This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it is obvious that the number of moments selected by the SP should exceed 30% of the overall EEG samples for accuracy to be over 90%.
Root-finding is an oldest classical problem, which is still an important research topic, due to its impact on computational algebra and geometry. In communications systems, when the impulse response of the channel is minimum phase the state of equalization algorithm is reduced and the spectral efficiency will improved. To make the channel impulse response minimum phase the prefilter which is called minimum phase filter is used, the adaptation of the minimum phase filter need root finding algorithm. In this paper, the VHDL implementation of the root finding algorithm introduced by Clark and Hau is introduced.
VHDL program is used in the work, to find the roots of two channels and make them minimum phase, the obtained output results are
This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria’s value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded fro
... Show MoreFinger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network
... Show MoreIn this research we will present the signature as a key to the biometric authentication technique. I shall use moment invariants as a tool to make a decision about any signature which is belonging to the certain person or not. Eighteen voluntaries give 108 signatures as a sample to test the proposed system, six samples belong to each person were taken. Moment invariants are used to build a feature vector stored in this system. Euclidean distance measure used to compute the distance between the specific signatures of persons saved in this system and with new sample acquired to same persons for making decision about the new signature. Each signature is acquired by scanner in jpg format with 300DPI. Matlab used to implement this system.
In this paper, we calculate and measure the SNR theoretically and experimental for digital full duplex optical communication systems for different ranges in free space, the system consists of transmitter and receiver in each side. The semiconductor laser (pointer) was used as a carrier wave in free space with the specification is 5mW power and 650nm wavelength. The type of optical detector was used a PIN with area 1mm2 and responsively 0.4A/W for this wavelength. The results show a high quality optical communication system for different range from (300-1300)m with different bit rat (60-140)kbit/sec is achieved with best values of the signal to noise ratio (SNR).
In this work, satellite images classification for Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented. Proposed supervised classification method (Modified Vector Quantization) using MATLAB software and supervised classification method (Maximum likelihood Classifier) using ERDAS imagine have been used, in order to get most accurate results and compare these methods. The changes that taken place in year 2000 comparing with 1990 and in year 2015 comparing with 2000 are calculated. The results from classification indicated that water and vegetation are decreased, while barren land, alluvial soil and shallow water
... Show Morehe Orthogonal Frequency Division Multiplexing is a promising technology for the Next Generation Networks. This technique was selected because of the flexibility for the various parameters, high spectral efficiency, and immunity to ISI. The OFDM technique suffers from significant digital signal processing, especially inside the Inverse/ Fast Fourier Transform IFFT/FFT. This part is used to perform the orthogonality/De-orthogonality between the subcarriers which the important part of the OFDM system. Therefore, it is important to understand the parameter effects on the increase or to decrease the FPGA power consumption for the IFFT/FFT. This thesis is focusing on the FPGA power consumption of the IFFT/FFT uses in the OFDM system. This researc
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