X-ray diffraction, scanning electron microscopy and transmission electron microscopy showed that TiO 2 particles synthesized by a sol-gel procedure exhibited uniform size about 16-20 nm. This nanopowder was deposited on a porous quartz tube (D = 74 mm, L = 418 mm, deposit density ∼16.4 mg cm −2 ) through an intermediate adhesive polymethylmethacrylate layer to manufacture a photocatalytic filter tube. A polypropylene pre-filter was coated with a nanosilver layer (particle size ∼20 nm) prepared by aqueous molecular solution method. An air cleaner of 250 m 3 h −1 capacity equipped with this pre-filter, an electrostatic air filter, 4 photocatalytic filter tubes and 4 UV-A lamps (36 W) presented the high degradation ability for certain volatile organic compounds (VOCs), bacteria and fungi. The VOCs degradation performances of the equipment with respect to divers compounds are different: in a 10 m 3 box, 91.6% of butanol was removed within 55 min, 80% of acetone within 100 min, 70.1% of diethyl ether within 120 min and only 43% of benzene was oxidized within 150 min. Over 99% of bacteria and fungi were killed after the air passage through the equipment. For application, it was placed in the intensive care room (volume of 125 m 3 ) of E hospital in Hanoi; 69% of bacteria and 63% of fungi were killed within 6 h.
This paper presents a single FPGA implementation of a realtime sound localization system using two microphones. The implementation, utilizing a cross-correlation technique based on a modified version of the phase transform, successfully localizes sound sources in noisy environments with as low an SNR as 10dB. Using the same algorithm and similar hardware architecture, it is shown that up to 5 parallel systems (using 10 microphones), all real-time, can be implemented on a single FPGA while only utilizing an estimated 77mW-108mW per microphone.
: This paper presents a method using Maximum-Likelihood Linear Regression (MLLR) adaptation to improve recognition performance of Limabeam algorithm for speech recognition using microphone array. From our investigation on Limabeam algorithm, we can see that the performance of filtering optimization depends strongly on the supporting optimal state sequence and this sequence is created by using Viterbi algorithm trained with HMM model. So we propose an approach using MLLR adaptation for the recognition of speech uttered in a new environment to obtain better optimal state sequence that support for the filtering parameters' optimal step. Experimental results show that the system embedded with MLLR adaptation presents the word correct recognition rate 2% higher than that of original calibrate Limabeam and also present 7% higher than that of Delay and Sum algorithm. The best recognition accuracy of 89.4% is obtained when we use 4 microphones with 5 utterances for adaptation.
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