Multi-sensor Technology Research Articles

FCGP: Infrared and Visible Image Fusion via Joint Contrast and Gradient Preservation

With the fast development of multi-sensor technologies, image fusion has played an essential role in modern military and civilian. To better integrate thermal radiation information in infrared images and detailed appearance information in visible images, we investigate a novel norm formulation via joint contrast and gradient preservation, for infrared-visible image fusion. Specifically, we employ a structure tensor measurement to characterize the similarity between the fused image and the infrared image in terms of thermal radiation information, to better integrate visible appearance details. Since natural image gradients follow the hyper-Laplacian distribution, we employ $ \ell _{ p\in [ 0.5,0.8]}$ norm instead of $ \ell _0$ or $ \ell _1$ norm to measure the gradient term to further extract texture information from visible images. To cope with the computational problem introduced by the coupled structure tensor and non-convex $ \ell _{p\in [ 0.5,0.8]}$ norm, we propose a computational efficient solver based on half-quadratic splitting scheme. Experiments on public datasets validate the competitiveness of FCGP from both the subjective and objective evaluation perspective.

Aplication ADeV Aplikasi Air Detection Environment System (ADeV) Dalam Mendeteksi Kadar Kualitas Udara Di Area Parkiran Berbasis Android

Penelitian ini menyajikan Aplikasi Air Detection Environment System berbasis android menggunakan teknologi WSN. Teknologi WSN dengan multi sensor sebagai hardware sistem deteksi kualitas udara di lingkungan parkiran. Aplikasi yang dirancang telah berhasil secara otomatis mendeteksi kualitas udara akibat gas buang kendaraan berupa CO, CO2, HC, PM10, suhu dan kelembaban. Teknologi multi sensor ini dapat mengirimkan data kualitas udara ke server dan ditampilkan aplikasi android berupa tampilan status kualitas udara serta menampilkan waktu dan lokasi kejadian. Adapun kelebihan aplikasi ini ialah masyarakat bisa mengetahui kondisi lingkungan di parkiran secara praktis, real time dan mobile karena cukup membuka aplikasi yang ada di smartphone Diharapkan dengan adanya aplikasi ini dapat meningkatkan keselamatan dan kewaspadaan pada individu terhadap kualitas lingkungan yang tidak sehat.

A Co-Operative Autonomous Offshore System for Target Detection Using Multi-Sensor Technology

This article studies the design, modeling, and implementation challenges for a target detection algorithm using multi-sensor technology of a co-operative autonomous offshore system, formed by an unmanned surface vehicle (USV) and an autonomous underwater vehicle (AUV). First, the study develops an accurate mathematical model of the USV to be included as a simulation environment for testing the guidance, navigation, and control (GNC) algorithm. Then, a guidance system is addressed based on an underwater coverage path for the AUV, which uses a mechanical imaging sonar as the primary AUV perception sensor and ultra-short baseline (USBL) as a positioning system. Once the target is detected, the AUV sends its location to the USV, which creates a straight-line for path following with obstacle avoidance capabilities, using a LiDAR as the main USV perception sensor. This communication in the co-operative autonomous offshore system includes a decentralized Robot Operating System (ROS) framework with a master node at each vehicle. Additionally, each vehicle uses a modular approach for the GNC architecture, including target detection, path-following, and guidance control modules. Finally, implementation challenges in a field test scenario involving both AUV and USV are addressed to validate the target detection algorithm.

Protocol for PD SENSORS: Parkinson’s Disease Symptom Evaluation in a Naturalistic Setting producing Outcome measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson’s disease

IntroductionThe impact of disease-modifying agents on disease progression in Parkinson’s disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability...

Path Optimization Method for the Spherical Underwater Robot in Unknown Environment

One of the major respects of the autonomous capability of underwater robots in unknown environment is to be capable of global path planning and obstacles avoiding when encountering abrupt events. For the Spherical Underwater Robot (SUR) to fulfill autonomous task execution, this paper proposed a novel fuzzy control method that incorporates multi-sensor technology to guide underwater robots in unknown environment. To attain the objective, a SUR we designed is used to design the controller. According to its kinematic model, the safety distance was calculated and sensors (US1000-21A) were arranged. The novel fuzzy control method was then explored for robot’s path planning in an unknown environment through simulation. The simulation results demonstrate the capability of the proposed method to guide the robot, and to generate a safe and smooth trajectory in an unknown environment. The effectiveness of the proposed method was further verified through experiments with a SUR in a real platform. The real environment experiments by using the novel fuzzy control method were compared with the basic control method. The experimental results show that in unknown environments, the proposed method improves the execution efficiency and flexibility of the SUR.

Design of Automatic Sorting System of Workpiece Based on Multi-sensor Detection

The sorting system applies multi-sensor technology, PLC technology, pneumatic technology and frequency converter technology to realize the efficient automatic sorting of workpieces and solve the problem of automatic sorting of more complex shaped products. Through running test, the system has high efficiency, reliable operation, strong practicability, and great application value in automatic production lines such as mechanical processing, electronic assembly and article circulation.

CHARACTERIZATION OF A MOBILE MAPPING SYSTEM FOR SEAMLESS NAVIGATION

Abstract. Mobile Mapping Systems (MMS) are multi-sensor technologies based on SLAM procedure, which provides accurate 3D measurement and mapping of the environment as also trajectory estimation for autonomous navigation. The major limits of these algorithms are the navigation and mapping inconsistence over the time and the georeferencing of the products. These issues are particularly relevant for pose estimation regardless the environment like in seamless navigation. This paper is a preliminary analysis on a proposed multi-sensor platform integrated for indoor/outdoor seamless positioning system. In particular the work is devoted to analyze the performances of the MMS in term of positioning accuracy and to evaluate its improvement with the integration of GNSS and UWB technology. The results show that, if the GNSS and UWB signal are not degraded, using the correct weight to their observations in the Stencil estimation algorithm, is possible to obtain an improvement in the accuracy of the MMS navigation solution as also in the global consistency of the final point cloud. This improvement is measured in about 7 cm for planimetric coordinate and 34 cm along the elevation with respect to the use of the Stencil system alone.

Joint model for residual life estimation based on Long-Short Term Memory network

With the rapid development of industrial internet of things, multi-sensor technology has been widely used in system condition monitoring and residual useful life (RUL) estimation, which plays a crucial role in preventing catastrophic failures and reducing maintenance losses. However, current prognosis using multi-sensor data faces the following challenges: (i) Manual feature selection and extraction and exploration of degradation failure mechanism for a complex domain require a substantial amount of human labor and expertise from the practitioner, which is seldom available. (ii) Data fusion and prognosis are usually divided into two separate steps, resulting in the lack of intrinsic relationship between the two tasks. (iii) The end-to-end prediction methods directly based on deep learning behave like the black-box and provide no information for degradation progression. To overcome these drawbacks, a prediction framework comprising two deep learning models is proposed in this paper. Through the proposed supervised joint training scheme, the framework not only provides a continuous visualization progression of system degradation but also ensures that the generated fusion signal effectively performs in RUL prediction. As a framework application, a joint model of RUL estimation based on an ordinary long short-term memory network is designed. In the experiment section, a simulation study is firstly performed to show its RUL prediction performance. Then, a case study is conducted using two public experimental data sets (the aircraft turbofan engine data and the milling). Furthermore, the advantage of the proposed joint model is validated by carrying out a comparison with other methods based on the same experimental data sets.

Performance study of energy piles in different climatic conditions by using multi-sensor technologies

Abstract Energy pile is widely utilized for the thermal solicitation of the shallow geothermal energy in recent years. However, the thermo-mechanical performance of the energy pile under different climatic conditions has rarely been discussed in the existing literature. Thus, to investigate the performance of the energy pile in different climatic conditions, this study set up an experimental system consisting of multi-sensor technologies, especially the monitoring method of fiber Bragg grating. Then tests with a model pile are performed to study the performance of energy pile in heating and cooling demand dominated climatic conditions, respectively. The results show that the cumulative effect occurs in the soil temperature field in the cooling demand dominated climatic condition. Moreover, the thermally induced axial stress and irreversible displacement increase with thermal cycles in different climatic conditions. Also, this study confirms that FBG sensors are suitable for monitoring stress variations of energy piles.

Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion

As one of the main signal sources of underwater acoustic target recognition, the target noise signal is difficult to characterize the characteristics of the target from clearly comparing with the multi-sensor detection technology, which may lead to lower recognition rate and higher false alarm rate and seriously restricts the function of underwater acoustic detection system. In order to solve this problem, a multi-layers LSTM underwater acoustic target noise feature extraction model is established by using the long short term memory network. The information features such as time-domain envelope of target noise, Demon line spectrum and Mel frequency cepstrum coefficient are extracted, and a subset of multi-classes features is constructed. On this basis, the feature level fusion recognition and classification model based on the multi-classes features subset and the decision level fusion recognition and classification model based on the D-S evidence theory are established, and the above-mentioned models are tested by using the sample database. The difference of classification result between the multi-classes feature fusion and the single class feature recognition classification is compared, and the above model is tested and verified by using the relevant test data of port basin verification experiment. The correlation results show that the present intelligent recognition and classification method of underwater target noise based on the multi-classes feature fusion is more robust, and the recognition rate and false alarm rate of underwater target are better than those of single category feature discrimination method.

Sensors support machine learning

Sensors support machine learning

Application of multi-sensor fuzzy information fusion algorithm in industrial safety monitoring system

Chemical enterprises are characterized by many casualties and frequent serious accidents. In order to reduce the likelihood of such accidents and ensure safe production effectively, a new safety assessment method based on multi-sensor technology and fuzzy information fusion algorithm is proposed based on theory of fuzzy systems. The important parameters such as dust concentration, temperature and smoke concentration are monitored by various sensors in chemical enterprises, and information obtained by multiple sensors are fuzzified and optimized through the compositional operation and decision rules of a data fusion center, so as to fully utilize the information obtained from each monitoring point and obtain accurate estimations of the environmental safety state. In this paper, an example is presented to illustrate that the multi-sensor fuzzy information fusion algorithm increases the confidence level of industrial safety monitoring and improves the performance of the safety monitoring system.

On-line Analysis of Marine Water Quality Data Based on Large Electronic Computer

Cong, W.; Guo, J., and Yu, H., 2019. On-line analysis of marine water quality data based on large electronic computer. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 47–53. Coconut Creek (Florida), ISSN 0749-0208.In order to make full use of marine resources, develop marine economy and build a powerful marine country, in recent years, the state has focused on the on-line analysis technology of marine water. In order to solve the problems of rapid change of marine environment and difficulty in the detection and analysis of water quality data, this paper presents an online analysis method of marine water quality data based on large electronic computer after comparing various methods of marine water quality detection in China and abroad. This method uses a variety of marine water quality sensors to collect marine water quality data synthetically, and then uses multi-sensor fusion technology to fuse the collected water quality data. Finally, it combines multi-sensor D-S evidence theory data fusion technology with computer to analyze water quality data synthetically online. It is concluded that this method can effectively improve the ability of marine pollution early warning and marine ecological environment protection by real-time comprehensive analysis and research of marine water quality data.

Quality Monitoring and Analysis of Xinjiang ‘Korla’ Fragrant Pear in Cold Chain Logistics and Home Storage with Multi-Sensor Technology

Korla fragrant pear is prestigious for its special texture and unique flavor but suffers storage and supply chain difficulties for its deterioration-prone properties. In order to improve the storage quality of Korla fragrant pears during the whole cold chain from the orchard to the customers, the paper deeply researches multiple influencing factors of cold chain logistics and home storage of Korla fragrant pears with multi-sensor technology (MST), such as the temperature, relative humidity, concentrations of oxygen (O2), carbon dioxide (CO2) and ethylene (C2H4). Cold chain logistics are assessed by sensory evaluation and physiological index measurement, and home storage environments are classified by using back propagation neural network (BPNN) in both refrigerators and ordinary rooms. Experimental results show that the MST-based detectors can improve the accuracy of continuous sensor data acquisition, such that the preservation quality of Korla fragrant pears is effectively enhanced by data analysis on gas contents, firmness, pH, and total soluble solids. These results indicate that Korla fragrant pears stored in refrigerators have a higher acceptance for customers.

Can high-resolution reprocessed data replace the traditional 2D high-resolution seismic data acquired for site surveys?

Can high-resolution reprocessed data replace the traditional 2D high-resolution seismic data acquired for site surveys?

Multi-sensor technology for B-spline modelling and deformation analysis of composite structures

High-accuracy deformation analysis of composite structures is an important topic in the field of civil engineering and increasing attention is given to geometry-based analytical model, such as B-splines where the determination of the parameter values has a great influence on the accuracy of the B-spline approximation. In this paper, B-spline approximation is carried out with point cloud data of an arch structure obtained with terrestrial laser scanner (TLS) and the parameter control point is investigated with the aid of laser tracker (LT) and corner cube reflectors (CCRs). We focused on the discussion about the influence factors of the optimal parameters, such as CCR uncertainty, curvature of object, and order of the B-spline. The innovation of this paper is that we adopt LT to optimize the parameters of B-spline fitting, and the results are compared and verified by Bayesian information criterion.

Focus on materials, semiconductors, vacuum, and cryogenics

Focus on materials, semiconductors, vacuum, and cryogenics

Fine grained medical image fusion using type-2 fuzzy logic

<p>In recent years, many fast-growing technologies coupled with wide volume of medical data for the digitalization of that data. Thus, researchers have shown their immense interest on Multi-sensor image fusion technologies which convey image information based on data from various sensor modalities into a single image. The image fusion technique is a widespread technique for the diagnosis of medical instrumentation and measurement. Therefore, in this paper we have introduced a novel multimodal sensor medical image fusion method based on type-2 fuzzy logic is proposed using Sugeno model. Moreover, a Gaussian smoothen filter is introduced to extract the detailed information of an image using sharp feature points.Type-2 fuzzy algorithm is used to achieve highly efficient feature points from both the b images to provide high visually classified resultant image. The experimental results demonstrate that the proposed method can achieve better performance than the state-of-the- art methods in terms of visual quality and objective evaluation.</p>

A fiducial-aided data fusion method for the measurement of multiscale complex surfaces

Multiscale complex surfaces, possessing high form accuracy and geometric complexity, are widely used for various applications in fields such as telecommunications and biomedicines. Despite the development of multi-sensor technology, the stringent requirements of form accuracy and surface finish still present many challenges in their measurement and characterization. This paper presents a fiducial-aided data fusion method (FADFM), which attempts to address the challenge in modeling and fusion of the datasets from multiscale complex surfaces. The FADFM firstly makes use of fiducials, such as standard spheres, as reference data to form a fiducial-aided computer-aided design (FA-CAD) of the multiscale complex surface so that the established intrinsic surface feature can be used to carry out the surface registration. A scatter searching algorithm is employed to solve the nonlinear optimization problem, which attempts to find the global minimum of the transformation parameters in the transforming positions of the fiducials. Hence, a fused surface model is developed which takes into account both fitted surface residuals and fitted fiducial residuals based on Gaussian process modeling. The results of the simulation and measurement experiments show that the uncertainty of the proposed method was up to 3.97 × 10−5 μm based on a surface with zero form error. In addition, there is a 72.5% decrease of the measurement uncertainty as compared with each individual sensor value and there is an improvement of more than 36.1% as compared with the Gaussian process-based data fusion technique in terms of root-mean-square (RMS) value. Moreover, the computation time of the fusion process is shortened by about 16.7%. The proposed method achieves final measuring results with better metrological quality than that obtained from each individual dataset, and it possesses the capability of reducing the measurement uncertainty and computational cost.

Impact of Multi-Sensor Technology for Enhancing Global Security in Closed Environments Using Cloud-Based Resources

By nature, some jobs are always in closed environments and employees may stay for long periods. This is the case for many professional activities such as military watch tours of borders, civilian buildings and facilities that need efficient control processes. The role assigned to personnel in such environments is usually sensitive and of high importance, especially in terms of security and protection. With this in mind, we proposed in our research a novel approach using multi-sensor technology to monitor many safety and security parameters including the health status of indoor workers, such as those in watchtowers and at guard posts. In addition, the data gathered for those employees (heart rate, temperature, eye movement, human motion, etc.) combined with the room’s sensor data (temperature, oxygen ratio, toxic gases, air quality, etc.) were saved by appropriate cloud services, which ensured easy access to the data without ignoring the privacy protection aspect of such critical material. This information can be used later by specialists to monitor the evolution of the worker’s health status as well as its cost-effectiveness, which gives the possibility to improve productivity in the workplace and general employee health.