Identification of Musical Instruments by means of the Hough-Transformation

In order to distinguish between the sounds of different musical instruments, certain instrument-specific sound features have to be extracted from the time series representing a given recorded sound. The Hough Transform is a pattern recognition procedure that is usually applied to detect specific curves or shapes in digital pictures (Shapiro, 1978). Due to some similarity between pattern recognition and statistical curve fitting problems, it may as well be applied to sound data (as a special case of time series data). The transformation is parameterized to detect sinusoidal curve sections in a digitized sound, the motivation being that certain sounds might be identified by certain oscillation patterns. The returned (transformed) data is the timepoints and amplitudes of detected sinusoids, so the result of the transformation is another ?condensed? time series. This specific Hough Transform is then applied to sounds played by different musical instruments. The generated data is investigated for features that are specific for the musical instrument that played the sound. Several classification methods are tried out to distinguish between the instruments and it turns out that RDA (a hybrid method combining LDA and QDA) (Friedman, 1989) performs best. The resulting error rate is better than those achieved by humans (Bruderer, 2003). --

VLSI Implementierung eines parallelen Hough-Transformations-Prozessors mit dynamisch nachladbaren Mustern

In 1.0 5m CMOS Technik wurde ein Prozessor zur parallelen Verarbeitung einer speziellen Hough-Transformation entwickelt. Bei der auf 50 MHz ausgelegten Taktfrequenz koennen 6.4 x 10E+10 Objektmuster pro Sekunde detektiert werden. Bis zu 5 x 10E+7 zu detektierende Suchmuster koennen pro Sekunde in den Prozessor geladen werden. Damit koennen erstmals Echtzeitapplikationen in der Bildverarbeitung im Mikrosekundenbereich erschlossen werden

DESIGN OF A GAIT ACQUISITION AND ANALYSIS SYSTEM FOR ASSESSING THE RECOVERY OF MICE POST-SPINAL CORD INJURY

Current methods of determining spinal cord recovery in mice, post-directed injury, are qualitative measures. This is due to the small size and quickness of mice. This thesis presents a design for a gait acquisition and analysis system able to capture the footfalls of a mouse, extract position and timing data, and report quantitative gait metrics to the operator. These metrics can then be used to evaluate the recovery of the mouse. This work presents the design evolution of the system, from initial sensor design concepts through prototyping and testing to the final implementation. The system utilizes a machine vision camera, a well-designed walkway enclosure, and image processing techniques to capture and analyze paw strikes. Quantitative results gained from live animal experiments are presented, and it is shown how the measurements can be used to determine healthy, injured, and recovered gait

Identification of Musical Instruments by means of the Hough-Transformation

In order to distinguish between the sounds of different musical instruments, certain instrument-specific sound features have to be extracted from the time series representing a given recorded sound. The Hough Transform is a pattern recognition procedure that is usually applied to detect specific curves or shapes in digital pictures (Shapiro, 1978). Due to some similarity between pattern recognition and statistical curve fitting problems, it may as well be applied to sound data (as a special case of time series data). The transformation is parameterized to detect sinusoidal curve sections in a digitized sound, the motivation being that certain sounds might be identified by certain oscillation patterns. The returned (transformed) data is the time points and amplitudes of detected sinusoids, so the result of the transformation is another condensed time series. This specific Hough Transform is then applied to sounds played by different musical instruments. The generated data is investigated for features that are specific for the musical instrument that played the sound. Several classification methods are tried out to distinguish between the instruments and it turns out that RDA (a hybrid method combining LDA and QDA) (Friedman, 1989) performs best. The resulting error rate is better than those achieved by humans (Bruderer, 2003)

Computer Vision System-On-Chip Designs for Intelligent Vehicles

Intelligent vehicle technologies are growing rapidly that can enhance road safety, improve transport efficiency, and aid driver operations through sensors and intelligence. Advanced driver assistance system (ADAS) is a common platform of intelligent vehicle technologies. Many sensors like LiDAR, radar, cameras have been deployed on intelligent vehicles. Among these sensors, optical cameras are most widely used due to their low costs and easy installation. However, most computer vision algorithms are complicated and computationally slow, making them difficult to be deployed on power constraint systems. This dissertation investigates several mainstream ADAS applications, and proposes corresponding efficient digital circuits implementations for these applications. This dissertation presents three ways of software / hardware algorithm division for three ADAS applications: lane detection, traffic sign classification, and traffic light detection. Using FPGA to offload critical parts of the algorithm, the entire computer vision system is able to run in real time while maintaining a low power consumption and a high detection rate. Catching up with the advent of deep learning in the field of computer vision, we also present two deep learning based hardware implementations on application specific integrated circuits (ASIC) to achieve even lower power consumption and higher accuracy. The real time lane detection system is implemented on Xilinx Zynq platform, which has a dual core ARM processor and FPGA fabric. The Xilinx Zynq platform integrates the software programmability of an ARM processor with the hardware programmability of an FPGA. For the lane detection task, the FPGA handles the majority of the task: region-of-interest extraction, edge detection, image binarization, and hough transform. After then, the ARM processor takes in hough transform results and highlights lanes using the hough peaks algorithm. The entire system is able to process 1080P video stream at a constant speed of 69.4 frames per second, realizing real time capability. An efficient system-on-chip (SOC) design which classifies up to 48 traffic signs in real time is presented in this dissertation. The traditional histogram of oriented gradients (HoG) and support vector machine (SVM) are proven to be very effective on traffic sign classification with an average accuracy rate of 93.77%. For traffic sign classification, the biggest challenge comes from the low execution efficiency of the HoG on embedded processors. By dividing the HoG algorithm into three fully pipelined stages, as well as leveraging extra on-chip memory to store intermediate results, we successfully achieved a throughput of 115.7 frames per second at 1080P resolution. The proposed generic HoG hardware implementation could also be used as an individual IP core by other computer vision systems. A real time traffic signal detection system is implemented to present an efficient hardware implementation of the traditional grass-fire blob detection. The traditional grass-fire blob detection method iterates the input image multiple times to calculate connected blobs. In digital circuits, five extra on-chip block memories are utilized to save intermediate results. By using additional memories, all connected blob information could be obtained through one-pass image traverse. The proposed hardware friendly blob detection can run at 72.4 frames per second with 1080P video input. Applying HoG + SVM as feature extractor and classifier, 92.11% recall rate and 99.29% precision rate are obtained on red lights, and 94.44% recall rate and 98.27% precision rate on green lights. Nowadays, convolutional neural network (CNN) is revolutionizing computer vision due to learnable layer by layer feature extraction. However, when coming into inference, CNNs are usually slow to train and slow to execute. In this dissertation, we studied the implementation of principal component analysis based network (PCANet), which strikes a balance between algorithm robustness and computational complexity. Compared to a regular CNN, the PCANet only needs one iteration training, and typically at most has a few tens convolutions on a single layer. Compared to hand-crafted features extraction methods, the PCANet algorithm well reflects the variance in the training dataset and can better adapt to difficult conditions. The PCANet algorithm achieves accuracy rates of 96.8% and 93.1% on road marking detection and traffic light detection, respectively. Implementing in Synopsys 32nm process technology, the proposed chip can classify 724,743 32-by-32 image candidates in one second, with only 0.5 watt power consumption. In this dissertation, binary neural network (BNN) is adopted as a potential detector for intelligent vehicles. The BNN constrains all activations and weights to be +1 or -1. Compared to a CNN with the same network configuration, the BNN achieves 50 times better resource usage with only 1% - 2% accuracy loss. Taking car detection and pedestrian detection as examples, the BNN achieves an average accuracy rate of over 95%. Furthermore, a BNN accelerator implemented in Synopsys 32nm process technology is presented in our work. The elastic architecture of the BNN accelerator makes it able to process any number of convolutional layers with high throughput. The BNN accelerator only consumes 0.6 watt and doesn\u27t rely on external memory for storage

Real-time phasefront detector for heterodyne interferometers

We present a real-time differential phasefront detector sensitive to better than 3 mrad rms, which corresponds to a precision of about 500 pm. This detector performs a spatially resolving measurement of the phasefront of a heterodyne interferometer, with heterodyne frequencies up to approximately 10 kHz. This instrument was developed as part of the research for the LISA Technology Package (LTP) interferometer, and will assist in the manufacture of its flight model. Due to the advantages this instrument offers, it also has general applications in optical metrology