High resolution electrocardiography in healthy dogs: time domain parameters and comparison of the non-stationary (Wigner distribution) versus standard stationary frequency domain analysis methods

Fractionated heart activation can be detected as late potentials from surface recordings of signal-averaged electrocardiograms (SA ECG) which are considered as a marker of sustained ventricular tachycardia. For animal studies, reference values in time and frequency domain analyses are essentially missing. In the present study, we have established reference values in SA ECG time domain analysis and time-frequency representation of heart activation in healthy dogs. A group of 25 healthy mongrel dogs (body weight 12-15 kg) was investigated. Wigner distribution and our modification of Fast Fourier transform (FFT), gliding window FFT, was applied in SA ECG frequency domain analysis. Reference values in time domain SA ECG were established. Time and voltage criteria were adapted to short duration of heart cycle and fast voltage decrement of the QRS complex in dogs. Wigner distribution and gliding window FFT were applied in order to describe mean heart activation in the frequency domain. Contribution of higher frequencies (30-80 Hz) was detected by both frequency analysis methods in the second third of ventricular activation in healthy animals. Presented results could offer a basis for further experimental arrhythmologic studies.     

Efficient localization of hot spots in proteins using a novel S-transform based filtering approach

Protein-protein interactions govern almost all biological processes and the underlying functions of proteins. The interaction sites of protein depend on the 3D structure which in turn depends on the amino acid sequence. Hence, prediction of protein function from its primary sequence is an important and challenging task in bioinformatics. Identification of the amino acids (hot spots) that leads to the characteristic frequency signifying a particular biological function is really a tedious job in proteomic signal processing. In this paper, we have proposed a new promising technique for identification of hot spots in proteins using an efficient time-frequency filtering approach known as the S-transform filtering. The S-transform is a powerful linear time-frequency representation and is especially useful for the filtering in the time-frequency domain. The potential of the new technique is analyzed in identifying hot spots in proteins and the result obtained is compared with the existing methods. The results demonstrate that the proposed method is superior to its counterparts and is consistent with results based on biological methods for identification of the hot spots. The proposed method also reveals some new hot spots which need further investigation and validation by the biological community.     

A Controller Design Method Based on a Neural Network for an Outdoor Mobile Robot

A wheeled mobile mechanism with a passive and/or active linkage mechanism for travel in rough terrain is developed and evaluated. In our previous research, we developed a switching controller system for wheeled mobile robots in rough terrain. This system consists of two sub-systems: an environment recognition system using a self-organizing map and an adjusted control system using a neural network. In this paper, we propose a new controller design method based on a neural network. The proposed method involves three kinds of controllers: an elementary controller, adjusted controllers, and simplified controllers. In the experiments, our proposed method results in less oscillatory motion in rough terrain and performs better than a well tuned PID controller does.     

Frequency tuning in the electroreceptive periphery.

Our studies are concerned with the frequency tuning that is provided by the electrical resonance of tuberous electroreceptors. Frequency selectivity had previously been measured in the electroreceptor's afferent fibers, and resonant conductances in the electroreceptor cell membrane had been implicated in producing the selectivity. With transdermal application of sinusoidal current, we measured the frequency dependence of the impedance of small areas of the electroreceptor/skin structure of the weakly electric fish Sternopygus and Eigenmannia, and used our data to make a quantitative linear model of the structure. The qualitative form of the model was proposed by Bennett (1). The quantitative model allows us to estimate the frequency selectivity of the voltage across the innervated membrane of the electroreceptor cells. The frequency selectivity of electroreceptor cell voltage derived from our data are as sharp as the neural selectivity at frequencies close to the most sensitive frequency. Many of our measurements supported the linear system model. However, spontaneous electroreceptor voltage oscillations were detected in some of our specimens, suggesting that the electroreceptors can operate in a regime of active nonlinearity. A simple explanation for the observed oscillations is that they arise when damping in the electroreceptor cell's resonant membrane is negative for a limited span of membrane voltage surrounding the resting voltage. The response of oscillating units to sinusoidal current was compatible with this explanation. We report experimental observations bearing on the consequences of active nonlinearity for the frequency tuning of a resonant system.     

Sound representation methods for spectro-temporal receptive field estimation

The spectro-temporal receptive field (STRF) of an auditory neuron describes the linear relationship between the sound stimulus in a time-frequency representation and the neural response. Time-frequency representations of a sound in turn require a nonlinear operation on the sound pressure waveform and many different forms for this non-linear transformation are possible. Here, we systematically investigated the effects of four factors in the non-linear step in the STRF model: the choice of logarithmic or linear filter frequency spacing, the time-frequency scale, stimulus amplitude compression and adaptive gain control. We quantified the goodness of fit of these different STRF models on data obtained from auditory neurons in the songbird midbrain and forebrain. We found that adaptive gain control and the correct stimulus amplitude compression scheme are paramount to correctly modelling neurons. The time-frequency scale and frequency spacing also affected the goodness of fit of the model but to a lesser extent and the optimal values were stimulus dependant. Action Editor: Israel Nelken     

Neural adaptive control for vibration suppression in composite fin-tip of aircraft

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H(infinity) control scheme.     

A model of neuro-musculo-skeletal system for human locomotion under position constraint condition

The human locomotion was studied on the basis of the interaction of the musculo-skeletal system, the neural system and the environment. A mathematical model of human locomotion under position constraint condition was established. Besides the neural rhythm generator, the posture controller and the sensory system, the environment feedback controller and the stability controller were taken into account in the model. The environment feedback controller was proposed for two purposes, obstacle avoidance and target position control of the swing foot. The stability controller was proposed to imitate the self-balancing ability of a human body and improve the stability of the model. In the stability controller, the ankle torque was used to control the velocity of the body gravity center. A prediction control algorithm was applied to calculate the torque magnitude of the stability controller. As an example, human stairs climbing movement was simulated and the results were given. The simulation result proved that the mathematical modeling of the task was successful.     

A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition

Transient neural assemblies mediated by synchrony in particular frequency ranges are thought to underlie cognition. We propose a new approach to their detection, using empirical mode decomposition (EMD), a data-driven approach removing the need for arbitrary bandpass filter cut-offs. Phase locking is sought between modes. We explore the features of EMD, including making a quantitative assessment of its ability to preserve phase content of signals, and proceed to develop a statistical framework with which to assess synchrony episodes. Furthermore, we propose a new approach to ensure signal decomposition using EMD. We adapt the Hilbert spectrum to a time-frequency representation of phase locking and are able to locate synchrony successfully in time and frequency between synthetic signals reminiscent of EEG. We compare our approach, which we call EMD phase locking analysis (EMDPL) with existing methods and show it to offer improved time-frequency localisation of synchrony. Action Editor: Carson C. Chow     

Voltage clamp simulations for multifiber bundles in a double sucrose gap: cable complications

A theoretical model is presented for voltage clamp of a bundle of cylindrical excitable cells in a double sucrose gap. The preparation in the test node is represented by a single one-dimensional cable (length/diameter ratio approximately) with standard Hodgkin-Huxley kinetics for transmembrane Na current. Imperfections of voltage control due to internal (longitudinal) resistivity and external (radial) resistance in series to the membrane are analysed. The electrical behavior of a fiber is described by the cable equation with appropriate boundary conditions and subsidiary equations reflecting the membrane characteristics. Membrane voltage and current distribution in response to a step command was obtained by numerical integration. The results are described in two papers. The present paper deals with the effect of internal resistivity with the external resistance being neglected. The closed loop response of a fiber displays a strong tendency to oscillate. To stabilize the system a phase lead was inserted and the gain of the control amplifier was reduced. Conditions for stability were examined by Nyquist analysis. When the Na system was activated by a command pulse below ENa, a voltage gradient developed between a depolarization (relative to the command signal) at the end where voltage was monitored and a hyperpolarization at the site of current injection. In spite of a poor voltage control the total measured current appeared to have a smooth transient. With large voltage gradients a small, second inward current was seen. At a low (high) Na conductance maximum peak inward current was larger (smaller) that the current expected from ideal space clamping.     

Increase of slow periodic modulation of EEG in a patient with Alzheimer's disease.

The recently described slow oscillations of amplitude of theta and alpha waves of the EEG (with a frequency below 0.08 Hz) in healthy subjects are attributed to the autonomic nervous system with control at the brain stem level. In the present pilot study, the slow brain rhythms were analyzed in a patient with Alzheimer's disease and were compared to a healthy subject. Dynamic analysis of the EEG was performed using time-frequency mapping which gives simultaneous time and frequency representation of the brain signal. This method comprises a transform of the filtered EEG signal into its analytic form and application of the Wigner distribution modified by time and frequency smoothing. It has been shown that the envelope of both theta and alpha activities oscillates at 0.04 Hz and 0.07 Hz in the healthy subject and at 0.03 Hz and 0.06 Hz in a patient with Alzheimer's disease. The amplitude of the slow oscillations of theta activity was substantially higher in the patient with Alzheimer's disease as compared with the healthy subject. It is being proposed that the increase of slow brain rhythms in the patient with Alzheimer's disease reflects an abnormal activity of the autonomic nervous system. However, the underlying pathophysiological mechanisms need to be further studied.     

Motion control of musculoskeletal systems with redundancy

Motion control of musculoskeletal systems for functional electrical stimulation (FES) is a challenging problem due to the inherent complexity of the systems. These include being highly nonlinear, strongly coupled, time-varying, time-delayed, and redundant. The redundancy in particular makes it difficult to find an inverse model of the system for control purposes. We have developed a control system for multiple input multiple output (MIMO) redundant musculoskeletal systems with little prior information. The proposed method separates the steady-state properties from the dynamic properties. The dynamic control uses a steady-state inverse model and is implemented with both a PID controller for disturbance rejection and an artificial neural network (ANN) feedforward controller for fast trajectory tracking. A mechanism to control the sum of the muscle excitation levels is also included. To test the performance of the proposed control system, a two degree of freedom ankle–subtalar joint model with eight muscles was used. The simulation results show that separation of steady-state and dynamic control allow small output tracking errors for different reference trajectories such as pseudo-step, sinusoidal and filtered random signals. The proposed control method also demonstrated robustness against system parameter and controller parameter variations. A possible application of this control algorithm is FES control using multiple contact cuff electrodes where mathematical modeling is not feasible and the redundancy makes the control of dynamic movement difficult.     

Feedforward-Feedback Hybrid Control for Magnetic Shape Memory Alloy Actuators Based on the Krasnosel'skii-Pokrovskii Model

As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to improve control precision and mitigate the effects of the hysteresis nonlinearity of magnetic shape memory alloy actuators. First, hysteresis nonlinearity compensation for the magnetic shape memory alloy actuator is implemented by establishing a feedforward controller which is an inverse hysteresis model based on Krasnosel''skii-Pokrovskii operator. Secondly, the paper employs the classical Proportion Integration Differentiation feedback control with feedforward control to comprise the hybrid control system, and for further enhancing the adaptive performance of the system and improving the control accuracy, the Radial Basis Function neural network self-tuning Proportion Integration Differentiation feedback control replaces the classical Proportion Integration Differentiation feedback control. Utilizing self-learning ability of the Radial Basis Function neural network obtains Jacobian information of magnetic shape memory alloy actuator for the on-line adjustment of parameters in Proportion Integration Differentiation controller. Finally, simulation results show that the hybrid control method proposed in this paper can greatly improve the control precision of magnetic shape memory alloy actuator and the maximum tracking error is reduced from 1.1% in the open-loop system to 0.43% in the hybrid control system.     

Identification of protein-coding regions in DNA sequences using a time-frequency filtering approach

Accurate identification of protein-coding regions (exons) in DNA sequences has been a challenging task in bioinformatics. Particularly the coding regions have a 3-base periodicity, which forms the basis of all exon identification methods. Many signal processing tools and techniques have been applied successfully for the identification task but still improvement in this direction is needed. In this paper, we have introduced a new promising model-independent time-frequency filtering technique based on S-transform for accurate identification of the coding regions. The S-transform is a powerful linear time-frequency representation useful for filtering in time-frequency domain. The potential of the proposed technique has been assessed through simulation study and the results obtained have been compared with the existing methods using standard datasets. The comparative study demonstrates that the proposed method outperforms its counterparts in identifying the coding regions.     

Equivalent Voltage and Current Waves for TM SPP in Graphene Sheets

We present a theoretical model to describe the propagation of a transverse magnetic surface plasmon polariton in graphene based on equivalent voltage and charge current waves, which includes the spatial dispersion effect. Electrons and holes in graphene are governed by the Boltzmann equation in the particle conserving relaxation time approximation. First, we deduce expressions for the non-equilibrium distributions when there are charge oscillations in graphene as a response to the electromagnetic field applied to it. These distribution functions are used in the Boltzmann equations to derive other equations for the following four local macroscopic averages: the oscillating electron and hole densities, and the electron and hole current densities. Then, for a specific structure, we solve the wave equations for the electric and vector magnetic potentials to obtain the relations between the charge oscillations and the potentials. So, we reach a homogeneous system of four coupled equations relating the amplitudes of the voltage and the current waves. The non-trivial solutions of the system allow us to compute the dispersion and loss curves for such waves. As it is already known, for a given frequency, we can see that the higher the Fermi level is, the lesser the spatial-dispersion effect is. Following the analysis, a distributed-element circuit for the equivalent transmission line in which would propagate the waves, is developed. Finally, we analyze the dependence of these circuit elements and the impedance on both the frequency and Fermi level.

     

An improved vaseline gap voltage clamp for skeletal muscle fibers

A Vaseline gap potentiometric recording and voltage clamp method is developed for frog skeletal muscle fibers. The method is based on the Frankenhaeuser-Dodge voltage clamp for myelinated nerve with modifications to improve the frequency response, to compensate for external series resistance, and to compensate for the complex impedance of the current-passing pathway. Fragments of single muscle fibers are plucked from the semitendinosus muscle and mounted while depolarized by a solution like CsF. After Vaseline seals are formed between fluid pools, the fiber ends are cut once again, the central region is rinsed with Ringer solution, and the feedback amplifiers are turned on. Errors in the potential and current records are assessed by direct measurements with microelectrodes. The passive properties of the preparation are simulated by the "disk" equivalent circuit for the transverse tubular system and the derived parameters are similar to previous measurements with microelectrodes. Action potentials at 5 degrees C are long because of the absence of delayed rectification. Their shape is approximately simulated by solving the disk model with sodium permeability in the surface and tubular membranes. Voltage clamp currents consist primarily of capacity currents and sodium currents. The peak inward sodium current density at 5 degrees C is 3.7 mA/cm2. At 5 degrees C the sodium currents are smoothly graded with increasing depolarization and free of notches suggesting good control of the surface membrane. At higher temperatures a small, late extra inward current appears for small depolarizations that has the properties expected for excitation in the transverse tubular system. Comparison of recorded currents with simulations shows that while the transverse tubular system has regenerative sodium currents, they are too small to make important errors in the total current recorded at the surface under voltage clamp at low temperature. The tubules are definitely not under voltage clamp control.     

An Assessment of the Double Sucrose-Gap Voltage Clamp Technique as Applied to Frog Atrial Muscle

The homogeneity of voltage clamp control in small bundles of frog atrial tissue under double sucrose-gap voltage clamp conditions was assessed by intracellular microelectrode potential measurements from cells in the test node region. The microelectrode potential measurements demonstrated that (1) good voltage control of the impaled cell existed in the absence of the excitatory inward currents (e.g., during small depolarizing clamp pulses of 10-15 mV), (2) voltage control of the impaled cell was lost during either the fast or slow excitatory inward currents, and (3) voltage control of the impaled cell was regained following the inward excitatory currents. Under nonvoltage clamp conditions the transgap recorded action potential had a magnitude and waveform similar to the intracellular microelectrode recorded action potentials from cells in the test node. Transgap impedance measured with a sine-wave voltage of 1,000 Hz was about 63% of that measured either by a sine-wave voltage of 10 Hz or by an action potential method used to determine the longitudinal resistance through the sucrose-gap region. The action potential data in conjunction with the impedance data indicate that the extracellular resistance (R_s) through the sucrose gap is very large with respect to the longitudinal intracellular resistance (R_i); the frequency dependence of the transgap impedance suggests that at least part of the intracellular resistance is paralleled by a capacitance. The severe loss of spatial voltage control during the excitatory inward current raises serious doubts concerning the use of the double sucrose-gap technique to voltage clamp frog atrial muscle.     

Optimal Placement of Unified Power Flow Controllers to Improve Dynamic Voltage Stability Using Power System Variable Based Voltage Stability Indices

This study examines a new approach to selecting the locations of unified power flow controllers (UPFCs) in power system networks based on a dynamic analysis of voltage stability. Power system voltage stability indices (VSIs) including the line stability index (LQP), the voltage collapse proximity indicator (VCPI), and the line stability index (L_mn) are employed to identify the most suitable locations in the system for UPFCs. In this study, the locations of the UPFCs are identified by dynamically varying the loads across all of the load buses to represent actual power system conditions. Simulations were conducted in a power system computer-aided design (PSCAD) software using the IEEE 14-bus and 39- bus benchmark power system models. The simulation results demonstrate the effectiveness of the proposed method. When the UPFCs are placed in the locations obtained with the new approach, the voltage stability improves. A comparison of the steady-state VSIs resulting from the UPFCs placed in the locations obtained with the new approach and with particle swarm optimization (PSO) and differential evolution (DE), which are static methods, is presented. In all cases, the UPFC locations given by the proposed approach result in better voltage stability than those obtained with the other approaches.     

Fuzzy Self-Tuning PID Control of Hydrogen-Driven Pneumatic Artificial Muscle Actuator

In this paper, a fuzzy self-tuning Proportional-Integral-Derivative (PID) control of hydrogen-driven Pneumatic Artificial Muscle (PAM) actuator is presented. With a conventional PID control, non-linear thermodynamics of the hydrogen-driven PAM actuator still highly affects the mechanical actuations itself, causing deviation of desired tasks. The fuzzy self-tuning PID controller is systematically developed so as to achieve dynamic performance targets of the hydrogen-driven PAM actuator. The fuzzy rules based on desired characteristics of closed-loop control are designed to finely tune the PID gains of the controller under different operating conditions. The empirical models and properties of the hydrogen-driven PAM actuator are used as a genuine representation of mechanical actuations. A mass-spring-damper system is applied to the hydrogen-driven PAM actuator as a typical mechanical load during actuations. The results of the implementation show that the viability of the proposed method in actuating the hydrogen-driven PAM under mechanical loads is close to desired performance.     

Addition of a non-photic component to a light-based mathematical model of the human circadian pacemaker

Mathematical models have become vital to the study of many biological processes in humans due to the complexity of the physiological mechanisms underlying these processes and systems. While our current mathematical representation of the human circadian pacemaker has proven useful in many experimental situations, it uses as input only a direct effect of light on the circadian pacemaker. Although light (a photic stimulus) has been shown to be the primary synchronizer of the circadian pacemaker across a number of species, studies in both animals and humans have confirmed the existence of non-photic effects that also contribute to phase shifting and entrainment. We modified our light-based circadian mathematical model to reflect evidence from these studies that the sleep-wake cycle and/or associated behaviors have a non-photic effect on the circadian pacemaker. In our representation, the sleep-wake cycle and its associated behaviors provides a non-photic drive on the circadian pacemaker that acts both independently and concomitantly with light stimuli. Further experiments are required to validate fully our model and to understand the exact effect of the sleep-wake cycle as a non-photic stimulus for the human circadian pacemaker.