Analog Components Research Articles

РОБОТОТЕХНІЧНА СИСТЕМА З ОДНОТИПНИМИ ЧАСТОТНО-ЗАЛЕЖНИМИ КОМПОНЕНТАМИ У ТРАКТІ ОБРОБКИ СИГНАЛІВ ДАТЧИКІВ

Abstract. The issue of building a sensor signal processing path on the same type of components in computer robotic systems, which can comprehensively rebuild their characteristics by software and hardware means depending on the operating conditions to increase the system efficiency, is considered. Different types of connections of analog and digital second-order frequency-dependent components are considered, since a high-order processing path is easier to form from the same type of low-order components. This is due to the low cost of calculating the coefficients of the transfer function and the number of coefficients, ease of adjustment or reconstruction, moderate power consumption and processing time. It is shown that a serial connection is best suited for the sensor signal processing path, since signal distortion occurs in other connections. To increase the order of the processing path, it is suggested to use a serial connection of the same type of frequency-dependent components of the second order. Obtained ratios that allow to calculate the frequency of the main typical component based on the required frequency of several components connected in series. These ratios are very difficult to implement in robotic systems, as they do not have large computing resources. Since the range of frequencies and the number of components are limited, it is recommended to calculate the necessary frequencies in advance and summarize them in the table. When working in real time, it is advisable to use these tables for rebuilding. When connecting analog and digital basic filters of the same type in series, it is advisable to use the main filters of the second order of Butterworth and Chebyshev of the second kind with flat frequency response in the passband, which will ensure a good increase in the steepness of the frequency response decline and make it possible to predict the value of the cutoff frequency for the low-pass filter. And for bandpass filters, only Butterworth filters. Keywords: Industry 4.0-5.0, robotic systems, frequency-dependent components, component connection types, series connection, main typical component, amplitude-frequency characteristic.

Analog/digital circuit simplification for Hopfield neural network

Circuit realization of neural networks is a significant approach in neuromorphic computing. Researchers have simplified the circuit for single neuron model, but one for neural network is rarely reported, yet. This paper presents a ReLU-type Hopfield neural network (RHNN) model using simple ReLU function instead of traditional hyperbolic-type function as the activation function. RHNN with three neurons is focused on. Its boundedness and stability are confirmed theoretically, and complex chaotic dynamics are simulated numerically. Further, the printed circuit board (PCB)-based analog circuit is fabricated, and the RHNN circuit has 49.3 % fewer analog components than the Tanh-type HNN circuit. Meanwhile, with logical shift method, an efficient low-cost multiplierless digital circuit is developed on field-programmable gate array (FPGA) platform. Experimental results manifest that resource consumptions of one are less than that of IP core-based digital implementation. Particularly, the RHNN model is well applied to image encryption for satisfying requirements on transmission security.

Highly-dependable printed neuromorphic circuits based on additive manufacturing

The rapid development of emerging domains, such as the Internet of Things and wearable technologies, necessitates the development of flexible, stretchable, and non-toxic devices that can be manufactured at an ultra-low cost. Printed electronics has emerged as a viable solution by offering not only the aforementioned features but also a high degree of customization, which enables the personalization of products and facilitates the low-cost product development process even in small batches. In the context of printed electronics, printed neuromorphic circuits offer highly customized and bespoke realization of artificial neural networks to achieve desired functionality with very small number of hardware components. However, since analog components are utilized, the performance of printed neuromorphic circuits can be influenced by various factors. In this work, we focus on three main factors that perturb the circuit output from the designed values, namely, variations due to printing errors, aging effects of printed resistors, and input variations originating from sensing uncertainty. In the described approach, these variations are taken into account during the design (training) to ensure the dependability of the printed neuromorphic circuits. With this approach, the expected accuracy and the robustness of printed neural networks can be increased by 27% and 74%, respectively. Moreover, the ablation study suggests that, aging effect and printing variation may have similar effects on the functionality of printed neural networks. In contrast, the impact of sensing uncertainty on printed neural networks is almost orthogonal to aging and printing variations.

Interpretable machine learning in VLSI physical design

Today's popularisation of portable devices largely depends on the progress in integrated circuits. Modern Very Large Scale Integration technology (VLSI) allows billions of transistors to be packed into the same chip. In the past years, digital design in VLSI has been developed compared to analogue design. The traditional method is hard to model the performance change in analogue or mixed-signal components caused by physical design. In the early 2000s, rapid advances in machine learning and computing power made analogue design automation possible. Despite their outstanding performance, the transparency issue has become significant. This paper introduces the history of VLSI physical design, which includes placement and routing in the early stages. The change that machine learning (ML) has made is mentioned in the third section. Analysis of the potential problem has been proposed, followed by a brief category of some well-known work in interpretable Machine Learning, which could be the primary direction for VLSI automation to be further popularised in the future.

Biomimetic synthesis of hydroxytyrosol from conversion of tyrosol by mimicking tyrosine hydroxylase

Hydroxytyrosol, one of the most powerful natural antioxidants, exhibits certificated benefits for human health. In this study, a biomimetic approach to synthesize hydroxytyrosol from the hydroxylation of tyrosol was established. EDTA-Fe2+ coordination complex served as an active center to simulate tyrosine hydroxylase. H2O2 and ascorbic acid were used as oxygen donor and hydrogen donor, respectively. Hydroxy radical and singlet oxygen contributed to active species. The biomimetic system displayed analogous component, structure, and activity with TyrH. Hydroxytyrosol titer of 21.59mM, and productivity of 9985.92mg·L-1·h-1 was achieved with 100mM tyrosol as substrate. The proposed approach provided efficient and convenient route to quickly produce high amount of hydroxytyrosol.

Endoscopic iliopsoas lengthening for treatment of recalcitrant iliopsoas tendinitis after total hip arthroplasty.

Iliopsoas (IP) tendinitis from impingement upon the acetabular component after total hip arthroplasty (THA) has been treated with open and endoscopic IP tenotomy or acetabular component revision. This study describes the results of a consecutive series of patients treated with endoscopic IP tenotomy as a less invasive alternative. Twenty-eight patients with IP impingement after THA underwent endoscopic IP lengthening from 2012 to 2021 at a single-center academic institution. The follow-up of 24 of these patients was achieved with a mean follow-up of 7.6 months (range 1-28). Outcomes included the modified Harris Hip Score (mHHS), visual analog pain scale (VAS), satisfaction, component positioning and complications. Seventy-one percent of patients were satisfied or very satisfied after their operation. The median mHHS preoperatively was 57 (Interquartile range [IQR] 43-60) and postoperatively was 75 (IQR 66-92, P < 0.001). Clinically meaningful improvements in mHHS were seen in patients with VAS pain scores <5, cup prominence >8 mm, body mass index >30, and less than 2 years from their index THA. Two patients developed a deep infection 7 and 10 months postoperatively (neither related to the release), and one patient underwent open psoas release for persistent impingement. Endoscopic IP tenotomy is a safe and effective treatment for impingement after THA. Patients with cup prominence >8 mm, body mass index >30 and less than 2 years since their index THA may have more clinically meaningful improvements in pain and function.

Time-Interleaving Sigma–Delta Modulator-Based Digital-to-Analog Converter With Time Multiplexing in the Analog Domain

Sigma-delta modulator based DACs are simple circuits with low accuracy requirements in their analog components. However, their signal bandwidth is limited by speed constrains. Time-Interleaving allows the designer to trade-off between complexity and speed by replacing the original architecture by a number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> of parallel paths clocked at a frequency <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> times smaller. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> digital outputs of the low-rate parallel paths are time multiplexed in a unique path at the high rate and converted to analog by means of a DAC. Unfortunately, this DAC must be also clocked at the high rate, it imposes severe restrictions on performances, and can limit the maximum frequency of operation. In this brief, the time multiplexing is performed in the analog domain by means of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> DACs clocked at the low rate. The resulting architecture is more robust against dynamic effects. The main limitation introduced by the proposed architecture comes from mismatch in the DACs’ gain, which can drastically impact performances. A new technique of dynamic elements matching is proposed here to overcome this problem. The proposed architecture benefits from the advantages of the return-to-zero pulses but, unlike a conventional DAC, maintains the robustness against clock jitter.

Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants

• A comprehensive quantitative evaluation of common cause failures is performed for safety-critical digital control systems; • Both hardware and software common cause failures are tracked and identified; • Failure probabilities of digital control systems are estimated via a multiscale reliability analysis approach. • The impact of common cause failures to plant safety is evaluated. Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. This paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework's capabilities is documented in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety.

Security Analysis of IoT Devices: From the system level to the logic level

IoT devices have become more and more widespread and are playing an important role in the evolving digital society. At the same time, IoT nodes are embedded devices that are always online, often making them interesting targets for potential attackers. Since many IoT applications are closely linked to humans, e. g., medical implants, smart appliances and speakers, and (autonomous) cars, it is of crucial importance that they are trusted to provide safety. Trust in embedded devices is therefore a challenge that concerns industry and policy makers alike. As engineers know, today’s embedded systems are often very complex, consisting of software and firmware, PCBs with digital and analog components and, of course, very complex ICs, which include CPUs that make a device “smart.”

A Review of Recent Developments on Secure Authentication Using RF Fingerprints Techniques

The Internet of Things (IoT) concept is widely used today. As IoT becomes more widely adopted, the number of devices communicating wirelessly (using various communication standards) grows. Due to resource constraints, customized security measures are not possible on IoT devices. As a result, security is becoming increasingly important in IoT. It is proposed in this study to use the physical layer properties of wireless signals as an effective method of increasing IoT security. According to the literature, radio frequency (RF) fingerprinting (RFF) techniques are used as an additional layer of security for wireless devices. To prevent spoofing or spoofing attacks, unique fingerprints appear to be used to identify wireless devices for security purposes (due to manufacturing defects in the devices' analog components). To overcome the difficulties in RFF, different parts of the transmitted signals (transient/preamble/steady-state) are used.&#x0D; This review provides an overview of the most recent RFF technique developments. It discusses various solution methods as well as the challenges that researchers face when developing effective RFFs. It takes a step towards the discovery of the wireless world in this context by drawing attention to the existence of software-defined radios (SDR) for signal capture. It also demonstrates how and what features can be extracted from captured RF signals from various wireless communication devices. All of these approaches' methodologies, classification algorithms, and feature classification are explained. &#x0D; In addition, this study discusses how deep learning, neural networks, and machine learning algorithms, in addition to traditional classifiers, can be used. Furthermore, the review gives researchers easy access to sample datasets in this field.

Design and Development of an FPGA Board for Software-Defined Radio Research

This paper describes the design and development of a cost-affordable yet powerful software-defined radio system. The main application environment of the board is in academia where it is used to deliver a multidisciplinary approach in a number of fundamental engineering topics in communications. A moderately fast ADC together with an audio DAC are used for the analog components while an off-the-shelf FPGA is used as the digital processing element. To demonstrate the boards capabilities, hardware blocks to demodulate amplitude modulation and digital modulation were developed.

Widely-Linear Digital Self-Interference Cancellation in Full-Duplex USRP Transceiver.

Full-duplex (FD) communication systems allow for increased spectral efficiency but require effective self-interference cancellation (SIC) techniques to enable the proper reception of the signal of interest. The underlying idea of digital SIC is to estimate the self-interference (SI) channel based on the received signal and the known transmitted waveform. This is a challenging task since the SI channel involves, especially for mass-market FD transceivers, many nonlinear distortions produced by the impairments of the analog components from the receiving and transmitting chains. Hence, this paper first analyzes the power of the SI components under practical conditions and focuses on the most significant one, which is proven to be produced by the I/Q mixer imbalance. Then, a widely-linear digital SIC approach is adopted, which simultaneously deals with the direct SI and its image component caused by the I/Q imbalance. Finally, the performances achieved by linear and widely-linear SIC approaches are evaluated and compared using an experimental FD platform relying on software-defined radio technology and GNU Radio. Moreover, the considered experimental framework allows us to set different image rejection ratios for the transmission path I/Q mixer and to study its influence on the SIC capability of the discussed approaches.

Robust Distributed Hybrid Beamforming in Coordinated Multi-User Multi-Cell mmWave MIMO Systems Relying on Imperfect CSI

Novel hybrid beamformer designs are conceived for a multi-user multi-cell (MUMC) mmWave system relying on base station (BS) coordination and total transmit power minimization subject to realistic signal-to-interference-plus-noise ratio (SINR) constraints at each mobile station (MS). Initially, a semidefinite relaxation (SDR)-based approach is developed for a centralized MUMC system to determine the fully digital beamformer having perfect CSI. Subsequently, a Bayesian learning (BL) technique is harnessed for decomposing the fully-digital (FD) solution into its analog and digital components for constructing a hybrid transceiver. Next, an alternating direction method of multipliers (ADMM) based distributed hybrid beamformer is designed for the same system, which requires only local CSI and limited information exchange among the BSs, thus avoiding the excessive signalling overheads required by the centralized approach. Then we further extend both the centralized and the above distributed hybrid designs to construct robust beamformers that minimize the worst-case transmit power with imperfect CSI. Our robust beamforming techniques leverage the S-lemma, which is eminently suitable for the infinitely many constraints arising from the associated CSI uncertainty. Finally, our simulation results demonstrate the improved performance of the proposed centralized and distributed methods over the system having no coordination.

A Modulated Wideband Converter Model Based on Linear Algebra and Its Application to Fast Calibration.

In the context of cognitive radio, smart cities and Internet-of-Things, the need for advanced radio spectrum monitoring becomes crucial. However, surveillance of a wide frequency band without using extremely expensive high sampling rate devices is a challenging task. The recent development of compressed sampling approaches offers a promising solution to these problems. In this context, the Modulated Wideband Converter (MWC), a blind sub-Nyquist sampling system, is probably the most realistic approach and was successfully validated in real-world conditions. The MWC can be realized with existing analog components, and there exist calibration methods that are able to integrate the imperfections of the mixers, filters and ADCs, hence allowing its use in the real world. The MWC underlying model is based on signal processing concepts such as filtering, modulation, Fourier series decomposition, oversampling and undersampling, spectrum aliasing, and so on, as well as in-flow data processing. In this paper, we develop an MWC model that is entirely based on linear algebra, matrix theory and block processing. We show that this approach has many interests: straightforward translation of mathematical equations into simple and efficient software programming, suppression of some constraints of the initial model, and providing a basis for the development of an extremely fast system calibration method. With a typical MWC acquisition device, we obtained a speed-up of the calibration computation time by a factor greater than 20 compared with a previous implementation.

Statistical Digital Predistortion of 5G Millimeter-Wave RF Beamforming Transmitter Under Random Amplitude Variations

The nonlinearity of the fifth-generation (5G) millimeter-wave (mmWave) phased array transmitter (TX) depends on the variations in the beamforming coefficients, e.g., due to unwanted amplitude errors of the phase shifters or purposely introduced amplitude variations to shape the beam, as well as other analog component variations. Therefore, also the digital predistortion (DPD) coefficients depend on the beamforming coefficients and require a continuous update for different beamsteering directions in a conventional DPD. We propose a robust DPD approach that uses the average array response for DPD training. The average array response is estimated using the known experimental histogram of power amplifier (PA) input power variation resulting from the amplitude variations due to beamforming. The DPD training based on the average array response makes the DPD coefficients insensitive to beamforming variations. The proposed DPD strategy requires a shared feedback path for training and only a single set of DPD coefficients to linearize the array response in all beamsteering directions. The performance of the proposed DPD strategy is validated by over-the-air (OTA) measurements of a 28-GHz phased array TX over different steering angles. Experimental results show that the proposed DPD method using one set of DPD coefficients provides similar linearization performance across the steering angles compared with the DPD trained to all steering angles through the OTA reference antenna.

Fast-Tracking Optical Coherent Receiver Tolerating Transmitter Component Distortion

In this paper, we propose a receiver architecture capable of compensating for a time-varying signal distortion induced by the transmitter analog components. In the proposed architecture, the novel transmitter distortion compensating (TDC) block is implemented outside of the feedback control loop of the adaptive equalizer to suppress the feedback delay by optimizing the parameter used in the least mean square (LMS) algorithm. The proposed TDC block is designed for compensating for the DC offset, the IQ amplitude imbalance, the IQ orthogonality error, and the IQ timing skew. Our simulation results show that the proposed scheme significantly reduces the required signal-to-noise ratio penalty from the theoretical limit, which is imposed due to the transmitter components distortion. Furthermore, our theoretical analysis confirms that the delay induced in the feedback loop of the adaptive equalizer determines the upper bound of the LMS step size under the stable condition, hence allowing us to maximize the tracking speed of our receiver.

Digital Regulation of Wireless Power Transfer Systems Using an Embedded Lock-In Amplifier

High-performance operation of inductive wireless power transfer (WPT) systems requires judicious tuning of the inverter connected to the transmission hardware for high-performance operation and reliability. In many applications, disturbances to both the coupling condition and the secondary-side load reshape the system dynamics, motivating the design of closed-loop regulation and impedance detection techniques. Although several analog methods have been presented, they require multiple additional components and are dependent upon fixed component values and operating frequency. In this article, a novel digital approach is presented for use in series–series compensated WPT systems. The proposed system extracts both the phase and the magnitude of the primary current in real time. Through closed-loop regulation, zero-voltage switching is sustained without additional compensation components, while the output current is controlled. By utilizing a digital framework, no additional analog components are required, improving system reliability and cost. The state identification method is self-tuning, providing robust suppression of switching noise even with a variable switching frequency. A computationally efficient construction ensures a fast converging measurement and enables high-frequency operation. Analysis, simulations, and experimental results are included to comprehensively verify these characteristics.

On target selection as reflected by posterior ERP components in feature-guided visual search.

The N2pc event-related potential is a widely studied ERP component that reflects the covert deployment of visuo-spatial attention to target stimuli displayed laterally relative to fixation. Recently, an analogous ERP component, named N2pcb, has been proposed as a marker of the deployment of visuo-spatial attention to targets displayed on the vertical midline. Two studies that investigated the N2pcb component found analogous results, using however two different algorithms to compute the amplitude of N2pcb. One study subtracted the ipsilateral activity elicited by a lateral target from the bilateral activity elicited by a target displayed on the vertical midline, whereas the other study subtracted the bilateral activity elicited by target-absent displays from the bilateral activity elicited by a target displayed on the vertical midline. Here we show both algorithms estimate properly the N2pc as well as the N2pcb components. In addition, we explored whether the singleton detection positivity (SDP) component, a posterior bilateral positivity temporally concomitant to N2pc recently reported in studies using singleton search, could be observed in the present study in which a target was defined by a combination of features. Given that such component was indeed found using feature search, we named this component posterior processing positivity (PPP), and showed that bilateral activity elicited by target-absent displays is an adequate baseline for its correct isolation.

Analysis of Verbal Language from the Theory of Human Movement as a Complex System

The objective of this manuscript is to carry out a conceptual analytical review of verbal language from the theory of Human Body Movement as a Complex System (HMCS). This theory is proposed by the Body-Movement Research Group from the Autonomous University of Manizales, Colombia. This model assumes that language production is essentially an embodied motor function. The analysis here presented involves postulates of both structural and functional theories of language, as well as cognitive linguistics, since the HMCS theory encompasses these multiple possibilities of approach. It is this conjecture of a holistic vision of language from its different approaches that leads to the proposition that, in effect, the different levels of linguistic analysis find several analogous components in the HMCS model. A conceptual analytical review is carried out, and structured in two main components. In the first part, the HMCS theory is exposed with its three levels of interaction: the objectified aspects of movement, the potentiality of movement and the external context of human movement. In the second part, an analysis of verbal language is carried out from this conceptual framework, also analyzed from the three levels of interaction: the objectified aspects of verbal language as a motor expression, the potentiality of verbal language and the context in the production of verbal language. Definitions, concepts, interactions and analogies are presented during the development of the article. In conclusion, body movement is language and language is, in essence, human movement. Both are intricate, integrated, indivisible, and inherently constitutive aspects of the same cognitive processes. The analogy achieved highlights the theory of the shared resource between motor control and cognitive control. In the same way that verbal language allows communication based on communicative intentions, body movement allows the exchange of information, constituting a bridge between cognition and the world.

Complementary genomic and epigenomic adaptation to environmental heterogeneity.

While adaptation is commonly thought to result from selection on DNA sequence-based variation, recent studies have highlighted an analogous epigenetic component as well. However, the relative roles of these mechanisms in facilitating population persistence under environmental heterogeneity remain unclear. To address the underlying genetic and epigenetic mechanisms and their relationship during environmental adaptation, we screened the genomes and epigenomes of nine global populations of a predominately sessile marine invasive tunicate, Botryllus schlosseri. We detected clear population differentiation at the genetic and epigenetic levels. Patterns of genetic and epigenetic structure were significantly influenced by local environmental variables. Among these variables, minimum annual sea surface temperature was identified as the top explanatory variable for both genetic and epigenetic variation. However, patterns of population structure driven by genetic and epigenetic variation were somewhat distinct, suggesting possible autonomy of epigenetic variation. We found both shared and specific genes and biological pathways among genetic and epigenetic loci associated with environmental factors, consistent with complementary and independent contributions of genetic and epigenetic variation to environmental adaptation in this system. Collectively, these mechanisms may facilitate population persistence under environmental change and sustain successful invasions across novel environments.