This paper presents a methodology to create a Simultaneous Switching Noise (SSN) predictive SPICE model for a 32-bit microcontroller. Here, the purpose is to predict the noise interference generated by switching IOs a...
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ISBN:
(纸本)9781728135496
This paper presents a methodology to create a Simultaneous Switching Noise (SSN) predictive SPICE model for a 32-bit microcontroller. Here, the purpose is to predict the noise interference generated by switching IOs and establish new design/layout rules for the next product generation in order to reduce these interferences. This model achieves an accurate correlation between measurements done on a 32-bit MCU and Eldo simulations. Then, features are defined in order to build this accurate model. Having this, a deeper research work can be done only by simulation.
The Electrocardiogram (ECG) provides a detailed representation of the heart's electrical activity, emerging as a crucial resource for continuous cardiac health monitoring. Recent advances in Artificial Intelligenc...
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ISBN:
(纸本)9798350387032;9798350387025
The Electrocardiogram (ECG) provides a detailed representation of the heart's electrical activity, emerging as a crucial resource for continuous cardiac health monitoring. Recent advances in Artificial Intelligence (AI) techniques have revolutionized ECG signal processing, creating new possibilities for every-day health monitoring. The exploitation of these AI technologies has driven a growing interest in wearable devices where the challenge is to implement these functionalities on limited memory resource hardware. In this scenario, the Edge Computing paradigm, where computation occurs near the data source rather than in a remote data center, emerges as a promising solution. This article proposes an efficient approach for Myocardial Infarction (MI) detection based on Deep Learning (DL) methods using spectrogram and a 1D Convolutional Neural Network (1D-CNN). The aim is to strike a balance between computational efficiency and accuracy enabling practical application on wearable devices. In the presented work, a study on the impact of the spectrogram parameters and results on the 1D-CNN was conducted. The final training phase yielded a remarkable accuracy of 95.94%, showcasing the efficacy of the proposed approach. Notably, the trained model was successfully deployed on a 32-bit microcontroller featuring an ARM Cortex-M4 architecture, underscoring the feasibility of real-world implementation for embedded systems in healthcare applications.
In this paper, a 32-bit RISC-V microcontroller in a 65-nm Silicon-On-Thin-BOX (SOTB) chip is presented. The system is developed based on the VexRiscv Central Processing Unit (CPU) with the Instruction Set Architecture...
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In this paper, a 32-bit RISC-V microcontroller in a 65-nm Silicon-On-Thin-BOX (SOTB) chip is presented. The system is developed based on the VexRiscv Central Processing Unit (CPU) with the Instruction Set Architecture (ISA) extensions of RV32IM. Besides the core processor, the System-on-Chip (SoC) contains 8KB of boot ROM, 64KB of on-chip memory, UART controller, SPI controller, timer, and GPIOs for LEDs and switches. The 8KB of boot ROM has 7KB of hard-code in combinational logics and 1KB of a stack in SRAM. The proposed SoC performs the Dhrystone and Coremark benchmarks with the results of 1.27 DMIPS/MHz and 2.4 Coremark/MHz, respectively. The layout occupies 1.32-mm(2) of die area, which equivalents to 349,061 of NAND2 gate-counts. The 65-nm SOTB process is chosen not only because of its low-power feature but also because of the back-gate biasing technique that allows us to control the microcontroller to favor the low-power or the high-performance operations. The measurement results show that the highest operating frequency of 156-MHz is achieved at 1.2-V supply voltage (V-DD) with +1.6-V back-gate bias voltage (V-BB). The best power density of 33.4-mu W/MHz is reached at 0.5V V-DD with +0.8-V V-BB. The least current leakage of 3-nA is retrieved at 0.5-V V-DD with -2.0-V.V V-BB.
This research presents an improved mobile inverted pendulum robot called Two-wheeled Self-balancing robot (TWSBR) using a Proportional-Derivative Proportional-Integral (PD-PI) robust control design based on 32-bit mic...
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This research presents an improved mobile inverted pendulum robot called Two-wheeled Self-balancing robot (TWSBR) using a Proportional-Derivative Proportional-Integral (PD-PI) robust control design based on 32-bit microcontroller in a sensed environment (SE). The robot keeps itself balance with two wheels and a PD-PI controller based on the Kalman filter algorithm during the navigation process and is able to stabilize while avoiding acute and dynamic obstacles in the sensed environment. The Proportional (P) control is used to implement turn control for obstacle avoidance in SE with ultrasonic waves. Finally, in a SE, the robot can communicate with any of the Internet of Things (IoT) devices (mobile phone or Personal Computer) which have a Java-based transmission application installed and through Bluetooth technology connectivity for wireless control. The simulation results prove the efficiency of the proposed PD-PI controller in path planning, and balancing challenges of the TWSBR under several environmental disturbances. This shows an improved control system as compared to the existing improved Adaptive Fuzzy Controller.
With the advancement in personalized healthcare technology, the usage of wearable devices for continuous monitoring and analysis of long-term biomedical signals, such as electrocardiography (ECG) has shown explosive g...
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With the advancement in personalized healthcare technology, the usage of wearable devices for continuous monitoring and analysis of long-term biomedical signals, such as electrocardiography (ECG) has shown explosive growth. However, the existing ECG monitoring devices exhibit limited performance, such as they only store the ECG data, have low accuracy and their inability to perform event-by-event diagnosis at the place of data acquired. Therefore, the personalized healthcare demands an efficient method and point-of-care platform capable of providing real-time feedback to consumers as well as subjects. In this paper, a novel ECG signal analysis method using discrete cosine stockwell transform for feature extraction and artificial bee colony optimized least-square twin support vector machines as classifier is developed and prototyped using commercially available 32-bit microcontroller test platform. The prototype is evaluated under two schemes, i.e., the class and personalized schemes and validated on the benchmark MIT-BIH arrhythmia data. A higher overall accuracy of 96.14% and 86.5% respectively is reported by the prototype in the aforesaid two evaluation schemes than the existing works. The platform can be utilized as an early warning system in detecting abnormal ECG in home care environment to the state-of-art diagnosis.
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