Digital Signal Processing Tools Research Articles

A programmable reaction-diffusion system for spatiotemporal cell signaling circuit design

Cells self-organize molecules in space and time to generate complex behaviors, but we lack synthetic strategies for engineering spatiotemporal signaling. We present a programmable reaction-diffusion platform for designing protein oscillations, patterns, and circuits in mammalian cells using two bacterial proteins, MinD and MinE (MinDE). MinDE circuits act like "single-cell radios," emitting frequency-barcoded fluorescence signals that can be spectrally isolated and analyzed using digital signal processing tools. We define how to genetically program these signals and connect their spatiotemporal dynamics to cell biology using engineerable protein-protein interactions. This enabled us to construct sensitive reporter circuits that broadcast endogenous cell signaling dynamics on a frequency-barcoded imaging channel and to build control signal circuits that synthetically pattern activities in the cell, such as protein condensate assembly and actin filamentation. Our work establishes a paradigm for visualizing, probing, and engineering cellular activities at length and timescales critical for biological function.

Complex infrastructure systems analysis and management: the theory of faults

PurposeThe purpose of this research is to provide a theoretical and practical theory and application that provides understanding and means to manage complex infrastructures.Design/methodology/approachIn this research, complexity, nonlinear, noncontinuous effects and aleatoric and data unknowns are bypassed by directly addressing systems' responses. Graph theory, statistics and digital signal processing (DSP) tools are applied within a theoretical framework of the theory of faults (ToF). Motivational complex infrastructure systems (CISs) are difficult to model. Data are often missing or erroneous, changes are not well documented and processes are not well understood. On top of it, under complexity, stalwart analytical tools have limited predictive power. The aleatoric risk, such as rain and risk cascading from interconnected infrastructures, is unpredictable. Mitigation, response and recovery efforts are adversely affected.FindingsThe theory and application are presented and demonstrated by a step-by-step development of an application to a municipal drainage system. A database of faults is analyzed to produce system statistics, spatio-temporal morphology, behavior and traits. The gained understanding is compared to the physical system's design and to its modus operandi. Implications for design and maintenance are inferred; DSP tools to manage the system in real time are developed.Research limitations/implicationsSociological systems are interest driven. Some events are intentionally created and directed to the benefit and detriment of the opposing parties in a project. Those events may be explained and possibly predicted by understanding power plays, not power functions. For those events, sociological game theories provide better explanatory value than mathematical gain theories.Practical implicationsThe theory provides a thematic network for modeling and resolving aleatoric uncertainty in engineering and sociological systems. The framework may be elaborated to fields such as energy, healthcare and critical infrastructure.Social implicationsToF provides a framework for the modeling and prediction of faults generated by inherent aleatoric uncertainties in social and technological systems. Therefore, the framework and theory lay the basis for automated monitoring and control of aleatoric uncertainties such as mechanical failures and human errors and the development of mitigation systems.Originality/valueThe contribution of this research is in the provision of an explicatory theory and a management paradigm for complex systems. This theory is applicable to a wide variety of fields from facilities and construction project management to maintenance and from academic studies to commercial use.

A Review of Digital Watermarking Approaches for Forensic Applications

Abstract: There has been remarkable growth in the digital domain. This has led to the production of a huge amount of digital data in the form of text messages, documents, audio/speech signals, digital images, videos, etc. In forensics, digital information (i.e., images of crime scenes, etc.) is very important to examine cases and collect the proof regarding the incident. This further leads to judge the victims and culprits by the court for appropriate actions. So, even a slight change (intentional/ unintentional) may lead to the wrong judgement. Several digital signal processing tools are easily available to process this type of digital data, which means data may get disrupted, altered, or deform in such a way that the authenticity gets compromised. In such cases, it can be difficult to recognize the source information as well as to check the originality/intactness of the digital data. In the recent past, several techniques have come into existence to protect digital data against security issues. Steganography, cryptography, and watermarking are examples of security techniques, which have been widely used for data security and forensic applications for a long time. Specifically, digital watermarking techniques provide many useful features to help users regarding data security and authentication without affecting other data characteristics. This work is an attempt to provide a review of recent digital image watermarking approaches for forensic applications.

A Fragile Image Watermarking Scheme in DWT Domain Using Chaotic Sequences and Error-Correcting Codes.

With the rapid development of digital signal processing tools, image contents can be easily manipulated or maliciously tampered with. Fragile watermarking has been largely used for content authentication purposes. This article presents a new proposal for image fragile watermarking algorithms for tamper detection and image recovery. The watermarked bits are obtained from the parity bits of an error-correcting code whose message is formed from a binary chaotic sequence (generated from a secret key known to all legitimate users) and from bits of the original image. Part of the codeword (the chaotic bits) is perfectly known to these users during the extraction phase, adding security and robustness to the watermarking method. The watermarked bits are inserted at specific sub-bands of the discrete wavelet transform of the original image and are used as authentication bits for the tamper detection process. The imperceptibility, detection, and recovery of this algorithm are tested for various common attacks over digital images. The proposed algorithm is analyzed for both grayscale and colored images. Comparison results reveal that the proposed technique performs better than some existing methods.

Advanced DSP-Based Monitoring for Spatially Resolved and Wavelength-Dependent Amplifier Gain Estimation and Fault Location in C+L-Band Systems

The development of efficient anomaly detection schemes is a key element for the implementation of autonomous optical networks as they can help telecom operators to automate the location of defective devices and track the overall performance of the network infrastructure. In that regard, the exploitation of receiver based digital signal processing (DSP) for optical performance monitoring has shown to be a promising enabler for detection of spatially resolved and wavelength-dependent properties and anomalies in optical fiber links. In this work, we study the benefits of applying DSP-based longitudinal power estimation on multiple wavelength division multiplexing (WDM) channels allocated in the optical grid to infer wavelength-wise characteristics of a C+L-band optical line system. In that context, we show that the applied scheme can successfully recreate a visualization of the spatial evolution of the gain tilt imposed by in-line optical amplifiers. Additionally, we propose the utilization of advanced DSP tools based on wavelet-denoising to enhance the performance of an anomaly detection approach. The proposed method not only can improve accuracy of the fault location, by reducing positioning uncertainty, but it also delivers more uniform readings of the anomaly signatures.

Exploring the need for language-specific hearing aid signal processing

Speech phonemes in higher frequencies have varying acoustic characteristics in different languages. For example, /s/ and /ʃ/, fricatives commonly used in English, have spectral peaks at higher frequencies than /ʂ/ and /x/, fricatives commonly used in Mandarin. Data on the relationship between the acoustic characteristics of a listener’s native language and their hearing aid signal prescription requirements could provide important clinical guidance. Non-linear frequency compression is a digital signal processing tool that compresses auditory information above a pre-determined cutoff frequency. In hearing aids, this compression is applied with the intention of making higher-frequency speech sounds more audible to listeners with high-frequency hearing loss. Here, we studied the effect of different frequency compression settings on fricative perception between Mandarin and English listeners. Normal-hearing participants between age 18 to 50 years were presented with fricative identification and fricative discrimination tasks under various frequency compression settings. Participants were grouped between those with exclusive English language background birth to 6 years to those with exclusive Mandarin language background during the same period. Results display different responses for presented phonemes and frequency compression settings, suggesting that language specificity could be considered for hearing aid signal prescription.

Discrete Quadratic-Phase Fourier Transform: Theory and Convolution Structures

The discrete Fourier transform is considered as one of the most powerful tools in digital signal processing, which enable us to find the spectrum of finite-duration signals. In this article, we introduce the notion of discrete quadratic-phase Fourier transform, which encompasses a wider class of discrete Fourier transforms, including classical discrete Fourier transform, discrete fractional Fourier transform, discrete linear canonical transform, discrete Fresnal transform, and so on. To begin with, we examine the fundamental aspects of the discrete quadratic-phase Fourier transform, including the formulation of Parseval’s and reconstruction formulae. To extend the scope of the present study, we establish weighted and non-weighted convolution and correlation structures associated with the discrete quadratic-phase Fourier transform.

Algebraic fields and rings as a digital signal processing tool

It is shown that algebraic fields and rings can become a very promising tool for digital signal processing. This is mainly due to the fact that any digital signals change in a finite range of amplitudes and, therefore, there are only a finite set of levels that can correspond to the amplitudes of a signal reduced to a discrete form. This allows you to establish a one-to-one correspondence between the set of levels and such algebraic structures as fields, rings, etc. This means that a function that takes values in any of the algebraic structures containing a finite set of elements can serve as a model of a signal reduced to a discrete form. A special case of such a signal model are functions that take values in Galois fields. It is shown that, along with Galois fields, in certain cases, algebraic rings contain zero divisors can be used to construct signal models. This representation is convenient because in this case it becomes possible to independently operate with the digits of the number that enumerates the signal levels. A simple and intuitive method for constructing rings is proposed, based on an analogy with the method of algebraic extensions.

Low-Complexity Multi-Task Learning Aided Neural Networks for Equalization in Short-Reach Optical Interconnects

With the rapid development of machine learning technologies in recent years, different types of neural network (NN)-based equalizers have been proposed and proved to be efficient digital signal processing tools to deal with the nonlinear impairments in short-reach direct detection optical interconnects. However, one major concern for these NN-based equalizers is their computational complexity (CC), since only a few tens of multiplications per symbol can be practically handled considering real-time implementation. In this paper, we propose an NN-based multi-symbol equalization scheme inspired by multi-task learning. Compared with traditional single-output NN-based equalizers, the CC can be significantly reduced with the help of the proposed scheme. A 50-Gb/s 25-km pulse amplitude modulation (PAM)-4 direct detection optical link is experimentally carried out and 3 different types of NNs, i.e., feedforward NN (FNN), cascade FNN (C-FNN), and recurrent NN (RNN), are employed for the proposed multi-symbol equalization scheme. Experimental results show that a maximum CC reduction of 43.2%/41.1%/44.0% can be achieved with FNN/C-FNN/RNN-based multi-symbol equalization to achieve their best performance, compared with the corresponding single-output NNs. The best performance of the proposed and the traditional FNN/C-FNN-based schemes are the same, while the RNN-based scheme sacrifices a slight system performance. Aided by multi-symbol equalization, the CC needed to recover 1 symbol for various NNs can all be reduced to about a few tens of multiplications, indicating the feasibility of real-time implementation of these NN-based equalizers.

A Comprehensive Overview on Modified Versions of Stockwell Transform For Power Quality Monitoring

The increasing trends toward the accurate identification of power quality disturbances (PQD) via power quality (PQ) monitoring require an appropriate digital signal processing (DSP) technique and a robust classifier. To this end, Stockwell transform (ST), one of the most efficient feature extraction DSP tools, and its several variants play an utmost role in PQ assessment framework. Its time-varying spectral characteristics generally extract the local instantaneous frequency spectrum from the global temporary behavior of PQD signal. However, the Standard ST suffers from thepoor time-frequency resolution because of its frequency-dependent Gaussian window (GW). While the analysis of the statistically time-varying signals requires a suitable balance between time and frequency resolution. To this end, this paper provides a comprehensive literature review on several modified versions of Standard ST for the first time to reduce the computational complexity of the algorithm as well as maximize the energy concentration of the time-frequency plane. A comparative analysis of all the modified STs has been presented in tabular form to provide the key characteristics of each technique. Additionally, a case study has been presented to substantiate the highest accuracy of the proposed algorithm over the other ST variants. Apart from the PQD classification, miscellaneous applications of Standard ST and its modified variants have been indicated. This review paper may provide a valuable resource to the researchers for further improvement of the time-frequency resolution of ST not only in classifying PQD but also for its other wide applications.

Combined Neural Network and Adaptive DSP Training for Long-Haul Optical Communications

Machine Learning (ML) algorithms have shown to complement standard digital signal processing (DSP) tools in mitigating fiber nonlinearity and improving long-haul transmission performance. However, dynamic transmission impairments such as polarization effects and carrier phase noise corrupt the training data and conventional cost functions for neural network (NN) training become unsuitable. Simple cascade of ML and standard adaptive DSP blocks will also result in suboptimal transmission performance or require impractical training methodologies in presence of such dynamic transmission impairments. We show how the adaptive DSP blocks can be treated as extra stateful NN layers and be combined with the main NN so that standard backpropagation-like training algorithms in ML can be applied. In this case, the adaptive filters are viewed as NN states which are updated in the forward pass of the backpropagation. We study the combined training of linear and nonlinear parameters in the digital backpropagation (DBP) algorithm for fiber nonlinearity compensation (named generalized DBP (GDBP) hereafter), residual impairments, polarization effects, frequency offsets and carrier phase noise compensation filters as a single NN in a 7 × 288 Gb/s polarization multiplexed (PM)-16QAM transmission experiment over 1125 km. We derived the complete set of backpropagation-like gradients and state update equations for the static and dynamic parameters of the combined NN. We further proposed and open-sourced a JAX-based coding framework for their easy and practical implementation. GDBP is more generalized than other DBP variants proposed in literature and for a given total number of steps, GDBP is the first experimental demonstration of optimal single-channel DBP based-fiber nonlinearity compensation algorithm. In addition, for complexity constrained situations with shortened filter taps, GDBP enables a 1 dB performance improvement over other DBP variants.

Joint Equalization of Linear and Nonlinear Impairments for PAM4 Short-Reach Direct Detection Systems

Various neural network (NN)-based equalizers have been proposed as effective digital signal processing (DSP) tools for short-reach optical direct detection (DD) communication systems. However, they are mainly designed for solving nonlinear problems due to the intrinsic nonlinear activation function. In this letter, we propose novel DSP schemes to deal with both linear and nonlinear impairments in short-reach DD optical links, including cascade feedforward NN (FNN)/cascade recurrent NN (RNN)-based equalizers, and combination of feedforward equalization (FFE) and traditional non-cascade FNN/RNN-based equalizers. A 50-Gb/s pulse amplitude modulation (PAM)-4 directly modulated laser (DML)-based system is experimentally carried out and the bit-error-rate (BER) performance of the proposed DSP algorithms are compared. Experimental results show that cascade FNN/RNN and FNN/RNN-FFE could help improve the BER performance compared with traditional non-cascade FNN/RNN, since linear impairments are taken into account by either FFE or the cascade structure. With the help of the joint optimization of both linear and nonlinear effects through the network training process, cascade FNN/RNN shows great superiority over other DSP schemes. We also investigate on the impact of decision feedback equalization (DFE) concatenated with FNN/RNN. Compared with using FNN/RNN followed by FFE, no significant BER improvement can be found using DFE in place of FFE.

RESEARCH OF MEDIAN FILTERING OF ONE-DIMENSIONAL SIGNALS

This paper considers the principles of digital signal processing, the general provisions of digital filtering, existing methods of noise filtering in electrical signals, the median filters of one-dimensional signals are studied in detail. To solve this problem, the classification of digital signal processing tools is presented. Since the most effective for filtering noise in electrical signals are digital filters, they are given the most attention. The main purpose of signal filtering is the need to extract the information contained in them. This information, which is usually present in the amplitude of the signal (absolute or relative), in frequency or spectral composition, in phase or in the relative time dependences of several signals. The classification of existing digital filters is carried out. For further development, a median filter was selected, which belongs to the class of heuristics and is one of the most effective in filtering signals from impulse noise and white noise. Highlighting the advantages and disadvantages, a review of existing software that implements the median filter. It is established that the urgent task is to increase the processing speed and reduce resource costs in the implementation of such filters, developed an algorithm for fast median filtering, conducted an experimental test of software-implemented median filters with different apertures at different levels of fluctuation noise. This program meets all the requirements of modern norms and standards, allows its practical use to solve real problems of signal processing. In order to increase the speed of information processing, a median filtering algorithm based on difference matrices using the threshold saturation function has been developed. Developed software that implements the proposed algorithm. Schemes of the main program, reading of values of a signal from a file, filtering, sorting of data on amplitude, a choice of a window of elements, a choice of the registered values are presented. The conditions of data registration and ADC parameters to ensure efficient operation of filters are also defined

Efficient and Privacy-Preserving Outsourcing of 2D-DCT and 2D-IDCT

As a subset of discrete Fourier transform (DFT), discrete cosine transform (DCT), especially two-dimensional discrete cosine transform (2D-DCT), is an important mathematical tool for digital signal processing. However, the computational complexity of 2D-DCT is quite high, which makes it impossible to meet the requirements in some signal processing fields with large signal sizes. In addition, to optimize the 2D-DCT algorithm itself, seeking help from a cloud platform is considered to be an excellent alternative to dramatically speeding up 2D-DCT operations. Still, there are three key challenges in cloud computing outsourcing that need to be addressed, including protecting the privacy of input and output data, ensuring the correctness of the returned results, and ensuring adequate local cost savings. In this paper, we explore the design of a practical outsourcing protocol for 2D-DCT and 2D-IDCT, which well solves the above three challenges. Both theoretical analysis and simulation experiment results not only confirm the feasibility of the proposed protocol but also show its outstanding performance in efficiency.

A new numerical approach for DNA representation using modified Gabor wavelet transform for the identification of protein coding regions

The fundamental step in genomic signal processing applications is to assign mathematical descriptor to nucleotides {A, T, G, C} of DNA molecule for discrete representation. The discrete representation should replicate biological information of gene when analyzed with digital signal processing tools. In this aspect, a novel binary representation of DNA sequence by combining structural and chemical information of original DNA sequence has been proposed for the identification of protein coding regions of eukaryotes. The identification model comprises two stages, mainly, numerical encoding in first stage, and analysis of biological behavior through digital signal processing algorithms in second stage. In the first stage, a new numerical encoding method based on Walsh codes of order-4 is proposed to obtain 1-D binary discrete sequence. In the second stage, the modified Gabor wavelet transform (MGWT) is employed on the discretized DNA sequence for spectrum analysis. The optimal gene numerical encoding and multiresolution approach of MGWT has readily identified the structures of coding regions of unknown gene sequences. The proposed model is validated by analyzing prediction efficiency in terms of statistical metrics such as sensitivity, specificity, accuracy on both sequence and data base level. Furthermore, the results are compared by plotting receiver operating curves (ROC) for all classification thresholds for the state-of-art encoding methods. Area under curve (AUC) value of 0.86 at sequence level and 0.84 at database level is achieved. Performance metrics indicate that the proposed encoding method exhibits relatively better performance than other numerical encoding methods.

The Algorithm and Structure for Digital Normalized Cross-Correlation by Using First-Order Moment.

Normalized cross-correlation is an important mathematical tool in digital signal processing. This paper presents a new algorithm and its systolic structure for digital normalized cross-correlation, based on the statistical characteristic of inner-product. We first introduce a relationship between the inner-product in cross-correlation and a first-order moment. Then digital normalized cross-correlation is transformed into a new calculation formula that mainly includes a first-order moment. Finally, by using a fast algorithm for first-order moment, we can compute the first-order moment in this new formula rapidly, and thus develop a fast algorithm for normalized cross-correlation, which contributes to that arbitrary-length digital normalized cross-correlation being performed by a simple procedure and less multiplications. Furthermore, as the algorithm for the first-order moment can be implemented by systolic structure, we design a systolic array for normalized cross-correlation with a seldom multiplier, in order for its fast hardware implementation. The proposed algorithm and systolic array are also improved for reducing their addition complexity. The comparisons with some algorithms and structures have shown the performance of the proposed method.

Fast structured illumination three-dimensional color microscopic imaging method based on Hilbert-transform

As a wide-field microscopy, structured illumination microscopy (SIM) enables super-resolution and three-dimensional (3D) imaging. It has recently received lots of attention due to the advantages of high spatial resolution, short image recording time, and less photobleaching and phototoxicity. The SIM has found numerous important applications in time-lapse imaging of living tissues and cellular structures in the field of biomedical science. Color information is an important physical quantity describing the characteristics of living creatures and reflects the differences in its microstructure and optical property to some extent. Although HSV (hue, saturation, value) color space based structured illumination full-color 3D optical sectioning technique can recover the full color information on the surface of the samples without color distortion. However, for each optical sectioning, three raw images with fixed phase shift are required to calculate the sectioning images by the rootmean square (RMS) algorithm. This will dramatically increase the data acquisition time and data storage space, especially for a large-scaled sample that needs image stitching strategy. The image processing progress operated in HSV color space need to run the RMS algorithm three times in each channel of HSV space for every section, and transform the images between RGB (red-green-blue) space and HSV space twice. This will absolutely extend the data processing time and put forward higher requirements for computer hardware and software for data storage and processing. To this end, in this paper, a fast 3D color optical sectioning SIM algorithm based on Hilbert-transform is proposed. The Hilbert-transform has proved to be a powerful tool in digital signal and image processing and has successfully applied to the SIM. Here, only two raw images with structured illumination are needed to reconstruct a full-color optical sectioned image for each slice. This fast 3D color sectioning method has the advantage of insensitivity to phase-shift error and has better adaptability to noise, high quality color sectioning images can be obtained under the phase-shift error or noise disturbed environment. The image acquisition data are reduced by 1/3 and the color optical sectioning reconstruction time is saved by about 28%, this new method effectively improves the efficiency and speed for 3D color imaging and will bring a wider application range for SIM.

Extracting Signals from High-Frequency Trading with Digital Signal Processing Tools

As algorithms replace a growing number of tasks performed by humans in the markets, there have been growing concerns about an increased likelihood of cascading events, similar to the Flash Crash of May 6, 2010. To address these concerns, researchers have employed a number of scientific data analysis tools to monitor the risk of such cascading events. As an example, the authors of this article investigate the natural gas (NG) futures market in the frequency domain and the interaction between weather forecasts and NG price data. They observe that Fourier components with high frequencies have become more prominent in recent years and are much stronger than could be expected from an analytical model of the market. Additionally, a significant amount of trading activity occurs in the first few seconds of every minute, which is a tell-tale sign of time-based algorithmic trading. To illustrate the potential of cascading events, the authors further study how weather forecasts drive NG prices and show that, after separating the time series by season to account for the different mechanisms that relate temperature to NG price, the temperature forecast is indeed cointegrated with NG price. They also show that the variations in temperature forecasts contribute to a significant percentage of the average daily price fluctuations, which confirms the possibility that a forecast error could significantly affect the price of NG futures. TOPICS:Statistical methods, simulations, big data/machine learning Key Findings • High-frequency components in the trading data are stronger than expected from a model assuming uniform trading during market hours. • The dominance of the high-frequency components have been increasing over the years. • Relatively small changes in temperature could create a large price fluctuation in natural gas futures contracts.

One Technique to Enhance the Resolution of Discrete Fourier Transform

Discrete Fourier transform (DFT) is a common analysis tool in digital signal processing. This transform is well studied and its shortcomings are known as well. Various window functions (e.g., Hanning, Blackman, Kaiser) are often used to reduce sidelobes and to spread the spectrum. In this paper, we introduce a transformation that allows removing the sidelobes of the Fourier transform and increasing the resolution of the DFT without changing the time sample. The proposed method is based on signal phase analysis. We give the comparison of the proposed approach with known methods based on window functions. The advantages and disadvantages of the proposed technique are explicitly shown. We also give a set of examples illustrating the application of our technique in some practical applications, including engine vibration analysis and a short-range radar system.

Low‐delay and high‐functioning digital differentiators in the big data era

AbstractSince the big data signal has large amount of data, low‐delay signal processing algorithm is required. Digital differentiators are a type of digital signal processing tool, and are useful to estimate the velocity from the position or obtain the image outline. In this article, we introduce finite impulse response (FIR) and infinite impulse response (IIR) digital differentiators. The differentiators can achieve a low‐delay and high‐functioning processing such as low‐pass differentiation.