Channel state information (CSI) can provide phase and amplitude of multichannel subcarrier to better describe signal propagation characteristics. Therefore, CSI has become one of the most commonly used features in indoor Wi-Fi localization. In addition, compared to the CSI geometric localization method, the CSI fingerprint localization method has the advantages of easy implementation and high accuracy. However, as the scale of the fingerprint database increases, the training cost and processing complexity of CSI fingerprints will also greatly increase. Based on this, this article proposes to combine backpropagation neural network (BPNN) and adaptive genetic algorithm (AGA) with CSI tensor decomposition for indoor Wi-Fi fingerprint localization. Specifically, the tensor decomposition algorithm based on the parallel factor (PARAFAC) analysis model and the alternate least squares (ALSs) iterative algorithm are combined to reduce the interference of the environment. Then, we use the tensor wavelet decomposition algorithm for feature extraction and obtain the CSI fingerprint. Finally, in order to find the optimal weights and thresholds and then obtain the estimated location coordinates, we introduce an AGA to optimize BPNN. The experimental results show that the proposed algorithm has high localization accuracy, while improving the data processing ability and fitting the nonlinear relationship between CSI location fingerprints and location coordinates.

Adaptive Genetic Algorithm-Aided Neural Network With Channel State Information Tensor Decomposition for Indoor Localization

ABSTRACT Malaria fever is a potentially fatal disease caused by the Plasmodium parasite. Identifying Plasmodium parasites in blood smear images can help diagnose malaria fever rapidly and precisely. According to the World Health Organization (WHO), there were 241 million malaria cases and 627 000 deaths worldwide in 2020, while 95% of malaria cases and 96% of malaria deaths occurred in Africa. Also in Africa, children that are less than five years old accounted for an estimated 80% of all malaria deaths. To address the menace of malaria, this paper proposes a novel deep learning model, called a data augmentation convolutional neural network (DACNN), trained by reinforcement learning to tackle this problem. The performance of the proposed DACNN model is compared with CNN and directed acyclic graph convolutional neural network (DAGCNN) models. Results show that DACNN outperforms previous studies in processing and classification images. It achieved 94.79% classification accuracy in malaria blood sample images of balanced class dataset obtained from the Kaggle dataset. The proposed model can serve as an effective tool for the detection of malaria parasites in blood smear images.

https://doi.org/10.1080/08839514.2022.2033473

A Novel Data Augmentation Convolutional Neural Network for Detecting Malaria Parasite in Blood Smear Images

This article presents an automatic real-time object detection method using sidescan sonar (SSS) and an onboard graphics processing unit (GPU). The detection method is based on a modified convolutional neural network (CNN), which is referred to as self-cascaded CNN (SC-CNN). The SC-CNN model segments SSS images into object-highlight, object-shadow, and seafloor areas, and it is robust to speckle noise and intensity inhomogeneity. Compared with typical CNN, SC-CNN utilizes crop layers which enable the network to use local and global features simultaneously without adding convolution parameters. Moreover, to take the local dependencies of class labels into consideration, the results of SC-CNN are postprocessed using Markov random field. Furthermore, the sea trial for real-time object detection via the presented method was implemented on our autonomous underwater vehicle (AUV) named SAILFISH via its GPU module at Jiaozhou Bay Bridge, Qingdao, China. The results show that the presented method for SSS image segmentation has obvious advantages when compared with the typical CNN and unsupervised segmentation methods, and is applicable in real-time object detection task.

Real-Time Object Detection for AUVs Using Self-Cascaded Convolutional Neural Networks

2D Object Detection and Recognition: Models, Algorithms and Networks

This study presents a novel active contour model (ACM) driven by weighted global and local region-based signed pressure force (SPF) to segment images in the presence of intensity inhomogeneity and noise. First, an adaptive weighted global region-based SPF (GRSPF) function as the driving centers is designed based on the global image information, which is based on the normalized global intensity to update the weights of the inner and outer regions of the curve during iterations. Second, by introducing the normalized absolute local intensity differences as the weighs of the inner and outer regions, an adaptive weighted local region-based SPF (LRSPF) function is similarly defined. Third, instead of setting a fixed force, a force propagation function is introduced to automatically balance the interior and exterior forces according to the image feature. Meanwhile, by combing the adaptive GWSPF and LWSPF functions, a weighted hybrid region-based SPF function is defined, which can improve the efficiency and accuracy of the proposed model. The experimental results on real images demonstrate that the proposed model is more robust than the popular region-based ACMs for segmenting images with intensity inhomogeneity and noise. The code is available at https://github.com/fangchj2002/WHRSPF .

/pdf/active-contour-driven-by-weighted-hybrid-signed-pressure-h35r8u1o9n.pdf

Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image Segmentation

Machine Learning today in data analysis field robotize interperative model framework. Classification algorithms in Machine learning have grown to be among the leading research topics and its utilization in therapeutic datasets are in discussion all over. Acknowledging the fact that combining multiple predictions leads to more accurate results than merely depending on a single prediction, a single dataset has been trained on various algorithms and the highest voted class is predicted as the result. Liver disease is the only major instigation of death still perennial, hence early detection and treatment of not very symptomatic liver disease is must which can significantly reduce the chances of death. In the proposed work, the dataset of Indian Liver Patient has been utilized and it clearly states that grouping classification algorithms efficiently improves the rate of prediction of illnesses.

Prediction of Liver Disease Using Grouping of Machine Learning Classifiers

Cancer is a dreadful heterogeneous disease that refers to abnormal growth of cell tissue. Millions of people die every year due to unnoticed recognition or late detection of the disease. Hence, detecting presence of cancer cells correctly becomes important. Putting into attention the number of people affected by cancer, we need to find better ways of diagnosis and treatment. Neural networks are a burning research area in medical science, especially in the areas of radiology, cardiology, oncology, urology and etc.. A variety of these techniques as Decision Trees (DTs), Support Vector Machines (SVM) and Artificial Neural Network (ANN) have been widely applied in cancer detection. This paper presents a method of classification of cancer cells into Benign and Malignant using deep learning Convolutional Neural Network.

Cancer Detection Using Convolutional Neural Network

In the Multimedia era, removal of the Noises from an image becomes a key challenge in the field of Digital Image Processing (DIP) and Computer Vision. Noise may be mixed with an image during capturing time, transmission time or due to dust particle on the screen of capturing device. Therefore, removal of these unwanted signals from the image is urgently required for the better analysis of the image and the de-noised image is more meaningful for Object detection, Edge detection and many more. There are various types of image noise, however, Gaussian Noise and Impulse Noise are commonly found in the image. This work focuses on the outliers and Mean Filter to improve the performance for Gaussian noise reduction from the image. In experimental assessments, artificial noise has been mixed using MATLAB to MSRA (10k images) dataset, this dataset is used to evaluate our proposed technique. The experiment results show that the proposed approach improves the performance in noise reduction over other filter approaches.

An efficient Gaussian Noise Reduction Technique For Noisy Images using optimized filter approach

In the domain of computer vision research, human’s capability to distinguish salient objects from background has become an important ingredient. So far, the researchers are not able to propose an approach that balances the tradeoff between the computation time needed by humans and detection accuracy. This paper highlights an improved salient object detection (SOD) accuracy technique which does not demand high computation time. The accuracy of convex hull enabled active contour technique (CHACT) model is assessed with area under the curve, recall, precision, F-measure, mean absolute error, and computation time on six available publicly image datasets. The computation time of CHACT is comparatively less than many existing approaches and also improved accuracy for SOD.

CHACT: Convex Hull Enabled Active Contour Technique for Salient Object Detection

During the last decade, there has been profound progress in the field of visual saliency. However, there still exist various major challenges that hinder the detection performance for scenes with complex composition, presence of additive noise, objects of diverse scale and rotations etc. Generally, images with additive noise have low spatial resolution and blurred edges, which affects the learning capability of the network and causes inaccurate detection. In order to address these issues, in this study, the authors propose a fully convolutional neural network which jointly denoise the input maps by learning edges and contrast details, followed by learning of residing salient details via colour spatial maps in an end-to-end fashion. Their framework employs convolutional layers that use gradient and contrast details of images to denoise the areas with high edge density. After denoising, the denoised images are subjected to salient object detection (SOD) using convolutional layers. The effectiveness of the proposed network is evaluated on benchmark datasets. The experimental results demonstrate the significant performance improvement of the proposed method over state-of-the-art detection techniques.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2018.5814

Maheep Singh

Papers

Prediction of Liver Disease Using Grouping of Machine Learning Classifiers

Cancer Detection Using Convolutional Neural Network

An efficient Gaussian Noise Reduction Technique For Noisy Images using optimized filter approach

CHACT: Convex Hull Enabled Active Contour Technique for Salient Object Detection

SOD-CED: salient object detection for noisy images using convolution encoder–decoder