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Glycemic control is essential to manage metabolic diseases such as diabetes. Frequent measurements of systemic glucose levels with prompt managements can prevent organ damages. The eye is a glucose highly demanding organ in our body, and the anterior chamber (AC) in the eye has been suggested for a noninvasive blood glucose monitoring site. However, calculating blood glucose levels from measuring glucose levels in AC has been difficult and unclear. In this study, we aimed to examine glucose levels from AC and find a correlation with blood glucose levels. A total of 30 patients with cataracts (men and women, study 1; 7 and 3, study 2; 9 and 11) who visited Keio University Hospital from 2015 to 2018 and agreed to participate in this study were recruited. Glucose levels from AC and the blood were examined by a UV-hexokinase or H2O2-electrode method before/during the cataract surgery. These values were analyzed with regression analyses depending on the groups (blood glucose-ascending and descending groups). In the blood glucose-descending group, glucose levels from AC were strongly correlated with blood glucose levels (a high R2 value, 0.8636). However, the relatively moderate correlation was seen in the blood glucose-ascending group (a low R2 value, 0.5228). Taken together, we showed different correlation ratios on glucose levels between AC and the blood, based on blood glucose dynamics. Stacking data regarding this issue would enable establishing noninvasive blood glucose monitoring from measuring glucose levels in AC more correctly, which will be helpful for proper and prompt managements for glucose-mediated complications.

In this paper we propose an associative memory based system for real-time character recognition. Based on an associative memory with 128 reference patterns of size 256 bits designed in 0.35 µm technology we could get an average nearest-match search time of 130 ns for classification of different samples of characters written in Times and Arial fonts. Comparing to other OCR systems, although this prototype model is not yet robust enough but is advantageous in terms of classification time and hardware size.

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An associative memory based learning model is proposed which uses a short and long-term memory and a ranking mechanism to manage the transition of reference vectors between two memories. The memorizing process is similar to that in human memory. In addition, an optimization algorithm is used to adjust the reference vectors components as well as their distribution, continuously. Comparing to other learning models like neural networks, the main advantage of the proposed model is no need to pre-training phase as well as its hardware-friendly structure which makes it implementable by an efficient LSI architecture without requiring a large amount of resources. The system was implemented on an FPGA platform and tested with real data of handwritten and printed English characters and the classification results found satisfactory.