Intelligent measuring systems for distance estimation based on fuzzy data fusion

To promote underwater construction, the tools such as underwater robotic manipulator system, underwater vehicles and so on are necessary. Furthermore, these system need an accurate 3D position parameters. If no position information for automatic control system that system is difficult to be operated. Hence, it is important to take position information. Although 3D position camera can offer 3D position parameters, it has still some deviation. In addition, the manipulator system need accurate distance information for fast and accurate operation. Hence, an accurate distance measurement system is necessary for a remotely teleoperated robotic manipulator system for underwater constructions. This paper focus on the distance measurement study to offer a accurate target distance information. The information can combine with 3D position camera to obtain more accurate 3D position parameters to offer a useful position information for underwater constructions, with a remotely teleoperated robotic manipulator system. In this paper, three distance measurement system, includes commercial ultrasonic distance measurement system, fabricated ultrasonic system and a laser distance measurement system is study. The commercial distance measurement system have a distance measurement system rang 8~35 cm, plusmn0.1 cm. However, a short distance measurement is difficult to be measured with the measurement system. On the other side, the fabricated measurement system can measure a short distance. Information from 0~5 cm, accuracy plusmn0.3 cm. The fabricated distance measurement system fabricated with PZT thin film. In this study, a horn is added to improve the accuracy of distance measurement. In addition, the laser distance measurement system, measurement system is used to measure the distance information also in this study. The combination of these distance measurement system to form a multiple range distance measurement can offer a long measurement range for accurate distance measurement underwater. A detailed experiment is to be studied in the future. Furthermore, the distance measurement system has been offered a distance measurement in a remotely teleoperated robotic manipulator system for underwater constructions

In multi-target hospital illness tracking, for data fusion, data with noise as input must be sent to fusion center to be filtered, associated, combined and made final decision as output. In this paper, an efficient fuzzy fusion approach for hospital illness tracking is proposed. The proposed approach is developed based on the fuzzy clustering means algorithm, which differs from many other fuzzy logic data fusion algorithms. Performance evaluation and results using simulations are reported, and a comparison with other fuzzy logic approaches based on the results described in other reference is also shown. The efficiency of the new approach has been demonstrated by the fuzzy system performance evaluation.

Design, development, fabrication and evaluation of the dynamics of a graphene based underwater acoustic vector sensor: A simulation and experimental study

Progress in science and technology has accelerated the pace of development in the intelligent vehicle system. Multiple sensors distributed in the system are used to gather and determine vehicle environmental information. Regardless of the accuracy of sensors, the data fusion algorithm employed in multi-sensors system affects the decision-making directly and deeply. According, it is necessary to design appropriate algorithms to achieve sensors' data processing. This paper presents a novel fuzzy adaptive data fusion strategy based on the federal Kalman filter and fuzzy inference so as to achieve more accurate results. In the strategy, fuzzy inference is used to adjust the level of measurement noise, while the purpose of federal Kalman filter is to obtain the smooth data. Besides, calculating the mean value of residuals, we illustrate a method to reject measurement defects and substitute the previous effective value for them. Experimental results show that the proposed strategy can effectively detect and correct defects, and obtain more accurate data processing results compared with the federal Kalman filter.

This paper presents a novel person to camera distance measuring system based on eye-distance. The distance between centers of two eyes is used for measuring the person to camera distance. The variation in eye-distance (in pixels) with the changes in camera to person distance (in inches) is used to formulate the distance measuring system. The system starts with computing the distance between two eyes of a person and then person to camera distance is measured. The proposed distance measurement system is relatively simple and inexpensive to implement as it does not require any other external distance measuring tools. Experimental results show the effectiveness of the system with an average accuracy of 94.11%.

This paper presents a multi-resolution masks based pattern matching method for person identification. The system is commenced with the construction of multi-resolution mask cluster pyramid, where the mask size is chosen depending on the distance between two eyes, computed from the detected face. Experimental results show the effectiveness of the system with significantly higher precision, recall rates and matching probability comparing with conventional single resolution mask based person identification systems. This paper also presents a novel person to camera distance measuring system based on eye-distance. The distance between centers of two eyes (interocular distance) is used for measuring the person to camera distance. The variation in eye-distance (in pixels) with the changes in camera to person distance (in inches) is used to formulate the distance measuring system. Experimental results show the effectiveness of the distance measurement system with an average accuracy of 94.11%. Key words: Single resolution mask, multi-resolution masks, person to camera distance, person identification.

A cellphone has been put at our bedside and used as an alarm clock frequently. When an earthquake occurred, it is easy to find a sufferer to detect the buried position of a cellphone. So, the sufferer detection system with a cellphone which is consists of two components has been developed. One component is a distance measurement system (DSMS) which measures the distance to make use of the difference of propagation velocity between an electric wave and a sound wave. The other component is a direction measurement system (DRMS) which measures the direction to utlize the radio field intensity. This paper is described about the principle of DRMS and the results of cellphone detection experiment.

To establish a parallel fusion approach of processing high dimensional information, the model and criterion of multi-sensor fuzzy stochastic data fusion were presented. In order to design genetic algorithm fusion ( the fusion parameter coding ) initial population and fitness function establishing, and fuzzy logic controller designing for genetic operations and probability choosing were complete d. The discussion on the highly dimensional fusion was given. For a moving target with the division of 1.64 (velocity) and 1.75 (acceleration), the precision of fusion is 0.94 and 0.98 respectively. The fusion approach can improve the reliability and decision precision effectively.

Fuzzy systems have been proven very successfully in many important applications and are rapidly growing to become a powerful technique for multisenosr-multitarget data fusion. The functional paradigm for fuzzy multisenosr-multitarget data fusion consists of fuzzification, fuzzy knowledge-base, fuzzy inference mechanism, and defuzzification. In fuzzy system design, users start with some fuzzy rules, which are chosen heuristically based on their experience, and membership functions, which in many cases are chosen subjectively based on understanding the problem, and they use the developed system to tune these rules and membership functions. Constructing membership function is the most important step in the fuzzy system design. This paper addresses the problem of constructing the optimal membership functions from input data in a multisenosr-multitarget environment. This analysis has been applied to clustering of multisensor information in a two-dimensional multisenosr-multitarget data fusion system. Clustering performance using optimal membership functions is compared to that of clustering using non-optimal membership functions. The results show that there is a significant performance improvement when using optimal membership functions.

Estimation of harvested fruit weight using volume measurements with distance sensors: A case study with olives in a big box

Underwater wireless sensor network (UWSN) has limited bandwidth, long propagation delays, and unrechargeable batteries, which forces the development of techniques minimizing the energy consumption of UWSNs. One solution is clustering that outperforms in terms of energy optimization and, in addition, can also provide scalability and data reliability. The development of clustering algorithms depends on UWSN architectures. In our case, we have proposed a hybrid UWSN network architecture named ACOP-UWSN (ACoustic OPtical Underwater Wireless Sensor Network), which aims for reliable data transmission at the fastest speed with higher data rates and minimized propagation delays. In ACOP-UWSN, optical sensors sense data, whereas acoustic sensors only relay data towards the surface buoy. Clustering of all these nodes may result in unnecessary wastage of the node’s energy and memory. Therefore, we undergo clustering of only optical sensors, as comparatively, sensing and processing of data consume maximum energy and memory than relaying data. In the proposed clustering algorithm, the criteria for cluster head selection are OSNR (optical signal-to-noise ratio) and residual energy. The mathematical derivation of OSNR for underwater wireless optical communication is a key part of the algorithm. The results achieved using the simulation tool show that our proposed OSNR-based energy efficient clustering algorithm (OECA) is efficient in terms of energy and as well data reliability as compared to state of art. The quantitative analysis of the results showed that the total energy consumed for the existing scheme is 450 J, which is more as compared to 400 J of OECA. Similarly, the residual energy of nodes using OECA is 1% greater than the existing scheme. To our knowledge, research on the clustering of optical sensors in UWSN is least available, and we are among the few to work on an OSNR-based clustering algorithm for underwater optical nodes.

This paper presents a nonvisible field-of-view (NFOV) target estimation approach that incorporates optical and acoustic sensors. An optical sensor can accurately localize a target in its field-of-view whereas the acoustic sensor could estimate the target location over a much larger space, but only with limited accuracy. A recursive Bayesian estimation framework where observations of the optical and acoustic sensors are probabilistically treated and fused is proposed in this paper. A technique to construct the observation likelihood when two microphones are used as the acoustic sensor is also described. The proposed technique derives and stores the interaural level difference of observations from the two microphones for different target positions in advance and constructs the likelihood through correlation. A parametric study of the proposed acoustic sensing technique in a controlled test environment, and experiments with an NFOV target in an actual indoor environment are presented to demonstrate the capability of the proposed technique.

We investigate an optical system for the absolute distance measurement of objects with rough surfaces, using the principle of asynchronous optical sampling (ASOPS). A telescope system collects the scattered laser light that returns from the rough surface and directs it into a collimator with high precision. The contrastive absolute distance measurement experiments are carried out using a plane mirror and objects with rough surfaces. The residual error is kept within 10 μm within a 2-ms sampling time for the rough surfaces. Our results indicate that this ASOPS method is a promising candidate for metrological applications in some industrial sites.

Ultrasonic system for accurate distance measurement in the air

We present a system for intraocular distance measurement of the human eye in vivo with high sensitivity. The instrument is based on Fourier domain low coherence interferometry (FD-LCI). State-of-the-art FD-LCI systems are capable to image a depth range of only a few mm, because the depth range is determined by the spectral resolution of the spectrometer. To measure larger distances (e.g. human eye length) we implemented two separate reference arms with different arm lengths into the interferometer. Each reference arm length corresponds to a different depth position within the sample (e.g. cornea and retina). Therefore two different depth sections, each with a depth range of a few mm can be imaged simultaneously. With the new system axial distances could be measured with a precision of 8&mgr;m. We demonstrate the performance of the instrument by measuring the axial eye length of 9 patients with cataract and compare our results with those obtained using the IOL Master (Carl Zeiss Meditec Inc.).