Signal Processing
The sensitivity of an ultrasonic transducer is an important parameter for evaluating its effective frequency band, electroacoustic conversion efficiency, and the measurement capability of the system. Determining the sensitivity of a traditional immersion or contact piezoelectric transducer has been well investigated. However, due to the high attenuation of wave propagation in air and the large acoustic impedance mismatch between the active piezoceramic material and the load medium, there are few reports on the calibration of an air-coupled piezoelectric transducer.
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Data processing procedure. Pictures from (a) to (d) are corresponding to the four data processing steps respectively. The units of vertical axis are arbitrary and the number is not equal to the actual received power. The measurement range of 400 m corresponds to the beam angle of about 46°
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In order to increase the diversity in signal datasets, we create a new dataset called HisarMod, which includes 26 classes and 5 different modulation families passing through 5 different wireless communication channel. During the generation of the dataset, MATLAB 2017a is employed for creating random bit sequences, symbols, and wireless fading channels.
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Oxygen is one of the most adverse gas that contaminates sterile drugs in glass medicine bottle, so it is of great significance to detect oxygen concentration for glass medicine bottle in order to ensure the asepsis of drug and the stability of ingredients. Wavelength modulation spectroscopy (WMS) is applied to achieve online oxygen concentration detection by the single-line spectrum analysis for the advantages of non-contact and high sensitivity.
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7200 .csv files, each containing a 10 kHz recording of a 1 ms lasting 100 hz sound, recorded centimeterwise in a 20 cm x 60 cm locating range on a table. 3600 files (3 at each of the 1200 different positions) are without an obstacle between the loudspeaker and the microphone, 3600 RIR recordings are affected by the changes of the object (a book). The OOLA is initially trained offline in batch mode by the first instance of the RIR recordings without the book. Then it learns online in an incremental mode how the RIR changes by the book.
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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (CURE-OR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In CURE
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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the
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This data set contains 50 low resolution (640 x 360) short videos containing a variety real life activities.
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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed.
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3951 Views