Low-resolution Sensor Research Articles

FT-IR metrology aspects for on-line monitoring of CO2 and CH4 in underground laboratory conditions

The French National Radioactive Waste Management Agency (ANDRA) has recently developed a new experimental set-up which allows sampling water from marl rock formations, together with an in situ characterisation of the composition and migration mechanisms of the gases dissolved in the marl porewater. Gases and liquids are collected from vertical borehole drillings in underground laboratories. The analytical design, Fourier transformed infrared spectroscopy based, allows powerful and long term on-line monitoring of gases released by low-permeability media. The IR system is designed to cope with the unfavourable measurement conditions occurring in an experimental underground laboratory (moisture, dust, etc.). Because the working conditions in such an underground laboratory make complete purging of the IR spectrometer difficult, the IR spectra of geological gases are often perturbed by contributions from atmospheric CO2 and water vapour. The metrology aspect is based on an IR low resolution sensor equipped with two measurement compartments. In the internal compartment linked to the borehole layout, gases are monitored on-line through a cell with a variable optical path, whereas in the external compartment, atmospheric CO2 is measured through a short open path configuration. The experimental method and data processing procedure used to determine the real partial pressure of CO2 arising from the marl rock formation are described in this paper. Results of the on-line gas (CO2 and CH4) monitoring conducted in the Mont Terri underground laboratory are presented and compared with punctual gas chromatography analyses.

Hybrid modeling and identification of dry friction systems, application to a clutch actuator

This paper proposes a new general framework, i.e. hybrid modeling, to model dry friction systems. The proposed hybrid model has two modes (states): one models the system sliding, the second the system sticking. The model's main advantage is the adaptation capability to observable phenomena. It is shown for the most common behaviors encountered experimentally and comparisons with other models presented in the literature are carried out. The models and methods of parameters identification of the literature are generally validated experimentally on system with sensors and actuators of high precision. This paper focuses on modeling and identifying complex mechanical system with low resolution sensors. The presented theoretical work is validated experimentally on a clutch-by-wire.

Edge identification of a small object through a low-resolution tactile sensor array

Edge detection is always an interesting field for researchers in the area of artificial intelligence, including machine visions and robotics. In the context of tactile sensors, many studies have been conducted on edge-identification algorithms. These investigations often use high-resolution tactile sensor arrays to track an edge of a large object. In this work, edge identification is examined in which a coarse-resolution tactile sensor, specif., a 2×2 array, is used to detect and track edges of a small object. To simplify the control, we control the sensor just in 2D and in just one direction at a time. We propose two methods to solve this problem: a scanning method, of which the principle of operation is similar to that of a scanner, and an edge-following method that can make the sensor follow the boundary of the object. The flowchart of each method will be presented in detail in this paper. Some simulations in MATLAB, including four shapes, viz., square, triangle, ellipse, and hexagon, have been executed to verify our proposal. A comparison and a critique of the two methods are also presented for improving the methods.

An enhanced edge tracking method using a low resolution tactile sensor

Edge detection plays an important role in object recognition and exploration. In this paper, we propose an efficient tracking algorithm, which uses a coarse resolution tactile sensor set, for edge detection of a 2D shape object. Although many researchers have often used a tactile sensor with high resolution such as a 10×10 or higher for edge detection, in this research, we use a 2×2 tactile sensor to track the edges of an object. Using this type of low resolution sensor, we can reduce the manufacturing cost of the sensor and simplify the calculation and control process. In our algorithm, we only need to control the sensor to make it move along the vertical axis, Oy, or the horizontal axis, Ox. The sensor is moved along a suitable direction according to the sensing signals receiving from it. A set of simulations for both convex and concave shapes has been done to verify the algorithm. In addition, methods to increase the accuracy of the algorithm are also discussed.

Depth reconstruction uncertainty analysis and improvement – The dithering approach

The depth spatial quantization uncertainty is one of the factors which influence the depth reconstruction accuracy caused by a discrete sensor. This paper discusses the quantization uncertainty distribution, introduces a mathematical model of the uncertainty interval range, and analyzes the movements of the sensors in an Intelligent Vision Agent System. Such a system makes use of multiple sensors which control the deployment and autonomous servo of the system. This paper proposes a dithering algorithm which reduces the depth reconstruction uncertainty. The algorithm assures high accuracy from a few images taken by low-resolution sensors. The dither signal is estimated and then generated through an analysis of the iso-disparity planes. The signal allows for control of the camera movement. The proposed approach is validated and compared with a direct triangulation method. The simulation results are reported in terms of depth reconstruction error statistics. The physical experiment shows that the dithering method reduces the depth reconstruction error.

Novel Speed and Rotor Position Estimation Strategy Using a Dual Observer for Low-Resolution Position Sensors

Recently, high-performance AC motor drive technologies, including field-oriented control (FOC), have been widely used in home appliance products such as direct-drive drum washing machines. In low-cost AC machine drive systems, speed or position sensors with high resolution are not available due to high costs. Instead, a low-resolution sensor is installed to calculate the speed and rotor position. In the FOC, however, accurate rotor angle information is essential for high performance. In addition to the low resolution of the sensor, its accuracy is poor due to mechanical errors and signal processing. The inaccurate and delayed position information degrades the performance of current regulation and also increases torque ripples, which is the source of mechanical vibration and acoustic noise. In order to overcome this hardware problem, this study proposes a software technique based on a novel dual observer and a position sensor offset compensation strategy. The dual observer can estimate the rotor speed and position without time delay or bumps. Also, with the compensation algorithm for position sensor offset, the misaligned synchronous reference frame d- and q-axis can be corrected. Experimental results verify the effectiveness of the proposed method.

Implementation of H-Infinity Control Algorithms for Sensor-Constrained Mechatronic Systems Using Low-Cost Microcontrollers

This paper introduces a novel method which is intended to assist in the design and implementation of optimal H-infinity ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> ) algorithms in low-cost mechatronic applications. The particular problem considered is position control in a situation where there are both sensor-related uncertainties (caused by low-resolution sensors) and limited computational resources. The first part of the method presented in this paper describes how to design the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> algorithm based on dynamic features of the sensor. The second part of the method involves finding a suitable numerical controller representation in order to reduce memory and CPU load. Evaluation of the method is based on empirical studies using three industrial sensors employed in a sub-acted robot. Results for a classic proportional integral derivative (PID) controller are included, in order to provide comparisons with the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> approach. In the empirical evaluation, the PID implementation shows marginal stability when the low-resolution sensor is employed; by contrast, the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> implementation is found to remain stable in the same circumstances.

Adaptive framework for robust high-resolution image reconstruction in multiplexed computational imaging architectures

In multiplexed computational imaging schemes, high-resolution images are reconstructed by fusing the information in multiple low-resolution images detected by a two-dimensional array of low-resolution image sensors. The reconstruction procedure assumes a mathematical model for the imaging process that could have generated the low-resolution observations from an unknown high-resolution image. In practical settings, the parameters of the mathematical imaging model are known only approximately and are typically estimated before the reconstruction procedure takes place. Violations to the assumed model, such as inaccurate knowledge of the field of view of the imagers, erroneous estimation of the model parameters, and/or accidental scene or environmental changes can be detrimental to the reconstruction quality, even if they are small in number. We present an adaptive algorithm for robust reconstruction of high-resolution images in multiplexed computational imaging architectures. Using robust M-estimators and incorporating a similarity measure, the proposed scheme adopts an adaptive estimation strategy that effectively deals with violations to the assumed imaging model. Comparisons with nonadaptive reconstruction techniques demonstrate the superior performance of the proposed algorithm in terms of reconstruction quality and robustness.

A comparative analysis of different spatial sampling schemes: Modelling of SSRB data

Low spatial resolution satellite sensors provide information over relatively large targets with typical pixel resolutions of hundreds of km2. However, the spatial scales of ground measurements are usually much smaller. Such differences in spatial scales makes the interpretation of comparisons between quantities derived from low resolution sensors and ground measurements particularly difficult. It also highlights the importance of developing appropriate sampling strategies when designing ground campaigns for validation studies of low resolution sensors. We make use of statistical modelling of high resolution surface shortwave radiation budget (SSRB) data to look into this problem. A spatial model that describes the SSRB over a selected region is proposed, and the impact of different sampling schemes in the performance of the model is analysed. Both systematic and random sampling schemes can efficiently represent the full observations set. **Current affiliation: Meteorological Office, Hadley Centre for Climate Prediction and Research, UK

Increasing depth lateral resolution based on sensor fusion

Technologies for measuring range data were intensively developed in recent years. The Time-Of-Flight matrix range sensor is one of the most active range acquisition devices, due to its effective suppression of background illumination, relative low cost and high portability. However, the resolution of such 3D range sensor is much lower than that of a modern colour image. This paper presents a solution to the problem of deriving a high-resolution depth image from a low-resolution sensor and a high-resolution colour image. The proposed algorithm is a modification of the Markov Random Fields fusion method that was presented by Diebel and Thrun (2005). Two extensions are suggested to improve the optimisation criteria.

Driver tracking and posture detection using low-resolution infrared sensing

Intelligent sensors are playing an ever-increasing role in automotive safety. This paper describes the development of a low-resolution infrared (IR) imaging system for continuous tracking and identification of driver postures and movements. The resolution of the imager is unusually low at 16 − 16 pixels. An image processing technique has been developed using neural networks operating on a segmented thermographic image to categorize driver postures. The system is able reliably to identify 18 different driver positions, and results have been verified experimentally with 20 subjects driving in a car simulator. IR imaging offers several advantages over visual sensors; it will operate in any lighting conditions and is less intrusive in terms of the privacy of the occupants. Hardware costs for the low-resolution sensor are an order of magnitude lower than those of conventional IR imaging systems. The system has been shown to have the potential to play a significant role in future intelligent safety systems.

Spectral unmixing for tracking human motion in low resolution imagery

This paper discusses how colour/hue information can be used to track people in image sequences received from very low resolution sensors. The main colour spaces such as Normalised RGB, HSV, CIE L*a*b*and YCbCr have different advantages and disadvantages when applied to tracking in very low resolution imagery, where the objects of interest may be sub-pixel in size. Tests have been carried out to find out the best colour space or combination of colour spaces. Spectral unmixing algorithms, commonly used in remote sensing and multi-spectral image processing, were used to assist object tracking algorithms by decomposing pixel colour information into fractional contributions from backgrounds and skin.

Improved land surface emissivities over agricultural areas using ASTER NDVI

Land surface emissivity retrieval over agricultural regions is important for energy balance estimations, land cover assessment and other related environmental studies. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) produces images of sufficient spatial resolution (from 15 m to 90 m) to be of use in agricultural studies, in which fields of crops are too small to be well-resolved by low resolution sensors. The ASTER project generates land surface emissivity images as a Standard Product (AST05) using the Temperature/Emissivity Separation (TES) algorithm. However, the TES algorithm is prone to scaling errors in estimating emissivities for surfaces with low spectral contrast if the atmospheric correction is inaccurate. This paper shows a comparison between the land surface emissivity estimated with the TES algorithm and from a simple approach using the Normalized Difference Vegetation Index (NDVI) for five ASTER images (28 June 2000, 15 August 2000, 31 August 2000, 28 April 2001 and 02 August 2001) of the agricultural area of Barrax (Albacete, Spain). The results indicate that differences are < 1% for ASTER band 13 (10.7 μm) and < 1.5% for band 14 (11.3 μm), but > 2% for bands 10 (8.3 μm), 11 (8.6 μm) and 12 (9.1 μm). The emissivities for the five ASTER bands were tested against in situ measurements carried out with the CIMEL CE 312-2 field radiometer, the NDVI method giving root mean square errors (RMSE) < 0.005 over vegetated areas and RMSE < 0.015 over bare soil, and the TES algorithm giving RMSE ∼ 0.01 for vegetated areas but RMSE > 0.03 over bare soil. The errors and inconsistencies for ASTER bands 13 and 14 are within those anticipated for TES, but the greater errors for bands 10–12 suggest the presence of problems related to atmospheric compensation and model assumptions about soil spectra. The NDVI method uses visible/near-infrared data co-acquired with the thermal images to estimate vegetation cover and, hence, provides an independent constraint on emissivity. The success of this approach suggests that it may be useful for daytime images of agricultural or other heavily vegetated areas, in which the TES algorithm has occasional failures.

AC brushless drive with low-resolution Hall-effect sensors for surface-mounted PM Machines

An ac brushless drive in which Hall-effect sensors are used as rotor position sensors is presented in this paper. Three different methods to obtain a high-resolution position estimation from the low-resolution sensors are described and compared through simulation and experimental testing. The proposed control algorithm's most innovative feature is its adaptability to the entire speed range, including startup, when using any of the three estimation algorithms. The control algorithm has been implemented and tested in order to drive a slotless axial-flux permanent-magnet (PM) machine for domestic appliance applications.

A Hybrid Sensorless SRM Drive With Eight- and Six-Switch Converter Topologies

This paper presents a hybrid sensorless controller used with both the classical eight-switch converter and a reduced six-switch converter topology for switched reluctance motor (SRM) drives. The hybrid sensorless scheme consists of a sliding mode observer (SMO)-based position sensorless approach for high speeds along with a low-resolution discrete sensor for low speeds and standstill. The hybrid position estimation switches between the SMO and the low-resolution sensor according to the speed sign and the position error difference between the SMO and the low-resolution sensor. The experimental results show the robustness of this hybrid low-cost control approach with the two converter topologies.

Cross-Sensor Calibration Between Ikonos and Landsat ETM+ for Spectral Mixture Analysis

Spectral mixture analysis is an algorithm that is developed to overcome the weakness in traditional land-use/land-cover (LULC) classification where each picture element (pixel) from remote sensing is assigned to one and only one LULC type. In reality, a remotely sensed signal from a pixel is often a spectral mixture from several LULC types. Spectral mixture analysis can derive subpixel proportions for the endmembers from remotely sensed data. However, one frequently faces the problem in determining the spectral signatures for the endmembers. This study provides a cross-sensor calibration algorithm that enables us to obtain the endmember signatures from an Ikonos multispectral image for spectral mixture analysis using Landsat ETM+ images. The calibration algorithm first converts the raw digital numbers from both sensors into at-satellite reflectance. Then, the Ikonos at-satellite reflectance image is degraded to match the spatial resolution of the Landsat ETM+ image. The histograms at the same spatial resolution from the two images are matched, and the signatures from the pure pixels in the Ikonos image are used as the endmember signatures. Validation of the spectral mixture analysis indicates that the simple algorithm works effectively. The algorithm is not limited to Ikonos and Landsat sensors. It is, in general, applicable to spectral mixture analysis where a high spatial resolution sensor and a low spatial resolution sensor with similar spectral resolutions are available as long as images collected by the two sensors are close in time over the same place.

Improvement of Performance and Stability of a Drive System with a Low-Resolution Position Sensor by Multirate Sampling Observer

Low-resolution encoders are normally used as speed sensors in many traction drive systems, such as electric trains, electric vehicles, etc. For example, 60-pulse-per-revolution pulse generators are used to detect speed for electric railway vehicles and low-resolution Hall-effect sensors are commonly used in many electric vehicles. Fig. 1 illustrates the sensors. The signals from these kinds of sensors are pulses, which are then used to calculated speed and acceleration. The speed and the acceleration, therefore, are updated only when the next pulse is detected. As a result, the speed and the acceleration cannot be achieved precisely, especially at low speed. Nowadays, high-frequency digital sampling processors (DSP) are commonly used as controllers. This allows us to precisely control the system. However, it is very difficult to realize in the mentioned systems since the low-resolution sensors are installed. Fig. 2 illustrates the condition at low speed. The period between two consecutive pulses, T1, is longer than the control period, T2. It implies that precise control cannot be achieved even though the high-frequency DSPs are used. To solve this problem, an instantaneous speed observer was proposed to estimate the speed between two consecutive encoder’s pulses. The instantaneous speed observer is a specific discrete-time observer, which estimates the speed at every sampling time and corrects the estimated speed when the next pulse is detected. As a result, the precise speed is achieved at every sampling time. The instantaneous speed observer can also be used to improve the performance of linear scales for linear drives. Its concept is also useful for visual servo, where the sampling rate of the CCD camera is much slower than the sampling rate of the controller. However, the model of the instantaneous speed observer is very specific and limited to motor drive, and its application cannot be extended for the higher order plant such as robot manipulators. For traction control, one of the difficulties is that it deals with a wide speed range. The authors also applied the instantaneous speed observer for speed estimation of train speed. To apply the instantaneous speed observer, which is designed in discrete-time domain, it is necessary to change the observer gains according to the speed to prevent the noise problem at high speed and instability problem at low speed. The observer gain was tuned by simply considering only the relation of pole locations on sand zplanes. As a result, the system can operate stably in a Improvement of Performance and Stability of a Drive System with a Low-Resolution Position Sensor by Multirate Sampling Observer

High‐resolution image reconstruction with displacement errors: A framelet approach

AbstractHigh‐resolution image reconstruction arises in many applications, such as remote sensing, surveillance, and medical imaging. The Bose and Boo (1998) model can be viewed as the passage of the high‐resolution image through a blurring kernel built from the tensor product of a univariate low‐pass filter of the form [½ + ϵ, 1, …, 1, ½ − ϵ], where ϵ is the displacement error. When the number L of low‐resolution sensors is even, tight‐frame symmetric framlet filters were constructed (Chan et al., 2004b) from this low‐pass filter using Ron and Shen's (1997) unitary extension principle. The framelet filters do not depend on ϵ, and hence the resulting algorithm reduces to that of the case where ϵ = 0. Furthermore, the framelet method works for symmetric boundary conditions. This greatly simplifies the algorithm. However, both the design of the tight framelets and extension to symmetric boundary are only for even L and cannot, be applied to the case when L is odd. In this article, we design tight framelets and derive a tight‐framelet algorithm with symmetric boundary conditions that work for both odd and even L. An analysis of the convergence of the algorithms is also given. The details of the implementations of the algorithm are also given. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 91–104, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20012

Examining the potential of using remotely sensed fire data to predict areas of rapid forest change in South America

Major advances have been made in the last decades estimating deforestation by using satellite data. This has been mostly done by detecting differences in a time series of high resolution data. However, the interpretation of the images and the detection of change is cumbersome and expensive. Furthermore, the area covered by a high resolution image is relatively small and only available on a monthly bases. This work investigates if detected fire of the low resolution (AVHRR) sensor can be used as a quick and inexpensive way of locating areas of rapid forest cover change. It tests the hypothesis that vegetation fires, as detected by earth orbiting satellites, are a reliable indicator of “hot spots” of deforestation in tropical South America. Fine spatial resolution satellite data were used to measure the deforestation that occurred in the mid 1990s in 41 sites spread across the tropical forests of South America. These data were then compared to the number of fires occurring in these sites, as detected by the NOAA-AVHRR satellite between April 1992 and December 1993. At the individual site level, 25 of the test sites showed a significant statistical relationship between the fires and the deforestation occurring in the following years. A further 6 sites showed neither fire activity nor significant deforestation. When aggregated to the regional level, only the set of sites lying in relatively dry areas with the same land use practices (located in Brazil) showed a significant correlation between fire and deforestation. This correlation enabled us to produce a map of high deforestation risk occurring in 1992 in the Brazilian Amazon, which showed close correspondence to the actual deforestation fronts of the year 1992.

Resolution enhancement of low resolution wavefields with POCS algorithm

The problem of enhancing the resolution of wavefield or beam profile measurements obtained using low resolution sensors is addressed by solving the problem of interpolating signals from partial fractional Fourier transform information in several domains. The iterative interpolation algorithm employed is based on the method of projections onto convex sets (POCS).