Programmable Signal Processor Research Articles

Visibilis 433: Sensory Extension and Audiovisual Reinterpretation of 4′33″

Abstract This article introduces Visibilis 433, an audiovisual installation piece inspired by and conceived as an homage to John Cage’s legendary 4′33″. The piece utilizes practically inaudible components of the ambient sound in the gallery to present an audible artificial soundscape and an accompanying virtual landscape in real time, aiming to reinterpret Cage’s original intentions from a new perspective through signal processing and multimedia programming. In addition to introducing notable features of Visibilis 433, the article discusses its significance as a re-mediated synesthetic variation of 4′33″ that extends the limit of our auditory perceptions and crosses the border between audio and visual senses.

Voice as an Indicator for Laryngeal Disorders Using Data Mining Approach

Introduction: Laryngeal disorders are a common health problem that affects people of all ages, genders, and races. One of the main symptoms of laryngeal disorders is changes in the voice, which can be used as an indicator for the presence of such disorders. In this paper, we present a data mining approach for using voice as an indicator for laryngeal disorders.Material and Methods: We collected a dataset of voice recordings from individuals with and without laryngeal disorders including 434 people from two clinical centers in Tehran. The dataset was created using a powerful signal processing program and then based on the difference between male and female voice, the dataset was separated into two datasets. Finally, a Deep Neural Network was implemented for modelling using Python programming language and F1-score, Accuracy, Sensitivity, Specificity, and AUC as the model’s evaluation metrics were reported.Results: Among all the acoustic features, 23 features were selected for the male dataset and 25 features for the female data set. For the male dataset the final model achieved F1-Score of 0.915 and Accuracy of 0.910. For the female dataset the result was 0.884 of F1-Score and 0.896 of Accuracy.Conclusion: Our results show that machine learning algorithms can accurately classify voice recordings into two groups: individuals with laryngeal disorders and those without. The high accuracy achieved by the algorithms suggests that voice can be used as an objective and automated diagnostic tool for laryngeal disorders.

Freeform direct-write and rewritable photonic integrated circuits in phase-change thin films.

Photonic integrated circuits (PICs) with rapid prototyping and reprogramming capabilities promise revolutionary impacts on a plethora of photonic technologies. We report direct-write and rewritable photonic circuits on a low-loss phase-change material (PCM) thin film. Complete end-to-end PICs are directly laser-written in one step without additional fabrication processes, and any part of the circuit can be erased and rewritten, facilitating rapid design modification. We demonstrate the versatility of this technique for diverse applications, including an optical interconnect fabric for reconfigurable networking, a photonic crossbar array for optical computing, and a tunable optical filter for optical signal processing. By combining the programmability of the direct laser writing technique with PCM, our technique unlocks opportunities for programmable photonic networking, computing, and signal processing. Moreover, the rewritable photonic circuits enable rapid prototyping and testing in a convenient and cost-efficient manner, eliminate the need for nanofabrication facilities, and thus promote the proliferation of photonics research and education to a broader community.

24-Hour Monitoring of Intraocular Pressure Fluctuations Using a Contact Lens Sensor: Diagnostic Performance for Glaucoma Progression.

The purpose of this study was to compare 24-hour intraocular pressure (IOP) related fluctuations monitoring between 2 groups of visual field progression rates in patients with open angle glaucoma (OAG). Cross-sectional study performed at Bordeaux University Hospital. Twenty-four-hour monitoring was performed using a contact lens sensor (CLS; Triggerfish; SENSIMED, Etagnières, Switzerland). Progression rate was calculated using a linear regression of the mean deviation (MD) parameter of the visual field test (Octopus; HAAG-STREIT, Switzerland). Patients were allocated into two groups: group 1 with an MD progression rate <-0.5dB/year and group 2 with an MD progression rate ≥-0.5dB/year. An automatic signal-processing program was developed and a frequency filtering of the monitoring by wavelet transform analysis was used to compare the output signal between the two groups. A multivariate classifier was performed for prediction of the faster progression group. Fifty-four eyes of 54 patients were included. The mean progression rate was -1.09 ± 0.60 dB/year in group 1 (n = 22) and -0.12 ± 0.13 dB/year in group 2 (n = 32). Twenty-four-hour magnitude and absolute area under the monitoring curve were significantly higher in group 1 than in group 2 (group 1: 343.1 ± 62.3 millivolts [mVs] and 8.28 ± 2.10 mVs, respectively, group 2: 274.0 ± 75.0 mV and 6.82 ± 2.70 mVs respectively, P < 0.05). Magnitude and area under the wavelet curve for short frequency periods ranging from 60 to 220 minutes were also significantly higher in group 1 (P < 0.05). The 24-hour IOP related fluctuations characteristics, as assessed by a CLS, may act as a risk factor for progression in OAG. In association with other predictive factors of glaucoma progression, the CLS may help adjust treatment strategy earlier.

Synaptic Resistor Circuits Based on Al Oxide and Ti Silicide for Concurrent Learning and Signal Processing in Artificial Intelligence Systems.

Neurobiological circuits containing synapses can process signals while learning concurrently in real time. Before an artificial neural network (ANN) can execute a signal-processing program, it must first be programmed by humans or trained with respect to a large and defined data set during learning processes, resulting in significant latency, high power consumption, and poor adaptability to unpredictable changing environments. Inthiswork, a crossbar circuit of synaptic resistors (synstors) is reported, each synstor integrating a Si channel with an Al oxide memory layer and Ti silicide Schottky contacts. Individual synstors are characterized and analyzed to understand their concurrent signal-processing and learning abilities. Without any prior training, synstor circuits concurrently execute signal processing and learning in real time to fly drones toward a target position in an aerodynamically changing environment faster than human controllers, and with learning speed, performance, power consumption, and adaptability to the environment significantly superior to an ANN running on computers. The synstor circuit provides a path to establish power-efficient intelligent systems with real-time learning and adaptability in the capriciously mutable real world.

Low-loss chip-scale programmable silicon photonic processor

Chip-scale programmable optical signal processors are often used to flexibly manipulate the optical signals for satisfying the demands in various applications, such as lidar, radar, and artificial intelligence. Silicon photonics has unique advantages of ultra-high integration density as well as CMOS compatibility, and thus makes it possible to develop large-scale programmable optical signal processors. The challenge is the high silicon waveguides propagation losses and the high calibration complexity for all tuning elements due to the random phase errors. In this paper, we propose and demonstrate a programmable silicon photonic processor for the first time by introducing low-loss multimode photonic waveguide spirals and low-random-phase-error Mach-Zehnder switches. The present chip-scale programmable silicon photonic processor comprises a 1×4 variable power splitter based on cascaded Mach-Zehnder couplers (MZCs), four Ge/Si photodetectors, four channels of thermally-tunable optical delaylines. Each channel consists of a continuously-tuning phase shifter based on a waveguide spiral with a micro-heater and a digitally-tuning delayline realized with cascaded waveguide-spiral delaylines and MZSs for 5.68 ps time-delay step. Particularly, these waveguide spirals used here are designed to be as wide as 2 µm, enabling an ultralow propagation loss of 0.28 dB/cm. Meanwhile, these MZCs and MZSs are designed with 2-µm-wide arm waveguides, and thus the random phase errors in the MZC/MZS arms are negligible, in which case the calibration for these MZSs/MZCs becomes easy and furthermore the power consumption for compensating the phase errors can be reduced greatly. Finally, this programmable silicon photonic processor is demonstrated successfully to verify a number of distinctively different functionalities, including tunable time-delay, microwave photonic beamforming, arbitrary optical signal filtering, and arbitrary waveform generation.

Procedural Data Processing for Single-Molecule Identification by Nanopore Sensors.

Nanopores are promising single-molecule sensing devices that have been successfully used for DNA sequencing, protein identification, as well as virus/particles detection. It is important to understand and characterize the current pulses collected by nanopore sensors, which imply the associated information of the analytes, including the size, structure, and surface charge. Therefore, a signal processing program, based on the MATLAB platform, was designed to characterize the ionic current signals of nanopore measurements. In a movable data window, the selected current segment was analyzed by the adaptive thresholds and corrected by multi-functions to reduce the noise obstruction of pulse signals. Accordingly, a set of single molecular events was identified, and the abundant information of current signals with the dwell time, amplitude, and current pulse area was exported for quantitative analysis. The program contributes to the efficient and fast processing of nanopore signals with a high signal-to-noise ratio, which promotes the development of the nanopore sensing devices in various fields of diagnosis systems and precision medicine.

The Optimization of Parallel Resonance Circuit for Wear Debris Detection by Adjusting Capacitance

Wear debris in lubrication oil provides important information for marine engine condition monitoring and faults diagnosis. Inductive sensors have been widely used to detect wear debris in lubrication oil. To improve the sensitivity, the inductive coil is always connected with a capacitor in parallel to form parallel LC resonance-sensing circuit. A previous study optimized the parallel resonance circuit by adjusting the excitation frequency. However, multiple parameters (namely, excitation signal, signal detection circuits, and signal-processing program, etc.) need to be adjusted accordingly for a series of the testing frequencies. To simplify the optimization, we propose a method based on adjusting the parallel capacitance in this work. The impedance (inductance and internal resistance) of the sensing coil and its variation induced by particles are first measured, which are the necessary parameters for establishing the function relationship between the parallel capacitance and the relative impedance variation. With the function relationship, the relative impedance variation is calculated directly, and the optimal capacitance is located at the highest absolute value of it. The experimental results for the ferrous and nonferrous particles match the calculation results well. Interestingly, the optimal capacitance for the nonferrous particle was larger than that of the ferrous particle. We speculate that the difference is generated due to the increased resistance induced by the eddy current effect.

Time-frequency analyses applied to sound brought about by continual impacts for time-varying axial stress measurement of round bar

We developed the method to measure a time-varying stress of a round bar by time-frequency analyses. The larger tension we give to a guitar string, the higher tone the string emits, i.e. the string is changing its natural frequency to be higher. The frequency of the string is a function of the tension. When we look at the relation conversely, we can estimate tension by the frequency. We conceived the idea that if we could measure the variation of the frequency, we would be able to evaluate how the tension varied with time. The same condition holds for a round bar. We are able to estimate the time-varying axial stress of the bar by measuring the variation of the natural frequency of the bar. In order to verify of this idea, we conducted the experiment. We applied a sinusoidal stress to the bar. We gave continual impacts to the bar to bring about free vibrations. The vibrations produce sound. The sound provides data to follow the variation of the natural frequency. The procedure which consisted of a short-time Fourier transform application and signal processing programs we developed was applied to the sound. We could obtain the sequence of points which described the relation between the first mode natural frequency of the bar and time. We converted the sequence of the frequency to the sequence of the axial stress employing the relation between the natural frequency and the axial stress of the bar. We compared the stress sequence estimated by the procedure with the stress variation measured by the strain gauge attached to the bar. The sequences by the procedure and the variation by the strain gauge agreed well for various sinusoidal applied stresses for the loading cycle of less than 10 Hz.

Development of a Digital Model of a Beta Function Based on Mellin’s Integral Transforms

The implementation of a digital model of the beta function for use in computer algorithms is a time-consuming task. This is due to the complexity of the high-precision representation of its integrand functions, which require a large number of intermediate operations, which entails a large load on the computational power. Purpose: Development of the basic theoretical provisions of the integral Mellin transform in relation to the theory of signal processing against the background of noise and research of their discrete representation. Results: It is shown in the paper that the beta function can be considered as a special case of Mellin’s integral transforms. Based on this statement, a mathematical model of the beta function was developed. Using the properties of parametrically periodic oscillations belonging to the class of trigonometric-logarithmic functions, it was possible to create a digital model for representing the beta function. Practical relevance: Based on the established digital model can be realized a high-speed algorithm for calculating the beta function with a given accuracy. Such algorithms can serve as a basis for creating signal processing programs in order to detect wideband phase-shift keyed signals against a background of noise with an unknown phase sequence. An example of using such algorithms is the search for Wi-Fi bugs.

Measuring distance using ultra-wideband radio technology enhanced by extreme gradient boosting decision tree (XGBoost)

Measuring distance is critical for safety and quality in construction and operation of engineering structures. This paper proposes a framework to utilize cost-effective and robust ultra-wideband radio technology for wireless sensing of distance, presents a machine learning method based on extreme gradient boosting decision tree, and incorporates error mitigation methods to improve the measurement accuracy. In-situ measurement of distance for a highway bridge in operation was conducted to evaluate the performance of the proposed methods which demonstrated a sub-millimeter accuracy of distance measurement. The proposed methods show desired accuracy, cost-effectiveness, and robustness to the environment, and reveal a tradeoff between the accuracy and frequency of distance measurement. The tradeoff can be used to optimize the sensing system and signal processing program to satisfy the requirements in different applications. This study is expected to advance the capability of measuring distance in various automation processes for construction and operation of engineering structures.

On-Chip Programmable Nonlinear Optical Signal Processor and Its Applications

We introduce an integrated device based on microring resonator (MRR) assisted Mach-Zehnder interferometer (MZI) preceded by a tunable MZ coupler for nonlinear optical signal processing. The novel structure of this device provides both high programmability and switching contrast. These desirable features suggest its potential use as a multi-purpose optical processor providing different high-performance functionalities. We experimentally demonstrate two functionalities using the same device fabricated on a silicon-on-insulator substrate. One functionality is an all-optical thresholder enabling 40× signal contrast improvement, and the other functionality is clock-less pulse carving technique that converts long-pulse signals to short-pulse signals. With these functionalities, we discuss system-level applications of our device in optical interconnects and photonic neural networks.

Multirotors Video Synthetic Aperture Radar: System Development and Signal Processing

360-degree video synthetic aperture radar (VideoSAR) presents a powerful capability for the information perception of dynamic scenes. In comparison to time-consuming airborne SAR, miniature SAR (MiniSAR) mounted on an unmanned aerial vehicle platform is a cost-effective configuration that provides a higher operation flexibility for video acquisition. Nevertheless, MiniSAR system development and signal processing limited by multiple factors are still challenging issues, including restricted weight, volume, power consumption, complicated trajectory imaging, and high-efficient video processing. In this article, we develop a multirotors VideoSAR system at X-band in Nanjing University of Aeronautics and Astronautics, concerning hardware architecture, field experiment, and video signal processing. It has the capability of ultrahigh resolution imaging with three available channels up to 1.8-GHz transmission bandwidth. Field programmable gate array-based unified signal processing architecture can further accelerate the generation of massive VideoSAR sequences in terms of both circular spotlight and stripmap modes. Several experimental results have been presented to demonstrate the validity of our developed system.

Research on Building Gas Pipe Leakage Detection Based on LabVIEW and Multisim

According to the micro energy vibration signal which is generated by the internal gas injection when the building gas pipe leaks, a set of building gas pipe leakage detection and analysis system is designed. The final analysis results of the system can provide the basic analysis data for the timely detection of building gas pipe leakage and the quantitative and qualitative change results caused by leakage. The system selects 801S vibration sensor to extract the vibration signal generated by pipeline leakage, and then amplifies the weak voltage signal converted by vibration signal through signal conditioning circuit. The signal conditioning circuit completes the simulation through Multisim platform and verifies that the expected results meet the requirements. Finally, the signal processing program and detection program based on HHT algorithm are designed and constructed by LabVIEW platform to decompose the data of the conditioned signal and determine whether there is leakage. The results show that the dynamic and static characteristics of the system are fine, and the accuracy is high, especially the effect of the low-frequency and low-pressure (200Hz and below 5V) signal detection is very well.

A 1.5 mW Programmable Acoustic Signal Processor for Hearing Assistive Devices With Speech Intelligibility Enhancement

This paper presents a low-power, programmable acoustic signal processor for hearing assistive devices with speech intelligibility enhancement. The reprogrammable design provides considerable flexibility for the devices to deal with personal conditions of hearing loss. A spectral-change enhancement (SCE) algorithm is implemented to improve speech intelligibility. The power consumption is minimized by adding dedicated hardware accelerators. The short-time objective intelligibility (STOI) measure is utilized for optimizing the datapath architecture. Optimization on the critical MAC operations results in 34% power and area reductions when compared to the direct-mapped design. A 50% reduction in SRAM storage is also achieved owing to the reduced memory storage for the associated MAC operations. With the aid of the optimized MAC unit and data buffer, the overall execution time is reduced by 99.2%. Designed in a 40-nm CMOS technology, the processor integrates 431k gates in area of 0.3 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The design dissipates 1.5 mW at a clock frequency of 10.5 MHz from a 0.7V supply, with a processing latency of 1.05 ms.

Theoretical and Experimental Study and Compensation for Temperature Drifts of Micro Thermal Convective Accelerometer

We present an ambient temperature compensation method for micro thermal convective accelerometers (MTCA) to improve the performance of temperature drift of sensitivity (TDS) and temperature drift of bias (TDB). Based on the theoretical and experimental analysis, we revealed that the temperature drift of sensitivity (sensitivity attenuation) of the MTCA is not only attributed to the changing properties of the working fluids under varying ambient temperature but also resulting from the redistribution of the temperature field within the enclosing chamber of MTCA. Besides, the temperature drift of bias is analyzed, which is proportional to the changing ambient temperature. By means of the newly proposed one-dimensional (1D) model, the sensitivity attenuation function could be easily predicted without time-consuming experiments. Furthermore, with the characterized linear function of temperature drift of bias, and the digital signal processing (DSP) programming (Arduino Yun), the sensitivity compensation error could be controlled within 4% and the temperature drift of bias could be controlled within ±0.05V (sensitivity: 1.4V/g) based on on-site test results. Above all, it is meaningful that this newly designed MTCA is promising to be implemented to ambient temperature changing the environment for precise detection. [2019-0262]

Enhanced Optimal Feature Selection Techniques for Fetal Risk Prediction using Machine Learning Algorithms

Cardiotocography (CTG) records fetal heart rate (FHR) and uterine contractions (UC) simultaneously. The CTG,*which is one of the*most common*diagnostic techniques used during pregnancy and before delivery to evaluate maternal and fetal well-being. Doctors can understand the state of the fetus by observing the*Cardiotocography trace patterns. There are several techniques for interpreting a typical cardiotocography data based on signal processing and computer programming. Only a few decades after cardiotocography has been implemented into clinical*practice, the predictive potential of these approaches remains controversial and still unreliable This paper presents MRMR feature selection algorithms with four classification for Fetal risk prediction using python.

Self-Configuring and Reconfigurable Silicon Photonic Signal Processor

Photonic signal processing is essential in the optical communication and optical computing. Numerous photonic signal processors have been proposed, but most of them exhibit limited reconfigurability and automaticity. A feature of fully automatic implementation and intelligent response is highly desirable for the multipurpose photonic signal processors. Here, we report and experimentally demonstrate a fully self-learning and reconfigurable photonic signal processor based on an optical neural network chip. The proposed photonic signal processor is capable of performing various functions including multichannel optical switching, optical multiple-input-multiple-output descrambler and tunable optical filter. All the functions are achieved by complete self-learning. Our demonstration suggests great potential for chip-scale fully programmable optical signal processing with artificial intelligence.

A TMS320C6000 digitális jelprocesszorok hardveres és szoftveres fejlesztőeszközei

A digitális jelprocesszorok (DSP) gyártása a megjelenésük óta külön iparággá fejlődött. A DSP-k kifejlesztéséhez hozzájárult a digitális módszerek elterjedése a telekommunikációban és ennek következtében az olcsó programozható jelfeldolgozó eszközök iránti kereslet növekedése. A digitális jelprocesszorok fontossága és fejlődése piaci forgalmukkal rendkívül jól jellemezhető. A fejlődés rendkívül gyors volt, mely a hardver mellett a szoftverfejlesztés terén is megfigyelhető volt. A jelprocesszorok folyamatosan bővítik a digitális jelfeldolgozás alkalmazási területeit és lehetőségeit, melyek funkcionalitásából eredően a rendelkezésre álló fejlesztőeszközök is folyamatosan bővülnek. Növekvő fontosságának megfelelő súllyal kell szerepelnie. Sokféle felhasználóbarát integrált fejlesztői környezet áll a felhasználók rendelkezésére alkalmazásfejlesztésre és szimulációra egyaránt, és természetesen már grafikus fejlesztői környezetben is programozhatjuk a jelprocesszorokat. A cikk a TMS320C6000 digitális jelprocesszorok hardveres és szoftveres fejlesztőeszközeit mutatja be.

Fully Reconfigurable Waveguide Bragg Gratings for Programmable Photonic Signal Processing

Fiber Bragg gratings have been invented for over three decades now and have been intensively employed in optical communication and sensor systems. The key limitation of a fiber Bragg grating is that once fabricated, it is hard to be tuned or with very limited tunability via thermal or mechanical tuning. Recently, great efforts have been directed into the study of realizing Bragg gratings on silicon-on-insulator (SOI) platform. The key advantage of Bragg gratings based on SOI is that the gratings can be fast and electrically reconfigured with the use of free-carrier plasma dispersion effect in silicon, which can be employed for fast programmable photonic signal processing. In this article, Bragg gratings based on SOI that are electrically programmable will be reviewed and their use for advanced programmable optical and microwave signal processing will be discussed.