Analog Signal Research Articles

Designing an artificial intelligence-based model for electric motor fault diagnosis to support maintenance decision-making

Motors play a crucial role in production systems. However, not everything always goes smoothly, and motor failures are one of the common challenges in the production process. Misalignment of the drive shaft is a frequent motor fault caused by improper installation or damage to machine components. This study proposes the design of a monitoring and fault diagnosis model for DC motors, which includes: (i) a PID controller for motor speed control; (ii) a vibration signal acquisition unit; and (iii) a motor monitoring unit via Blynk and signal processing for fault diagnosis. In the model, motor faults are classified using a convolutional neural network (CNN) based on analog signals that have been transformed to the frequency domain and denoised. Experimental results demonstrate that classification using the convolutional neural network is highly accurate and stable.

TRENDS OF THE MICROWAVE PHOTONIC RADARS

Today, the world's leading countries are intensively working on the development of new generation radars - microwave photonic radars. Microwave photonic radars make it possible to significantly reduce the mass and size characteristics of radar stations, to increase the information capability and range of target detection due to the reduction of losses in long communication lines when using optical fiber, to ensure high immunity due to the significantly lower sensitivity of optical-electronic equipment and fiber-optic lines of communication connection to external electromagnetic influences. Microwave photonics provides wide bandwidth, flat response, low loss transmission, multi-dimensional multiplexing, ultra-fast analog signal processing and immunity to electromagnetic interference. Radar implementation in the optical domain can provide better resolution, coverage, and speed performance, which would be difficult to implement with traditional electronics. The review article examines the state of development and system architectures of such photonic radars as optoelectronic hybrid radars, all-optical radars, multifunctional microwave photonic radar systems, distributed microwave photonic radars, software-defined radars, and cognitive radars. New technologies in this field and possible future directions of research are discussed. As an example, a broadband microwave photon radar reproduced on the basis of a microcircuit is considered. The broadband signal generator and receiver are built into the silicon crystal on the insulator. A high-precision distance measurement with a resolution of 2.7 cm and an error of less than 2.75 mm was obtained. Visualization of multiple targets with complex profiles has been implemented. But the performance of most integrated microwave photonic chips is not yet satisfactory for practical radar applications. Monolithic integration of key microwave photonic subsystems is also not mature enough for practical applications, so hybrid integration of devices fabricated on their optimal integration platforms is of practical interest. At present, indium phosphide, silicon nitride and silicon on insulator are the three leading platforms for photonic integration

Smartphone-based and non-invasive sleep stage identification system with piezo-capacitive sensors

A non-invasive, wireless, smartphone-based electronic measurement system for sleep stage identification is presented in this work. Ballistocardiograph signals are collected by two piezo-capacitive thin film strips located on the mattress base. Suitable analog conditioning circuits and digital pre-processing techniques are applied to obtain the heart and breathing rates (HR, BR), and an activity index (ACT) related to the body movements during the sleep. An initial calibration stage is proposed where analog signal amplification is fitted to each subject, from which activity index is derived. Features considered for machine learning classifications were the mentioned data and the time variabilities of HR and BR represented by the features R(k) and B(k), respectively. Support Vector Machine (SVM) and K-Nearest-Neighbour (KNN) classifiers are employed in both flat and hierarchical classification scenarios for Wake – Non Rapid Eye Movement – Rapid Eye Movement (WAKE/NREM/REM) sleep stage identification. Twelve healthy subjects were recorded with the developed system using a polysomnograph (PSG) as reference data. When compared with PSG, the presented system achieved an average accuracy of 69 % using only three features: R(k), B(k), and ACT, highlighting an 88.2 % recall for NREM stage identification. These findings suggest that accounting only for time variability features and activity, satisfactory results can be provided as a complementary alternative for sleep stage identification, with a smartphone-based electronic system designed as an affordable, versatile, and simple tool for household applications.

Calibration complex of radio measuring equipment

Modern trends in the development of radio measuring equipment require improvement of the principles of metrological support of these devices and corresponding reference base. The new approach is based on the digitalisation of equipment, programming of measurement procedures, and the transition from single measurement standards to measurement complexes. The paper describes the complex developed at the NSC “Institute of Metrology” and designed for verification and calibration of a wide range of radio measuring equipment, where all these approaches are realised, presents methodological, software and hardware features of its construction, as well as the advantages and possibilities of this approach in metrological practice. On the example of the method of “Bessel Function Zeros”, which was somewhat forgotten due to the cumbersome nature of its implementation by analogue methods and equipment, it is shown that with the new approach this method gets a “second birth”, moreover, at a higher metrological level. It is shown that the use of direct digital synthesis oscillators can significantly improve the “electronic counting frequency meter” method, in particular, prevent the phenomenon of “frequency capture”, which limited the capabilities of the method and introduced additional measurement uncertainty. Moreover, the additional properties of the complex associated with the use of the R&S FSL spectrum analyser with the K7 analogue signal demodulator option are explained in more detail. A list of other methods realised in the complex is given, among which the “combination frequencies” method, deserves a special attention, providing the highest resolution in measuring nonlinear distortions of both quasi-harmonic and modulated signals. It is shown that the complex is a flexible apparatus, the work programme of which can be adjusted and supplemented. Information on the structure of the complex, hardware solution of a number of nodes, programming language, etc. is given.

Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling

Mathematical modeling of epidemic dynamics is crucial to understand its underlying mechanisms, quantify important parameters, and make predictions that facilitate more informed decision-making. There are three major types of models: mechanistic models including the SEIR-type paradigm, alternative data-driven (DD) approaches, and hybrid models that combine mechanistic models with DD approaches. In this paper, we summarize our work in the COVID-19 Scenario Modeling Hub (SMH) for more than 12 rounds since early 2021 for informed decision support. We emphasize the importance of deep learning techniques for epidemic modeling via a flexible DD framework that substantially complements the mechanistic paradigm to evaluate various future epidemic scenarios. We start with a traditional curve-fitting approach to model cumulative COVID-19 based on the underlying SEIR-type mechanisms. Hospitalizations and deaths are modeled as binomial processes of cases and hospitalization, respectively. We further formulate two types of deep learning models based on multivariate long short term memory (LSTM) to address the challenges of more traditional DD models. The first LSTM is structurally similar to the curve fitting approach and assumes that hospitalizations and deaths are binomial processes of cases. Instead of using a predefined exponential curve, LSTM relies on the underlying data to identify the most appropriate functions, and is capable of capturing both long-term and short-term epidemic behaviors. We then relax the assumption of dependent inputs among cases, hospitalizations, and death. Another type of LSTM that handles all input time series as parallel signals, the independent multivariate LSTM, is developed. Independent multivariate LSTM can incorporate a wide range of data sources beyond traditional case-based epidemiological surveillance. The DD framework unleashes its potential in big data era with previously neglected heterogeneous surveillance data sources, such as syndromic, environment, genomic, serologic, infoveillance, and mobility data. DD approaches, especially LSTM, complement and integrate with the mechanistic modeling paradigm, provide a feasible alternative approach to model today’s complex socio-epidemiological systems, and further leverage our ability to explore different scenarios for more informed decision-making during health emergencies.

High-precision flood detection and mapping via multi-temporal SAR change analysis with semantic token-based transformer

Flood detection in crisis and disaster management is significantly facilitated by the analysis of synthetic aperture radar (SAR) imagery. Traditional flood detection techniques focus more on SAR image pairs than on the optical level. However, the distinctive characteristics of SAR images, characterized by limited visual information, pervasive speckle noise, and analogous backscatter signals, present formidable obstacles to accurately identifying water bodies and extracting change features. Consequently, the performance of existing methods remains unsatisfactory. This paper addresses this challenge by focusing on disparities between SAR image pairs and introducing a pioneering semantic token-based transformer network, denoted as SemT-Former, to enhance flood detection accuracy. SemT-Former operates by prioritizing changes of interest rather than fully comprehending the entire image scene. This is achieved through the integration of temporal-wise feature representation and the introduction of a class token to capture high-level segmentation associated with changes in water bodies. These innovations augment the model’s capacity to discriminate between genuine changes in water bodies and spurious changes induced by similar signals or speckle noise. The effectiveness of SemT-Former is evaluated through a case study in Khartoum, Sudan, focusing on flood detection and the estimation of damaged farmland near river confluences. Experimental results demonstrate that SemT-Former outperforms existing methods, exhibiting a 90.6% improvement in F1-score and an 88.5% enhancement in IoU. This underscores SemT-Former as a promising solution for precise and effective flood mapping from SAR images.

Synergistic Effect of Ferroelectric and HfO2/SiO2 Hetero dielectrics in Junctionless FET for Analog and RF Applications

AbstractThe synergistic effect of ferroelectric and HfO2/SiO2 (Hafnium dioxide/ Silicon dioxide) hetero dielectrics in double gate Junctionless Field Effect Transistor is investigated using TCAD Tool. The study encompasses a wide range of parameters, allowing for a detailed examination of the impact of hysteresis on the overall functionality of the double gate Junctionless FET. One crucial aspect of the investigation involves examining the analog and RF performance of the device. The high transconductance (gm) of 5.42 mS, a gain of 95 dB (decibel), a cut‐off frequency of 553.9 GHz (gigahertz), a gain transconductance frequency product (GTFP) of 50.9 THz (terahertz), and a gain bandwidth product (GBW) of 188 GHz shows its great potential for analog and RF signal processing applications as well as for other high data rate wireless applications such as Machine Learning (ML), Artificial Intelligence (AI) and Internet‐of‐Thing (IoT). The low transit time of 17 ns (nano second), with high ION/IOFF ratio range of 107 shows that the device has good switching behavior also. This aspect of the research aims to unveil the device's viability for analog and high‐frequency applications, contributing valuable insights to the emerging electronic technologies field.

On chip broadband and high extinction ratios Mach-Zehnder interferometer for DPSK signal demodulation

Differential phase shift keying (DPSK) signals are widely employed in optical communication systems due to the high sensitivity and simple self-delay interference demodulation scheme. In this paper, we demonstrate a broadband on-chip Mach-Zehnder interferometer (MZI) for multiple channel DPSK signal demodulation. The chip is constructed using a single-mode high-index doped silica glass waveguide with fiber-to-fiber insert loss less than 3 dB and small second-order dispersion. The MZI interference fringe extinction ratio is over 25 dB in the range of C- and L-bands. The MZI is employed to demodulate four parallel DPSK signals simultaneously with similar performance, indicating that the broad bandwidth MZI is suitable for a receiver of massive parallel optical communications.

Stabilizing Analog Signal Processing of Artificial Synapse Under Heat Fluctuations Through Light‐Temperature Antagonistic Operation

Stabilizing Analog Signal Processing of Artificial Synapse Under Heat Fluctuations Through Light‐Temperature Antagonistic Operation

Development of Visual System for Flight Simulation Training Device

The complexity of avionics systems and aircraft equipment is increasing day by day. To operate the avionics equipment, rigorous training is required. The training of pilots to handle these advanced equipment is quite costly. To address this issue a novel flight simulation training device is proposed in this paper. The proposed flight simulator can train the pilot on the ground. The development of visual system for Flight Simulation Training Device (FSTD) is discussed in detail in this paper. The visual system for FSTD is designed using DAQ cards, multifunctional displays, serial communication interface, pilot controls and commercially available off-the-shelf (COTS) controller. This proposed system displays various flight parameters. OpenGL is used to create graphics pages to display flight parameters. DAQ cards were utilizedto connect the analog and digital signals of cockpit to the FSTD’s visual system. A visual system was used in development of actual FSTD by assimilating symbols developed in OpenGL and creating typical pages for simultaneous display on four display surfaces.

High Wideband Digital Oscilloscope Design

In most test applications, acquisition and analysis involving simultaneous processing of analog and digital signals. However, the bandwidth of most mainstream digital oscilloscopes is limited to 100 MHz, which is unable to meet the testing needs of high-frequency signals in complex electronic systems [1], and therefore, high-bandwidth digital oscilloscopes have emerged. Based on this background, this paper designs a digital oscilloscope hardware platform with high bandwidth by integrating FPGA and ARM technologies, aiming to meet the rigorous testing requirements of modern electronic systems. The FPGA module is based on the xc7s75fgga676 chip, which is mainly responsible for ADC control, data processing and frequency measurement functions. AM5708 is selected as the ARM module to realize the trigger, time base, amplitude and automatic setting functions of the oscilloscope. In order to ensure the accuracy and fidelity of waveform changes, the Sinc function interpolation method is used. This design further improves the acquisition bandwidth and processing speed on the basis of traditional MSO (Mixed Signal Oscilloscope) oscilloscopes, which is of great significance for the acquisition and processing of high-speed signals.

Innovative graph-based video processing methodology for collapse early warning of historic masonry buildings

In the present study an innovative video processing methodology is proposed for application to vibration data of historic masonry structures. The proposed methodology is based on graph theory and topology analysis applied to magnified videos in search of effective parameters for early-warning signals before the collapse of structures in case of earthquakes. The proposed method was validated through seismic tests of a brick-masonry mockup representing a vault of the mosque in the Palace of the Dey, Algiers. In particular, the seismic tests were carried out at increasing earthquake intensity up to the final collapse of the mockup. After processing the videos of the seismic tests by motion magnification method, the magnified video frames were transformed into a graph of the structure. Finally, several graph indices were calculated and monitored during the vibration. The monitored parameters were analyzed in search of potential threshold values suitable to generate an early warning signal. In particular, the inverse algebraic connectivity provided an early warning signal in the order of a few seconds before collapse. This was validated by comparison with an analogous signal provided at similar time by an accurate displacement lab measurements system based on optical markers positioned at several points of the tested mock-up.

Method for measuring radiation patterns of digital antenna arrays

Formulation of the problem. The Digital Beamforming Antenna Arrays (DBAA) are widely used as the directional antennas in radio systems today. However, such arrays typically do not have a radiofrequency output. Consequently, the standard methods for measuring the Radiation Patterns (RP), based on the transmitting of a monochromatic probing signals, reception of the signals in the far field, as well as measuring the output power of the antenna array, cannot be used to measure the RP of the DBAA. The method considered in this work involves the transmitting a probing signal modulated by a pseudonoise sequence and processing the DBAA output signal at the zero intermediate frequency (base-band) using a matched filter or a correlator. Target. A method for measuring the radiation pattern of the DBAA is considered, based on the correlation processing of its output signal. Results. The paper presents a description of the setup for measuring the DBAA RP, as well as a mathematical explanation of the measurement procedure. It is shown that the accuracy of the RP measurement depends on the Signal-to-Noise Ratio (SNR) at the outputs of the receivers of the DBAA channels, and the number of array channels and the number of symbols of the pseudonoise sequence used for the modulation of the radiated signal. The error in the RP measurement decreases by 3 dB each time as the number of the DBAA channels or the number of symbols of the pseudonoise sequence used in the measurements is doubled. This result is confirmed by simulation the measurement of RP of the eight- and sixteen-channel linear DBAA using pseudo-noise sequences of maximum length (M-sequences) with a number of symbols equal to 1023 and 2047. Practical significance. The proposed method does not require the usage of the up-converter to convert the base-band DBAA output signal from the base-band to a carrier frequency, and it also does not require the usage of a power meter for measurement the analog signal at the output of this converter.

Magnetic and acoustic sensors in the age of digitalization for industrial production

The challenges of digitalization are giving rise to new generations of measurement technology. This does not necessarily involve the use of new physical principles; rather, the importance of handling and generating valuable data has increased. We present a new measuring system that samples and digitizes analogue signals at 50 MHz with 24-bit conversion. Every 43 µs, a Fourier step is generated for 1,024 data points via an FPGA-based measurement card. The resulting spectral data diagram can then be electronically filtered to improve the signal-to-noise ratio. With this procedure, further possibilities have been created to improve data analysis and data handling, one of which is a parametric pattern recognition, which transfers selectable signals to a library based on the spectral data diagram and searches for them in future data using a similarity algorithm. The use of Python is another building block. With Python, machine learning methods can be transferred from training directly to the measurement system and can run there directly using the extensive library standards. Sensor-fusion is a module that allows analog data to be analyzed via the measurement card, but at the same time also links digital data with analog data by connecting DAQ modules from a third source as virtual ports and transmitting values to shared databases. This article shows the application of the described measurement concept using magneto-inductive sensors and piezoelectric sensors. Magnetic sensors react sensitively to material changes and can reliably determine mechanical parameters such as hardness, residual stresses and grain size. The mentioned software functions enable non-contact measurement of ferromagnetic materials at high throughput speeds. So far limited to laboratory applications, magnetic sensors can also be used safely in harsh industrial conditions. Applications include rapid differentiation of heat treatment conditions, detection of grinding burn and hardness measurements. Acoustic sensors are used in areas that are difficult to access or whose environmental conditions make the use of optical systems in particular difficult. The sensors can mainly detect cracks, frictional behaviour and wear quantities. Applications are in particular straightening processes of hardened steel components, wire drawing, welding, deep drawing and machining. In general, this method can also be used to monitor other sensors and thus raise them to a new level of evaluation technology and data analysis.

Уточнение границ областей уверенного приема телевизионного сигнала в одночастотной сети DVB-T2

One of the ways to clarify the zones of a television signal’s confident reception in single-frequency networks (SFN) DVB-T2 deployed on previously existing infrastructure of analog signal’s television is considered. A simulation model of the SFN broadcasting consist of module of the propagation of radio waves, module of a transmitting station, module of a receiving station and a geoinformation system. It has been revealed that the DVB-T2 broadcasting modes, the effective antenna suspension heights and the effective radiated power of transmitters, which are used in DVB-T2 broadcasting, can lead not only to the appearance of areas of intra-character interference with reduced television signal quality inside the service area, but also to the effect of the so-called "intra-network amplification". The discrepancy between the used protective interval (PI) and the topology of the studied OSS has been established.

Design of the offline test electronics for the nozzle system of proton therapy.

A set of nozzle equipment for proton therapy is currently under development at China Institute of Atomic Energy (CIAE). To facilitate the off-line commissioning of the whole equipment, a set of ionization chamber signal generation system, known as the test electronics, was designed. The results showed that the system can simulate the beam position, beam fluence (which exhibits a positive correlation with the dose), and other related analog signals generated by the proton beam when it traverses the ionization chamber. Moreover, the accuracy of the simulated beam position is within ± 0.33mm, and the accuracy of the simulated beam fluence signal is within ± 1%. The test electronics can output analog signals representing environmental parameters. The test electronics meets the design requirements, which can be used for the commissioning of the nozzle system as well as the treatment control system without the presence of the proton beam.

Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets

Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. ‘Binary’ stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and −1, with the probability of being in either level determined by an external bias. ‘Analog’ stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence ‘analog’) and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer whereby hardware for very different functionalities such as combinatorial optimization and temporal sequence learning can be integrated in the same substrate in the same processing run. This is somewhat reminiscent of heterogeneous integration, except this is integration of functionalities or computational fabrics rather than components. The energy cost of reconfiguration is miniscule. There are also other applications of strain mediated barrier control that do not involve reconfiguring a BSN to an ASN or vice versa, e.g. adaptive annealing in energy minimization computing (Boltzmann or Ising machines), emulating memory hierarchy in a dynamically reconfigurable fashion, and control over belief uncertainty in analog stochastic neurons. Here, we present a study of strain engineered barrier control in unconventional computing.

Electronically Tunable Grounded and Floating Capacitance Multipliers Using a Single Active Element

A capacitance multiplier is an active circuit designed specifically to increase the capacitance of a passive capacitor to a significantly higher capacitance level. In this paper, the use of a voltage differencing differential difference amplifier (VDDDA), an electronically controllable active device for designing grounded and floating capacitance multipliers, is proposed. The capacitance multipliers proposed in this study are extremely simple and consist of a VDDDA, a resistor, and a capacitor. The multiplication factor (Kc) can be electronically controlled by adjusting the external bias current (IB). It offers an easy way of controlling it by utilizing a microcontroller for modern analog signal processing systems. The multiplication factor has the potential to be adjusted to a value that is either less than or greater than one, hence widening the variety of uses. The grounded capacitance multiplier can be easily transformed into a floating one by utilizing Zc-VDDDA. PSpice simulation and experimentation with a VDDDA realized from commercially available integrated circuits were used to test the performance of the proposed capacitance multipliers. The multiplication factor is electronically adjustable, ranging in approximation from 0.56 to 13.94. The operating frequency range is approximately three frequency decades. The realization of the lagging and leading phase shifters using the proposed capacitance multiplier is also examined and proven. The results reveal that the lagging and leading phase shifts are electronically tuned via the multiplication factor of the proposed capacitance multipliers.

High-speed continuous X-ray imaging method based on SiPM auto-encoding detector

High-speed X-ray imaging can be used to observe the internal evolution of objects in the microsecond range. However, obtaining data and processing images continuously are difficult when the speed of X-ray imaging is more than one million frames per second (fps). The novel method of high-speed X-ray imaging, which is based on a SiPM auto-encoding detector, was proposed. The different response to X-ray is realized by adjusting the bias voltage of different pixels to encode the analog signals. The analog signals from multiple SiPM pixels are simplified into a single signal. In this way, SiPM is constructed as the SiPM auto-encoding detector, which greatly reduces the amount of analog signals to be sampled for each frame of X-ray image, and then the image is reconstructed in the upper computer. The feasibility of the imaging method was verified by coupling the pixelated SiPM and lutetium-ytterbium scintillation crystals to construct a SiPM auto-encoding detector. Six patterns were exposed when pulsed X-rays were performed at frequencies of 100 and 500 kHz. Then, the number of image pixels was compressed from 16 to 4. The peak signal-to-noise ratio of the reconstructed images was above 41, and the structural similarity was above 0.99. Experimental results demonstrated that the speed of continuous X-ray imaging reached 500, 000 fps for six kinds of 4 × 4 pixel patterns. This method can be applied to improve the speed of high-speed continuous X-ray imaging.

A New CMOS FTFNDITA Implementation and Its Application

ABSTRACT This research article presents a new design of a CMOS analog building block, termed a Four Terminal Floating Nullor Differential Input Transconductance Amplifier (FTFNDITA). This new analog building block can operate up to the MHz range and offers low-power dissipation circuitry. The proposed FTFNDITA works with ± 1.65 V power supply. Moreover, a ICs-based FTFNDITA is also developed using an off-self commercially available Current Feedback Operational Amplifier (CFOA) as IC AD844 and Operational Transconductance Amplifier (OTA) as IC LM13700 or IC CA3080. To extend the application perspective of the proposed FTFNDITA blocks, a few fundamental analog signal processing circuits like the design of Floating Inductor Simulator (FIS), First-Order Low-Pass Filter (FLPF), Floating Capacitance Multiplier (FCM), and its extension for active filter design, are incorporated. In addition, a non-ideality investigation is also performed to test the deviation between the ideal and practical design. Finally, PSPICE and experimental verifications are observed to validate the functionality of FTFNDITA.