Noise Density Level Research Articles

Modern Solutions to Improve the Energy Efficiency of Radio Lines for the Technical Renewal of Radio Stations of Integrated Communication Systems

Promising tasks for improving radio equipment with increased energy efficiency, related to in-depth preliminary study and efficiency research using the stand of the chief designer, are considered. Initially, a theoretical factor analysis of the interference radio terminal energy efficiency with respect to evenly distributed over the spectrum is given. The problem of choosing a variant of digital modulation and parameters of a noise-resistant code from the condition of obtaining the least energy for the transmission of one bit of information at a given error probability per bit and the level of spectral noise density is examined. With regard to broadband signals, energy-efficient linearly frequency-modulated signals are allocated. In the case of narrowband signals, cognitive technologies for improving energy efficiency for radio paths with partially filled spectrum, tuning radio lines to the 'windows of the best transparency' of the spectrum, are studied. Their importance for VHF radio lines and HF radio systems with tuning to the optimal frequency by regular ionosphere sounding is noted. The features of cognitive radio networks development to improve the energy efficiency of radio facilities are considered. The ways of algorithms design for modem and noise-resistant encoder energy effective tuning to a given radio line conditions for testing in the stand of the Chief Designer are discussed. Priority tasks for improving radio equipment with increased energy efficiency have been specified. Urgent specific subtasks for elaboration at the stand of the Chief Designer are highlighted.

Pretreatment of Ultra-Weak Fiber Bragg Grating Hydrophone Array Based on Cubic Spline Interpolation Using Intensity Compensation.

The demodulation algorithm based on 3 × 3 coupler in a fiber-optic hydrophone array has gained extensive attention in recent years. The traditional method uses a circulator to construct the normal path-match interferometry; however, the problem of increasing the asymmetry of the three-way signal to be demodulated is easily overlooked. To provide a solution, we report a pretreatment method for hydrophone array based on 3 × 3 coupler demodulation. We use cubic spline interpolation to perform nonlinear fitting to the reflected pulse train and calculate the peak-to-peak values of the single pulse to determine the light intensity compensation coefficient of the interference signal, so as to demodulate the corrected three-way interference signal. For experimental verification, ultra-weak fiber Bragg gratings (uwFBGs) with reflectivity of −50 dB are applied to construct a hydrophone array with 800 sensors, and a vibratory liquid column method is set up to generate a low-frequency hydroacoustic signal. Compared to the traditional demodulation algorithm based on a 3 × 3 coupler, the pretreatment method can improve the consistency of interference signals. The Lissajous figures show that cubic spline interpolation can improve the accuracy of monopulse peak seeking results by about 1 dB, and intensity compensation can further lead to a much lower noise density level for the interference pulse amplitude—specifically, more than 7 dB at 5~50 Hz—and the signal-to-noise ratio is improved by approximately 10 dB at 10 Hz. The distinct advantages of the proposed pretreatment method make it an excellent candidate for a hydrophone array system based on path-match interferometry.

A new effective denoising filter for high density impulse noise reduction

Today, thanks to the rapid development of technology, the importance of digital images is increasing. However, sensor errors that may occur during the acquisition, interruptions in the transmission of images and errors in storage cause noise that degrades data quality. Salt and pepper noise, a common impulse noise, is one of the most well-known types of noise in digital images. This noise negatively affects the detailed analysis of the image. It is very important that pixels affected by noise are restored without loss of image fine details, especially at high level of noise density. Although many filtering algorithms have been proposed to remove noise, the enhancement of images with high noise levels is still complex, not efficient or requires very long runtime. In this paper, we propose an effective denoising filter that can restore the image effectively in terms of quality and speed with less complexity for high density noise level. In the experimental studies, we compare the denoising results of the proposed method with other state-of-the-art methods and the proposed algorithm is quantitatively and visually comparable to these algorithms when the noise intensity is up to 90%.

RETRACTED: Design of embedded digital image processing system based on ZYNQ

Zynq- real time edge detection system based on execution of the image reconstructed from the sequence. Although the object edge detection is an essential tool in computer vision, edge detection results in the negative image frame noise significantly. Further, due to the high computational complexity of an image filtering operation, implemented in the hardware configuration of the re-configurable hardware it is necessary. In this portion, the proposed dynamic Zynq embedded system reconfiguration capability to detect according to the input image frame to the noise level of the different density filters perform during runtime bit stream re Configuration.The results show that the accuracy of edge detection is highly evaluated. We analyze various noise density levels, and the results show that the edge detection results of the proposed filter bit stream are more accurate, while effectively providing computing capacity to support the real-time processing of the image frame. In the configuration time, CPU usage, and hardware resource utilization performance test results were compared.

Impulse noise reduction using hybrid neuro‐fuzzy filter with improved firefly algorithm from X‐ray bio‐images

AbstractNoise filtering performance in medical images is improved using a neuro‐fuzy network developed with the combination of a post processor and two neuro‐fuzzy (NF) filters. By the fact, the Sugeno‐type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro‐fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.

A Quartile Based Novel Approach For The Reduction Of Salt And Pepper Noise

In the modern era, people prefer short and informative data like images over the large texts. In text messages, we are focusing on security of data in a similar way in images along with the security of data the noise in an image is a big issue. Image noise is basically a pixel affected by some false value which may be the cause of misleading the result especially in an object and edge detection in an image. Hence for performing the operation of an object and edge detection in the noisy environment some pre-processing must be required. Noise reduction from an image is active research problem in Digital Image Processing. Gaussian noise, Speckle noise, Poisson noise, Salt and Pepper noise are the most common types of noises in an image. In this paper the authors proposed a quartile based novel noise reduction approach for salt and pepper noise which perform better than the median filter in noisy images having a high density of salt and pepper noise. For experimental result analysis, a qualitative and quantitative comparison is done with the traditionally widely accepted approach for salt and pepper noise i.e median filter, which is considered best for the salt and pepper noise. All experimental analysis is done with the randomly selected images from the MSRA dat-aset (Salient Object images data-set). Noisy images are prepared by mixing the artificial noise using MATLAB with different level of noise density.

An Enhanced Distributed Acoustic Sensor Based on UWFBG and Self-Heterodyne Detection

We demonstrate an enhanced distributed acoustic sensing (DAS) system based on ultra-weak fiber Bragg grating (UWFBG) array and self-heterodyne detection. By using the interaction process between nearby UWFBGs, the distribution of acoustic field along the sensing fiber could be fully captured. Self-heterodyne detection was applied, leading to much lower noise density level for the proposed sensing system. The experimental results have shown that the acoustic field could be reconstructed with high fidelity even at the rear end of a 42 km long sensing fiber with the spatial resolution of 11.7 m. The proposed sensing system has a linear intensity response of R2 = 0.9988, while the peak-to-peak value (VPP) of dynamic strain was tuned from 9.28 nϵ to 176.96 nϵ. The root-mean-square (RMS) measurement error of our sensing system was less than 1.32 nϵ. The frequency response range of the proposed system was from 8 Hz to 1 kHz. The proposed sensing system offered a promising solution for the performance enhancement of DAS system that could achieve high spatial resolution, high sensing sensitivity, long sensing distance and dead zone free measurement at the same time.

Model to estimate the salt and pepper noise density level on gray-scale digital image

In this research paper, we proposed a probabilistic analysis to find the relationship between entropy of image and salt & pepper noise density. For this estimation, we have employed entropy inspection of spatial domain technique. Based on the fact that entropy of image signal decreases with increase

Random Valued Impulse Noise Removal Using Region Based Detection Approach

Removal of random valued noisy pixel is extremely challenging when the noise density is above 50%. The existing filters are generally not capable of eliminating such noise when density is above 70%. In this paper a region wise density based detection algorithm for random valued impulse noise has been proposed. On the basis of the intensity values, the pixels of a particular window are sorted and then stored into four regions. The higher density based region is considered for stepwise detection of noisy pixels. As a result of this detection scheme a maximum of 75% of noisy pixels can be detected. For this purpose this paper proposes a unique noise removal algorithm. It was experimentally proved that the proposed algorithm not only performs exceptionally when it comes to visual qualitative judgment of standard images but also this filter combination outsmarts the existing algorithm in terms of MSE, PSNR and SSIM comparison even up to 70% noise density level.

Medical Development Platform Using ZyCAP-Based Partial Reconfiguration on ZynqSoC

AbstractAnalysis of medical image is important in many biomedical applications such as abnormality detection, diagnosis, and surgical planning. Especially, edge detection of medical images is essential for segmentation and automatic recognition of the human organs. However, performance of edge detection filter degrades significantly if medical images/videos are corrupted with noises. In this paper, Zynq SoC is used as a medical development platform to implement real-time edge detection of image/video sequences of up to 1080p-resolution. Dynamic partial reconfigurable capability is utilized so that adaptive reconfiguration of partial filter bitstreams is performed according to the detected noise density level in the image/video sequences. Moreover, open source controller, called ZyCAP, is implemented on the proposed platform to further increase the reconfiguration speed through DMA controller, which is connected to high performance port for efficient access to external DRAM memory. We demonstrate that the ...

On a hybrid use of structural vibration signatures for damage identification: a virtual vibration deflection (VVD) method

A damage identification method named virtual vibration deflection (VVD) was developed, the principle of which was formulated based on the “weak” modality of the pseudo-excitation (PE) approach previously established. In essence, VVD is based on locating structural damage within a series of “sub-regions” divided from the entire structure under inspection, and each sub-region was considered as a “virtual” structure undergoing independent vibration. The corresponding vibration deflection of the “virtual” structure was then used to derive the damage index of VVD. Besides various advantages inheriting from the PE approach, for example, capability of detecting damage without baseline signals and pre-developed benchmark structures, VVD exhibits improved detection accuracy and particularly enhanced noise immunity compared with the PE approach, attributed to a hybrid use of multi-types of vibration signatures (MTVS). As a proof-of-concept investigation, a beam model was used in a numerical study to examine the philosophy of VVD. And the influences from different factors (i.e., level of measurement noise and measurement density) on the detection accuracy of VVD were discussed based on the numerical model. An experiment was carried out subsequently to identify the locations of multiple defects contained in an aluminum beam-like structure. Identification results constructed by the PE approach, VVD using single-type of vibration signatures, and VVD using MTVS, were presented, respectively, for the purpose of comparison.

Entropy-guided switching trimmed mean deviation-boosted anisotropic diffusion filter

This report describes the experimental analysis of a proposed switching filter-anisotropic diffusion hybrid for the filtering of the fixed value (salt and pepper) impulse noise (FVIN). The filter works well at both low and high noise densities though it was specifically designed for high noise density levels. The filter combines the switching mechanism of decision-based filters and the partial differential equation-based formulation to yield a powerful system capable of recovering the image signals at very high noise levels. Experimental results indicate that the filter surpasses other filters, especially at very high noise levels. Additionally, its adaptive nature ensures that the performance is guided by the metrics obtained from the noisy input image. The filter algorithm is of both global and local nature, where the former is chosen to reduce computation time and complexity, while the latter is used for best results.

An automatic filtering convergence method for iterative impulse noise filters based on PSNR checking and filtered pixels detection

Whether input images are corrupted by impulse noise and what the noise density level is are unknown a priori, and thus published iterative impulse noise filters cannot adaptively reduce noise, resulting in a smoothing image or unclear de-noising. For this reason, this paper proposes an automatic filtering convergence method using PSNR checking and filtered pixel detection for iterative impulse noise filters. (1) First, the similarity between the input image and the 1st filtered image is determined by calculating MSE. If MSE is equal to 0, then the input image is unfiltered and becomes the output. (2) Otherwise, one applies PSNR checking and filtered pixel detection to estimate the difference between the tth filtered image and the t–1th filtered image. (3) Finally, an adaptive and reasonable threshold is defined to make the iterative impulse noise filters stop automatically for most image details preservation in finite steps. Experimental results show that iterative impulse noise filters with the proposed automatic filtering convergence method can remove much of the impulse noise and effectively maintain image details. In addition, iterative impulse noise filters operate more efficiently.

Zynq-Based Reconfigurable System for Real-Time Edge Detection of Noisy Video Sequences

We implement Zynq-based self-reconfigurable system to perform real-time edge detection of 1080p video sequences. While object edge detection is a fundamental tool in computer vision, noises in the video frames negatively affect edge detection results significantly. Moreover, due to the high computational complexity of 1080p video filtering operations, hardware implementation on reconfigurable hardware fabric is necessary. Here, the proposed embedded system utilizes dynamic reconfiguration capability of Zynq SoC so that partial reconfiguration of different filter bitstreams is performed during run-time according to the detected noise density level in the incoming video frames. Pratt’s Figure of Merit (PFOM) to evaluate the accuracy of edge detection is analyzed for various noise density levels, and we demonstrate that adaptive run-time reconfiguration of the proposed filter bitstreams significantly increases the accuracy of edge detection results while efficiently providing computing power to support real-time processing of 1080p video frames. Performance results on configuration time, CPU usage, and hardware resource utilization are also compared.

K-MEANS BASED IMAGE DENOISING USING BILATERAL FILTERING AND TOTAL VARIATION

Bilateral filter and Total variation image denoising are widely used in image denoising. In low noisy level, bilateral filtering is better than TV denoising for it reveals better SNR and sharper edges. However, in high noisy level, TV denoising outperforms bilateral filtering in terms of SNR and more details of non edges. It is very difficult to perform denoising of a very noisy image for the resulted image rarely improves its SNR comparing to the original noisy one. Even though Total variation image denoising could be used for a very noisy image, the resulted SNR still needs some improvement. In this research, the K-means-based Bilateral-TV denoising (K-BiTV) approach using pixel-wise bilateral filtering and TV denoising has been derived based on the gradient magnitude calculation of the guideline map using K-means clusters. The denoising result of K-BiTV was depended on the level of noise density and the appropriate cluster. The experimental result showed that comparing to the conventional TV denoising and bilateral filter, K-BiTV gave the higher SNRs for some images with higher level of noise density.

Low-dose CT coronary angiography using iterative reconstruction with a 256-slice CT scanner

To explore whether computer tomography coronary angiography (CTCA) using iterative reconstruction (IR) leads to significant radiation dose reduction without a significant loss in image interpretability compared to conventional filtered back projection (FBP). A consecutive series of 200 patients referred to our institution to undergo CTCA constituted the study population. Patients were sequentially assigned to FBP or IR. All studies were acquired with a 256-slice CT scanner. A coronary segment was considered interpretable if image quality was adequate for evaluation of coronary lesions in all segments ≥ 1.5 mm. The mean age was 56.3 ± 9.6 years and 165 (83%) were male, with no significant differences between groups. Most scans were acquired using prospective ECG triggering, without differences between groups (FBP 84% vs IR 82%; P = 0.71). A total of 3198 (94%) coronary segments were deemed of diagnostic quality. The percent assessable coronary segments was similar between groups (FBP 91.7% ± 4.0% vs IR 92.5% ± 2.8%; P = 0.12). Radiation dose was significantly lower in the IR group (2.8 ± 1.4 mSv vs 4.6 ± 3.0 mSv; P < 0.0001). Image noise (37.8 ± 1.4 HU vs 38.2 ± 2.4 HU; P = 0.20) and signal density (461.7 ± 51.9 HU vs 462.2 ± 51.2 HU; P = 0.54) levels did not differ between FBP and IR groups, respectively. The IR group was associated to significant effective dose reductions, irrespective of the acquisition mode. Application of IR in CTCA preserves image interpretability despite a significant reduction in radiation dose.

Removal of random-valued impulsive noise from corrupted images

This paper presents a novel method for the suppression of random-valued impulsive noise from corrupted images. The proposed method is composed of an efficient noise detector and a pixel-restoration operator. The noise detector has been used to discriminate the uncorrupted pixels from the corrupted pixels. The noise-free intensity values of the corrupted pixels have been computed by using triangle-based linear interpolation and the values of tuning parameters of the proposed method have been optimized with differential evolution algorithm. Extensive simulation experiments indicate that the proposed method significantly outperforms all of the comparison methods mentioned in this paper. The success of the proposed method over comparison methods is due to its excellent detail preservation performance independent from the level of noise density.