An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.

Ionospheric Clutter Suppression with an Auxiliary Crossed-Loop Antenna in a High-Frequency Radar for Sea Surface Remote Sensing

Ionospheric clutter is one of the main problems for high-frequency surface wave radars (HFSWRs), as it severely interferes with sea surface state monitoring and target detection. Although a number of methods exist for ionospheric clutter suppression, most are suitable for radars with a large-sized array and are inefficient for small-aperture radars. In this study, we added an auxiliary crossed-loop antenna to the original compact radar antenna, and used an adaptive filter to suppress the ionospheric clutter. The experimental results of the HFSWRs data indicated that the suppression factor of the ionospheric clutter was up to 20 dB. Therefore, the Bragg peaks that were originally submerged by the ionospheric clutters could be recovered, and the gaps in the current maps can, to a large extent, be filled. For an oceanographic radar, the purpose of suppressing ionospheric clutter is to extract an accurate current speed; the radial current fields that were generated by our method showed an acceptable agreement with those generated by GlobCurrent data. This result supports the notion that the ionospheric suppression technique does not compromise the estimation of radial currents. The proposed method is particularly efficient for a compact HFSWRs, and can also be easily used in other types of antennas.

Large Tunable 16-Tupled Millimeter Wave Generation Utilizing Optical Carrier Suppression With a Tunable Sideband Suppression Ratio

Coping up with the rising bandwidth demands for 5G ultra-high speed applications, utilizing millimeter (MM) wave spectrum for data transmission over the radio over a fiber-based system is the ideal approach. In this study, a highly conversant and spectrally pure photonic generation of a 16-tupled MM wave signal using a series-connected DD-MZM with a lower modulation index, a splitting ratio, and a wider tunable range is presented. A 160-GHz MM wave is generated through a double sideband optical carrier suppression technique having an optical sideband suppression ratio (OSSR) of 69 dB and a radio frequency sideband suppression ratio (RSSR) of 40 dB. However, the OSSR and the RSSR are tunable with values greater than 15 dB when the modulation index (M.I.) varies from 2.778 to 2.873, ±8° phase drift, and a 15-dB enhancement in the OSSR with a wider nonideal parameter variation range giving acceptable performance can be seen in the model as compared with previous research works.

Primary Signal Phase Suppression Technique Based On Mutual Information Improved Variational Mode Decomposition

Specific emitter identification (SEI) identifies targets mainly by unintentional modulation of the signal. However, due to the high energy of the primary signal, once the primary signal changes, the recognition becomes less effective or even impossible using a feature database that is not updated. In this paper, we propose to use a mutual information improved variable mode decomposition (VMD) algorithm to suppress the primary signal phase of the transmitter. Furthermore, we simulate the feature extraction of the unintentional phase modulation of the transmitter signal and use support vector machine (SVM) for individual identification. The simulation results show that the algorithm improves the recognition rate by about 6% (0 dB) compared to the retained primary signal. The results demonstrate that our proposed phase suppression technique improves the adaptability and accuracy of individual identification of transmitters.

A Neural Recording and Stimulation Chip with Artifact Suppression for Biomedical Devices

This paper presents chip implementation of the integrated neural recording and stimulation system with stimulation-induced artifact suppression. The implemented chip consists of low-power neural recording circuits, stimulation circuits, and action potential detection circuits. These circuits constitute a closed-loop simultaneous neural recording and stimulation system for biomedical devices, and a proposed artifact suppression technique is used in the system. Moreover, this paper also presents the measurement and experiment results of the implemented 4-to-4 channel neural recording and stimulation chip with 0.18 µm CMOS technology. The function and efficacy of simultaneous neural recording and stimulation is validated in both in vivo and animal experiments.

Raft attitude control and elastic deformation suppression technique for large-scale floating raft air spring mounting system

The structure of floating rafts-equipped vessels will be inevitably undermined due to application of large-scale designs. When encountering strong external disturbances, not only the rafts will deviate from the original balanced position but also greater elastic deformation will be generated, which may lead to the relative displacement between devices on rafts and endanger the operation safety of equipment. In this study, a class of raft elastic deformation monitoring and discrimination methods is put forward via analyzing the features of large-scale raft elastic deformation. Air springs layout is optimized through finite element method (FEM). A flexible raft control responding model is established and a novel raft elastic deformation suppression technique is proposed based on air spring pressure parameter identification that adjusts the air spring pressure distribution. The experimental results indicate that this technique can effectively control the attitude of the rafts and reduce elastic deformation, leading to a largely improved control precision and a faster convergence speed of the raft.

Variation of inversion delay for wrist joint MR imaging with SPAIR technique: which ID is optimal?

Background: The current fat suppression technique in magnetic resonance imaging (MRI) significantly diagnoses abnormalities in musculoskeletal disorders. The spectral attenuated inversion recovery (SPAIR) fat suppression (FS) technique had an inversion delay (ID) parameter that allows choosing between full or partial FS. This was the first research related to the optimal setting of the ID time variation in the SPAIR technique on T2-weighted MRI wrist joint images.Objectives: This study aims to find out anatomical information with the most optimal ID value of the MRI wrist joint image T2 turbo spin-echo (TSE) FS SPAIR coronal slice sequence.Method: This study was a pre-experimental post-test only. Scanning MR wrist joint 16 volunteers took data with the qualitative analysis used three radiologists (visual grading) with statistical data analysis.Results: Image information of the MRI wrist joint T2 TSE FS SPAIR coronal slices sequence showed differences in the variation of ID (p<0.001), where the ID of 85 ms produced the most optimal image information.Conclusion: MRI image of the wrist joint of the T2 TSE FS SPAIR coronal slices sequence the most optimal with an ID variation of 85 ms compared to ID 70 ms and 100 ms.