An Adaptable Continuous Restricted Boltzmann Machine in VLSI for Fusing the Sensory Data of an Electronic Nose.

An embedded system capable of fusing sensory data is demanded for many portable or implantable microsystems. The continuous restricted Boltzmann machine (CRBM) is a probabilistic neural network not only capable of classifying data reliably but also amenable to very-large-scale-integration (VLSI) implementation. Although the embedded system based on the CRBM has been demonstrated with analog VLSI, the precision required by the learning algorithm is hardly achievable with analog circuits. Therefore, this paper investigates the feasibility of realizing the CRBM as a digital embedded system for fusing the sensory data of an electronic nose (eNose). The fusion here refers to data clustering and dimensional reduction that facilitates reliable classification. The capability of the CRBM to model different types of eNose data is first examined by MATLAB simulation. Afterward, the CRBM algorithm is customdesigned as a digital embedded system within an eNose microsystem. The functionality of the embedded CRBM system is then tested and discussed. With on-chip learning ability, the CRBM-embedded eNose is able to adapt its parameters in response to new data inputs or environmental changes.

A fully integrated nose-on-a-chip for rapid diagnosis of ventilator-associated pneumonia.

Ventilator-associated pneumonia (VAP) still lacks a rapid diagnostic strategy. This study proposes installing a nose-on-a-chip at the proximal end of an expiratory circuit of a ventilator to monitor and to detect metabolite of pneumonia in the early stage. The nose-on-a-chip was designed and fabricated in a 90-nm 1P9M CMOS technology in order to downsize the gas detection system. The chip has eight on-chip sensors, an adaptive interface, a successive approximation analog-to-digital converter (SAR ADC), a learning kernel of continuous restricted Boltzmann machine (CRBM), and a RISC-core with low-voltage SRAM. The functionality of VAP identification was verified using clinical data. In total, 76 samples infected with pneumonia (19 Klebsiella, 25 Pseudomonas aeruginosa, 16 Staphylococcus aureus, and 16 Candida) and 41 uninfected samples were collected as the experimental group and the control group, respectively. The results revealed a very high VAP identification rate at 94.06% for identifying healthy and infected patients. A 100% accuracy to identify the microorganisms of Klebsiella, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida from VAP infected patients was achieved. This chip only consumes 1.27 mW at a 0.5 V supply voltage. This work provides a promising solution for the long-term unresolved rapid VAP diagnostic problem.

Novel oxygen-releasing immobilized cell beads for bioremediation of BTEX-contaminated water.

Novel oxygen-releasing bead (ORB) and oxygen-releasing immobilized cell bead (ORICB) were prepared. Their oxygen releasing characteristics and effect on degradation of benzene, toluene, ethylbenzene, and xylene (BTEX)-contaminated groundwater were evaluated in a column. ORB prepared by CaO(2)-encapsulated freezing had much better oxygen-releasing capacity (0.526 mg O(2) per ORB) than that by the mixing-freezing method. The encapsulated-ORB did not influence groundwater pH. Two BTEX degraders were utilized to prepare the ORICB. The ORICBs-column rapidly (hydraulic retention time: 0.872 day) degraded BTEX after a 2-5 day acclimation period. The BTEX removal increased as flow distances increased. At BTEX concentration of 120 mg L(-1), 67% of benzene and 81-90% of TEX were removed. The SEM shows that micropores existed in the ORBs and BTEX degraders were immobilized. The denaturing gradient gel electrophoresis profiles indicate that BTEX degraders were distributed throughout the column. The BTEX concentration of 120 mg L(-1) markedly altered the structure of the indigenous microbial community.