There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The PASCAL Visual Object Classes Challenge

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Remote Sensing And Image Interpretation

On account of environmental concerns, the fate and adverse effects of plastics have attracted considerable interest in the past few years. Recent studies have indicated the potential for fragmentation of plastic materials into nanoparticles, i.e., "nanoplastics," and their possible accumulation in the environment. Nanoparticles can show markedly different chemical and physical properties than their bulk material form. Therefore possible risks and hazards to the environment need to be considered and addressed. However, the fate and effect of nanoplastics in the (aquatic) environment has so far been little explored. In this review, we aim to provide an overview of the literature on this emerging topic, with an emphasis on the reported impacts of nanoplastics on human health, including the challenges involved in detecting plastics in a biological environment. We first discuss the possible sources of nanoplastics and their fates and effects in the environment and then describe the possible entry routes of these particles into the human body, as well as their uptake mechanisms at the cellular level. Since the potential risks of environmental nanoplastics to humans have not yet been extensively studied, we focus on studies demonstrating cell responses induced by polystyrene nanoparticles. In particular, the influence of particle size and surface chemistry are discussed, in order to understand the possible risks of nanoplastics for humans and provide recommendations for future studies.

/pdf/emergence-of-nanoplastic-in-the-environment-and-possible-y3p26c4dn5.pdf

Emergence of Nanoplastic in the Environment and Possible Impact on Human Health

Over the past three decades, significant developments have been made in hyperspectral imaging due to which it has emerged as an effective tool in numerous civil, environmental, and military applications. Modern sensor technologies are capable of covering large surfaces of earth with exceptional spatial, spectral, and temporal resolutions. Due to these features, hyperspectral imaging has been effectively used in numerous remote sensing applications requiring estimation of physical parameters of many complex surfaces and identification of visually similar materials having fine spectral signatures. In the recent years, ground based hyperspectral imaging has gained immense interest in the research on electronic imaging for food inspection, forensic science, medical surgery and diagnosis, and military applications. This review focuses on the fundamentals of hyperspectral image analysis and its modern applications such as food quality and safety assessment, medical diagnosis and image guided surgery, forensic document examination, defense and homeland security, remote sensing applications such as precision agriculture and water resource management and material identification and mapping of artworks. Moreover, recent research on the use of hyperspectral imaging for examination of forgery detection in questioned documents, aided by deep learning, is also presented. This review can be a useful baseline for future research in hyperspectral image analysis.

Modern Trends in Hyperspectral Image Analysis: A Review

In this paper, we present a new sliding window based local thresholding technique 'NICK' and give a detailed
comparison of some existing sliding-window based thresholding algorithms with our method. The proposed method aims
at achieving better binarization results, specifically, for ancient document images. NICK has been inspired from the
Niblack's binarization method and exhibits its robustness and effectiveness when evaluated on low quality ancient
document images.

Comparison of Niblack inspired binarization methods for ancient documents

Breast cancer is among world's second most occurring cancer in all types of cancer. Most common cancer among women worldwide is breast cancer. There is always need of advancement when it comes to medical imaging. Early detection of cancer followed by the proper treatment can reduce the risk of deaths. Machine learning can help medical professionals to diagnose the disease with more accuracy. Where deep learning or neural networks is one of the techniques which can be used for the classification of normal and abnormal breast detection. CNN can be used for this detection. Mammograms-MIAS dataset is used for this purpose, having 322 mammograms in which almost 189 images are of normal and 133 are of abnormal breasts. Promising experimental results have been obtained which depict the efficacy of deep learning for breast cancer detection in mammogram images and further encourage the use of deep learning based modern feature extraction and classification methods in various medical imaging applications especially in breast cancer detection. It is an ongoing research and further developments are being made by optimizing the CNN architecture and also employing pre-trained networks which will hopefully lead to higher accuracy measures. Proper segmentation is mandatory for efficient feature extraction and classification.

Khurram Khurshid

Papers

Modern Trends in Hyperspectral Image Analysis: A Review

Comparison of Niblack inspired binarization methods for ancient documents

Breast cancer detection in mammograms using convolutional neural network

Role of deep learning in brain tumor detection and classification (2015 to 2020): A review.

Hyperspectral document image processing: Applications, challenges and future prospects