Signal Processing Functions Research Articles

Reconfigurable Homojunction Phototransistor for Near-Zero Power Consumption Artificial Biomimetic Retina Function.

Semiconductor photodetectors integrating preliminary signal-processing functions play a vital role in artificial biomimetic retina systems. Herein, we propose a tungsten diselenide (WSe2) phototransistor with a dual-layer gate dielectric and an asymmetric graphene insert structure. This phototransistor exhibits a bidirectional self-powered photocurrent by controlling the gate voltage via the formation of reconfigurable p+-p and n-p homojunctions in the channel from the asymmetric graphene insert. At the same time, the nonvolatile electron and hole stored in the dual-layer gate dielectric are generated using a temporary gate voltage, which can replace the gate voltage to regulate the channel charge. Moreover, the photocurrent shows a linear relation with the temporary programming gate voltage. The phototransistor exhibits a rectification ratio of >4 orders of magnitude without the gate voltage, indicating its significant capability to operate in a fully self-powered mode with near-zero power consumption. Based on the device characteristics, we successfully simulate the biological functions of the photoreceptor layer and bipolar cell layer in the retinal receptive field. The identification of the object motion direction in the receptive field can be realized by integrating three programmable devices on the chip. Furthermore, edge enhancement of the image is achieved by independently modulating the light response of each pixel in the sensor by varying the programming gate voltage. This study will promote the developing progress of future artificial biomimetic retina systems.

Wearable and Multiplexed Biosensors based on Oxide Field-Effect Transistors.

Wearable sensors designed for continuous, non-invasive monitoring of physicochemical signals are important for portable healthcare. Oxide field-effect transistor (FET)-type biosensors provide high sensitivity and scalability. However, they face challenges in mechanical flexibility, multiplexed sensing of different modules, and the absence of integrated on-site signal processing and wireless transmission functionalities for wearable sensing. In this work, a fully integrated wearable oxide FET-based biosensor array is developed to facilitate the multiplexed and simultaneous measurement of ion concentrations (H+, Na+, K+) and temperature. The FET-sensor array is achieved by utilizing a solution-processed ultrathin (≈6nm thick) In2O3 active channel layer, exhibiting high compatibility with standard semiconductor technology, good mechanical flexibility, high uniformity, and low operational voltage of 0.005V. This work provides an effective method to enable oxide FET-based biosensors for the fusion of multiplexed physicochemical information and wearable health monitoring applications.

A Basic Tutorial on Novelty and Activation Functions for Music Signal Processing

A Basic Tutorial on Novelty and Activation Functions for Music Signal Processing

A microfluidic patch for wireless wearable electrochemical detection of sweat metabolites

Wearable sweat sensors provide an innovative detection method for analyzing human physiological and biochemical parameters and play an important role in detecting the potential risks of chronic diseases. Existing commercial wearable sensors (Apple iWatch, HUAWEI smart band) have problems such as single detection index (heart rate or blood pressure), and mainly focus on the physical information. This work studied an integrated microfluidic patch to simultaneously detect several biomarkers (pH, sodium ions, and uric acid) in sweat. We achieved the detection of pH level, sodium, and uric acid in linear ranges of 4–8, 0–160 mM, and 5–160 μM, respectively, covering well the clinically relevant ranges in the healthy human sweat. Laser engraving was chosen to prepare a microfluidic layer, and the sweat collection function was realized by combining hydrophilic and hydrophobic microfluidic synergy. It could efficiently collect the required sweat sample volume (80 μL) within 30 s while avoiding the contamination caused by directly contact with the skin. In addition, a wireless signal acquisition and transmission system was designed with the signal reading, processing, and wireless transmission functions, which can display the testing results in real time on Android phone-specific application software. We combined the wireless system with a microfluidic patch for on-body testing to verify the good wearability of the sweat sensor. This research realized the wireless data processing and transmission while improving sweat collection efficiency. In addition, it could detect various sweat physiological components in real-time, and provides a multiparameter, real-time, and portable sensing platform.

Performance Analysis of Microwave Photonic Spectral Filters based on Optical Microcombs

AbstractMicrowave transversal filters, which are implemented based on the transversal filter structure in digital signal processing, offer a high reconfigurability for achieving a variety of signal processing functions without changing hardware. When implemented using microwave photonic (MWP) technologies, also known as MWP transversal filters, they provide competitive advantages over their electrical counterparts, such as large operation bandwidth, strong immunity to electromagnetic interference, and low loss when processing signals at high frequencies. Recent advances in high‐performance optical microcombs provide compact and powerful multi‐wavelength sources for MWP transversal filters that require a larger number of wavelength channels to achieve high performance, allowing for the demonstration of a diverse range of filter functions with improved performance and new features. Here, a comprehensive performance analysis for microcomb‐based MWP spectral filters based on the transversal filter approach is presented. First, the theoretical limitations are investigated in the filter spectral response induced by finite tap numbers. Next, the distortions are analyzed in the filter spectral response resulting from experimental error sources. Finally, the influence of input signal's bandwidth on the filtering errors is assessed. These results provide a valuable guide for the design and optimization of microcomb‐based MWP transversal filters for a variety of applications.

Synthesis of an electronic control module for a selected technological node with a focus on its automation

The article presents problems related to the design of sequential control systems using algorithmic design method. Based on a graph describing the functions of signal processing, the method of fast programming of sequential electro-pneumatic systems and systems with logic elements is presented. The developed sequential system was verified through simulation using the FluidSim computer-aided design software from Festo.

Hybrid Radix-16 booth encoding and rounding-based approximate Karatsuba multiplier for fast Fourier transform computation in biomedical signal processing application

Multiplication is an essential biomedical signal processing function implemented in the Digital Signal Processing (DSP) cores. To enhance the speed, area and energy efficiency of DSP cores, approximate multiplication is used. Also, low power multiplier unit design is one of the requirements of DSP processor to meet the increasing demands. To balance both the design and error metrics of a multiplier design, an efficient Hybrid Radix-16 Booth Encoding and rounding-based approximate Karatsuba Multiplier (RBEKM-16) is proposed. This research introduces an Approximate Karatsuba multiplier based on rounding, utilizing rounding approximation to compute the least significant part of the product. Simple operators, like adders and multiplexers, replace complex and costly conventional Floating-Point (FP) multipliers in this process. Radix-4 logarithms are incorporated to further minimize hardware complexity and calculate the product's most significant part. Subsequently, an approximate 4-2 compressor is applied in the partial product reduction stage to generate the most significant bit result. In the experimental scenario, the efficiency of the multiplier is evaluated in terms of energy efficiency, area utilization and error rate by using Xilinx ISE 8.1i tool. The results from the experiments indicate that the suggested multiplier demonstrates improved energy efficiency, utilizes space more effectively, and performs well in applications related to biomedical signal processing. Further, the accomplished area utilization of the proposed 16-bit multiplier is 1068 μm2, delay is 3.01 ns, power consumption is 0.021 mW and power delay product is 119 fJ.

Engineering topological interface states in metal-wire waveguides for broadband terahertz signal processing.

Innovative terahertz waveguides are in high demand to serve as a versatile platform for transporting and manipulating terahertz signals for the full deployment of future six-generation (6G) communication systems. Metal-wire waveguides have emerged as promising candidates, offering the crucial advantage of sustaining low-loss and low-dispersion propagation of broadband terahertz pulses. Recent advances have opened up new avenues for implementing signal-processing functionalities within metal-wire waveguides by directly engraving grooves along the wire surfaces. However, the challenge remains to design novel groove structures to unlock unprecedented signal-processing functionalities. In this study, we report a plasmonic signal processor by engineering topological interface states within a terahertz two-wire waveguide. We construct the interface by connecting two multiscale groove structures with distinct topological invariants, i.e., featuring a π-shift difference in the Zak phases. The existence of this topological interface within the waveguide is experimentally validated by investigating the transmission spectrum, revealing a prominent transmission peak in the center of the topological bandgap. Remarkably, we show that this resonance is highly robust against structural disorders, and its quality factor can be flexibly controlled. This unique feature not only facilitates essential functions such as band filtering and isolating but also promises to serve as a linear differential equation solver. Our approach paves the way for the development of new-generation all-optical analog signal processors tailored for future terahertz networks, featuring remarkable structural simplicity, ultrafast processing speeds, as well as highly reliable performance.

Space Domain Awareness Observations Using the Buckland Park VHF Radar

There is increasing interest in space domain awareness worldwide, motivating investigation of the use of non-traditional sensors for space surveillance. One such class of sensor is VHF wind profiling radars, which have a low cost relative to other radars typically applied to this task. These radars are ubiquitous throughout the world and may potentially offer complementary space surveillance capabilities to the Space Surveillance Network. This paper updates an initial investigation on the use of Buckland Park VHF wind profiling radars for observing resident space objects in low Earth orbit to further investigate the space surveillance capabilities of the sensor class. The radar was operated during the Australian Defence “SpaceFest” 2019 activity, incorporating new beam scheduling and signal processing functionality that extend upon the capabilities described in the initial investigation. The beam scheduling capability used two-line element propagations to determine the appropriate beam direction to use to observe transiting satellites. The signal processing capabilities used a technique based on the Keystone transform to correct for range migration, allowing the development of new signal processing modes that allow the coherent integration time to be increased to improve the SNR of the observed targets, thereby increasing the detection rate. The results reveal that 5874 objects were detected over 10 days, with 2202 unique objects detected, representing a three-fold increase in detection rate over previous single-beam direction observations. The maximum detection height was 2975.4 km, indicating a capability to detect objects in medium Earth orbit. A minimum detectable RCS at 1000 km of −10.97 dBm2 (0.09 m2) was observed. The effects of Faraday rotation resulting from the use of linearly polarised antennae are demonstrated. The radar’s utility for providing total electron content (TEC) measurements is investigated using a high-range resolution mode and high-precision ephemeris data. The short-term Fourier transform is applied to demonstrate the radar’s ability to investigate satellite rotation characteristics and monitor ionospheric plasma waves and instabilities.

INTEGRATED CIRCUITS AND THEIR APPLICATIONS IN ELECTRONICS

The article deals with the integrated circuits and their applications in electronics. Integrated circuits (ICs) have revolutionized the field of electronics by enabling the integration of multiple components onto a single chip. This miniaturization has led to significant advancements in various electronic devices and systems. ICs offer numerous benefits, including compact size, energy efficiency, reliability, high speed, cost-effectiveness, flexibility, and scalability. The ability to combine different functions on a single chip has made ICs indispensable in a wide range of applications, from consumer electronics to industrial automation. The integration of digital signal processing, memory management, communication interfaces, and other functions on a single IC has enabled the development of sophisticated and high-performance devices.

Interpretable parallel channel encoding convolutional neural network for bearing fault diagnosis

Interpretability plays a crucial role in the application of neural networks for fault diagnosis. Integrating preprocessing methods into neural networks can enhance interpretability while preserving their ‘end-to-end’ characteristics. However, when only redesigning the first layer, subsequent structures still exhibit limited transparency. Additionally, traditional convolution structure is ill-suited for analyzing the readable feature maps derived from vibration signals. To address these challenges, this paper proposes a novel convolution structure for the parameterized signal processing function of the first-layer convolution kernel. This structure incorporates channel mixing for feature augmentation, designs a condensed feature encoder for aggregating and compressing features channel-by-channel, ensures the interpretability of feature map processing, and obtains condensed feature codes to propose smooth activation layer-wise relevance propagation (SA-LRP) method that to perform interpretability analysis. Additionally, cubic feature screening is implemented for diagnostic classification to improve structural fitness. We design experiments using multiple datasets to test various indicators of the structure. The results confirm that connecting our convolution architecture for subsequent analysis outperforms other convolution architectures for the convolution kernel of the first-layer parameterized signal processing function. The interpretability of the model is evaluated through SA-LRP method and validates the interpretability of the model.

Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools

Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.

3D-integrated pixel circuit for a low power and small pitch SOI sensor

Targeting on low power consumption and high spatial resolution, the CPV-4 SOI pixel sensor has been developed. Its pixel circuit requires about 120 transistors to implement the analog-digital mixed signal processing functionality within a compact pixel area of 17 μm × 21 μm. By utilizing 3D vertical integration, sensing diode and analog front-end are realized in the lower-tier chip, while hit information storage and sparse readout are achieved in the upper-tier chip, thereby minimizing its pixel size and power consumption. This work presents the pixel circuit design and the test results on the completed 3D chips. The feature of SOI pixel process and the 3D integration are also highlighted in this article.

Biotin-cGMP and -cAMP are able to permeate through the gap junctions of some amacrine cells in the mouse retina despite their large size.

Gap junctions transmit electrical signals in neurons and serve metabolic coupling and chemical communication. Gap junctions are made of intercellular channels with large pores, allowing ions and small molecules to permeate. In the mammalian retina, intercellular coupling fulfills many vital functions in visual signal processing but is also implicated in promoting cell death after insults, such as excitotoxicity or hypoxia. Conversely, some studies also suggested a role for retinal gap junctions in neuroprotection. Recently, gap junctions were also advocated as conduits for therapeutic drug delivery in neurodegenerative disorders. This requires the permeation of rather large molecules through retinal gap junctions. However, the permeability of retinal networks for molecules >0.6 kDa has not been tested systematically. Here, we used the cut-loading method and probed gap junctional networks in the mouse retina for their permeability to cGMP and cAMP coupled to Biotin, using the well-characterized tracer Neurobiotin as control. Biotin-cGMP and -cAMP have a molecular weight of >0.8 kDa. We show that they cannot pass the gap junctions of horizontal cells but can permeate through the gap junctions of specific amacrine cells in the inner retina. These amacrine cells do not comprise AII amacrine cells and nitric oxide-releasing amacrine cells but some unknown type. In summary, we show that some retinal gap junctions are large enough to let molecules >0.8 kDa pass, making the intercellular delivery of therapeutic agents - already successfully exploited, for example, in cancer - also feasible in neurodegenerative diseases.

Hermite-Hadamard Type Inequalities and Convex Functions in Signal Processing

Hermite-Hadamard Type Inequalities and Convex Functions in Signal Processing

Filtering of Complex Signals Based on a Two-Level Fuzzy-Logic Model

Purpose of research. Development of a method and algorithm of complex analog radio signals filtering and binarization, such as the signal of Automatic dependent surveillance-broadcast (ADS-B), which allows to increase the sensitivity of the receiver of the AZN-B signal and increase the number of correctly detected received messages.Methods. To solve this problem, the basics of the theory of signal filtering and the theory of fuzzy sets were applied in the work. The proposed method is based on combining signal filtering by known filters and a two-level fuzzy model. The first and second levels of the fuzzy model contain three operations: automatic formation of membership functions, compositional output and defuzzification. Input variables of both levels are given by trapezoidal membership functions. At the first level, they are formed automatically depending on the characteristics of the complex signal. The output function at the first level is given by a singleton function, and defuzzification is carried out using a simplified center of gravity model.Results. The proposed algorithm was implemented in the developed device based on a programmable logic integrated circuit (FPGA). In addition to filtering, the developed device implements all signal processing functions, such as: receiving input data, decoding, checking the correctness of decoded data, storing them, transmitting ADS-B messages for further processing. A distinctive feature of the device is its small size and low power consumption, which allows use it in small spacecraft and unmanned aerial vehicles.Conclusion. A method of filtering complex signals based on a fuzzy logic model is considered, which can be used to filter complex signals, such as ADS-B messages in small spacecraft modules. The proposed implementation of the filtering method makes it possible to increase the sensitivity of the AZN-B signal receiver by 20% and correctly decode the received signal. The method was implemented by an FPGA-based device, which made it possible to reduce the size and power consumption compared to analogues.

Channel-Agnostic Training of Transmitter and Receiver for Wireless Communications.

Wireless communications systems are traditionally designed by independently optimising signal processing functions based on a mathematical model. Deep learning-enabled communications have demonstrated end-to-end design by jointly optimising all components with respect to the communications environment. In the end-to-end approach, an assumed channel model is necessary to support training of the transmitter and receiver. This limitation has motivated recent work on over-the-air training to explore disjoint training for the transmitter and receiver without an assumed channel. These methods approximate the channel through a generative adversarial model or perform gradient approximation through reinforcement learning or similar methods. However, the generative adversarial model adds complexity by requiring an additional discriminator during training, while reinforcement learning methods require multiple forward passes to approximate the gradient and are sensitive to high variance in the error signal. A third, collaborative agent-based approach relies on an echo protocol to conduct training without channel assumptions. However, the coordination between agents increases the complexity and channel usage during training. In this article, we propose a simpler approach for disjoint training in which a local receiver model approximates the remote receiver model and is used to train the local transmitter. This simplified approach performs well under several different channel conditions, has equivalent performance to end-to-end training, and is well suited to adaptation to changing channel environments.

Engineering Nonlinear Effects of Quantum-Well Semiconductor Optical Amplifiers for Applications in Optical Signal Processing

Quantum-well semiconductor optical amplifiers (QW-SOAs) have been widely used in all-optical signal processing functions because of their various nonlinear effects. For different optical signal processing functions, the requirements for performance of the QW-SOAs are different, and nonlinear effects of the QW-SOAs should be controlled selectively. Engineering nonlinear effects of QW-SOA is very important for different applications in optical signal processing. Here, a comprehensive QW-SOA model by combining the QW band structure calculation with the dynamic model is established to connect the structure parameters with the nonlinear effects of QW-SOA. Those characterized parameters of the QW-SOAs, such as gain coefficient, differential gain coefficient, refractive index change, differential refractive index change, linewidth enhancement factor, third-order susceptibility and polarization dependent gain, are used to characterize the nonlinear effects, and are calculated with different structure parameters based on this developed model. Take the wavelength conversion based on cross gain modulation effect and four wave mixing effect as examples, the structure parameters of the QW-SOAs are optimized for engineering the nonlinear effects and achieving the best output performance. This theoretical work presents a guide for choosing the optimized structure parameters while fabricating the QW-SOAs for different optical signal processing functions

Quantifying the Accuracy of Microcomb-Based Photonic RF Transversal Signal Processors

Photonic RF transversal signal processors, which are equivalent to reconfigurable electrical digital signal processors but implemented with photonic technologies, have been widely used for modern high-speed information processing. With the capability of generating large numbers of wavelength channels with compact micro-resonators, optical microcombs bring new opportunities for realizing photonic RF transversal signal processors that have greatly reduced size, power consumption, and complexity. Recently, a variety of signal processing functions have been demonstrated using microcomb-based photonic RF transversal signal processors. Here, we provide detailed analysis for quantifying the processing accuracy of microcomb-based photonic RF transversal signal processors. First, we investigate the theoretical limitations of the processing accuracy determined by tap number, signal bandwidth, and pulse waveform. Next, we discuss the practical error sources from different components of the signal processors. Finally, we analyze the contributions of the theoretical limitations and the experimental factors to the overall processing inaccuracy both theoretically and experimentally. These results provide a useful guide for designing microcomb-based photonic RF transversal signal processors to optimize their accuracy.

Integrated near-infrared fiber-optic photoacoustic sensing demodulator for ultra-high sensitivity gas detection

An integrated near-infrared fiber-optic photoacoustic sensing demodulator was established for ultra-high sensitivity gas detection. The demodulator has capacities of interference spectrum acquisition and calculation, laser modulation control as well as digital lock-in amplification. FPGA was utilized to realize all the control and signal processing functions, which immensely improved the integration and stability of the system. The photoacoustic signal detection based on fiber-optic Fabry–Perot (F-P) acoustic sensor was realized by applying ultra-high resolution spectral demodulation technique. The detectable frequency of photoacoustic signal achieved 10 kHz. The system integrated lock-in amplification technology, which made the noise sound pressure and dynamic response range of sound pressure detection reached 3.7 μPa/√Hz @1 kHz and 142 dB, respectively. The trace C2H2 gas was tested with a multi-pass resonant photoacoustic cell. Ultra-high sensitivity gas detection was accomplished, which was based on high acoustic detection sensitivity and the matching digital lock-in amplification. The system detection limit and normalized noise equivalent absorption (NNEA) coefficient were reached 3.5 ppb and 6.7 × 10−10 cm−1WHz−1/2, respectively. The devised demodulator can be applied for long-distance gas measurement, which depends on the fact that both the near-infrared photoacoustic excitation light and the probe light employ optical fiber as transmission medium.