Editorial

Asymmetric windows in digital signal processing

Design of equiripple FIR filters using a feedback neural network

Chapter 4 - Frequency Analysis: The Fourier Series

Time-dependent mass spectra and breakdown graphs.I. 1,5-hexadiyne

Slope to intercept ratio of low-field photogeneration efficiency in β-metal-free phthalocyanine

The simple particle-in-a-sphere model of quantum dot excitons is the basis for understanding the excitonic peak positions, line widths, and relaxation dynamics in many spectroscopic experiments. Recent multiresonant coherent multidimensional spectroscopy (CMDS) with picosecond excitation pulses measured the two-dimensional spectra of PbSe quantum dots and successfully used this simple model of an inhomogeneous distribution of spherically confined exciton and biexciton states and rate constants to describe the dephasing and population relaxation dynamics. The long excitation pulses prevented resolution of faster dynamics. This work reports the development of multiresonant CMDS with femtosecond excitation pulses to resolve the spectra and dynamics associated with the 1S exciton line shape of PbSe quantum dots. The experiments use different combinations of excitation frequencies, excitation pulse time delays, and a monochromator to display and measure correlations between the spectral features and their dynamics. Line-narrowing of the inhomogeneous distribution occurs at short time delays where the excitation excites a subset of the quantum dots within the 1S line shape and the last pulse probes this subset. The line-narrowing disappears at longer delay times. Three pulse photon echo peak shifts (3PEPS) also occur when the line-narrowing is present, but the shifts disappear as the correlation between the first and last coherence frequencies disappears. Wigner plots reveal the spectral dynamics accompanying the peak shift and the disappearance of the line-narrowing. This work shows there is rapid relaxation dynamics occurring within the line profile of the quantum confined excitonic states that is not consistent with current understanding of the excitonic line broadening. The data suggest that the relaxation dynamics play a more dominant role in defining the excitonic line widths than the inhomogeneous broadening of the quantum dot size distribution. These observations are consistent with other spectroscopic experiments on CdSe and PbS quantum dots. The experiments also show the presence of a higher energy feature that lies outside the 1S line shape and undergoes very rapid relaxation.

Field programmable gate arrays (FPGAs) are attractive for a digital spectrometer due to its advantages of digital signal processing. However, how to improve the versatility of the spectrometer and increase the operating frequency of the digital shaper has gradually become a limitation that needs to be resolved in the FPGA-based spectrometer. A solution to improve the universality of the FPGA-based digital spectroscopy system is presented in this work, and the implementation of the real-time digital signal processing unit is improved to obtain a higher operating frequency, and then the optimal parameters of the digital trapezoidal shaper and the processing unit are also discussed through a series of experiments; finally, a FPGA-based digital spectrometer is developed. With the HPGe detector, the spectrometer achieves excellent energy resolution performance of 0.35% at 662 keV, 0.25% at 1173.2 keV, and 0.23% at 1332.5 keV.

A biosignal is used as a control signal for electrical stimulation to restore weakened muscle function due to damage to the central nervous system. In patients with central nervous system damage, sufficient muscle contraction does not occur spontaneously. In this case, applying electrical stimulation can cause normal muscle contraction. However, it is necessary to remove the electrical stimulation artifact caused by the electrical stimulation. This paper describes a system design that removes electrical stimulation artifact in real time using a Cortex-M4-based STM32F processor. The STM32F is a very advantageous MCU for such DSPs, especially because it has a built-in floating point operator. Using STM32F's various high-performance peripherals (12-bit parallel ADC and 12-bit DAC, UART, Timer), an optimized embedded system was implemented.In this paper, the simulated and real-time results were compared and evaluated with the designed fir filter. In addition, the performance of the filter was evaluated through frequency analysis. As a result, it was verified that a high-performance 32-bit STM32F with floating point calculator and various peripherals is suitable for real-time signal processing

Time-varying MMSE modulated lapped transform and its applications to transform coding for speech and audio signals

Field Programmable Gate Arrays (FPGAs) provide flexible computational architectures that are ideal for digital signal processing (DSP). With support of a NIH/NIDCD SBIR grant, we are developing an open FPGA platform for the speech, hearing, and acoustics research communities. The advantage of using FPGAs in a computational platform over conventional CPU approaches is the ability to implement low-latency high-performance signal processing with deterministic latencies. The hardware portion of the platform includes an audio codec and an Intel System-on-Chip (SoC) FPGA that contains ARM CPUs alongside the computational fabric that allows custom data plane designs. Development uses Mathwork’s Simulink that allows exploration and simulation of signal processing algorithms. Once a Simulink model has been developed to implement audio processing in either the time domain or frequency domain, VHDL code can be generated that implements the desired signal processing. The VHDL code is then implemented in the FPGA computational fabric where the FPGA functions as a real-time signal processor. We are currently soliciting ideas/feedback from the speech, hearing, and acoustic communities as to what features they would like to see in the next iteration of the open FPGA-based computational platform. [NIH/NIDCD R44DC015443: www.openspeechtools.com]

In order to fulfil real time signal processing tasks such as clutter rejection, moving target detection (MTD) and constant false alarm rate (CFAR) control in airborne radar, an airborne radar parallel signal processing system (ARPS2) is proposed with DSP chips as its kernel processing nodes. The DSP chips are used with parallel architecture. Each node has its private input and output memory. It adopts several parallel techniques, such as parallel storage, parallel processing, parallel code loading and parallel data organization to achieve high efficiency. It has a simple structure, excellent flexibility and easiness in developing. ARPS2 is going to be applied to an airborne radar. It can also be applied to perform high-speed real time signal processing algorithms in other kinds of radar.

There is a dearth of high quality reconfigurable hardware for DWT which can be extensively used in various real time signal and image processing applications. In this article, we have focused on proposing a dedicated customizable hardware for DWT applicable to 1D/2D signal processing. We have proposed bit-serial Distributed Arithmetic (DA) based VLSI architectures for ID/2D DWT. Exploitation of DA enables us to make our designs Multiplierless, thereby consuming less area. Bit serial configuration of DA is also exploited to introduce modularity and pipelining in the proposed convolution DWT based ID/2D architectures. Though the speed of the proposed designs may not be suitable for certain applications, a number of parallel channels can be introduced for the same. The provision of mode selection can efficiently be used to realize 9/7 and also 5/3 DWT filters in the same proposed architecture. In the proposed 1D/2D architecture, we have achieved about 50% memory size reduction, 30% reduction in area consumption and comparable speed in comparison to the other latest notable DWT architecture so far. We have verified the viability of our proposed 2D architecture by utilizing it in real time image decomposition.

Speech technologies have been developed for decades as a typical signal processing area, while the last decade has brought a huge progress based on new machine learning paradigms. Owing not only to their intrinsic complexity but also to their relation with cognitive sciences, speech technologies are now viewed as a prime example of interdisciplinary knowledge area. This review article on speech signal analysis and processing, corresponding machine learning algorithms, and applied computational intelligence aims to give an insight into several fields, covering speech production and auditory perception, cognitive aspects of speech communication and language understanding, both speech recognition and text-to-speech synthesis in more details, and consequently the main directions in development of spoken dialogue systems. Additionally, the article discusses the concepts and recent advances in speech signal compression, coding, and transmission, including cognitive speech coding. To conclude, the main intention of this article is to highlight recent achievements and challenges based on new machine learning paradigms that, over the last decade, had an immense impact in the field of speech signal processing.

The choice of the best parametric representation of acoustic signals is determinant in achieving a high level of accuracy in speech recognition applications. Most state of the art speech recognizers, rely on the Mel-Frequency cepstral coefficients (MFCC) as a feature extraction method, however, this method fails to capture nonlinearities related to the modulation patterns occurring in speech signals. In this contribution, we propose a novel, feature extraction method that partially simulates the frequency analysis and nonlinearities occurring in the human auditory system. This is achieved by using a passive Gammachirp filterbank for frequency analysis and the Dyn operator for nonlinear processing of the speech signals. The performance of the algorithm was tested in various noise conditions including white, pink and subway noises at various signal-to-noise ratios (SNRs). Results show that this method achieves a significant improvement over the MFCC.

A new feature extraction method for rolling bear fault signal is proposed based on the time-delayed feedback asymmetric tristable stochastic resonance (TFATSR). The generalized potential function and probability density function (PDF) of TFATSR system have been derived by using a short delay time. The generalized potentials with different parameters including feedback intensity, time-delay length, asymmetric term, system parameters have been discussed. The variation of the signal-to-noise ratio (SNR) curve of the TFATSR with different feedback intensities is obtained through the signal processing of fault signal. The comparison between three SR systems including symmetrical tristable stochastic resonance (STSR), time-delayed feedback symmetrical tristable stochastic resonance (TFSTSR) and TFATSR has been made to prove the superiorities of weak signal detection. Experimental data shows that the proposed method can realize the feature extraction for rolling bear fault signal.