Computational model for tactile sensing system with wrinkle’s morphological change

ABSTRACT

We previously developed Wrin’Tac, a tactile sensing system that can select sensing modalities by changing its morphology. In this paper, we present a computational model to estimate a wrinkle’s morphology and predict the output of embedded sensing elements in both static indentation and sliding action cases. We evaluated the wrinkle shape and the posture of the sensing element by calculating its height. The wrinkle’s mechanical change is assessed by ascertaining its stiffness under vertical indentation by a spherical indenter. The output voltages of the sensor under static indentation are calculated by the proposed model, and the experimental values have error less than 10%, which validates the accuracy of our proposed model. For dynamic sliding action, this proposed model clarified the capability of wrinkle morphology in an evaluation of such sliding action’s characteristics as the sliding direction and velocity. We also identified the role of the wrinkle’s morphology in the sensor’s sensitivity under different conditions of dynamic sliding motion, implying that this sensing system may select suitable sensitivity for specific sensation tasks. We expect this work to pave the way for assessing the role of morphological changes to tactile sensation and developing soft active tactile sensing systems.     

Multi-agent coverage control design with dynamic sensing regions

A cooperative region reconnaissance problem is considered in this paper where a group of agents are required to reconnoitre a region of interest. A main challenge of this problem is the sensing region of each agent varies with its altitude within an altitude constraint. Meanwhile, the reconnaissance ability of an agent is determined by its altitude and radial distance. First, the region reconnaissance is formulated as an effective coverage problem, which means that each point in the given region should be surveyed until a preset level is achieved. Then, an effective coverage control law is proposed to minimize coverage performance index by adjusting the altitude of an agent. Finally, the effectiveness of the proposed control law is verified through numerical simulations.     

Hysteresis correction of tactile sensor response with a generalized Prandtl–Ishlinskii model

Tactile sensors are basically arrays of force sensors. Most of these force sensors are made of polymers or conductive rubber at lower cost, especially in the case of large area low-medium resolution tactile sensors. The consequence of such a decrease in cost and complexity is a worsening in performance. Hysteresis and drift are two main sources of error. Other tactile sensors do not present such limitations per se, however they are covered by a protective elastic layer in their final location and this covering can also lead to limitations. This paper presents a method to reduce the error caused by hysteresis in tactile sensors. This method is based on the generalized Prandtl–Ishlinskii model that has been applied to characterize hysteresis and saturation nonlinearities in smart actuators. The approximation error depends on several parameters as well as on the envelope functions that are chosen. Different alternatives are explored in the paper. Moreover, the model can also be inverted. This inverse model allows the force values to be obtained from the tactile sensor output while reducing the errors caused by hysteresis. In this paper the results of such an inversion are compared with other alternatives to register the data that do not compensate hysteresis. The average value of the hysteresis error measured in the experimental curve is 7.20% for an input range of 206?kPa, while this error is 1.51% following the compensation procedure. Since the implementation of tactile sensors usually results in the electronics being close to the raw sensor, and this hardware is also commonly based on a microcontroller or even on a FPGA, it is possible to add the algorithms presented in this paper to the set of compensation and calibration procedures to run in the smart sensor.     

Remote sensing image fusion based on Bayesian linear estimation

A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spa- tial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.     

Satellite remote sensing of atmospheric optical depth spectrum†

An analysis of the atmospheric radiative transfer processes suggests that the diffuse radiance emerging at the top of the Earth's atmosphere in the position of a satellite can be approximated by a linear relationship with the optical depth. It is found that the variation of the optical depth is associated with the changing of aerosol size distribution. The optical depth increases with the presence of large aerosol particles. Thus, inhomogeneities in aerosol size distribution can affect the high-altitude atmospheric radiance in the visible wavelengths (400–800 nm). To demonstrate an application of ocean colour remote sensing for atmospheric studies, computations of diffusely reflected radiance at the top of the atmosphere, using the radiative transfer equation, have been made for known aerosol size distribution and oceanic surface albedo. The condition of a clear sky over clear water has been considered in the study. The computed radiance was compared with the radiance detected by the Coastal Zone Colour Scanner (CZCS) aboard the NIMBUS-7 satellite. The CZCS radiance was collected within 1–2 hours after the aerosol measurements from the same area (Monterey Bay, California). An optical depth of the Earth's atmosphere is inferred from the comparison. The procedure was carried out for wavelengths corresponding to the CZCS channels (443, 520, 550,670 and 750 nm). Values of optical depth are plotted against the wavelengths to provide an optical depth spectrum. The results indicate that there is a strong dependence of optical depth on the wavelength. The dependence, however, does not agree well with the relationship proposed by Angstrom (1964) for the aerosol optical depth in the atmosphere. Furthermore, the resulting optical depth ratio cannot be estimated by the approximation suggested by Gordon and Clark (1981) and Gordon et al. (1983).     

A study on the use of tactile instructions for developing robot’s motions

Developing motions for humanoid robots is time consuming. However, sport and dance instructors can easily adjust their students?? postures by simple touches. This suggests the possibility of exploiting touch for motion development, and allows us to propose a methodology based on this concept. To realize such a system, it is required to define how the robot should interpret touches. We propose a supervised learning approach to cope with this issue, and verify its feasibility experimentally. We then study the data collected by the algorithm, and show that the system is practical both for motion development and for studying human-robot tactile communication. In particular, we present considerations on the sparsity that characterize the whole process and suggest how sparsity can be exploited for efficient interpretation of tactile instructions.     

Transportation mode detection using cumulative acoustic sensing and analysis

1 Introduction Smartphone sensing has emerged as an active area of interest for researchers,as these are increasingly being used in smart transportation services.This paper focuses on a smartphone based vehicle monitoring system,viz.transportation mode de-tection.Detection of the transportation mode of a commuter is usually carried out through a combination of motion sensors such as an accelerometer and position sensors such as GPS[1].     

Reliable non-prehensile door opening through the combination of vision, tactile and force feedback

Whereas vision and force feedback—either at the wrist or at the joint level—for robotic manipulation purposes has received considerable attention in the literature, the benefits that tactile sensors can provide when combined with vision and force have been rarely explored. In fact, there are some situations in which vision and force feedback cannot guarantee robust manipulation. Vision is frequently subject to calibration errors, occlusions and outliers, whereas force feedback can only provide useful information on those directions that are constrained by the environment. In tasks where the visual feedback contains errors, and the contact configuration does not constrain all the Cartesian degrees of freedom, vision and force sensors are not sufficient to guarantee a successful execution. Many of the tasks performed in our daily life that do not require a firm grasp belong to this category. Therefore, it is important to develop strategies for robustly dealing with these situations. In this article, a new framework for combining tactile information with vision and force feedback is proposed and validated with the task of opening a sliding door. Results show how the vision-tactile-force approach outperforms vision-force and force-alone, in the sense that it allows to correct the vision errors at the same time that a suitable contact configuration is guaranteed.     

A survey on distributed compressed sensing： theory and applications

The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.     

Remote sensing of snow – a review of available methods

The use of satellite remote sensing for the mapping of snow-cover characteristics has a long-lasting history reaching back until the 1960s. Because snow cover plays an important role in the Earth's climate system, it is necessary to map snow-cover extent and snow mass in both high temporal and high spatial resolutions. This task can only be achieved by the use of remotely sensed data. Many different sensors have been used in the past decades with various algorithms and respective accuracies. This article provides an overview of the most common methods. The limitations, advantages and drawbacks will be illustrated while error sources and strategies on how to ease their impact will be reviewed. Beginning with a short summary of the physical and spectral properties of snow, methods to map snow extent from the reflective part of the spectrum, algorithms to estimate snow water equivalent (SWE) from passive microwave (PM) data and the combination of both spectra will be delineated. At the end, the reader should have an overarching overview of what is currently possible and the difficulties that can occur in the context of snow-cover mapping from the reflective and microwave parts of the spectrum.     

Remote sensing of atmospheric water content from Bhaskara SAMIR data‡

Abstract

The 19-35 GHz and 22-235 GHz passive microwave radiometers (SAMIR) on board the Indian satellite Bhaskara have provided very useful data. From these data has been demonstrated the feasibility of deriving atmospheric and ocean surface parameters such as water vapour content, liquid water content, rainfall rate and ocean surface winds. Different approaches have been tried for deriving the atmospheric water content. The statistical and empirical methods have been used by others for the analysis of the Nimbus data. A simulation technique has been attempted for the first time for 19 35 GHz and 22-235 GHz radiometer data. The results obtained from three different methods are compared with radiosonde data. A case study of a tropical depression has been undertaken to demonstrate the capability of Bhaskara SAMIR data to show the variation of total water vapour and liquid water contents.     

Bayesian Multi‐net Classifier for classification of remote sensing data

The purpose of this paper is to investigate the applicability of Bayesian Multi‐net Classifier (BMC) to classify remote sensing data. BMC is based on Bayesian Network (BN), which is a graphical model encoding probabilistic relationships among variables of interest. Different from the BNC that has a mere network, a BMC has as many local Bayesian Networks as the predefined classes, which means that the probabilistic relationships among the features can be different for different classes. Classification is done by computing the probability of the class, given the particular instance of the features, and then predicting the class with the highest posterior probability. This method was validated using a Landsat ETM+ image of Beijing acquired on 1 May 2003. Based on the confusion matrix, overall accuracy, Kappa statistic, total normalized probability of misclassification (TNPM), and McNemar's test, classification results of BMC were compared with those of MLC and BNC in the case study. The comparison results show that BMC performs slightly better than MLC and similar to BNC. The local Bayesian Networks of BMC can also lead to a better understanding of the dependencies between bands for different classes.     

Wideband underwater sonar imaging via compressed sensing with scaling effect compensation

By exploiting the sparsity of the imaging scene, compressed sensing(CS) imaging may provide an effective solution to cope with the tradeoff of high azimuth resolution and wide swath in synthetic aperture sonar imaging. However, existing CS imaging methods are based on narrowband signal model and the scaling effect on the echoes is normally omitted, which may seriously affect the ultimate image reconstruction performance.This paper establishes the wideband CS underwater sonar imaging model at first, where the scaling effect and Doppler frequency shift are jointly considered. Furthermore, two wideband CS imaging approaches are discussed in uniform scale-range space and velocity-range space, respectively. Besides, modified l1-norm minimization algorithms are proposed for the scene reconstruction in velocity-range space. Finally, numerical experiments are provided to demonstrate the effectiveness of the proposed model and the reconstruction algorithms.     

The potential and challenge of remote sensing‐based biomass estimation

Remotely sensed data have become the primary source for biomass estimation. A summary of previous research on remote sensing‐based biomass estimation approaches and a discussion of existing issues influencing biomass estimation are valuable for further improving biomass estimation performance. The literature review has demonstrated that biomass estimation remains a challenging task, especially in those study areas with complex forest stand structures and environmental conditions. Either optical sensor data or radar data are more suitable for forest sites with relatively simple forest stand structure than the sites with complex biophysical environments. A combination of spectral responses and image textures improves biomass estimation performance. More research is needed to focus on the integration of optical and radar data, the use of multi‐source data, and the selection of suitable variables and algorithms for biomass estimation at different scales. Understanding and identifying major uncertainties caused by different stages of the biomass estimation procedure and devoting efforts to reduce these uncertainties are critical.     

Compressed sensing digital receiver and orthogonal reconstructing algorithm for wideband ISAR radar

This paper proposes a novel design of intermediate frequency(IF) digital receiver for wideband inverse synthetic aperture radar(ISAR) based on compressed sensing(CS). For the convenience in engineering application, we use random sampling in the digital receiver and make it possible to digitize the wideband IF signal using a commercial off-the-shelf analog-to-digital converter with sub-Nyquist sample rate. Besides, a novel basis for the sparse representation of real-valued ISAR radar echoes is built in this paper, and an orthogonal CS reconstructing algorithm is proposed based on this. Using our proposed method, the complex-valued range profile of target can be directly reconstructed from the subsampled real raw echo. The phase information of target range profile, which is very important for the coherent processing in ISAR imaging, is well reserved during the reconstruction. As a result, the down converter and matched filter, which are essential in conventional radar receiver, can be eliminated in our CS digital receiver. A series of simulation validates our design and demonstrates the feasibility of the sub-Nyquist sampling. The simulation results of ISAR imaging verify the validity and superiority of the proposed orthogonal reconstructing method.     

Small satellite remote sensing and applications – history,current and future

The small satellite renaissance began in the 1980s and is changing the economics of space. Technological trends have supported the advancement of small satellites in the 1–500 kg range. The number of countries actively participating has grown substantially during the past years. Satellite constellations (groups of satellites working in concert) are emerging as a powerful and effective application. In this paper, we focus on the small satellites than can perform remote sensing or Earth observation tasks. An overview is presented of the small satellite literature on Earth observation. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field and a reference for those searching for literature on a specific application for Earth observation. Small satellite programmes are classified according to the geographic regions. The small satellite industry and small satellite systems are addressed. In terms of applications, small satellite constellations are discussed in more detail. Finally, future developments are put forward. Telegeoprocessing combined with grid computing will provide the infrastructure and technologies for the development of Processing on Demand for Small Satellite Constellation systems.     

Peat resource mapping in Lewis using remote sensing techniques and automated cartography†

Abstract

The application of methodology developed for peat resource survey has illustrated the usefulness of multi-level and multi-band aerial photography in classifying Landsat multispectral imagery. Ground truthing to characterize the thematic maps plotted from the aerial photography and Landsat imagery was based primarily on vegetation surveys. The results of this work are illustrated with a series of maps and photographs and the usefulness of the approach is assessed.     

Microinstrument contact force sensing based on cable tension using BLSTM–MLP network

Minimally invasive surgical robotic systems established the foundation for precise and refined surgery, and the intelligentization of robotic systems is an important direction for future development. Among the methods of intelligentization, microinstrument external force sensing is an open and challenging research area. Force sensing information is used not only to ensure that surgeons apply the appropriate amount of force but also to prevent unintentional tissue damage. Because a microinstrument is a compact and small-sized construction, indirect force sensing method instead of the integration of sensors into the microinstrument is used, yielding better biocompatibility, sterilizability and monetary cost savings. This paper focuses on microinstrument-tissue contact force sensing, and the microinstrument used is a three degrees of freedom cable-driven manipulator. A contact force estimation strategy based on the differences in cable tension is established with consideration of the kinematics, dynamics and friction of the manipulator. A principle prototype of a surgical microinstrument force measurement system is developed, and then zero-drift, hysteresis and force loading experiments are studied. Based on the experimental data of the force loading experiments, the relationship between cable tension and contact forces is established by using a bidirectional long short-term memory plus multilayer perceptron network. The results show that the L2 cost of the network in the training set converges to 0.006 and that the RMSE of the network in the testing set converges to 0.053, and the network can meet the measurement requirements without overfitting. Therefore, the indirect force estimation method is a viable method of measuring forces of cable-driven microinstrument and can be used to integrate force sensing information into surgical robotic systems to improve the operability of surgical robots.