results of permalloy Ni80Fe20 films of different thickness (10–250 nm). The films were grown by sputter deposition at an angle, with in-situ magnetic field assisting the definition of uniaxial anisotropy. The results show that there is negligible change in anisotropy field (Hk) and resistivity with thickness down to 50 nm. However, for thinner films (<50 nm) Hk and resistivity increase rapidly with decrease in film thickness. The coercivity (Hc) of our films was found to be independent of the thickness, in all cases below 1.5 Oe. The AMR increases with the film thickness and saturates at higher thicknesses.
In this paper, we present a novel optical method to observe lithium iron phosphate (LFP) cathodes, where optical effects are usually suppressed by carbon additives. Our approach to utilize electrochromic markers in the cathode overcomes this restriction. During cell cycling, reflectance and color of the cathode were found to precisely correlate with state of charge. In addition, the spatial and temporal progression of ion intercalation has been made visible. Data gained from these experiments can be used to determine reaction kinetics and improve electrochemical battery models. This can directly benefit battery research as a novel method for materials characterization in the laboratory. As a next step, we propose a complementary sensor system for commercial battery cells, where an optical fiber is placed between cell electrodes as an evanescent field sensor. The cell-integrated fiber optic system has the potential to directly determine the state of charge and additionally act as a redundant safety level to prevent critical cell states.
Pressure-sensitive mat (PSM) technology offers several advantages as a sensor modality for patient monitoring since it is non-contact and unobtrusive. However, as we move to deploy PSM for long-term continuous patient monitoring, we must consider and characterize their metrological properties that arise due to their electrical, mechanical or optical construction. We evaluate the dynamic metrological properties of rise time, creep, percent change in creep, drift, and repeatability for three different PSM technologies from three vendors, namely, S4 (Kinotex fiber-optics), Tekscan (resistive ink), and XSensor (capacitive). Both long-term (14.5 hrs) and repeated short-term experiments (1 min) were conducted using two anthropometric models exhibiting contact pressures representative of adult and neonatal patients. Long-term experiments were conducted to characterize rise time, creep, percent change in creep, and drift for each sensor. With both pressure models, the XSensor exhibited the fastest dynamic response in terms of rise and recovery times, while Tekscan exhibited the slowest responses. S4 and Tekscan present with an expected decrease in drift with application of the adult model, but XSensor shows the opposite trend. Short-term experiments were conducted to measure repeatability with four application-removal repetitions for 1 min each. The coefficient of variation (CoV) was computed for each sensor as a measure of repeatability. For both pressure models, the smaller CoV of XSensor implies greater repeatability and hence, greater reliability.
With the aging of the population, comes increased incidence of chronic diseases affecting the cardiac and respiratory systems. Monitoring of these chronic conditions at home via family members or in institutions via healthcare providers is usually adequate during the day. Non-intrusive video-based monitoring approaches have been proposed using optical cameras whose performance significantly deteriorates in low light conditions. This paper proposed the use of infrared night vision cameras to monitor the heart and respiration rates in low light conditions and in complete darkness. An infrared camera in conjunction with video magnification method is used to capture and analyze the video of subjects in dark conditions. To validate the extracted heart rate, a finger photoplethysmograph (PPG) device that can display the real-time heart rate was used. To validate the respiration rate a BioHarness chest strap was used. The proposed framework was tested on different sizes of regions of interest (ROIs) and different distances between the subject and the camera. A post-processing procedure was applied on the video magnification signal to reduce noise. To characterize and rule out artifacts, an experiment on inanimate objects was also conducted. Results indicate that the non-intrusive approach based on infrared night vision cameras and video magnification method can accurately extract heart and respiration rates, and can be used for continuous healthcare monitoring in the night.
