Mean trophic level of coastal fisheries landings in the Persian Gulf(Hormuzgan Province), 2002–2011

Fishing activities can alter the structure of marine food webs by the selective removal of some species. The changes in the marine food webs of the Hormuzgan waters of the Persian Gulf, Iran were assessed, based on estimates of the mean trophic index(MTI) and Fishing in Balance index(FiB), and on landing profile of the exploited marine community(49 species) during the period, 2002–2011. The total landings( Y_t)( R =0.88, P <0.001) increased gradually while the Y_t of carnivores has slightly declined, and the Y_t of herbivores, detritivores and omnivores has increased. Consequently, the MTI significantly decreased( R =-0.69, P <0.05) at a rate of 0.11 during this decade. The MTI showed a decreasing trend, which indicates exploitation of marine resources. The Fi B index also showed a downward trend and negative values from 2002 to 2009, which may be associated with unbalanced structure in the fisheries, but an upward trend from 2009 to 2011. The time variation of the landing profile showed two periods with significant differences in their species composition( R =0.88; P =0.005), and based on analysis of similarity, species have been identified as discriminator species, namely Thunnus albacores and Benthosema pterotum. Results indicate that changes in MTI reflected changes in the Hormuzgan landing structure. The examination of the MTI, FBI, and landing profile(LP) temporal pattern suggests that the status of fishery resources in Hormuzgan inshore waters is overexploited, and provides evidence of the probability that a fishing down process is occurring in this area, and that this trend may continue in the long-term. Therefore, environmental fisheries management and conservation programs should be prioritized for these valuable resources.

Efficient hemodynamic states stimulation using fNIRS data with the extended Kalman filter and bifurcation analysis of balloon model

This paper introduces a stochastic hemodynamic system to describe the brain neural activity based on the balloon model. A continuous-discrete extended Kalman filter is used to estimate the nonlinear model states. The stability, controllability and observability of the proposed model are described based on the simulation and measurement data analysis. The observability and controllability characteristics are in- troduced as significant factors to validate the preference of different hemodynamic factors to be considered for diagnosis and monitoring in clinical applications. This model also can be efficiently applied in any monitoring and control platform include brain and for study of hemodynamics in brain imaging modalities such as pulse oximetry and functional near infrared spectroscopy. The work is on progress to extend the proposed model to cover more hemodynamic and neural brain signals for real-time in-vivo application.

Fully On-Chip Integrated Photodetector Front-End Dedicated to Real-Time Portable Optical Brain Imaging

Optical brain imaging using functional near infra-red spectroscopy (fNIRS) offers a portable and noninvasive tool for monitoring of blood oxygenation. In this paper we have introduced a new miniaturized photodetector front-end on achip to be applied in a portable fNIRS system. It includes silicon avalanche photodiodes (SiAPD), Transimpedance amplifier (TIA) front-end and Quench-Reset circuitry to operate in both linear and Geiger modes. So it can be applied for both continuous-wave fNIRS (CW-fNIRS) and also single-photon counting. Proposed SiAPD exhibits high-avalanche gain (>100), low-breakdown voltage ( V) and high photon detection efficiency accompanying with low dark count rates. The proposed TIA front-end offer a low power consumption ( mW), high-transimpedance gain (up to 250 MV/A), tunable bandwidth (1 kHz - 1 GHz) and very low input and output noise (~few fA/√Hz and few μV/√Hz). The Geiger-mode photon counting front-end also exhibits a controllable hold-off and rest time with an ultra fast quench-reset time (few ns). This integrated system has been implemented using submicron (0.35 μm) standard CMOS technology.

Real-Time Internet-Based Teleoperation

Internet-based teleoperation employs robots and internet a two breakthrough technologies to manipulate robots from distance for different applications. Variable and unknown time delay dynamics of internet is the main obstacle for realtime teleoperation via internet. In this paper the internet delay dynamics and its characteristics have been studied based on the measurement in different nodes. Then a black-box model for end-to-end internet delay dynamics has been developed using system identification and Auto-Regressive eXogeneous (ARX) model. Our experimental studies show a regular periodic behaviour in long-term intervals of internet delay variation and also confirm the accuracy and reliability of our theoretical and modelling derivations. This paper also introduces a novel multivariable control method for real-time telerobotic operations via Internet. Random communications delay of the Internet can cause instability in realtime closed-loop telerobotic systems. When a single identification model is used, it will have to adapt itself to the operating condition before an appropriate control mechanism can be applied. Slow adaptation may result in a large transient error. As an alternative, we propose to use a Multiple Model framework. The control strategy is to determine the best model for the current operating condition and activate the corresponding controller. We propose the use of Multi-Model Adaptive Control Theory and Multivariable Wave prediction method to capture the concurrency and complexity of Internet-based teleoperation. The results confirm the efficiency of the proposed technique in dealing with constant and variable delay dynamics of internet.

Detecting the Stable, Observable and Controllable States of the Human Brain Dynamics

A new technique is proposed in this paper for real-time monitoring of brain neural activity based on the balloon model. A continuous-discrete extended Kalman filter is used to estimate the nonlinear model states. The stability, controlla- bility and observability of the proposed model are described based on the simulation and measured clinical data analysis. By introducing the controllable and observable states of the hemodynamic signal we have developed a numerical tech- nique to validate and compare the impact of brain signal parameters affecting on BOLD signal variation. This model increases significantly the signal-to-noise-ratio (SNR) and the speed of brain signal processing. A linear-quadratic regulator (LQR) also has been introduced for optimal control of the model.