1 Introduction- 2 Algorithmic Operators- 3 Convergence of Iterative Methods- 4 Algorithmic Projection Operators- 5 Projection methods

Iterative Methods for Fixed Point Problems in Hilbert Spaces

This paper presents a literature review of image steganography techniques in the spatial domain for last 5 years. The research community has already done lots of noteworthy research in image steganography. Even though it is interesting to highlight that the existing embedding techniques may not be perfect, the objective of this paper is to provide a comprehensive survey and to highlight the pros and cons of existing up-to-date techniques for researchers that are involved in the designing of image steganographic system. In this article, the general structure of the steganographic system and classifications of image steganographic techniques with its properties in spatial domain are exploited. Furthermore, different performance matrices and steganalysis detection attacks are also discussed. The paper concludes with recommendations and good practices drawn from the reviewed techniques.

Image steganography in spatial domain: A survey

For agricultural applications, regularized smart-farming solutions are being considered, including the use of unmanned aerial vehicles (UAVs). The UAVs combine information and communication technologies, robots, artificial intelligence, big data, and the Internet of Things. The agricultural UAVs are highly capable, and their use has expanded across all areas of agriculture, including pesticide and fertilizer spraying, seed sowing, and growth assessment and mapping. Accordingly, the market for agricultural UAVs is expected to continue growing with the related technologies. In this study, we consider the latest trends and applications of leading technologies related to agricultural UAVs, control technologies, equipment, and development. We discuss the use of UAVs in real agricultural environments. Furthermore, the future development of the agricultural UAVs and their challenges are presented.

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Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications

As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.

https://www.mdpi.com/2071-1050/13/3/1224/pdf

A Review of Deep-Learning-Based Medical Image Segmentation Methods

Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification

Segmentation of red blood cells (RBCs) and white blood cells (WBCs) in peripheral blood smear images plays an important role in the evaluation and diagnosis a vast of disorders, including infection, leukemia, and some types of cancer. Generally, various image processing techniques are used to enhance the quality of images before the segmentation step. Therefore, the segmentation of blood cells is still a challenge. However, in this research, deep learning semantic segmentation - cutting-edge technology is applied for segmentation red blood cells and white blood cells in blood smear images. The experiment result shows that the global accuracy of our model yielded 89.45%. Besides, the accuracy of the segmentation of white blood cells, red blood cells, and the background of blood smear image reached 94.93%, 91.11%, and 87.32%, respectively.

Blood Cell Images Segmentation using Deep Learning Semantic Segmentation

In our current society, Unmanned Aerial Vehicles (UAVs), also known as drones have become common. These vehicles are controlled remotely from a Ground Control Station (GCS) or autonomously by a programmed mission. Data-link between drone and GCS can be established by using a communication protocol. The Micro Air Vehicle link (MAVLink) protocol is an open source, point-to-point networking protocol used to carry telemetry and to command and control many small unmanned aircrafts. In this paper, communication requirements, such as network latency and data loss of the MAVLink protocol have been analyzed.

Data analysis of the MAVLink communication protocol

Leukemia induced death has been listed in the top ten most dangerous mortality cause for human being. Among many reasons, one of them is the slow decision-making process which make suitable medical treatment cannot be applied on time. Therefore, good clinical decision support system for acute leukemia type classification has always become necessity. In this paper, the author proposed a novel approach to perform acute leukemia type classification using sequential neural network classifier. Our experimental result only cover the first classification process which shows an excellent performance in differentiating normal and abnormal cells. Further research is needed to proof the second neural network classifier effectiveness performance.

Acute lymphoid leukemia classification using two-step neural network classifier

A geographical information services (GIS) can be provided on the basis of a digital map, which is the fundamental form of representation of data in a GIS. Because the process of producing a digital map is considerably complex and the maintenance of a digital map requires substantial monetary and human resources, a digital map is very valuable and requires copyright protection. A digital map consists of a number of layers that are categorized in terms of topographical features and landmarks. Therefore, any unauthorized person can forge either an entire digital map or the feature layers of the map. In this paper, we present a robust and invisible watermarking scheme based on polylines and polygons for the copyright protection of a GIS digital map. The proposed scheme clusters all polylines and polygons in the feature layers of the map on the basis of the polyline length and the polygon area. And then a watermark is embedded in GIS vector data on the basis of the distribution of polyline length and polygon area in each group by moving all vertices in polylines and polygons within a specified tolerance. Experimental results confirm that the proposed scheme is more robust against geometric attacks, such as rotation, scaling, and translation (RST) transformations, data addition, cropping, breaking, and filleting attacks, and layer attacks with rearrangement and cropping, when compared with conventional schemes. Moreover, the scheme also satisfies data position accuracy.

Ki-Ryong Kwon

Papers

Blood Cell Images Segmentation using Deep Learning Semantic Segmentation

Data analysis of the MAVLink communication protocol

Acute lymphoid leukemia classification using two-step neural network classifier

Vector watermarking scheme for GIS vector map management

CAD drawing watermarking scheme