A 2.4GHz ASK transmitter suitable for a low power wireless capsule endoscope system is presented. A mixer-based frequency up-conversion transmitter architecture is employed to achieve a high data rate. A pseudodiffere...A 2.4GHz ASK transmitter suitable for a low power wireless capsule endoscope system is presented. A mixer-based frequency up-conversion transmitter architecture is employed to achieve a high data rate. A pseudodifferential stacked class-A power amplifier using the current reuse technique is proposed to save power. The transmitter mainly includes two parts: a 20MHz ASK modulator based on a constant amplitude phase lock loop (PLL) and a direct up-conversion RF circuit. This design, implemented in a TSMC 0.25μm CMOS process, achieves a - 23. 217dBm output power with a data rate of 1Mbps and dissipates 3.17mA of current with a single 2.5V power supply.展开更多
A smart image sensor was developed which integrates a digital pixel image sensor array with an image processor, designed for wireless endoscope capsules. The camera-on-a-chip architecture and its on-chip functionality...A smart image sensor was developed which integrates a digital pixel image sensor array with an image processor, designed for wireless endoscope capsules. The camera-on-a-chip architecture and its on-chip functionality facilitate the design of the packaging and power consumption of the integrated capsule. The power reduction techniques were carried out at both the architectural and circuit level. Gray coding and power gating in the sensor array to eliminate almost 50% of the switch activity on the data bus and more than 99% of the power dissipation in each pixel at a transmitting rate of 2 frames per second. Filtering and compression in the processor reduces the data transmission by more than 2/3. A parallel fully pipelined architecture with a dedicated clock management scheme was implemented in the JPEG-LS engine to reduce the power consumption by 15.7%. The smart sensor has been implemented in 0.18 μm CMOS technology.展开更多
The Internet of Medical Things (IoMT) emerges with the visionof the Wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the le...The Internet of Medical Things (IoMT) emerges with the visionof the Wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional NeuralNetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.展开更多
文摘A 2.4GHz ASK transmitter suitable for a low power wireless capsule endoscope system is presented. A mixer-based frequency up-conversion transmitter architecture is employed to achieve a high data rate. A pseudodifferential stacked class-A power amplifier using the current reuse technique is proposed to save power. The transmitter mainly includes two parts: a 20MHz ASK modulator based on a constant amplitude phase lock loop (PLL) and a direct up-conversion RF circuit. This design, implemented in a TSMC 0.25μm CMOS process, achieves a - 23. 217dBm output power with a data rate of 1Mbps and dissipates 3.17mA of current with a single 2.5V power supply.
文摘A smart image sensor was developed which integrates a digital pixel image sensor array with an image processor, designed for wireless endoscope capsules. The camera-on-a-chip architecture and its on-chip functionality facilitate the design of the packaging and power consumption of the integrated capsule. The power reduction techniques were carried out at both the architectural and circuit level. Gray coding and power gating in the sensor array to eliminate almost 50% of the switch activity on the data bus and more than 99% of the power dissipation in each pixel at a transmitting rate of 2 frames per second. Filtering and compression in the processor reduces the data transmission by more than 2/3. A parallel fully pipelined architecture with a dedicated clock management scheme was implemented in the JPEG-LS engine to reduce the power consumption by 15.7%. The smart sensor has been implemented in 0.18 μm CMOS technology.
基金the Universiti Teknologi Malaysia for funding this research work through the Project Number Q.J130000.2409.08G77.
文摘The Internet of Medical Things (IoMT) emerges with the visionof the Wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional NeuralNetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.