Mehmet Ali Cavuslu

Implementation of HOG algorithm for real time object recognition applications on FPGA based embedded system

Recent years HOG algorithm has been used to recognize objects in images, with complex content, with a very high success rate. Hardware implementation of this algorithm is very important because of the fact that it can be used in many object recognition applications. In this work HOG algorithm is implemented on FPGA to recognize different geometrical figures with a very high success rate. Objects vertical and horizontal edges have been sharpened using edge detection algorithms to calculate magnitude and angle of the local gradients. Obtained result are used to construct the histograms of gradient orientation. It is observed that each constructed histogram have distinctive features for every object. Rule based classifiers has been used to implement a successful real time object recognition approach on embedded system.

Implementation of edge dependent interpolation based de-interlacer on FPGA

Interlacing technique aims to lower the costs of high definition video systems by reducing the data amount sent to receiver unit. Regeneration of image at the receiver unit is an important point of interlacing method. In this study, regeneration (de-interlacing) of frames that are sent to receiver unit is implemented by using edge dependent interpolation method. The method is implemented using VHDL on Altera Cyclone-II FPGA. The method avoids reading of redundant data which yields to reduced operation time. Implementation occupies only %3 of the FPGA that is used in this study.

FPGA based moving objects controller using remote distance sensors

In this work, the change of sound wave speed by temperature is used in order to improve accuracy of ultrasonic distance measurement. Ambient temperature directly effects air density, thus air density effects sound wave speed. However most of ultrasonic distance measurement system assumes the sound speed is a constant. With this work, lower error rated system is suggested by considering adaptively changing soundwave speed according to ambient temperature. For the experimental work, a physical area is set and the ambient temperature is fluctuant changing, so several measurements performed with suggested and common way. In these conditions, highly sensitive sensors are used to measure ambient temperature and new soundwave speed is calculated for the temperature. When process repeated with new soundwave speed, it is observed that measurement accuracy is improved.

Hardware implementation of feature extraction from ship's underwater acoustic signals using LOFAR analysis

This study presents the FPGA implementation of a method for extraction of some features of a ship from its underwater acoustic signatures that are filtered from the environment noise. In this method, LOFAR (Low Frequency Analysis and Ranging) analysis is used to analyze the features in the low frequency bands. Signature frequencies are separated from background noise by using CFAR (Constant False Alarm Rate) based automatic threshold method. The method is implemented on an Artix-7 xc7a100tcsg324-1 FPGA.

Comparison of number formats on FPGA-based OFDM modem architecture

It is preferable for wireless operators to use the existing infrastructure as possible for different services such as GSM, 3G and Wimax. This becomes possible with the development of software defined base stations. But, to obtain desired performance and flexibility in signal processing at software defined base stations needs to be implemented instead of traditional processors and ASIC modem on programmable processors. The purpose of this thesis is implementing of the OFDM modem architecture on FPGA by using IEEE-754 floating point and IQ-Math fixed point number format. Two different OFDM modem design implemented by using IEEE-754 and IQ-Math, compared with basis of the usage of FPGA hardware resources, design clock frequency and the sensitivity of number formats criteria.

Simulation and estimation of underwater acoustical tonals emanating from naval platforms

In this study, a simulation environment, which can synthesize and analyze acoustical signals, has been developed in order to analyze the features and then to classify the naval platforms. In the simulation environment, multipath effects, in addition to transmission loss, have been taken into account to generate underwater acoustic signals. The real time analysis of the acoustic signals received by the hydrophones is accomplished by using LOFAR(Low Frequency Analysis and Recording) and DEMON(Detection of Enveloped Modulation on Noise) techniques. Order statistics based Constant False Alarm Rate type thresholding is applied in the frequency domain to the spectrogram obtained via LOFAR and DEMON analyses. This simulation environment enables to reduce the cost and time of conducting measurement campaigns in the sea, while it enables to test the performance of the feature extraction and classification algorithms.