Simultaneous optical signal-to-noise ratio and differential group delay monitoring based on degree of polarization measurements in optical communications systems

We present a simultaneous optical signal-to-noise ratio (OSNR) and differential group delay (DGD) monitoring method based on degree of polarization (DOP) measurements in optical communications systems. For the first time in the literature (to our best knowledge), the proposed scheme is demonstrated to be able to independently and simultaneously extract OSNR and DGD values from the DOP measurements. This is possible because the OSNR is related to maximum DOP, while DGD is related to the ratio between the maximum and minimum values of DOP. We experimentally measured OSNR and DGD in the ranges from 10 to 30 dB and 0 to 90 ps for a 10 Gb/s non-return-to-zero signal. A theoretical analysis of DOP accuracy needed to measure low values of DGD and high OSNRs is carried out, showing that current polarimeter technology is capable of yielding an OSNR measurement within 1 dB accuracy, for OSNR values up to 34 dB, while DGD error is limited to 1.5% for DGD values above 10 ps. For the first time to our knowledge, the technique was demonstrated to accurately measure first-order polarization mode dispersion (PMD) in the presence of a high value of second-order PMD (as high as 2071 ps(2)). (C) 2012 Optical Society of America

Multi-agent Reinforcement Learning for Traffic Signal Control

Optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for reducing the average delay experienced by the road users amidst the rapid increase in the usage of vehicles. In this paper, we formulate the TSC problem as a discounted cost Markov decision process (MDP) and apply multi-agent reinforcement learning (MARL) algorithms to obtain dynamic TSC policies. We model each traffic signal junction as an independent agent. An agent decides the signal duration of its phases in a round-robin (RR) manner using multi-agent Q-learning with either is an element of-greedy or UCB 3] based exploration strategies. It updates its Q-factors based on the cost feedback signal received from its neighbouring agents. This feedback signal can be easily constructed and is shown to be effective in minimizing the average delay of the vehicles in the network. We show through simulations over VISSIM that our algorithms perform significantly better than both the standard fixed signal timing (FST) algorithm and the saturation balancing (SAT) algorithm 15] over two real road networks.

Feedback communication using orthogonal signals

This research is concerned with block coding for a feedback communication system in which the forward and feedback channels are independently disturbed by additive white Gaussian noise and average power constrained. Two coding schemes are proposed in which the messages to be coded for transmission over the forward channel are realized as a set of orthogonal waveforms. A finite number of forward and feedback transmissions (iterations) per message is made. Information received over the feedback channel is used to modify the waveform transmitted on successive forward iterations in such a way that the expected value of forward signal energy is zero on all iterations after the first. Similarly, information is sent over the feedback channel in such a way that the expected value of feedback signal energy is also zero on all iterations after the first. These schemes are shown to achieve a lower probability of error than the best one-way coding scheme at all rates up to the forward channel capacity, provided only that the feedback channel capacity be greater than the forward channel capacity. These schemes make more efficient use of the available feedback power than existing feedback coding schemes, and therefore require less feedback power to achieve a given error performance.

Feedback from luminosity horizontal cells mediates depolarizing responses of chromaticity horizontal cells in the Xenopus retina.

It has been proposed that the depolarizing responses of chromaticity horizontal cells (C-HCs) to red light depend on a feedback signal from luminosity horizontal cells (L-HCs) to short-wavelength-sensitive cones in the retinas of lower vertebrates. In this regard we studied the C-HCs of the Xenopus retina. C-HCs and L-HCs were identified by physiological criteria and then injected with neurobiotin. The retina then was incubated with peanut agglutinin, which stains red-but not blue-sensitive cones. Electron microscopic examination revealed that L-HCs contact all cone classes, whereas C-HCs contact only blue-sensitive cones. Simultaneous recordings from C-HC/L-HC pairs established that when the L-HC was saturated by a steady bright red light, C-HCs alone responded to a superimposed blue stimulus. In response to red test flashes, the C-HC response was delayed by approximately 30 msec with respect to the L-HC response. Isolated HCs of both subtypes were examined by whole-cell patch clamp. Both responded to kainate with sustained inward currents and to quisqualate or alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) with desensitizing currents from a negative holding potential; i.e., both have AMPA-type glutamate receptors. gamma-Aminobutyric acid or glycine opened a chloride channel in the L-HC, whereas the C-HC was unresponsive to either inhibitory amino acid. Since glycine has been shown to abolish selectively the depolarizing response of the C-HC, this finding and other pharmacological data strongly implicate the L-HC in the underlying circuit. Moreover, because the C-HC does not respond to gamma-aminobutyric acid, the neurotransmitter of the L-HC, by elimination, a feedback synapse from L-HC to blue cone is the most plausible mechanism for the creation of depolarizing responses in C-HCs.

A Fast-Locking Phase-Locked Loop Using a Seven-State Phase Frequency Detector

A seven-state phase frequency detector (S.S PFD) is proposed for fast-locking charge pump based phase-locked loops (CPPLLs) in this paper. The locking time of the PLL can be significantly reduced by using the seven-state PFD to inject more current into the loop filter. In this stage, the bandwidth of the PLL is increased or decreased to track the phase difference of the reference signal and the feedback signal. The proposed architecture is realized in a standard 0.35 mu m 2P4M CMOS process with a 3.3V supply voltage. The locking time of the proposed PLL is 1.102 mu s compared with the 2.347 mu s of the PLL based on continuous-time PFD and the 3.298 mu s of the PLL based on the pass-transistor tri-state PFD. There are 53.05% and 66.59% reductions of the locking time. The simulation results and the comparison with other PLLs demonstrate that the proposed seven-state PFD is effective to reduce locking time.

Dynamic grey-box modeling for online monitoring of extrusion viscosity

Melt viscosity is a key indicator of product quality in polymer extrusion processes. However, real time monitoring and control of viscosity is difficult to achieve. In this article, a novel “soft sensor” approach based on dynamic gray-box modeling is proposed. The soft sensor involves a nonlinear finite impulse response model with adaptable linear parameters for real-time prediction of the melt viscosity based on the process inputs; the model output is then used as an input of a model with a simple-fixed structure to predict the barrel pressure which can be measured online. Finally, the predicted pressure is compared to the measured value and the corresponding error is used as a feedback signal to correct the viscosity estimate. This novel feedback structure enables the online adaptability of the viscosity model in response to modeling errors and disturbances, hence producing a reliable viscosity estimate. The experimental results on different material/die/extruder confirm the effectiveness of the proposed “soft sensor” method based on dynamic gray-box modeling for real-time monitoring and control of polymer extrusion processes. POLYM. ENG. SCI., 2012. © 2012 Society of Plastics Engineers

Analysis and Design of an Adaptive Polynomial Predistorter with the Loop Delay Estimator

Most adaptive linearization circuits for the nonlinear amplifier have a feedback loop that returns the output signal oj'tne eunplifier to the lineurizer. The loop delay of the linearizer most be controlled precisely so that the convergence of the linearizer should be assured lot this Letter a delay control circuit is presented. It is a delay lock loop (ULL) with it modified early-lute gate and can he easily applied to a DSP implementation. The proposed DLL circuit is applied to an adaptive linearizer with the use of a polynomial predistorter, and the simulalion for a 16-QAM signal is performed. The simulation results show that the proposed DLL eliminates the delay between the reference input signal and the delayed feedback signal of the linearizing circuit perfectly, so that the predistorter polynomial coefficients converge into the optimum value and a high degree of linearization is achieved

Multi-user bidirectional communication using isochronal synchronisation of array of chaotic directly modulated semiconductor lasers

Isochronal synchronisation between the elements of an array of three mutually coupled directly modulated semiconductor lasers is utilized for the purpose of simultaneous bidirectional secure communication. Chaotic synchronisation is achieved by adding the coupling signal to the self feedback signal provided to each element of the array. A symmetric coupling is effective in inducing synchronisation between the elements of the array. This coupling scheme provides a direct link between every pair of elements thus making the method suitable for simultaneous bidirectional communication between them. Both analog and digital messages are successfully encrypted and decrypted simultaneously by each element of the array.

A feasible study of EEG-driven assistive robotic system for stroke rehabilitation

Stroke is a medical emergency and can cause a neurological damage, affecting the motor and sensory systems. Harnessing brain plasticity should make it possible to reconstruct the closed loop between the brain and the body, i.e., association of the generation of the motor command with the somatic sensory feedback might enhance motor recovery. In order to aid reconstruction of this loop with a robotic device it is necessary to assist the paretic side of the body at the right moment to achieve simultaneity between motor command and feedback signal to somatic sensory area in brain. To this end, we propose an integrated EEG-driven assistive robotic system for stroke rehabilitation. Depending on the level of motor recovery, it is important to provide adequate stimulation for upper limb motion. Thus, we propose an assist arm incorporating a Magnetic Levitation Joint that can generate a compliant motion due to its levitation and mechanical redundancy. This paper reports on a feasibility study carried out to verify the validity of the robot sensing and on EEG measurements conducted with healthy volunteers while performing a spontaneous arm flexion/extension movement. A characteristic feature was found in the temporal evolution of EEG signal in the single motion prior to executed motion which can aid in coordinating timing of the robotic arm assistance onset.

Phase-Locked loops lock-in range in Frequency Modulated-Atomic Force Microscope nonlinear control system

Since the mid 1980s the Atomic Force Microscope is one the most powerful tools to perform surface investigation, and since 1995 Non-Contact AFM achieved true atomic resolution. The Frequency-Modulated Atomic Force Microscope (FM-AFM) operates in the dynamic mode, which means that the control system of the FM-AFM must force the micro-cantilever to oscillate with constant amplitude and frequency. However, tip-sample interaction forces cause modulations in the microcantilever motion. A Phase-Locked loop (PLL) is used to demodulate the tip-sample interaction forces from the microcantilever motion. The demodulated signal is used as the feedback signal to the control system, and to generate both topographic and dissipation images. As a consequence, a proper design of the PLL is vital to the FM-AFM performance. In this work, using bifurcation analysis, the lock-in range of the PLL is determined as a function of the frequency shift (Q) of the microcantilever and of the other design parameters, providing a technique to properly design the PLL in the FM-AFM system. (C) 2011 Elsevier B.V. All rights reserved.

Realimentação derivativa e modelo fuzzy Takagi-Sugeno paracontrole da articulação do joelho de pacientes paraplégicos com o uso de acelerômetros

Feedback control systems have been used to move the muscles and joints of the limbs of paraplegic patients. The feedback signal, related to the knee joint angle, can be obtained by using an electrogoniometer. However, the use of accelerometers can help the measurements due the facility of adhering these devices to the skin. Accelerometers are also very suitable for these applications due their small dimensions and weight. In this paper a new method for designing a control system that can vary the knee joint angle using Functional Electrical Stimulation (FES) is presented, as well as a simulation with parameters values available in the literature. The nonlinear control system was represented by a Takagi-Sugeno fuzzy model and the feedback signals were obtained by using accelerometers. The design method considered all plant nonlinearities and was efficient and reliable to control the leg position of a paraplegic patient with the angle of the knee ranging from 0° to 30°, considering electric stimulation at the quadriceps muscle. The proposed method is viable and offers a new alternative for designing control systems of the knee joint angle using more comfortable sensors for the patients.

Phase-Locked loops lock-in range in Frequency Modulated-Atomic Force Microscope nonlinear control system

Since the mid 1980s the Atomic Force Microscope is one the most powerful tools to perform surface investigation, and since 1995 Non-Contact AFM achieved true atomic resolution. The Frequency-Modulated Atomic Force Microscope (FM-AFM) operates in the dynamic mode, which means that the control system of the FM-AFM must force the micro-cantilever to oscillate with constant amplitude and frequency. However, tip-sample interaction forces cause modulations in the microcantilever motion. A Phase-Locked loop (PLL) is used to demodulate the tip-sample interaction forces from the microcantilever motion. The demodulated signal is used as the feedback signal to the control system, and to generate both topographic and dissipation images. As a consequence, a proper design of the PLL is vital to the FM-AFM performance. In this work, using bifurcation analysis, the lock-in range of the PLL is determined as a function of the frequency shift (Q) of the microcantilever and of the other design parameters, providing a technique to properly design the PLL in the FM-AFM system. (C) 2011 Elsevier B.V. All rights reserved.

Continuos Flow Single Cell Separation into Open Microwell Arrays

A novel design based on electric field-free open microwell arrays for the automated continuous-flow sorting of single or small clusters of cells is presented. The main feature of the proposed device is the parallel analysis of cell-cell and cell-particle interactions in each microwell of the array. High throughput sample recovery with a fast and separate transfer from the microsites to standard microtiter plates is also possible thanks to the flexible printed circuit board technology which permits to produce cost effective large area arrays featuring geometries compatible with laboratory equipment. The particle isolation is performed via negative dielectrophoretic forces which convey the particles’ into the microwells. Particles such as cells and beads flow in electrically active microchannels on whose substrate the electrodes are patterned. The introduction of particles within the microwells is automatically performed by generating the required feedback signal by a microscope-based optical counting and detection routine. In order to isolate a controlled number of particles we created two particular configurations of the electric field within the structure. The first one permits their isolation whereas the second one creates a net force which repels the particles from the microwell entrance. To increase the parallelism at which the cell-isolation function is implemented, a new technique based on coplanar electrodes to detect particle presence was implemented. A lock-in amplifying scheme was used to monitor the impedance of the channel perturbed by flowing particles in high-conductivity suspension mediums. The impedance measurement module was also combined with the dielectrophoretic focusing stage situated upstream of the measurement stage, to limit the measured signal amplitude dispersion due to the particles position variation within the microchannel. In conclusion, the designed system complies with the initial specifications making it suitable for cellomics and biotechnology applications.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.