Combined Signal Detection Method in Differential Frequency Hopping System

In order to enhance communication reliability of differential frequency hopping system, a receiver implemented with the concatenation of an optimal subblock-by-subblock maximum a posteriori probability (OBB-MAP) detector and a soft-decision Turbo decoder is proposed and validated in both AWGN and Rayleigh flat fading channels. It is shown that the OBB-MAP decoder can iteratively decode a cyclic trellis, and back-search the trellis for any state to obtain estimates for the prior information bits which can be employed by soft-decision Turbo decoder. The proposed receiver achieves a better bit error rate(BER) performance than maximum likelihood sequence estimation(MLSE) detector employing Viterbi algorithm. The simulation results demonstrate that the combined signal detection method improves communication quality.

A Multiscale Approach to Automatic Medical Image Segmentation Using Self-Organizing Map

In this paper, a new medical image classification scheme is proposed using selforganizing map (SOM) combined with multiscale technique. It addresses the problem of the handling of edge pixels in the traditional multiscale SOM classifiers. First, to solve the difficulty in manual selection of edge pixels, a multiscale edge detection algorithm based on wavelet transform is proposed. Edge pixels detected are then selected into the training set as a new class and a mu1tiscale SoM classifier is trained using this training set. In this new scheme, the SoM classifier can perform both the classification on the entire image and the edge detection simultaneously. On the other hand, the misclassification of the traditional multiscale SoM classifier in regions near edges is greatly reduced and the correct classification is improved at the same time.

Bayesian edge detector for SAR imagery using discontinuity-adaptive Markov random feld modeling

Synthetic aperture radar（SAR）image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld（DAMRF）and maximum a posteriori（MAP）estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly developed.In the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method.Four normalized ratios constructed in different directions are computed,based on which two edge strength maps（ESMs）are formed.The fnal edge detection result is achieved by fusing the results of two thresholded ESMs.The experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt＇s fgure of merit and visual evaluation in most cases.

Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review

Landslides are one of the geological disasters with wide distribution,high impact and serious damage around the world.Landslide risk assessment can help us know the risk of landslides occurring,which is an effective way to prevent landslide disasters in advance.In recent decades,artificial intelligence(AI)has developed rapidly and has been used in a wide range of applications,especially for natural hazards.Based on the published literatures,this paper presents a detailed review of AI applications in landslide risk assessment.Three key areas where the application of AI is prominent are identified,including landslide detection,landslide susceptibility assessment,and prediction of landslide displacement.Machine learning(ML)containing deep learning(DL)has emerged as the primary technology which has been considered successfully due to its ability to quantify complex nonlinear relationships of soil structures and landslide predisposing factors.Among the algorithms,convolutional neural networks(CNNs)and recurrent neural networks(RNNs)are two models that are most widely used with satisfactory results in landslide risk assessment.The generalization ability,sampling training strategies,and hyperparameters optimization of these models are crucial and should be carefully considered.The challenges and opportunities of AI applications are also fully discussed to provide suggestions for future research in landslide risk assessment.