Optimal Fitting Method of Nonlinear Simultaneous Equations Considering Structural Tensor Image Modeling

For the purpose of resolving the phenomenon of network congestion in the process of many-to-one communication in multimedia networks at present, the optimal fitting method of nonlinear simultaneous equations (OFMNSEs) is applied to the multimedia transmission congestion control system in this paper. The rate control and resource scheduling are effectively combined, and a clustering network structure is used to activate the congestion control method in turn based on the cluster header and intracluster-related indexes. Finally, it can be known through the analysis of the simulation results that the OFMNSE algorithm put forward in this paper can improve the congestion issue of the multimedia network transmission process and reduce the packet loss rate of data during the transmission effectively under the condition of different relative cache sizes compared with the conventional algorithm.

Three-Dimensional Stitching of Binocular Endoscopic Images Based on Feature Points

There are shortcomings of binocular endoscope three-dimensional (3D) reconstruction in the conventional algorithm, such as low accuracy, small field of view, and loss of scale information. To address these problems, aiming at the specific scenes of stomach organs, a method of 3D endoscopic image stitching based on feature points is proposed. The left and right images are acquired by moving the endoscope and converting them into point clouds by binocular matching. They are then preprocessed to compensate for the errors caused by the scene characteristics such as uneven illumination and weak texture. The camera pose changes are estimated by detecting and matching the feature points of adjacent left images. Finally, based on the calculated transformation matrix, point cloud registration is carried out by the iterative closest point (ICP) algorithm, and the 3D dense reconstruction of the whole gastric organ is realized. The results show that the root mean square error is 2.07 mm, and the endoscopic field of view is expanded by 2.20 times, increasing the observation range. Compared with the conventional methods, it does not only preserve the organ scale information but also makes the scene much denser, which is convenient for doctors to measure the target areas, such as lesions, in 3D. These improvements will help improve the accuracy and efficiency of diagnosis.

An Approach to 1/f Noise Detection Based on Adaptive T-ATFPF Algorithm

The generation of 1/f noise is closely related to the quality defects of IGBT devices. In the process of detecting IGBT single tube noise, thermal noise and shot noise show obvious white noise characteristics in the low frequency band, which are detected under the background of strong white noise 1/f noise can characterize the performance of IGBT devices. Therefore, on the basis of the Time-Frequency Peak Filtering (TFPF) algorithm, a two-dimensional time-domain adaptive T-ATFPF algorithm is proposed, and the adaptive segmentation is realized by means of the confidence interval crossing criterion based on Chebyshev’s inequality. Variable window length,use a small window length to process the signal section, which retains more detailed information of the effective signal.Use a larger window length to process the buffer section to ensure a smooth transition.Use the large window length to process the noise section, which more effectively suppresses randomness for noise, apply T-ATFPF to artificial synthesis model and actual model. Experimental results indicate that compared with the conventional algorithm, the improved method can better recover 1/f noise, and the ratio of signal to noise is greatly improved by about 1.3dB.

Application of AVAZ inversion algorithm based on exact formulas to a wide-azimuth seismic survey data

Results of applying of nonlinear AVAZ inversion optimization algorithm to data from 3D wide-azimuth seismic survey in the Republic of Serbia are presented. The algorithm is based on exact reflection coefficients formulas for PP reflection from anisotropic medium. We compare it with a conventional algorithm based on Ruger linear approximation of P-wave reflection from a boundary between isotropic and anisotropic (HTI) media. Maps of fracture orientation and anisotropy degree are more detailed in the case of using AVAZ inversion based on exact formulas. The results are in general accordance with the FMI well data, which indicates reliable performance of the algorithm.

Acoustic hologram optimisation using automatic differentiation

Acoustic holograms are the keystone of modern acoustics. They encode three-dimensional acoustic fields in two dimensions, and their quality determines the performance of acoustic systems. Optimisation methods that control only the phase of an acoustic wave are considered inferior to methods that control both the amplitude and phase of the wave. In this paper, we present Diff-PAT, an acoustic hologram optimisation platform with automatic differentiation. We show that in the most fundamental case of optimizing the output amplitude to match the target amplitude; our method with only phase modulation achieves better performance than conventional algorithm with both amplitude and phase modulation. The performance of Diff-PAT was evaluated by randomly generating 1000 sets of up to 32 control points for single-sided arrays and single-axis arrays. This optimisation platform for acoustic hologram can be used in a wide range of applications of PATs without introducing any changes to existing systems that control the PATs. In addition, we applied Diff-PAT to a phase plate and achieved an increase of > 8 dB in the peak noise-to-signal ratio of the acoustic hologram.

Novel Data Augmentation Employing Multivariate Gaussian Distribution for Neural Network-Based Blood Pressure Estimation

In this paper, we propose a novel data augmentation technique employing multivariate Gaussian distribution (DA-MGD) for neural network (NN)-based blood pressure (BP) estimation, which incorporates the relationship between the features in a multi-dimensional feature vector to describe the correlated real-valued random variables successfully. To verify the proposed algorithm against the conventional algorithm, we compare the results in terms of mean error (ME) with standard deviation and Pearson correlation using 110 subjects contributed to the database (DB) which includes the systolic BP (SBP), diastolic BP (DBP), photoplethysmography (PPG) signal, and electrocardiography (ECG) signal. For each subject, 3 times (or 6 times) measurements are accomplished in which the PPG and ECG signals are recorded for 20 s. And, to compare with the performance of the BP estimation (BPE) using the data augmentation algorithms, we train the BPE model using the two-stage system, called the stacked NN. Since the proposed algorithm can express properly the correlation between the features than the conventional algorithm, the errors turn out lower compared to the conventional algorithm, which shows the superiority of our approach.

A Survey on Scheduling Based Mac Protocol for Congestion Minimization in Internet of Things (IoT)

Recently, in Internet era the most common technology ubiquitous to develop smart environment is Internet of things (IoT) and Wireless Sensor Networks (WSNs).These technologies deployed enormously to formulate wide applications in area of Smart homes, Industrial automation, and security destined applications and information trailing. The huge development in wireless technology is due to great exploration in MEMS concept and Embedded Systems. Huge evolution in this technique leads to access different Medium Access Control (MAC) protocol and this protocol used to access multiple nodes peculiarly in wireless channel. The projected MAC protocol designed to enhance network lifetime. Essentially, the network leads to lot of congestion due to non-availability of IoT equipment and less available resources for various environmental applications. The simulated performance ensures that the conventional algorithm limits their dynamic service quality for IoT based applications. The above setbacks motivate the researches to develop the survey in existing scheduling based MAC protocol by highlighting their parameters.

Analysis Performance Of Conventional Algorithm And HMS Algorithm For Four-Way Intersection With Modified Round Robin

Traffic jam is currently one of the main problems for densely populated cities like Jakarta, Indonesia. One problem that causes traffic jams in Jakarta is that the traffic lights are too fast, which causes many cars to not be able to pass the traffic lights. There are already many algorithms to overcome this problem and get the right time for traffic lights based on how many vehicles are waiting in line, such as the HMS Algorithm and Conventional Algorithm. This research objective is to compare which algorithm has better performance to find the right amount of time for traffic lights to reduce traffic jams at four-way intersections with modified Round Robin method. And the result shown that the HMS algorithm is very suitable to be used in any condition for large or little vehicles, while conventional algorithms are only suitable to use for vehicles in the one little lane or the vehicles in one lane with other lane direction in the same place of lane

Adaptive Unscented Kalman Filter for Tracking GPS Signals in the Case of an Unknown and Time-Varying Noise Covariance

A new adaptive unscented Kalman filter (AUKF) is proposed to estimate the radio navigation parameters of a GPS signal tracking system in noisy environments and on a highly dynamic object. The experimental results have shown that the proposed AUKFbased method improves the GPS tracking margin by approximately 8 dB and 3 dB as compared to the conventional algorithm and the KF-based tracking, respectively. At the same time, the accuracy of Doppler frequency measurements increases as well.

A narrowband active noise control algorithm considering the harmonic distortion of the loudspeaker

Electrodynamic loudspeakers are the main actuators of the active noise control system, and their harmonic distortion has a detrimental effect on the noise reduction of the system. To improve the performance, this paper proposes a novel narrowband active noise control algorithm with compensating the nonlinearity of the loudspeaker. In the proposed algorithm, the parameters of the controller are obtained by iteration through the filtered-x least mean square algorithm. Meanwhile, they are adjusted in real-time by establishing the online inverse model of the loudspeaker using the Volterra expansion. The simulation experiments for the typical loudspeaker model show that the proposed algorithm can dramatically improve noise reduction compared to the conventional algorithm.