Real-time Signal Processing System Research Articles

Integrating Reconfigurable Foot Design, Multi-modal Contact Sensing, and Terrain Classification for Bipedal Locomotion

The ability of bipedal robots to adapt to diverse and unstructured terrain conditions is crucial for their deployment in real-world environments. To this end, we present a novel, bio-inspired robot foot design with stabilizing tarsal segments and a multifarious sensor suite involving acoustic, capacitive, tactile, temperature, and acceleration sensors. A real-time signal processing and terrain classification system is developed and evaluated. The sensed terrain information is used to control actuated segments of the foot, leading to improved ground contact and stability. The proposed framework highlights the potential of the sensor-integrated adaptive foot for intelligent and adaptive locomotion.

Physics-Based LED Modeling and Nonlinear Distortion Mitigating With Real-Time Implementation

In this paper, a nonlinear model for Light Emitting Diodes (LEDs) inspired by semiconductor physics, and a corresponding post-compensator are implemented in a Field Programmable Gate Array (FPGA) for real-time Visible Light Communications (VLC). Our experiments demonstrate that the LED model effectively characterizes the dynamic LED nonlinearity, including the memory effects. The output signal of this nonlinear LED model shows a good resemblance with the measured LED output. In addition, a dedicated nonlinear equalizer, say, a post-compensator, inspired by this LED physical model can mitigate the nonlinear distortion substantially. Thereby it facilitates high data rate over the bandwidth-limited LED. It shows that the nonlinear compensator is attractive for practical real-time digital signal processing systems due to its high performance and low complexity.

NCOG-14. INTRAOPERATIVE COGNITIVE-LINGUISTIC MAPPING GUIDED BY VISUALIZATION OF GAMMA BAND MODULATION ELECTROCORTICOGRAMS: PROOF OF CONCEPT IN A PATIENT WITH LEFT TEMPORAL AND OCCIPITAL LOW-GRADE ASTROCYTOMA

Abstract OBJECTIVE Determine the feasibility and preliminary utility of a novel approach to intraoperative brain mapping guided by visualization of electrocorticography (ECoG) heat maps. METHODS A 39-year-old male with a biopsy-proven left posterior temporal and occipital WHO grade II IDH-mutant astrocytoma underwent awake craniotomy with intraoperative language mapping. Language mapping utilized a dual iPad stimulus presentation system (NeuroMapper) coupled to a portable real-time neural signal processing system capable of both recording cortical activity and delivering direct cortical stimulation in a closed-loop fashion. An ECoG grid (4x8 with 1cm pitch) which covered the majority of the left temporal lobe was used to assess oscillatory cortical activity during administration of language paradigms including object, action, auditory descriptive, and written descriptive naming. ECoG recording and cortical stimulation were synchronized with stimulus presentation via a photosensor attached to the patient-facing tablet. Gamma band modulations in response to language paradigms at each electrode were processed in real-time and visualized as heat maps in MATLAB/Simulink. Following recording and visualization, bipolar direct cortical stimulation from the grid was conducted for each neighboring electrode pair (up to an intensity of 6 mA) during administration of language tasks. RESULTS Despite mild fluent aphasia, a large set of reliable baseline stimuli were obtained for the language mapping paradigms. All naming paradigms resulted in strongest heat map activation at electrode 12 located in the anterior to mid superior temporal gyrus. During stimulation, consistent speech arrest was observed across all paradigms when stimulating electrode pair 11-12, indicating good correspondence with ECoG heat map recordings. Additionally, this region corresponded well with posterior language network representation via resting-state fMRI. CONCLUSION Intraoperative real-time visualization of task-based ECoG gamma band modulation is feasible and may help identify targets for direct cortical stimulation. If validated, this may improve the efficiency and accuracy of intraoperative language mapping.

FPGA-based electronic system for the control and readout of superconducting quantum processors.

Electronic systems for qubit control and measurement serve as a bridge between quantum programming language and quantum information processors. With the rapid development of superconducting quantum circuit technology, synchronization in a large-scale system, low-latency execution, and low noise are required for electronic systems. Here, we present a field-programmable gate array (FPGA)-based electronic system with a distributed synchronous clock and trigger architecture. The system supports synchronous control of qubits with jitters of ∼5ps. We implement a real-time digital signal processing system in the FPGA, enabling precise timing control, arbitrary waveform generation, in-phase and quadrature demodulation for qubit state discrimination, and the generation of real-time qubit-state-dependent trigger signals for feedback/feedforward control. The hardware and firmware low-latency design reduces the feedback/feedforward latency of the electronic system to 125ns, significantly less than the decoherence times of the qubit. Finally, we demonstrate the functionalities and low-noise performance of this system using a fluxonium quantum processor.

Real-time signal processing system based on FPGA for motional Stark effect diagnostic on EAST.

The plasma current density profile plays a key role in the development of a high poloidal beta scenario, which is essential for long-pulse and high-performance plasma operation on a tokamak. Based on the polarimetry technique, a Motional Stark Effect (MSE) diagnostic has been built on the Experimental Advanced Superconducting Tokamak. To be prepared for real-time (RT) feedback control of the plasma current density profile in the future, a RT signal processing system has been developed. The RT signal processing system is composed of three functional modules: analog-to-digital conversion (ADC) module, polarization information extraction module, and digital-to-analog conversion (DAC) module. The final objective of this system is to acquire the polarization information of the MSE. Based on the field-programmable gate array unit, fast Fourier transformation is adopted to process the Photoelastic Modulator (PEM) digital signal, which was converted from a PEM signal via the ADC module. By means of frequency spectrum separation, the components around double modulating frequencies are restored through inverse fast Fourier transformation. Furthermore, the two amplitudes of their corresponding components can be obtained through a digital harmonic analyzer technique. Afterward, the ratio of the two amplitudes is calculated by arc tangent so that the polarization angle is obtained. Finally, the information of this polarization angle is converted into a voltage signal by the DAC module and then output in RT. The test results based on the RT signal processing system are in good agreement with those based on the phase lock-in amplifiers. The working cycle of this system is shorter than 10ms, which meets the requirements of the MSE diagnostic as a RT controller. The algorithm of RT signal processing and the relevant technology applied for building this system are presented in the main body of this paper in detail.

Echo signal simulation system

In order to meet the demand of realistic simulation for complex target echo signal in water, the indirect method of frequency domain is employed to simulate the target echo signal in this paper. The plate element method is first used to forecast complex target echo characteristics in typical water. After generating frequency response function data, the data is imported into the echo signal simulation system. The system adopts FPGA real-time signal processing system based on PXIe bus. It receives hydrophone output signal by A/D sampling, extracts the incident acoustic signal and makes frequency domain transformation. After multiplying the transformed signal and frequency response function, scattering echo spectrum is obtained. And then the time domain transformation is made to get the time series of the echo signal. The series is loaded into the transmitting transducer by the power amplifier and the echo signal is simulated. The system has the high fidelity of the model, stable and reliable hardware system, and software system for easy operation.

A Novel Paradigm of CORDIC-Based FFT Architecture Framed on the Optimality of High-Radix Computation

‘Fast Fourier transform’ (FFT), being a prevalent algorithm for the proficient computation of ‘discrete Fourier transform,’ constitutes one of the major sub-modules in numerous real-time signal processing systems. In this article, a new approach of CORDIC-based high-radix FFT architecture has been demonstrated. Having identified the complex rotation as the most time-consuming elementary operation of FFT, the number of such complex rotations has been optimized by adopting radix-8-based FFT computation. To add to this, CORDIC is employed to realize the complex rotation, keeping aside its multiplier–accumulator (MAC)-based counterpart, for further economizing the VLSI implementation of the proposed FFT architecture. Furthermore, the requirement of CORDIC blocks for last three stages of radix-8 FFT computation has totally been mitigated by utilizing SCALE blocks as the rotation in those stages can be expressed in terms of $$\pi /4$$ or its multiples. RAM is arranged in the form of memory banks to provide parallel data path operations, and RAM switching is performed in between stages for sustaining continuous data flow circumventing data access hazards. The throughput of the proposed radix-8 architecture is eight outputs per clock cycle, while the maximum clock frequency is limited only by the propagation delay of an adder. Hardware utilization and comparative performance evaluation have been reported to prove the proposed architecture’s supremacy. Our proposed prototype radix-8 architecture has been successfully implemented on Zynq UltraScale+ FPGA using Xilinx Vivado 18.2 software for verifying its feasibility in practical applications.

Improvements to the Robust Transform Based on the Weighted Median Operator Algorithm

The Robust Transform based on the Weighted Median operator algorithm calculates the transform of a signal when it has been exposed to impulsive noise. Since this algorithm demands very long execution time, it is not useful for real time signal processing systems. In this context, this work presents several strategies to improve its performance, such as the reduction of redundant calculations, optimization in the memory access, and a multithreads version of the algorithm. Besides, the original estimation method is modified to decrease even more the average execution time, keeping the quality level of the numeric results. The experimental results show a 30% performance improvement by reducing redundant calculations and optimizing the memory access, without making modifications to the estimation method and without using multi-threaded processing; 93% performance improvement by introducing modifications to the estimation method; and 97% performance improvement by incorporating the multi-threaded processing.

Design and implementation of a real-time digital signal processing system using PIC24 microcontroller and wireless GUI control

In this paper, a real-time Digital Signal Processing (DSP) system is designed and implemented by using a PIC24 microcontroller circuit and a C# GUI application running on PC. The wireless communication between the PIC24 subsystem and the GUI subsystem is implemented via Bluetooth modules on the subsystems. The DSP system first digitises an input square signal of a certain frequency through an on-chip ADC of PIC24 microcontroller, then uses different FIR digital filters to extract certain harmonics of the input signal, and outputs it as a sinusoidal signal to an on-chip DAC as well as sends the sampled data and filtered data over Bluetooth to the GUI. The GUI, besides plotting the input and output wave forms, can provide a means of controlling all functionalities of the system through a developed communication protocol. The design and implementation for the proposed DSP system are successfully demonstrated by experimental results. The hardware and software co-design method can be extended to other industrial applications and used as a good paradigm of engineering education for college students.

Design and implementation of a real-time digital signal processing system using PIC24 microcontroller and wireless GUI control

In this paper, a real-time Digital Signal Processing (DSP) system is designed and implemented by using a PIC24 microcontroller circuit and a C# GUI application running on PC. The wireless communication between the PIC24 subsystem and the GUI subsystem is implemented via Bluetooth modules on the subsystems. The DSP system first digitises an input square signal of a certain frequency through an on-chip ADC of PIC24 microcontroller, then uses different FIR digital filters to extract certain harmonics of the input signal, and outputs it as a sinusoidal signal to an on-chip DAC as well as sends the sampled data and filtered data over Bluetooth to the GUI. The GUI, besides plotting the input and output wave forms, can provide a means of controlling all functionalities of the system through a developed communication protocol. The design and implementation for the proposed DSP system are successfully demonstrated by experimental results. The hardware and software co-design method can be extended to other industrial applications and used as a good paradigm of engineering education for college students.

Dynamic partial reconfiguration scheme for fault‐tolerant FFT processor based on FPGA

The fast Fourier transform FFT processor is an important part of the space real-time signal processing system based on field programmable gate array (FPGA). Since occupying a large amount of logical resources and storage resources, FFT processor is more vulnerable to high-energy particles in space, resulting in single event upset (SEU). This paper presents a novel FPGA scrubbing framework base on dynamic partial reconfiguration technique for a FFT processor to mitigate SEU. The proposed scheme is compared with the blind scrubbing, the reconfiguration time is reduced by 78%. Then, the resource utilisation is 61.5% less than triple modular redundancy scheme. This paper also presents a DPR controller for FFT processor, which is evaluated in terms of hardware resources and reconfiguration time. A comparison to the Xilinx PRC IP shows that multipath delay feedback FFT controller saves 38.6% resources.

Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex

BackgroundRapidly changing excitability states in an oscillating neuronal network can explain response variability to external stimulation, but if repetitive stimulation of always the same high- or low-excitability state results in long-term plasticity of opposite direction has never been explored in vivo. Objective/hypothesisDifferent phases of the endogenous sensorimotor μ-rhythm represent different states of corticospinal excitability, and repetitive transcranial magnetic stimulation (rTMS) of always the same high- vs. low-excitability state results in long-term plasticity of different direction. MethodsState-dependent electroencephalography-triggered transcranial magnetic stimulation (EEG-TMS) was applied to target the EEG negative vs. positive peak of the sensorimotor μ-rhythm in healthy subjects using a millisecond resolution real-time digital signal processing system. Corticospinal excitability was indexed by motor evoked potential amplitude in a hand muscle. ResultsEEG negative vs. positive peak of the endogenous sensorimotor μ-rhythm represent high- vs. low-excitability states of corticospinal neurons. More importantly, otherwise identical rTMS (200 triple-pulses at 100 Hz burst frequency and ∼1 Hz repetition rate), triggered consistently at this high-excitability vs. low-excitability state, leads to long-term potentiation (LTP)-like vs. no change in corticospinal excitability. ConclusionsFindings raise the intriguing possibility that real-time information of instantaneous brain state can be utilized to control efficacy of plasticity induction in humans.

Real-time brain activity measurement and signal processing system using highly sensitive MI sensor

Superconducting Quantum Interference Devices (SQUIDs) are the most used sensor to detect the extremely weak magnetic field of brain. However, the sensor heads need to be kept at very low temperature to maintain superconductivity, and that makes the devices large-scale and inconvenient. In order to measure brain activity in normal environment, we had constructed a measurement system based on highly sensitive Magneto-Impedance (MI) sensor, and reported the study of measuring Auditory Evoked Field (AEF) brain waves. In this study, the system was improved, and the sensor signals can be processed in real-time to monitor brain activity. We use this system to measure the alpha rhythm in the occipital region and the Event-Related Field (ERF) P300 in the frontal, the parietal and both the temporal regions.

Perfect Dispersive Medium for Real-Time Signal Processing

Dispersion lies at the heart of real-time signal processing systems across the entire electromagnetic spectrum from radio to optics. However, the performance and applicability of such systems have been severely plagued by distortions due to the frequency-dependent nature of the amplitude response of the dispersive media. This frequency dependence is a fundamental consequence of causality, incarnated by Kramers–Kronig relations or, equivalently, by the Bode relations. In order to resolve this issue, we introduce here the concept of a perfect dispersive medium, which is a loss-gain medium characterized by a perfectly flat magnitude response along with a quasi-arbitrary phase response. This property results from equalized electric and magnetic dipole dispersions, whence the amplitude and phase of the transmission functions of the isolated loss and gain contributions become the inverse and remain the same, respectively, under sign reversal of the imaginary part of the equalized magnetodielectric polarizability. Such a perfect dispersive medium may be realized in the form of a metamaterial, and this paper demonstrates a corresponding stacked loss-gain metasurface structure for illustration. From a practical standpoint, perfect dispersive media may propel real-time signal processing technology to a new dimension, with a myriad of novel ultrafast communication, sensing, imaging, and instrumentation applications.

On-chip integratable all-optical quantizer using strong cross-phase modulation in a silicon-organic hybrid slot waveguide.

High performance all-optical quantizer based on silicon waveguide is believed to have significant applications in photonic integratable optical communication links, optical interconnection networks, and real-time signal processing systems. In this paper, we propose an integratable all-optical quantizer for on-chip and low power consumption all-optical analog-to-digital converters. The quantization is realized by the strong cross-phase modulation and interference in a silicon-organic hybrid (SOH) slot waveguide based Mach-Zehnder interferometer. By carefully designing the dimension of the SOH waveguide, large nonlinear coefficients up to 16,000 and 18,069 W−1/m for the pump and probe signals can be obtained respectively, along with a low pulse walk-off parameter of 66.7 fs/mm, and all-normal dispersion in the wavelength regime considered. Simulation results show that the phase shift of the probe signal can reach 8π at a low pump pulse peak power of 206 mW and propagation length of 5 mm such that a 4-bit all-optical quantizer can be realized. The corresponding signal-to-noise ratio is 23.42 dB and effective number of bit is 3.89-bit.

Improved synthetic-heterodyne Michelson interferometer vibrometer using phase and gain control feedback

Synthetic-heterodyne demodulation is a useful technique for dynamic displacement and velocity measurement using interferometric sensors as it can provide an output signal which is immune to interferometric drift. With the advent of cost effective, high-speed real-time signal processing systems and software, processing of the complex signals encountered in interferometry has become more feasible. In conventional synthetic-heterodyne demodulation schemes, to obtain the dynamic displacement or vibration of the object under test requires knowledge of the interferometer visibility and also the argument of two Bessel functions. In this paper, a new synthetic-heterodyne demodulation method is described leading to an expression for the dynamic displacement and velocity of the object under test that is significantly less sensitive to the received optical power. In addition, the application of two independent phase and gain feedback loops is used to compensate for the nonideal gain and phase response of the anti-aliasing filter required for the signal acquisition of the received wideband interferometer signal. The efficacy of the improved system is demonstrated by measuring the displacement sensitivity frequency response and linearity of a Piezoelectric Mirror-Shifter (PMS) over a range of 200Hz-9kHz. In addition, the system is used to measure the response of the PMS to triangular and impulse type stimuli. The experimental results show excellent agreement with measurements taken using two independent industry standard calibration methods.

Michelson interferometer vibrometer using self-correcting synthetic-heterodyne demodulation.

Synthetic-heterodyne demodulation is a useful technique for dynamic displacement and velocity detection in interferometric sensors, as it can provide an output signal that is immune to interferometric drift. With the advent of cost-effective, high-speed real-time signal-processing systems and software, processing of the complex signals encountered in interferometry has become more feasible. In synthetic heterodyne, to obtain the actual dynamic displacement or vibration of the object under test requires knowledge of the interferometer visibility and also the argument of two Bessel functions. In this paper, a method is described for determining the former and setting the Bessel function argument to a set value, which ensures maximum sensitivity. Conventional synthetic-heterodyne demodulation requires the use of two in-phase local oscillators; however, the relative phase of these oscillators relative to the interferometric signal is unknown. It is shown that, by using two additional quadrature local oscillators, a demodulated signal can be obtained that is independent of this phase difference. The experimental interferometer is a Michelson configuration using a visible single-mode laser, whose current is sinusoidally modulated at a frequency of 20kHz. The detected interferometer output is acquired using a 250kHz analog-to-digital converter and processed in real time. The system is used to measure the displacement sensitivity frequency response and linearity of a piezoelectric mirror shifter over a range of 500Hz to 10kHz. The experimental results show good agreement with two data-obtained independent techniques: the signal coincidence and denominated n-commuted Pernick method.

On the performance of multi-GPU-based expert systems for acoustic localization involving massive microphone arrays

Sound source localization is an important topic in expert systems involving microphone arrays, such as automatic camera steering systems, human–machine interaction, video gaming or audio surveillance. The Steered Response Power with Phase Transform (SRP-PHAT) algorithm is a well-known approach for sound source localization due to its robust performance in noisy and reverberant environments. This algorithm analyzes the sound power captured by an acoustic beamformer on a defined spatial grid, estimating the source location as the point that maximizes the output power. Since localization accuracy can be improved by using high-resolution spatial grids and a high number of microphones, accurate acoustic localization systems require high computational power. Graphics Processing Units (GPUs) are highly parallel programmable co-processors that provide massive computation when the needed operations are properly parallelized. Emerging GPUs offer multiple parallelism levels; however, properly managing their computational resources becomes a very challenging task. In fact, management issues become even more difficult when multiple GPUs are involved, adding one more level of parallelism. In this paper, the performance of an acoustic source localization system using distributed microphones is analyzed over a massive multichannel processing framework in a multi-GPU system. The paper evaluates and points out the influence that the number of microphones and the available computational resources have in the overall system performance. Several acoustic environments are considered to show the impact that noise and reverberation have in the localization accuracy and how the use of massive microphone systems combined with parallelized GPU algorithms can help to mitigate substantially adverse acoustic effects. In this context, the proposed implementation is able to work in real time with high-resolution spatial grids and using up to 48 microphones. These results confirm the advantages of suitable GPU architectures in the development of real-time massive acoustic signal processing systems.

Real-time Sound Source Localization on an Embedded GPU Using a Spherical Microphone Array

Abstract Spherical microphone arrays are becoming increasingly important in acoustic signal processing systems for their applications in sound field analysis, beamforming, spatial audio, etc. The positioning of target and interfering sound sources is a crucial step in many of the above applications. Therefore, 3D sound source localization is a highly relevant topic in the acoustic signal processing field. However, spherical microphone arrays are usually composed of many microphones and running signal processing localization methods in real time is an important issue. Some works have already shown the potential of Graphic Processing Units (GPUs) for developing high-end real-time signal processing systems. New embedded systems with integrated GPU accelerators providing low power consumption are becoming increasingly relevant. These novel systems play a very important role in the new era of smartphones and tablets, opening further possibilities to the design of high-performance compact processing systems. This paper presents a 3D source localization system using a spherical microphone array fully implemented on an embedded GPU. The real-time capabilities of these platforms are analyzed, providing also a performance analysis of the localization system under different acoustic conditions.

Hard Disk Drive Servo System Based on Field-Programmable Gate Arrays

Servo systems are essential components of current magnetic storage hard disk drives (HDDs). In this paper, we demonstrate a novel servo scheme using a field-programmable gate array (FPGA). Adjacent magnetic tracks with two different frequencies are recorded on the magnetic disk and are used as servo tracks to encode the position information. A discrete Fourier transform is employed to decode the magnetic head position and to obtain the position error signal (PES). The PES is defined in terms of the difference of the magnitudes of the two frequencies. The relationship between the displacement and the PES is obtained by offline calibration. A real-time signal acquisition and processing system using a commercial FPGA, analog-to-digital converter chips, and digital-to-analog converter chips is built to do the high-speed spectrum analysis and generate the PES. The PES is transmitted to a laboratory virtual instrument engineering workbench (LabVIEW) real-time control target to implement the control algorithm. The computed control output from the real-time control target is amplified to drive a piezoelectric stage, which moves the magnetic head along the radial direction to compensate for the position error. A simple repetitive and proportional-integral-derivative control algorithm is implemented for head positioning and to verify this new servo scheme in this paper. With the controller, most of the major disturbances, such as the harmonics of the spindle runout, have been effectively attenuated compared with an open-loop control scheme.