For almost a half-decade, the unique autocorrelation properties of Golay complementary pairs (GCP) have added a significant value to the key performance of conventional time-domain multiplexed fiber Bragg grating sensors (TDM-FBGs). However, the employment of the unipolar form of Golay coded TDM-FBG has suffered from several performance flaws, such as limited improvement of the signal-to-noise ratio (SNIR), noisy backgrounds, and distorted signals. Therefore, we propose and experimentally implement several digital filtering techniques to mitigate such limitations. Moving averages (MA), Savitzky-Golay (SG), and moving median (MM) filters were deployed to process the signals from two low reflectance FBG sensors located after around 16 km of fiber. The first part of the experiment discussed the sole deployment of Golay codes from 4 bits to 256 bits in the TDM-FBG sensor. As a result, the total SNIR of around 8.8 dB was experimentally confirmed for the longest 256-bit code. Furthermore, the individual deployment of MA, MM, and SG filters within the mentioned decoded sequences secured a further significant increase in SNIR of around 4, 3.5, and 3 dB, respectively. Thus, the deployment of the filtering technique alone resulted in at least four times faster measurement time (equivalent to 3 dB SNIR). Overall, the experimental analysis confirmed that MM outperformed the other two techniques in better signal shape, fastest signal transition time, comparable SNIR, and capability to maintain high spatial resolution.
We propose to cascade the Shape-Preserving Piecewise Cubic Hermite model with the Autoregressive Moving Average (ARMA) interpolator; we call this technique the Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average (SP2CHARMA) model. In a few test cases involving different images, this model is found to deliver an optimum solution for signal to noise ratio (SNR) estimation problems under different noise environments. The performance of the proposed estimator is compared with two existing methods: the autoregressive-based and autoregressive moving average estimators. Being more robust with noise, the SP2CHARMA estimator has efficiency that is significantly greater than those of the two methods.
This paper presents a two-level scheduling scheme for video transmission over downlink orthogonal frequency-division multiple access (OFDMA) networks. It aims to maximize the aggregate quality of the video users subject to the playback delay and resource constraints, by exploiting the multiuser diversity and the video characteristics. The upper level schedules the transmission of video packets among multiple users based on an overall target bit-error-rate (BER), the importance level of packet and resource consumption efficiency factor. Instead, the lower level renders unequal error protection (UEP) in terms of target BER among the scheduled packets by solving a weighted sum distortion minimization problem, where each user weight reflects the total importance level of the packets that has been scheduled for that user. Frequency-selective power is then water-filled over all the assigned subcarriers in order to leverage the potential channel coding gain. Realistic simulation results demonstrate that the proposed scheme significantly outperforms the state-of-the-art scheduling scheme by up to 6.8 dB in terms of peak-signal-to-noise-ratio (PSNR). Further test evaluates the suitability of equal power allocation which is the common assumption in the literature.
Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions.
In diversity combining at the receiver, the output signal-to-noise ratio (SNR) is often maximized by using the maximal ratio combining (MRC) provided that the channel is perfectly estimated at the receiver. However, channel estimation is rarely perfect in practice, which results in deteriorating the system performance. In this paper, an imperialistic competitive algorithm (ICA) is proposed and compared with two other evolutionary based algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA), for diversity combining of signals travelling across the imperfect channels. The proposed algorithm adjusts the combiner weights of the received signal components in such a way that maximizes the SNR and minimizes the bit error rate (BER). The results indicate that the proposed method eliminates the need of channel estimation and can outperform the conventional diversity combining methods.
We experimentally investigate the performance of L-band multiwavelength Brillouin-Raman fiber laser (MBRFL) under forward and backward pumped environments utilizing a linear cavity. A short length of 1.18 km dispersion compensating fiber is used as a nonlinear gain medium for both Brillouin and Raman gain. Experimental results indicate that the gain in the copumped laser configuration is higher than the gain in the counterpumped configuration. A stable and constant number of Brillouin Stokes lines up to 23 Stokes, with channel spacing of 0.08 nm and more than 20 dB of optical signal to noise ratio, can be generated as well as tuning over 20 nm in the L-band region from 1570 to 1590 nm. The laser generating the Brillouin Stokes lines exhibits flat amplitude bandwidth and high average output power of 0.8 and 1.6 dBm for the copropagation and counterpropagation pumps, respectively. Moreover, the tuning range bandwidth of the MBRFL can be predicted from the oscillated Brillouin pump gain profile.
In this paper, a variable threshold voice activity detector (VAD) is developed to control the operation of a two-sensor adaptive noise canceller (ANC). The VAD prohibits the reference input of the ANC from containing some strength of actual speech signal during adaptation periods. The novelty of this approach resides in using the residual output from the noise canceller to control the decisions made by the VAD. Thresholds of full-band energy and zero-crossing features are adjusted according to the residual output of the adaptive filter. Performance evaluation of the proposed approach is quoted in terms of signal to noise ratio improvements as well mean square error (MSE) convergence of the ANC. The new approach showed an improved noise cancellation performance when tested under several types of environmental noise. Furthermore, the computational power of the adaptive process is reduced since the output of the adaptive filter is efficiently calculated only during non-speech periods.
A widely tunable low stimulated Brillouin scattering (SBS) photonic crystal fiber (PCF) based multi-wavelength Brillouin-erbium fiber laser is presented. The fiber laser structure utilizes a pre-amplified Brillouin pump (BP) technique with 100 m of PCF and a tunable band-pass filter within a Fabry-Perot cavity. A total of 14 Brillouin Stokes lines can be tuned over 29 nm from 1540 nm to 1569 nm. The wide tunability was only limited by the bandwidth of the tunable band-pass filter. A constant channel spacing of 0.079 nm and signal to noise ratio (SNR) of more than 20 dB for each Brillouin Stokes lines were also observed.
We demonstrate a multiple-wavelength Brillouin comb laser with cooperative Rayleigh scattering that uses Raman amplification in dispersion-compensating fiber. The laser resonator is a linear cavity formed by reflector at each end of the dispersion-compensating fiber to improve the reflectivity of the Brillouin Stokes comb. Multiple Brillouin Stokes generation has been improved in terms of optical signal-to-noise ratio and power-level fluctuation between neighboring channels. Furthermore, the linewidth of the Brillouin Stokes is uniform within the laser output bandwidth.
Image watermarking embeds identifying information in an image in such a manner that it cannot easily be removed. For the past several years, image digital watermarking has become a necessary element used for hiding secret image and enabling secured communication such as
privacy, confidentiality, authentication and data integrity. Although numerous watermarking schemes are present in grayscale images, the present work focuses on the RGB color image. This study proposed a new hybrid method that would satisfy the essential needs of modern image watermarking. The color image watermarking is based on the 2D Discrete Cosine Transform and Elgamal cryptosystem. The 2D Discrete Cosine Transform depends on the matrix products, while the Elgamal cryptosystem depends on the discrete logarithm problem. The cryptosystem is combined with existing Arnold transform in watermarking algorithm to enhance the security of secret image. Value of Peak Signal to Noise Ratio was taken as performance evaluation parameters. On the whole, the performance evaluation shows that combining the two algorithms improved the performance of image watermarking.
Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a prediction tool that developed practically but not limited to small sample analysis. It was developed explicitly for multidimensional system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as help in eliminating variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multidimensionality restraint the optimization accuracy. Binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process to better prediction accuracy. The results show that if the historical data consist of samples with higher correlation of determination (R2) value for the model creation, the optimization process in reducing the number of variables would be much reliable and accurate. Comparing between T-Method+OA and T-Method+BPSO in four different case study, it shows that T-Method+BPSO performing better with greater R2 and means relative error (MRE) value compared to T-Method+OA.
In ultrasound imaging there is compromise between the penetration of signal at certain depths into the object and image resolution as the ultrasound probe only can transmit single frequency signals in one transmission. Using curvilinear ultrasound probe with 2 to 5 MHz frequency bandwidth, this study investigated the use of multi-frequency imaging to enhance the quality of phantom images.
Methods: Siemen Acuson X150 with curvilinear ultrasound transducer was used to scan the organs of interest (kidney, gallbladder and pancreas) of the ultrasound abdominal phantom. Different images at the different selected frequencies (2.5, 3.6 and 5.0 MHz) were created by fixing the position and the orientation of the transducer in each of the scanning process. Different-frequency images were generated and combined to produce composite (multi-frequency) image. Results: In this study, the quality of the composite image was evaluated based on signal-to noise ratio (SNR) and the obtained results were compared with the single frequency images. Besides, the comparison was also made in terms of overall image quality (noise and sharpness of organ outline) through perceived image quality analysis. Based on calculated SNR, the composite image of the kidney, gallbladder and pancreas recorded higher SNR value as compared to the single frequency images. However, through perceived image quality, most of the observers viewed that the quality of the composite image of the kidney, gallbladder and pancreas is poor as compared to the single frequency image. Conclusions: Image quality of ultrasound imaging is improved by combining multiple ultrasound frequency images into a single composite image. This is achieved as high SNR is obtained in the composite image. However, through perceived image quality, the overall image quality of the composite image was poor.
Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this paper, we propose a single-pixel object inspection system for the underwater environment based on compressive sensing super-resolution convolutional neural network (CS-SRCNN). With the CS-SRCNN algorithm, image reconstruction can be achieved with 30% of the total pixels in the image. We also investigate the impact of compression ratios on underwater object SPI reconstruction performance. In addition, we analyzed the effect of peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to determine the image quality of the reconstructed image. Our work is compared to the SPI system and SRCNN method to demonstrate its efficiency in capturing object results from an underwater environment. The PSNR and SSIM of the proposed method have increased to 35.44% and 73.07%, respectively. This work provides new insight into SPI applications and creates a better alternative for underwater optical object imaging to achieve good quality.
We demonstrate a multi-wavelength light source using a semiconductor optical amplifier (SOA) in conjunction with an array waveguide grating (AWG). The experimental results showed more than 20 channels with a wavelength separation of 0.8 nm and an optical signal-to-noise ratio of more than 10 dB under room temperature. The channels operated at the wavelength region from 1530.4 nm to 1548.6 nm, which corresponded to AWG filtering wavelengths with SOA drive current of 350 mA. The proposed light source had the advantages of a simple and compact structure, multi-wavelength operation and the system could be upgraded to generate more wavelengths.
This paper proposes a signal-to-noise-ratio (SNR) improvement by using an external phase modulator that allowed flexible control of the spectrum amplitude by varying the modulation index for linewidth measurements. Compared with the conventional self-heterodyne detection technique, the results obtained in this study showed an SNR improvement as high as 10 dB. This 10 dB improvement in SNR could help to reduce the usage of a particular length of a single mode fibre (normally about 50 Km) when measuring a linewidth in the region of 10 kHz.
This paper presents the improvement of quality factor (Q) estimation using shift frequency method. A new method was developed based on two previous methods; peak frequency shift (PFS) method and centroid frequency shift (CFS) method. The proposed algorithm has been tested to gauge its performance using three different scenarios; Q variation, travel
time variation, and signal to noise ratio (SNR) variation. The test was performed using the Ricker wavelet with random noise included. Based on the results obtained, it can be concluded that the new proposed method was able to improve Q estimation using shift frequency method. This method can also be implemented in the low and high Q condition, shallow and deep wavelet targets and in the low and high SNR conditions of seismic data. The limitations in the PFS and CFS methods can be reduced by this method.
Subei basin is the most promising onshore oil and gas bearing basin in South China. With the deepening of exploration, subtle hydrocarbon reservoirs have gradually become the major target of exploration. Seismic record often shows low signal to noise ratio (SNR), resulting that conventional seismic records have three shortcomings in the identification of subtle reservoirs: difficult to identify small faults; difficult to show the distribution law of sand body; and difficult to find traps. In order to solve this problem, we conducted the research on signal synthesis and decomposition. The research results showed that seismic record of different frequency bands can be restored from original seismic record and both of them contain real stratigraphic information. Based on this, when a certain band or several bands in the original seismic record is affected by noise and result in the reduction of SNR of seismic record, seismic information seriously affected by noise can be abandoned, leaving only less affected seismic information to obtain seismic record with higher SNR. In the collection of actual seismic record, the low and high band seismic information is seriously affected by noise, while medium-band seismic information is less affected. Therefore, based on this, the medium-band seismic information can be restored from the original seismic record to be new record, which is called predominant frequency band seismic record. In this paper, based on the research result, the predominant frequency band seismic record was applied to the two areas of Subei basin and the result showed the research result can be used as a good instruction on well placement and the improvement of drilling success rate.
A number of techniques have been proposed during the last three decades for noise variance and signal-to-noise ratio (SNR) estimation in digital images. While some methods have shown reliability and accuracy in SNR and noise variance estimations, other methods are dependent on the nature of the images and perform well on a limited number of image types. In this article, we prove the accuracy and the efficiency of the image noise cross-correlation estimation model, vs. other existing estimators, when applied to different types of scanning electron microscope images.
A new and robust parameter estimation technique, named image noise cross-correlation, is proposed to predict the signal-to-noise ratio (SNR) of scanning electron microscope images. The results of SNR and variance estimation values are tested and compared with nearest neighborhood and first-order interpolation. Overall, the proposed method is best as its estimations for the noise-free peak and SNR are most consistent and accurate to within a certain acceptable degree, compared with the others.
During the last three decades, several techniques have been proposed for signal-to-noise ratio (SNR) and noise variance estimation in images, with different degrees of success. Recently, a novel technique based on the statistical autoregressive model (AR) was developed and proposed as a solution to SNR estimation in scanning electron microscope (SEM) image. In this paper, the efficiency of the developed technique with different imaging systems is proven and presented as an optimum solution to image noise variance and SNR estimation problems. Simulation results are carried out with images like Lena, remote sensing, and SEM. The two image parameters, SNR and noise variance, are estimated using different techniques and are compared with the AR-based estimator.