Analog Integrated Circuits and Signal Processing最新文献

With the reduction of CMOS technology to nanometric dimensions, it is thought that the end of atomic limits in integrated circuit applications is almost approached and some problems are encountered in production. Carbon nanotube field effect transistors (CNTFETs) are considered a proper option to replace CMOS near term owing to their superior properties such as scalability and better channel electrostatics. For this purpose, a low-voltage, low-power Voltage Differencing Inverting Buffered Amplifier (VDIBA) structure is propose with a 32 nm CNTFET, in this article. The proposed CNTFET VDIBA structure operates with a bias current of 1 µA and consumes 14.32 µW of power with a supply voltage of ± 0.3 V. Compared to the traditional CMOS VDIBA structure, the power consumption is reduced by 733 times. Besides, proposed VDIBA structure has a bandwidth of 43.788 GHz.

This paper presents the development of a wireless data acquisition system for a wearable health sensor designed to measure glucose levels, pulse rate, and body temperature. The method emphasizes non-invasive and continuous monitoring to provide timely healthcare interventions. The designed system prioritizes wearability, flexibility, compactness, and low power consumption for user comfort and convenience. A transimpedance amplifier is designed to increase the glucose sensor signal with optimal gain and bandwidth, utilizing modeling tools for accurate signal processing. Filters, amplifiers, analog-to-digital converters, and a microcontroller for data processing and wireless transmission were used to create an integrated multi-input readout circuit for all three sensors. The work aims to develop a small and efficient circuit consuming less than 100 mW and occupying less than 6 cm². This research extensively covers the design and optimization of a transimpedance amplifier, the development of an integrated multi-input readout circuit, and the incorporation of low-power Bluetooth data transfer for a wearable health sensor system. The biosensor’s 10 uA signal range was effectively amplified to a voltage level that is readable, guaranteeing a minimum gain of 10,000 and converting it from current to voltage for measurement. An important milestone was achieved by integrating the communication of the amplified signal, heart rate, and temperature characteristics to the host application using Bluetooth. The complete system has been efficiently contained within a compact 6 cm² footprint.

The alarming mortality rates associated with cardiac abnormalities emphasize the critical need for early and accurate detection of heart disorders to mitigate severe health consequences for patients. Electrocardiograms (ECG) are commonly employed instruments for the examination of cardiac disorders, with a preference for noise-free ECG signals to ensure precise interpretation. However, ECG signal recordings are susceptible to environmental interferences, including patient movement and electrode positioning. This paper introduces a hardware implementation for denoising ECG signals, leveraging a novel method by integrating high-order Synchrosqueezing Transform, Detrended Fluctuation Analysis, and Non-Local-Mean filter optimized by Particle Swarm Optimization (HSST-DFA-PSO-NLM) techniques on Field-Programmable Gate Array (FPGA) platforms. FPGA-based processing units are chosen for their outstanding performance attributes, including high re-programmability, speed, architectural flexibility, and low power consumption, resulting in efficient signal processing. The effectiveness of the designed filtering algorithm is evaluated using key criteria, including Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE) for performance assessment. Additionally, resource utilization metrics such as Look-Up Tables (LUTs), Flip Flops, and DSP Blocks, as well as power consumption measures including dynamic power and static or leakage power, are analysed across various FPGA boards (Virtex and Zedboards) utilizing the VIVADO environment. Comparative analyses are conducted to identify the most suitable FPGA board for implementation, highlighting the superior performance of the proposed design. Remarkably, the proposed denoising solution gives excellent SNR of 29.56, 29.68, and 28.86 by denoising various ECG noises. The RMSE attained by the model is also less than 0.05. This research advances the field of cardiac disorder detection by providing a reliable and efficient FPGA-based solution for ECG signal denoising, thereby enhancing the accuracy of early diagnosis and treatment.

This paper comprehensively reviews the evolution and latest advancement of ultra-low All-Digital Phase Locked Loop (ADPLL) RF transceivers designed specifically for biomedical monitoring devices. With CMOS technology, these transceivers provide efficiency and simplicity, which are essential in the medical industry. As the size and power needs of these devices decrease due to CMOS scaling, they become more suitable for small and low-energy applications. In addition, this review also provides an insight into the ADPLL applications, Digital Controlled Oscillator (DCO), and Phase Frequency Detectors. The review highlights notable differences in performance between time-to-digital converters (TDC) and TDC-less designs. TDC-less design, like Digital Phase Frequency Detectors (DPFD), offers improvements in phase noise, small size, fast phase and frequency acquisition, and power efficiency at the expense of resolution. Comparing LC-DCO and ring-DCO revealed that at high operating frequencies, the ring-DCO consumes more power but has a simpler design and a smaller circuit area than LC-DCO. Future research should focus on enhancing the performance of the ADPLL RF transceiver for biomedical devices, specifically by using a low-voltage supply and implementing DPFD to achieve low power consumption, compact design and fast locking. The significant challenges remain in maintaining low power consumption at higher frequencies with Ring-DCO design. Using the Verilog HDL for ADPLL design and implementation provides modularity, simulation, synthesis, and flexibility, which makes it an excellent alternative to designing RF transceivers in biomedical applications which are efficient and reliable.

This paper offers the first demonstration of the design and layout of a fully integrated power converter in a monolithic Silicon Carbide (SiC) technology. A 400 V Buck Converter integrated with Gate-Drivers and Low-Voltage Control circuitry in a 25–600 V Mixed-Mode SiC CMOS technology has been presented in this paper. A new SiC technology has been developed for this design which has a feature size of 1 μm. This technology allows integration of High-Voltage Power FETs and Low-Voltage CMOS circuits on the same die with a common substrate. Both high-side and low-side Power FETs are N-type hence a bootstrap circuit is used, and the gate drivers use an isolated capacitive level shifter to translate the signals from the 25 V domain to the 400 V domain which is the input voltage of the Buck Converter. The load current is 1 A and the nominal output voltage is 100 V thereby meaning that the output power is 100 W. The switching frequency is up to 1 MHz, and the duty cycle can range from 10% to 90% signifying a wide range of operation of the converter.

This paper presents the design and investigation of a variable reactance-based RF-MEMS capacitive switch operating on multiple frequency bands in millimetre wave ranges used for B5G applications. The proposed switch has a built-in band switching capability to cover multiple frequency bands in FR-II mmWave band which can provide an inspirational and optimistic platform to tackle 5G and beyond challenges. The novel design utilizes lateral deflections to make and break the device’s connection and results in a very low pull-in voltage of < 3 V. The switch operates in different modes maximum up to 9 and switches between multiple frequencies by varying the reactance of the electromechanical structure. These modes are tuned to cover all the bands from n257 to n261, primarily used to provide 5G/B5G services in various countries. The RF performance, voltage requirement, and switching speed of the proposed device are as per the guidelines of the 5G/B5G communication system. The insertion losses are < 0.5 dB, and isolation is > 20 dB over the tuned frequency range (FR-II mmWave) with optimum isolation peaks at 12.1 GHz, 12.9 GHz, 21.2 GHz, 22.2 GHz, 23.5 GHz, 24.8 GHz, 26.1 GHz, and 39.5 GHz. The proposed device features a significant improvement in electromechanical and electromagnetic performance over a wide bandwidth with different structural configurations and thus can be used as an efficient IoT (Internet of Things) frequency reconfigurable device.

A novel hybrid method is proposed for designing a highly autonomous solar-powered lawnmower. The proposed hybrid method is a combination of the pelican optimization algorithm (POA) and the random forest algorithm (RFA); commonly, it is named the POARFA technique. The key objective of the proposed technique is to minimize errors while ensuring smooth and reliable operation. The solar lawnmower includes a rechargeable battery, Internet of Things (IoT), solar panel, and DC motor for control, monitoring, and user information. The IoT is utilized to control, monitor, and provide information to the user. The key components of the proposed lawnmower include a rechargeable battery, solar panel, IoT, and DC motor. This electrical energy is fed into the charging circuit. The controller of fractional order proportional integral derivative (FOPID) is used to regulate the motor that is utilized to track the path and improve the response of the system. The RFA approach is used to tune the parameters of the FOPID controller. The proposed solar lawnmower is extremely versatile, very durable, comfortable, and powerful, evading obstacles on the path. The proposed technique is executed in the MATLAB software and is compared with existing techniques. The peak overshoot of the POARFA approach is 0.712%, significantly lower than other approaches. In conclusion, the proposed POARFA approach showcases promising results for solar-powered lawnmowers, offering a more efficient, reliable, and sustainable solution compared to existing methods.

The most common non-invasive diagnostic model is the Electrocardiogram (ECG), which records the heart’s electrical activity over time and is used to diagnose various heart conditions. Due to the requirements of a typical eHealth system, it is necessary to compress ECG signals for long-term data recording and remote transmission. Moreover, cardiovascular diseases (CVDs) have been considered the most long-lasting disorders in recent years. The transmission of information from the patient to the distant hospital is necessary because rapid analysis and treatment are essential for the condition to be cured. Also, the data must be in the form of lossless and high-predictability data. So, the goal of this study was to create a two-stage lossless Integer Adaptive Predictor (IAP) compressor that could be implemented on a Field Programmable Gate Array (FPGA) without introducing any data loss during the compression process. Before compression, the ECG signals are denoised using a Fast Normalized Least Mean Square (FNLMS) algorithm-based adaptive filter, which removes the undesirable noise presented in the signal. Here, the adaptive filter is designed based on the hybrid systolic folding structure and compressor-based multiplier architecture to minimize the power, delay and area consumption of the filter while performing the signal-denoising process. Xilinx and MATLAB are used to run simulations using the MIT-BIH Arrhythmia and PTB diagnostic databases. Several performance parameters are used to assess the proposed design’s efficacy, and the results are compared to those of similar current designs. Consequently, the proposed compressor achieves a 45.23% compression ratio (CR) on MIT-BIH and a 10.87% average CR on the PTB diagnostic database, which demonstrates that the compression proficiency of the proposed design is high.

A controller is modelled and designed to optimize the power transfer in microgrid-connected hybrid renewable energy systems using an integrated DC/DC converter. To maximize the converter's output power and minimize the switching losses of the converter, a model is developed by including a simplified high conversion ratio converter, a maximal power point tracker, and an optimal controller with an effective control strategy. The proposed control system is a combination of the Artificial Transgender Longicorn Algorithm (ATLA) and the Gradient Boosting Decision Tree (GBDT) algorithm, named the ATLA-GBDT method. In the suggested technique, the ATLA is used as an assessment method to build up accurate control signals for the system and to improve the control signals database for offline use while considering the power exchange between the source and load. In addition, for training a GBDT system online, the data set received from the sensor is used to develop a control system for faster response. In addition, the goal function is defined by the system data, which is subject to equality and inequality constraints. Various constraints considered in the problem formulation are the output of renewable energy sources, power requirements, and the state of charge of storage components. The proposed control system is simulated using the MATLAB/Simulink platform, and the implementation is compared with the existing techniques. Various performance metrics like accuracy, specificity, recall and precision, RMSE, MAPE, and MBE of the proposed method and existing methods in the literature are presented.

Blind source separation (BSS) consists of recovering the independent source signals from their linear mixtures with unknown mixing channel. The existing BSS approaches rely on the fundamental assumption: the number of Gaussian source signals is no more than one, this limited the use of BSS seriously. To overcome this problem and the weakness of cosine index in measuring the dynamic similarity of signals, this study proposes the fuzzy statistical behavior of local extremum based on generalized Jaccard similarity as the measure of signal’s similarity to implement the separation of source signals. In particular, the imperialist competition algorithm is introduced to minimize the cost function which jointly considers the stationarity factor describing the dynamical similarity of each source signal separately and the independency factor describing the dynamical similarity between source signals. Simulation experiments on synthetic nonlinear chaotic Gaussian data and ECG signals verify the effectiveness of the improved BSS approach and the relatively small cross-talking error and root mean square error indicate that the approach improves the accuracy of signal separation.