In underwater acoustic channels, due to the limited bandwidth, the extension of the long delay due to the low speed of sound propagation, and also due to severe time changes, the establishment of a stable and efficient communication has always been accompanied by obstacles. Therefore, the use of orthogonal multi-input multi-Output multi-Carrier systems is common, but the challenge is that due to the use of long-distance rotary prefix for underwater channels, bandwidth gain and data transfer rate are greatly reduced. The proposed solution in this paper is to use multi-carrier systems based on offset bank filters. By using these systems, due to the lack of rotational prefix, the orthogonal frequency division system will no longer have problems and therefore will see an increase in interest and an increase in transmission rate in its multi-input-multi-output type. In this article, for the first time, a Hermit pulse forming filter is used, which has more accuracy and operational power than other filters. Also, in order to eliminate the destructive interference, the equalizer of at least average squares error has been used. The simulation results show the optimal performance of the proposed subsurface transmission system in shallow water. The output of simulations and numerical analysis has shown that at the rate equal to the error rate, the proposed system has a 15% higher send and receive rate, which is a valuable achievement in complex underwater conditions.

Identification and Verification using fingerprint has direct relation with quality of fingerprint. In this paper، novel method is proposed to enhance the quality of fingerprint using Arc-Gabor filter bank. Indeed proposed filter bank is an adaptive version of standard filter bank for fingerprint. Evaluating the ability of proposed method is done using two methods. In the first method، results of verification and identification using normalized histogram of Binarized Statistical Image Feature (BSIF) are compared for the fingerprints before and after using proposed method. And in the second method، Peak Signal to Noise Ratio (PSNR) is used. Two databases DBI and DBII are used for evaluation. Using proposed method، for verification Equal Error Rate، are decrease from (15. 89% and 11. 70%) to (11. 35% and 8. 00%)، respectively، and for identification، 1st rank correct recognition rate are increased from (69. 28% and 71. 16%) to (78. 80% and 81. 70%)، respectively. Average values of PSNR for enhanced fingerprint using proposed methods are more than other version of Gabor filter.

High peak-to-average power ratio (PAPR) has been a major drawback of filter bank Multicarrier (FBMC) in the 5G system. This research aims to calculate the PAPR reduction associated with the FBMC system. This research uses four techniques to reduce PAPR. They are classical tone reservation (TR). It combines tone reservation with sliding window (SW-TR). It also combines them with active constellation extension (TRACE) and with deep learning (TR-Net). TR-net decreases the greatest PAPR reduction by around 8.6 dB compared to the original value. This work significantly advances PAPR reduction in FBMC systems by proposing three hybrid methods, emphasizing the deep learning-based TRNet technique as a groundbreaking solution for efficient, distortion-free signal processing.

In this paper, exploitation of parallel matched filters bank based on binary phase code is proposed, to overcome shift Doppler in ground based radars which use pulse compression technique. The mismatch loss due to target Doppler shift in radar receiver reduced by odd or even number of these parallel filters. By increasing the number of filters over target velocity axis the central Doppler frequencies of the neighbor filters approach together and consequently the overlap increases and results in the straddling loss decrease in output. Using MATLAB software, the attenuation in the output of the receiver matched filter due to airborne target velocity was analyzed and a solution to calculate minimum number of filters is proposed for minimum loss of SNR. Finally, two methods for implementation of parallel matched filters on FPGA in the airborne targets radar receiver are presented and compared.

In traditional speech processing, feature extraction and classification were conducted as separate steps. The advent of deep neural networks has enabled methods that simultaneously model the relationship between acoustic and phonetic characteristics of speech while classifying it directly from the raw waveform. The first convolutional layer in these networks acts as a filter bank. To enhance interpretability and reduce the number of parameters, researchers have explored the use of parametric filters, with the SincNet architecture being a notable advancement. In SincNet's initial convolutional layer, rectangular bandpass filters are learned instead of fully trainable filters. This approach allows for modeling with fewer parameters, thereby improving the network's convergence speed and accuracy. Analyzing the learned filter bank also provides valuable insights into the model's performance. The reduction in parameters, along with increased accuracy and interpretability, has led to the adoption of various parametric filters and deep architectures across diverse speech processing applications. This paper introduces different types of parametric filters and discusses their integration into various deep architectures. Additionally, it examines the specific applications in speech processing where these filters have proven effective.

In this paper, two methods are proposed for the detection of a band-limited signal in unknown variance white Gaussian noise. The complex amplitude and the frequency of the signal and the noise variance are assumed as unknown parameters. Using wavelet concepts, an orthonormal, fully-decimated filter-bank is employed to decompose the signal into its subband components. It is shown that, in this process, the noise is also decomposed into orthonormal zero-mean components. In the output, if a band-limited target signal is present, the respective single subband component (or two components in marginal cases) containing the target signal presents a non-zero mean. The presence of a non-zero mean component(s) in this canonical form is tested using a well-known Generalized Likelihood Ratio (GLR) solution (F-test), which is based on the ratio between the output power of one (or two) subband(s) and the average output power of the other subbands (estimating the noise variance). Comparing to a threshold, a Constant FalseAlarm Rate (CFAR) detector is constructed. Since the target signal's central frequency is unknown, the proper subband(s) is selected as the one (or two) maximizing the F-test statistic and a GLRtest, namely a Wavelet Detector (WD), is obtained. It turns out that the performance of WD depends on the frequency of the signal. For instance, a low pass signal is detected better than a bandpass signal by this detector. To overcome this problem, the frequency band, where the signal may exist, is estimated, and the signal is down-converted such that the detection is always accomplished at the lowest subband in the new detector, a Modified WD (MWD). The performance of the proposed methods is evaluated in solving two well-known problems, compared with the existing DFT detector. A sinusoid with unknown amplitude, phase and frequency is detected by these detectors as an approximately band-limited signal. The proposed detectors are also applicable for the detection of a signal composed of a white component and an approximately band-limited component. A sinusoid, with unknown phase and frequency and Rayleigh-distributed amplitude, is also detected as such a signal.

proper choice for descripting images captured by ordinary optic sensors. In order to cover all spectrum and extracting better features filter banks are usually used. Although there is different scales and orientations in filter bank, but using proper values for other parameters such as maximum frequency, filters’ dimension and length of arc can effectively impact on final result. In this paper Meta-heuristic methods are used to estimate optimum values for these parameters. According to obtained results, in identification using Optimum Arc-Gabor filter bank (OAGFB) trained by Improved Gravitational Search Algorithm, the average of 1st Rank identification rate is increased from 79. 43 to 95. 71% and in verification by optimizing proposed filter bank using Simulated Annealing the average of Equal Error Rate is decreased from 8. 84 to 5. 12%.