Artificial Compound Eye Systems and Their Application: A Review.

The natural compound eye system has many outstanding properties, such as a more compact size, wider-angle view, better capacity to detect moving objects, and higher sensitivity to light intensity, compared to that of a single-aperture vision system. Thanks to the development of micro- and nano-fabrication techniques, many artificial compound eye imaging systems have been studied and fabricated to inherit fascinating optical features of the natural compound eye. This paper provides a review of artificial compound eye imaging systems. This review begins by introducing the principle of the natural compound eye, and then, the analysis of two types of artificial compound eye systems. We equally present the applications of the artificial compound eye imaging systems. Finally, we suggest our outlooks about the artificial compound eye imaging system.

Utilization of Electrical Impedance Spectroscopy and Image Classification for Non-Invasive Early Assessment of Meat Freshness.

This study presents a system for assessing the freshness of meat with electrical impedance spectroscopy (EIS) in the frequency range of 125 Hz to 128 kHz combined with an image classifier for non-destructive and low-cost applications. The freshness standard is established by measuring the aerobic plate count (APC), 2-thiobarbituric acid reactive substances (TBARS), and composition analysis (crude fat, crude protein, and moisture) values of the microbiological detection to represent the correlation between EIS and meat freshness. The EIS and images of meat are combined to predict the freshness with the Adaboost classification and gradient boosting regression algorithms. As a result, when the elapsed time of beef storage for 48 h is classified into three classes, the time prediction accuracy is up to 85% compared to prediction accuracy of 56.7% when only images are used without EIS information. Significantly, the relative standard deviation (RSD) of APC and TBARS value predictions with EIS and images datum achieves 0.890 and 0.678, respectively.

A Wearable EEG-HEG-HRV Multimodal System With Simultaneous Monitoring of tES for Mental Health Management.

A multimodal mental management system in the shape of the wearable headband and earplugs is proposed to monitor electroencephalography (EEG), hemoencephalography (HEG) and heart rate variability (HRV) for accurate mental health monitoring. It enables simultaneous transcranial electrical stimulation (tES) together with real-time monitoring. The total weight of the proposed system is less than 200 g. The multi-loop low-noise amplifier (MLLNA) achieves over 130 dB CMRR for EEG sensing and the capacitive correlated-double sampling transimpedance amplifier (CCTIA) has low-noise characteristics for HEG and HRV sensing. Measured three-physiology domains such as neural, vascular and autonomic domain signals are combined with canonical correlation analysis (CCA) and temporal kernel canonical correlation analysis (tkCCA) algorithm to find the neural-vascular-autonomic coupling. It supports highly accurate classification with the 19% maximum improvement with multimodal monitoring. For the multi-channel stimulation functionality, after-effects maximization monitoring and sympathetic nerve disorder monitoring, the stimulator is designed as reconfigurable. The 3.37 × 2.25 mm(2) chip has 2-channel EEG sensor front-end, 2-channel NIRS sensor front-end, NIRS current driver to drive dual-wavelength VCSEL and 6-b DAC current source for tES mode. It dissipates 24 mW with 2 mA stimulation current and 5 mA NIRS driver current.

Autonomous gain verification algorithm for a dual mode digital hearing aid chip.

A dual mode digital hearing aid chip with real-time autonomous gain verifications algorithm is proposed and implemented. Four proposed gain models due to the different internal sound paths are analyzed to verify its usefulness. By adopting this algorithm, a real-ear aided gain and a real-ear occluded gain which considers internal vent effect and leakage path effect are compared with conventional models. To evaluate this algorithm the implemented chip adopts two different modes; a hearing aid mode and a gain verification mode for a hearing aid operation and internal gain verification, respectively. The minimum and maximum convergence time of the presented algorithm covers 0.05 s at a 1 kHz input signal and 1.6 s at a 7.5 kHz input signal, respectively. The implemented chip dissipates less than 130 microW at a supply voltage of 0.9 V and occupies a 5.4 mm(2) core are at a 0.18 microm CMOS technology.