Power spectral density estimation for wireless fluctuation enhanced gas sensor nodes

Fluctuation enhanced sensing (FES) is a promising method to improve the selectivity and sensitivity of semiconductor and nanotechnology gas sensors. Most measurement setups include high cost signal conditioning and data acquisition units as well as intensive data processing. However, there are attempts to reduce the cost and energy consumption of the hardware and to find efficient processing methods for low cost wireless solutions. In our paper we propose highly efficient signal processing methods to analyze the power spectral density of fluctuations. These support the development of ultra-low-power intelligent fluctuation enhanced wireless sensor nodes while several further applications are also possible

Method of measuring cross-flow vortices by use of an array of hot-film sensors

The invention is a method for measuring the wavelength of cross-flow vortices of air flow having streamlines of flow traveling across a swept airfoil. The method comprises providing a plurality of hot-film sensors. Each hot-film sensor provides a signal which can be processed, and each hot-film sensor is spaced in a straight-line array such that the distance between successive hot-film sensors is less than the wavelength of the cross-flow vortices being measured. The method further comprises determining the direction of travel of the streamlines across the airfoil and positioning the straight-line array of hot film sensors perpendicular to the direction of travel of the streamlines, such that each sensor has a spanwise location. The method further comprises processing the signals provided by the sensors to provide root-mean-square values for each signal, plotting each root-mean-square value as a function of its spanwise location, and determining the wavelength of the cross-flow vortices by noting the distance between two maxima or two minima of root-mean-square values

Particles mass flow rate and concentration measurement using electrostatic sensor

In many industries where flow parameters measurement is essential to control manufacturing process, the use of a reliable, cost effective and high accuracy instrument is an important issue. Appropriate measurement method and design leads to improvement of pneumatic conveyors operation and process efficiency. This paper present an instrumentation design based on passive charge detection using a single electrostatic sensor. Two different sensor electrodes are applied to show the flexibility of electrostatic sensor application. A time domain signal processing algorithm is developed to measurement of mass flow rate and concentration profile from acquired electrical charge signal. The findings is led to a low cost and high accuracy design, the experimental test results of the design shows less than ±5% error between measured parameters and reference reading acquired from the manual weighing

Human mobility monitoring in very low resolution visual sensor network

This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics

Detection and Quantification of Multi-Analyte Mixtures Using a Single Sensor and Multi-Stage Data-Weighted RLSE

This work reports the development and experimental verification of a sensor signal processing technique for online identification and quantification of aqueous mixtures of benzene, toluene, ethylbenzene, xylenes (BTEX) and 1, 2, 4-trimethylbenzene (TMB) at ppb concentrations using time-dependent frequency responses from a single polymer-coated shear-horizontal surface acoustic wave sensor. Signal processing based on multi-stage exponentially weighted recursive leastsquares estimation (EW-RLSE) is utilized for estimating the concentrations of the analytes in the mixture that are most likely to have produced a given sensor response. The initial stages of EW-RLSE are used to eliminate analyte(s) that are erroneously identified as present in the mixture; the final stage of EW-RLSE with the corresponding sensor response model representing the analyte(s) present in the mixture is used to obtain a more accurate quantification result of the analyte(s). The success of this method in identifying and quantifying analytes in real-time with high accuracy using the response of just a single sensor device demonstrates an effective, simpler, lower-cost alternative to a sensor array that includes the advantage of not requiring a complex training protocol

Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination

In this paper, we consider multi-sensor classification when there is a large number of unlabeled samples. The problem is formulated under the multi-view learning framework and a Consensus-based Multi-View Maximum Entropy Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the stochastic agreement between multiple classifiers on the unlabeled dataset, the algorithm simultaneously learns multiple high accuracy classifiers. We demonstrate that our proposed method can yield improved performance over previous multi-view learning approaches by comparing performance on three real multi-sensor data sets.Comment: 5 pages, 4 figures, Accepted in 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 15

An Analysis of Optical Contributions to a Photo-Sensor's Ballistic Fingerprints

Lens aberrations have previously been used to determine the provenance of an image. However, this is not necessarily unique to an image sensor, as lens systems are often interchanged. Photo-response non-uniformity noise was proposed in 2005 by Luk\'a\v{s}, Goljan and Fridrich as a stochastic signal which describes a sensor uniquely, akin to a "ballistic" fingerprint. This method, however, did not account for additional sources of bias such as lens artefacts and temperature. In this paper, we propose a new additive signal model to account for artefacts previously thought to have been isolated from the ballistic fingerprint. Our proposed model separates sensor level artefacts from the lens optical system and thus accounts for lens aberrations previously thought to be filtered out. Specifically, we apply standard image processing theory, an understanding of frequency properties relating to the physics of light and temperature response of sensor dark current to classify artefacts. This model enables us to isolate and account for bias from the lens optical system and temperature within the current model.Comment: 16 pages, 9 figures, preprint for journal submission, paper is based on a thesis chapte

Strain and temperature sensors using multimode optical fiber Bragg gratings and correlation signal processing

Multimode fiber optic Bragg grating sensors for strain and temperature measurements using correlation signal processing methods have been developed. Two multimode Bragg grating sensors were fabricated in 62/125 m graded-index silica multimode fiber; the first sensor was produced by the holographic method and the second sensor by the phase mask technique. The sensors have signal reflectivity of approximately 35% at peak wavelengths of 835 nm and 859 nm, respectively. Strain testing of both sensors has been done from 0 to 1000 με and the temperature testing from 40 to 80°C. Strain and temperature sensitivity values are 0.55 pm/με and 6 pm/°C, respectively. The sensors are being applied in a power-by-light hydraulic valve monitoring system

Continuous Wavelet Transform and Hidden Markov Model Based Target Detection

Standard tracking filters perform target detection process by comparing the sensor output signal with a predefined threshold. However, selecting the detection threshold is of great importance and a wrongly selected threshold causes two major problems. The first problem occurs when the selected threshold is too low which results in increased false alarm rate. The second problem arises when the selected threshold is too high resulting in missed detection. Track-before-detect (TBD) techniques eliminate the need for a detection threshold and provide detecting and tracking targets with lower signal-to-noise ratios than standard methods. Although TBD techniques eliminate the need for detection threshold at sensor’s signal processing stage, they often use tuning thresholds at the output of the filtering stage. This paper presents a Continuous Wavelet Transform (CWT) and Hidden Markov Model (HMM) based target detection method for employing with TBD techniques which does not employ any thresholding