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A cyclocyanine (CC)-based organic small molecule two-photon (TP) fluorescent probe (CCNa1) was developed for mitochondrial sodium ion sensing. CCNa1 exhibits a low solvatochromic shift and strong TP fluorescence enhancement at 575 nm upon binding to Na+ and is insensitive to other metal ions and to pH. CCNa1 demonstrated fast cell loading ability, biocompatibility, and sensitive response to mitochondrial Na+ influx in live cells and mouse brain tissue.

This paper deals with estimating the acid concentration of pickling process using the Volterra inputs. To estimate the acid concentration, the whole pickling process is represented by the grey box model consists of the white box dealing with known system and the black box dealing with unknown system. Because there is a possibility of nonlinear term in the unknown system, the Volterra series are used to estimate the acid concentration. For the white box modeling, the acid tank solution level and concentration equations are used, and for the black box modeling, the acid concentration is estimated using the Volterra Least Mean Squares (LMS) algorithm and Least Squares (LS) algorithm. The LMS algorithm has the advantage of the simple structure and the low computation, and the LS algorithm has the advantage of lowest error. The simulation results compared to the measured data are included.

Mitochondrial viscosity affects respiratory state and signal transduction. As a result, abnormal mitochondrial viscosity leads to diabetes, accelerated aging, insulin resistance, Alzheimer's disease, Parkinson's disease, and Huntington's disease. Previously reported probes for mitochondrial viscosity have absorption and emission wavelengths in the green to orange region and thus are hampered by interference with biomolecules. In addition, rotors with disubstituted double bonds have low photostability, due to photoisomerization. Here, we report a new far-red viscosity probe, SFC-Cy007, which has high photostability, due to a tetrasubstituted double bond. This probe was selectively located in the intracellular mitochondria and immediately responded to changes in mitochondrial viscosity via swelling and inflammatory responses. Furthermore, SFC-Cy007 stained well at very low concentrations in the hippocampal tissue, allowing visual observation of viscosity distribution in hippocampal tissue for the first time.

For underwater vehicles, the use of sensors such as cameras and laser scanners is limited by the difference in environment compared to robots designed to work on dry land. In underwater environments, if use is made of sound signals, valuable information can be obtained. The most important application is the localization of underwater sound sources. The estimated location of a sound source can be used to control underwater robots or submarines. Thus, the purpose of this research is to estimate the source’s direction and location in a noisy underwater environment. The direction of the sound source is obtained using two hydrophones. Furthermore, if we assume that the robot or sound source is moving, the location of the sound source is estimated using more than two estimated directions. The feasibility of the developed algorithm is examined by experiments in a water tank and in the ocean. Keywords: hydrophone, time-delay estimation, generalized cross correlation, localization, particle filter I. 서론 주어진 임무수행을 위해 목표지점까지 정확하게 이동 할 수 있는 자율이동이 가능한 로봇의 개발을 위해 주변환경을 인식하고 로봇의 현재 위치를 추정하는 방법의 연구가 활발히 이루어 지고 있다. 이러한 방법들은 주로 초음파 센서, GPS, Laser Range Finder, 카메라, 마이크, RFID와 같은 센서를 사용하거나 여러 가지 센서 데이터들을 퓨전하여 사용한다[1-5]. 지상과 공중에서 동작하는 로봇들은 위에서 나열한 센서들을 큰 제약조건 없이 사용이 가능하지만 수중에서 작동하는 로봇이나 잠수정의 경우에는 사용할 수 있는 센서들이 음향관련 센서들과 관성센서 정도로 굉장히 제한적이다. 수중환경에서 음향신호를 활용하여 사용자로 하여금 주변의 환경적 변화 또는 로봇이나 잠수정의 운행상태를 추정하는데 도움을 주거나 주어진 신호에 대한 방위각 추정을 통해 신호발생기의 위치를 추정 할 수 있다. 수중 음원의 방위각을 추정하기 위해 여러 가지 방법들은 제안되었다. 그 중 가장 널리 사용되고 있는 방법은 TDE (Time-Delay Estimation)와 SBF (Steered Beamforming) 이다. 두 방법은 측정되는 음향데이터를 음원 위치일 때 최고 값을 가지는 함수로 변환하여 음원의 방향을 추정한다. 하지만 이러한 직접적인 방법은 수중에서 발생하는 잡음신호와 반사파의 영향에 따라 성능저하가 발생하게 된다. 따라서 본 논문에서는 반사파와 잡음신호가 존재하는 환경에서도 성능저하를 줄이기 위해 음원 방향을 추정하기 위한 파티클 필터(Particle filter) 방법을 제안한다. 제안하는 파티클 필터는 likelihood function을 생성하기 위해 GCC (Generalized Cross Correaltion) 함수를 사용하고 일반적인 TDE 방법과 달리 최고값 하나만을 사용하는 것이 아니라 여러 peak 값을 사용하여 반사파와 잡음신호의 영향을 줄인다. 또한 제안된 파티클 필터를 이용하여 얻어진 음원의 방위각과 음원 또는 로봇의 모션 정보를 이용하여 음원과 로봇의 상대위치를 추정하는 방법을 제안한다. II. 수중 음원의 방향 추정 1. GCC (Generalized Cross Correlation) 두 하이드로폰을 통해 얻어진 음향신호의 시간차를 추정하는 방법은 여러 가지가 있지만 그 중 가장 일반적으로 사용되는 알고리즘은 GCC이다. GCC는 두 입력신호의 Cross Correlation을30, 2013 이용하여 신호의 시간차이를 추정하는 방법으로 계산이 간단하기 때문에 시스템의 복잡도가 낮아도 사용이 가능하다[6]. 하지만 실제 수중 음원 신호의 방향을 추정함에 있어서 발생하는 여러 가지 문제점으로 GCC의 성능이 저하된다. 그 중 가장 대표적인 것이 반사파와 잡음신호이다. 수중 음원에서 발생된 신호가 물의 표면이나 바닥 면에 도달한 경우 반사되어 하이드로폰으로 입력될 수 있다. 이렇게 반사된 신호

This paper introduces a diffusion strategy for the multichannel active noise control. The diffusion strategy is designed to reduce the computational complexity by distributes computations to all nodes of multichannel active noise control system. Thus, the multichannel filtered-x normalized least mean square algorithm, which is the simplest way for real active noise control environments is used as a base algorithm for the diffusion strategy. From the structure of the multichannel active noise control system, the optimal weight vector at a node of the proposed strategy is calculated by considering the weight vector of neighbor nodes. With the proposed strategy, the computational complexity is distributed from one main processor to all nodes. With some simulation results, the performance of the proposed strategy is shown with the reduced computational complexity for one processor.

In this paper, we propose a moving object detection algorithm that enables to detect moving objects with collision risk in rear of the vehicle by using image sequences from a vehicle-mounted monocular rear-view fisheye camera.

This paper introduces a robust filtered-x NLMS algorithm with high performance which is designed for active control of impulsive noise system. High performance of the proposed algorithm is realized by obtaining the optimal step size of the filtered-x NLMS algorithm. The optimal step size is developed by analyzing the mean square deviation of filtered-x NLMS algorithm. The optimal step size is applied to the robust filtered-x NLMS algorithm which uses a step-size scaler against impulsive noise. The step-size scaler is constructed by taking the tanh cost function. The step size scaler scales down the step size of the filtered-x normalized least mean square algorithm, when impulsive noise is appeared. The proposed algorithm shows both fast convergence rate and low steady-state error although the simulation environments are constructed with the symmetric α-stable distribution for impulsive noise.

In this study, we consider a special situation that only a pair of hydrophone on a moving underwater vehicle is available to localize a fixed acoustic source of far distance. The trigonometry can be used in this situation by using two different DOA of different locations. Notice that the distance between the two locations should be measured. Therefore, we assume that the vehicle is sailing straightly and the moving distance for each unit time is measured continuously. However, the accuracy of the localization using the trigonometry is highly dependent to the accuracy of DOAs and measured moving distances. Therefore, we proposed another method based on the extended Kalman filter that gives more robust and accurate localization result., {"references":["Z. Zhou. J. Cui, and A. Bagatzoglou, Scalable Localization with Mobility\nPrediction for Underwater Sensor Networks, in 2008 IEEE INFOCOM\nConf., pp.2198-2206.","Z. Zhou , J. Cui , S. Zhou, Localization for large-scale underwater sensor\nnetworks, Proc. the 6th international IFIP-TC6 Conf. on Ad Hoc and\nsensor networks, wireless networks, next generation internet, May 14-18,\n2007, Atlanta, GA, USA, pp.108-119.","P. Corke, C. Detweiler, M Dumbabin, M. Hamilton, D. Rus, and I.\nVasilescu, Experiments with Underwater Robot Localization and Tracking,\nProc. IEEE Int. Conf. Robotics and Automation, pp.4556 4561, April\n2007.","N. Y. Ko, T. G. Kim, and S. W. Noh, Monte Carlo Localization of\nUnderwater Robot Using Internal and Exter nal Information, IEEE Conf.\nAsia-Pacific Services Computing, pp.410 415, Dec 2011.","B.A.A.P. Balasuriya, M. Takai, W.C. Lam, T. Ura, and Y. Kuroda,\nVision based autonomous underwater vehicle navigation: underwater\ncable tracking, Proc. OCEANS 97. MTS/IEEE Conf., 14181424 vol.2,\nOct 1997.","L. Stutters, H. Liu, C. Tiltman, and D. J. Brown, Navigation technologies\nfor autonomous underwater vehicles, IEEE Trans. Syst. Man Cybern. C,\nAppl. Rev., vol. 38, no. 4, pp. 581589, Jul. 2008.","K. T. Wong and M. D. Zoltowski, Closed-form underwater acoustic\ndirection-finding with arbitrarily spaced vector hydrophones at unknown\nlocations, IEEE J. Oceanic Eng., vol. 22, no. 3, pp. 566575, July 1997.","K. T. Wong and M. D. Zoltowski, Extended-aperture underwater acoustic\nmulti-source azimuth/elevation direction-finding using uniformly but\nsparsely spaced vector hydrophones, IEEE J. Oceanic Eng., vol. 22, no.\n4, pp. 659672, Oct. 1997.","K. T. Wong, and M. K. AWAD, Source Tracking with Multiple-\nForgetting-Factor RLS Using a Vector-Hydrophone Away From or Near\na Reflecting Boundary, IEEE Conf. OCEANS, pp. 1-4, May 2007.\n[10] N. Zou, C.C. Swee, B.A.L Chew, Vector Hydrophone Array Development\nand its Associated DOA Estimation Algorithms, in the Proceedings\nof OCEANS Asia Pacific, pp. 1-5, Singapore, May 2006.\n[11] J. Li, and X. Zhang, Improved Joint DOD and DOA Estimation for\nMIMO Array With Velocity Receive Sensors, IEEE Signal Processing\nLett., vol. 18, issue. 12, pp/ 717-720, Dec, 2011.\n[12] H. Lai and K. Bell and H. Cox, DOA Estimation using Vector Sensor\nArrays, 42nd Asilomar Conference on Signals, Systems and Computers,\n2009.\n[13] Q. Zhang, and J. Huang, N. Zou, Joint estimation of DOA and time-delay\nin underwater localization, Proc. IEEE International Conf. on Acoustic,\nSpeech, and Signal Processing, vol. 5, pp. 2817-2820, 1999.\n[14] D. Carevic, Automatic estimation of multiple target positions and velocities\nusing passive TDOA measurements of transients, IEEE Trans.Signal\nProcess., vol. 55, no. 2, pp. 424436, Feb. 2007.\n[15] L. Yang and K. C. Ho, An Approximately Efficient TDOA Localization\nAlgorithm in Closed-Form for Locating Multiple Disjoint Sources With\nErroneous Sensor Positions, IEEE Transactions on Signal Processing, vol.\n57, pp. 45984615, Dec. 2009.\n[16] D. Carevic, Detection and Tracking of Underwater Targets Using\nDirectional Sensors, Proc. of Intelligent Sensors, Sensor Networks and\nInformation, 2007 3rd International Conf., 2007, pp. 143-148.\n[17] Z. Yang, W. Xu, Z. Xiao, and X. Pan, Passive localization of an\nAutonomous Underwater Vehicle with periodic sonar signaling, IEEE\nConf. OCEANS, pp. 1-4, May 2010.\n[18] M. Cai, B. Bingham, Passive acoustic detection of a small remotely\noperated vehicle, IEEE Conf. OCEANS, pp. 1-7, June 2011."]}