Pseudo Random Binary Sequence Input Research Articles

Receiver Analog Front-End Cascading Transimpedance Amplifier and Continuous-Time Linear Equalizer for Signals of 5 to 30 Gb/s

A 5–30 Gb/s receiver analog front-end (AFE) cascading transimpedance amplifier (TIA) and continuous-time linear equalizer (CTLE) were implemented using a Taiwan Semiconductor 180 nm process. The system comprises a two-stage differential input pair CTLE, TIA, and a differential termination resistor Rm. A source-degenerated transconductance stage was adopted in the CTLE, and source follower and shunt feedback resistor stages were adopted in the TIA. The proposed CTLE could achieve high frequencies by altering the tail current with fixed degenerate capacitance CS and resistance RS. The proposed AFE achieved high bandwidth, and the use of a feedback resistor Rf and inductor Lf improved its high-frequency performance. Simulation results revealed that the CTLE can compensate for 16 dB of channel loss at a 3 GHz Nyquist frequency and can open closed eyes in a 6 Gb/s non-return-to-zero signal with a bit error rate of 0.16 × 10−12 for a 231 − 1 pseudorandom binary sequence input. The AFE could compensate for 12 dB of channel loss at a 15 GHz Nyquist frequency and can open closed eyes in a 30 Gb/s PAM4 signal from a pseudorandom binary sequence input; it consumed 27 mW of power at 1.8 V.

Identification of a quadcopter autopilot system via Box–Jenkins structure

This paper presents a method to precisely model a four rotor unmanned aerial vehicle, widely known as quadcopter autopilot system. Common system identification methods limit quadcopter models into first or second order systems, and do not count for noise characteristics. This leads to poor prediction accuracy of its longitudinal and lateral motion dynamics that ultimately affects the aircraft stabilization during flight and landing. To improve the quality of the estimated models, we utilized a statistically suitable discrete-time linear Box–Jenkins structure to model the plant and noise characteristics of the horizontal subsystems of a quadcopter autopilot system. The models were estimated using flight data acquired when the system were provided with pseudo-random binary sequence input. In this proposed method, by employing the prediction error method and least squares approach, the aircraft dynamics could be modeled up until the fifth order. The normalized root mean square fitness value showed that the predicted model output matches the experimental flight data by 94.72% in the one-step-ahead prediction test, and 84.52% in the infinite-step-ahead prediction test. These prediction results demonstrated an improvement of 52.8% when compared with a first and second order model structures proposed in previous works for the same quadcopter model. The output from this research works confirmed the effectiveness of the proposed method to adequately capture the autopilot dynamics and accurately predict the quadcopter outputs. These would greatly assist in designing robust flight controllers for the autopilot system.

PRBS based model identification and GPC PID control design for MIMO Process

Abstract This paper expounds the system identification of Multi Input and Multi Output (MIMO) process using Pseudo Random Binary Sequence (PRBS) input signal. Random input signal to the process procures the whole dynamics of the process around desired operating region. Capturing system dynamics using PBRS is more efficient, since it comprises of both positive and negative changes within the input sequence. Besides system modeling, this papers also gives an exposure on PID control design using Generalized Predictive Control (GPC) algorithm. Modeling using PRBS input signal and control design approach using GPC-PID is addressed by considering the case study of experimental MIMO processes. Results depict the efficiency of control design even in plant uncertainty conditions and performance is incorporated.

A Fully Integrated IEEE 802.15.7 Visible Light Communication Transmitter With On-Chip 8-W 85% Efficiency Boost LED Driver

This paper presents an IEEE 802.15.7 Physical Layer I (PHY-I) standard compliant visible light communication (VLC) transmitter system-on-a-chip (SoC), enabling the use of ordinary white LED lights as beacons or broadcasters for location-based applications. The mixed-signal SoC integrates a baseband digital signal processing (DSP) unit, a VLC modulator, and a switching boost LED driver with an on-chip power MOSFET. The DSP unit supports various coding schemes as well as both the on-off keying (OOK) modulation and the variable pulse-position modulation specified in the aforementioned standard. The boost LED driver is designed to power a matrix of 4 × 5 LEDs using Li-ion batteries for portable applications. The SoC simultaneously supports VLC and LED illumination with nine dimming levels. Implemented in a 0.35-μm CMOS process with 20-V devices, the SoC achieves a measured VLC data rate of up to 266 kb/s at a package error rate (PER) < 10% using a 211 −1 PRBS (pseudo random binary sequence) input. The communication distance measured is more than 2 m using no optical lens with the link efficiency of 5 nJ/bit. From an input at 3–5 V, the boost converter generates an output at 6–20 V with a 92% peak efficiency and an 8-W rating.

Frequency-Domain Identification of a Ventilated Room for Model Based Control

Efficient and accurate modeling techniques have become increasingly important in the context of model predictive control (MPC) for building automation. For modeling single-input single-output systems such as a ventilated room (with either constant air flow or constant supply temperature), system identification methods are promising and provide insight into the physical nature of these systems. In collaboration with the company SAUTER an office type test room was instrumented for experiments. Three models for the room were derived: i) an empirical transfer function estimate (ETFE) derived from a pseudo-random binary sequence input signal; ii) an ETFE derived from a relay feedback approach; iii) a physics based resistance-capacitance (RC) model.Using additional validation data, the different models and approaches were compared in terms of accuracy and efficiency. The effect of air mixing dynamics was demonstrated in an additional experiment to be one of the main differences between the experimentally identified and the RC model. An additional pole can be added to the RC model in order to compensate for the differences.

Clock- and Data-Recovery Circuit With Independently Controlled Eye-Tracking Loop for High-Speed Graphic DRAMs

An independently controlled eye-tracking clock- and data-recovery (CDR) circuit that achieves enhanced high-frequency jitter tolerance is presented in this brief. In the proposed CDR, a data-tracking loop compensates interchannel timing skews and rejects low-frequency jitter of the data, and an eye-tracking loop tracks asymmetric jitter distribution and high-frequency jitter of the data to enhance high-frequency jitter tolerance. This can be achieved by independently controlling two loops in the digital domain. The CDR is implemented using an 0.18- μm CMOS process, and a bit error rate of less than 10-12 was achieved for a data rate up to 5.8 Gb/s using a 231 - 1 pseudorandom binary-sequence input.

A 5-Gbit/s Clock- and Data-Recovery Circuit With 1/8-Rate Linear Phase Detector in 0.18-${\rm \mu}\hbox{m}$ CMOS Technology

With a new 1/8-rate linear phase detector (PD), a 5-Gbit/s clock and data recovery (CDR) circuit is implemented in a digital 0.18-mum CMOS technology. The outputs of the PD have much wider pulse than those of the conventional linear PDs. Thus, the design of circuits such as the PD and charge becomes much easier, and the maximum data rate is no longer limited by the speed of phase detection. The CDR shows 6.8-ps rms and 57.4-ps peak-to-peak jitter in the recovered clock and 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-12</sup> bit error rate for 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">31</sup> -1 pseudorandom binary-sequence input while consuming 144 mW from a 1.8-V supply.

Identification and Control for Heart Rate Regulation During Treadmill Exercise

This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate profile tracking performance for an automated treadmill system. For the identification of Hammerstein systems, the pseudorandom binary sequence input is employed to decouple the identification of dynamic linear part from input nonlinearity. The powerful epsilon-insensitivity support vector regression method is adopted to obtain sparse representations of the inverse of static nonlinearity in order to obtain an approximate linear model of the Hammerstein system. An Hinfinity controller is designed for the approximated linear model to achieve robust tracking performance. This new approach is successfully applied to the design of a computer-controlled treadmill system for the regulation of heart rate during treadmill exercise. Minimizing deviations of heart rate from a preset profile is achieved by controlling the speed of the treadmill. Both conventional proportional-integral-derivative (PID) control and the proposed approaches have been employed for the controller design. The proposed algorithm achieves much better heart rate tracking performance.

System identification and filtering using pseudo random binary inputs

The problem of identifying the impulse response of an unknown system is investigated when the input is restricted to a pseudo random binary sequence (PRBS). The well-known methods, such as the Least Mean Square (LMS) and the Recursive Least Square (RLS) algorithms, are studied for system modeling with a PRBS input and the results are compared. It is shown that post-processing the impulse response with a suitable filter may lead to even better identification. A new post-processing filter, namely, the Med-Mean (MM) filter, is proposed which smoothes the baseline noise of the identified impulse response while preserving the edges.

FNS parameter selection and upper limb characterization.

The design of a controller for functional neuromuscular stimulation requires characterization of the plant, i. e., the neuromuscular system involved. The present paper is concerned with the modeling and identification of the elbow flexion/extension and wrist pronation/supination systems. These movements can be performed successfully by the surface stimulation of the biceps, triceps, and pronator teres. The scatter in the plant parameters between subjects and for a given subject as a function of time, electrode placement, etc., is outlined. The nonlinear muscle gain is approximated by a threshold below which the torque is zero, followed by a linear region. A least squares identification technique using a pseudorandom binary sequence input returned best fit for a third-order model for both the elbow and wrist dynamics. The dependence of the complex poles on the input signal bandwidth and level of coactivation is examined. The differences in parameters between the wrist and elbow systems are calculated. The results are used to establish design requirements for an appropriate controller.

A method of determining the crosscorrelation functions for a class of non-linear systems†

A use is made of the method of finding output autocorrelation functions of continuous time-invariant nonlinear systems, when they are represented by their functional expansion. This enables the input-output crosscorrelation functions to be calculated by the use of multi-dimensional Laplace transforms. A simple example for a pseudo random binary sequence input is presented.

Elimination of the effect of nonlinearities on process crosscorrelations

An analysis is presented which shows how the small-signal gain of a continuous single-valued nonlinear process can be derived from large-signal crosscorrelations. In particular, it is shown how the technique can be used to overcome difficulties that arise when identifying nonlinear processes with a pseudorandom binary-sequence input.