Digital signal processing
Automatic white balance (AWB) is an important module for color constancy of cameras. The classification of the normal image and the color-distorted image is critical to realize intelligent AWB. One tenth of ImageNet is utilized as the normal image dataset for training, validating and testing. The distorted dataset is constructed by the proposed theory for generation of color distortion. To generate various distorted color, histogram shifting and matching are proposed to randomly adjust the histogram position or shape.
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The article presents a performance analysis of fully automated, in-house developed 2D ultrasound computerized tomography systems using different programming languages. The system is fully automated in four programming languages: LabVIEW, MATLAB, C and Python. It includes codes for sensors, instruments interfacing, real-time control, synchronized data acquisition, simultaneous raw data processing and analysis. A microcontroller integrated, multi-actuator based electro-mechanical assembly designed to provide complete scanning in three dimensions.
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Dataset1
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315 Views
A 5.76-second piano rendition of the Inspector Gadget Theme with a sampling rate of 44.1 kHz, played 2 mm from the multi-mode fiber. High-speed infrared camera data, derived from sound sampled at 1.93 kHz, consists of 10,000 frames capturing vibrations on a multi-mode fiber. It includes 128*8 pixel data and can be monitored, played, and processed through MATLAB.
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This dataset contains the synthetic stimuli used in the study published in the paper "A Comparative Study of 3D and 1D Acoustic
Simulations of the Higher Frequencies of Speech". The goal of this study was to evaluate the accuracy of the acoustic wave
propagation in the vocal tract in a source-filter synthesis paradigm with two perceptual experiments. The high frequencies (above 4 kHz) of the stimuli were
generated by three different methods: a source-filter method relying on a 1D and a 3D acoustic model, and a bandwith extension
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86 Views
The "Queue Waiting Time Dataset" is a detailed collection of information that records the movement of waiting times in queues. This dataset contains important details such as the time of arrival, the start and finish times, the waiting time, and the length of the queue. The arrival time denotes the moment when customers enter the queue, while the start and finish times track the duration of the service process. The waiting time measures the time spent waiting in the queue, and the queue length shows the number of customers in the queue when a new customer arrives.
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The data set contains raw channel-sounding data of 30 clinically relevant scenarios, captured in the university clinic of Dresden, Germany, and a script to analyze them. The measurement campaign was conducted in five environments:
- Infirmary (Inf)
- Emergency Room (ER)
- Intensive Care Unit (ICU)
- Hallway (Hall)
- Elevator (Elev)
The patients were performing various motion sequences:
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169 Views
Specific emitter identification (SEI) is a promising authentication paradigm in physical layer security (PLS). Despite the significant success of existing SEI schemes, most of them assume that the distributions of the training dataset and the test dataset are consistent. However, in most practical scenarios, when the signal parameters change, the distribution of the samples will changes, resulting in a significant performance degradation.
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314 Views
The file ExplanationForPaper.m contains the code that draws the figures in "Periodograms and The Method of Averaged Periodograms." It also produces many, many other, related figures and performs many related calculations. By reading through the code and running it, the reader will be able to "experience" the mateiral presented in the article. The reader will also see material related to the additional application discussed at the end of "The method of averaged periodograms" and just before the section "Numerical examples."
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The Intel D435i camera is selected to collect the point cloud data of lower limbs,the point cloud data source was 10 subjects including 4 females and 6 males, subjects are informed and voluntary, aged between 23-26 years, with an average age of 24.3 (±1.03) years, height of 172.1 (±6.46) cm, and weight of 71.3 (±9.58 kg.). The subjects were not trained prior to the testThe experimental data from these ten individuals were divided into two parts, half for training the long and short term memory neural network and half for validating the real-time and accuracy of the training model.
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