This paper presents the design and implementation of a pocket-sized electronic system for quantifying glucose levels in diabetic patients by measuring a byproduct of the chemical reaction of glucose oxidation, such as hydrogen peroxide. A novel Nano Wire sensor obtained the current versus the hydrogen peroxide (H2O2) concentration relationship. The system utilizes a modified amperometric electrochemical sensor circuit topology named "Strong Follower" (SF) to measure current signals from 1 μA to 11 μA accurately. The SF circuit includes active control loop feedback to maintain the output voltage in line with the input voltage, ensuring accurate and stable measurements. Simulation and experimental results demonstrate the effectiveness of the proposed system in quantifying H2O2 concentrations. Experimental tests were conducted using currents of 1, 5, 10, and 15 μA. Additionally, using general-purpose electronic components helps reduce the overall implementation cost, making it more accessible for diabetes healthcare applications.

As microprocessor design scales to the 10-nm technology and beyond, traditional pre- and post-silicon validation techniques are unsuitable to get a full system functional coverage. Physical complexity and extreme technology process variations severely limits the effectiveness and reliability of presilicon validation techniques. This scenario imposes the need of sophisticated post-silicon validation approaches to consider complex electromagnetic phenomena and large manufacturing fluctuations observed in actual physical platforms. One of the major challenges in electrical validation of high-speed input/output (HSIO) links in modern computer platforms lies in the physical layer (PHY) tuning process, where equalization techniques are used to cancel undesired effects induced by the channels. Current industrial practices for PHY tuning in HSIO links are very time consuming since they require massive laboratory measurements. An alternative is to use machine learning techniques to model the PHY, and then perform equalization using the resultant surrogate model. In this paper, a metamodeling approach based on neural networks is proposed to efficiently simulate the effects of a receiver equalizer PHY tuning settings. We use several design of experiments techniques to find a neural model capable of approximating the real system behavior without requiring a large amount of actual measurements. We evaluate the models performance by comparing with measured responses on a real server HSIO link.