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In this paper, an image adaptive, invisible digital watermarking algorithm with Orthogonal Polynomials based Transformation (OPT) is proposed, for copyright protection of digital images. The proposed algorithm utilizes a visual model to determine the watermarking strength necessary to invisibly embed the watermark in the mid frequency AC coefficients of the cover image, chosen with a secret key. The visual model is designed to generate a Just Noticeable Distortion mask (JND) by analyzing the low level image characteristics such as textures, edges and luminance of the cover image in the orthogonal polynomials based transformation domain. Since the secret key is required for both embedding and extraction of watermark, it is not possible for an unauthorized user to extract the embedded watermark. The proposed scheme is robust to common image processing distortions like filtering, JPEG compression and additive noise. Experimental results show that the quality of OPT domain watermarked images is better than its DCT counterpart. Keywords--Orthogonal Polynomials based Transformation, Digital Watermarking, Copyright Protection, Visual model., I. INTRODUCTION THE explosive growth of Internet and communication networks has led to the tremendous use of multimedia data like image, audio and video. Furthermore, due to the availability of [...]

Abstract--In this paper, a new algorithm for generating codebook is proposed for vector quantization (VQ) in image coding. The significant features of the training image vectors are extracted by using [...]

Here, we report the biosynthesis of molybdenum oxide (Mo5O14) nanoparticles (NPs) by co-precipitation methods using various vermiculture-based extracts and their impact on seed germination in Vigna radiata. The synthesis of Mo5O14 NPs was initially characterized by a color change from yellow to white and a surface plasmon resonance (SPR) peak at 360 nm in UV–visible spectroscopic analysis. In addition, X-ray diffraction (XRD) data revealed that the Mo5O14 NPs were crystalline with a tetragonal structure and a size of 35.32–41.86 nm. Furthermore, fourier transform infrared (FTIR) analysis of NPs absorption bands revealed the presence of unique functional groups, such as -OH stretching, CH2 stretching, and primary amide groups, providing strong evidence that the native protein served to a reduce, cap, and stabilize Mo5O14 NPs. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images revealed that the ultrastructural profile of Mo5O14 NPs formed rod particles with an average particle length of 102 nm and width of 33 nm. The energy dispersive X-ray (EDX) pattern analysis confirmed the presence of Mo and O ions. Finally, the effects of Mo5O14 NPs on Vigna radiata seed fresh and dry biomass characteristics, root and shoot length, and seed germination were investigated, and it was found that a significant increase in the seed germination rate of all seven vermiculture-based Mo5O14 NPs was observed at 200 ppm. Consequently, our work provides a step forward in the development of vermiculture-based Mo5O14 NPs as a promising green agrochemical nano-fertilizer.

Today, there is a great need for 3D instance segmentation, which has several uses in robotics and augmented reality. Unlike projective observations like 2D photographs, 3D models offer a metric reconstruction of the sceneries without occlusion or scale ambiguity of the environment. In agriculture, understanding Plant growth phenotyping enhances comprehension of complex genetic features and accelerates the advancement of contemporary breeding and smart farming. A reduction in crop production quality is caused by leaf diseases in agriculture. In order to increase productivity in the agricultural industry, it is therefore possible to automate the recognition of leaf diseases. Diverse leaf disease patterns affect the detection’s accuracy in the majority of systems. During phenotyping, 3D PCs (PC) of components of plants like the stems and leaves are segmented in order to follow autonomous growth and estimate the level of stress the crop has experienced. This research proposed a Point Sampling Method with occupancy grid representation for segmenting PCs of different plant species, which was developed. To handle unordered input sets, this approach mainly relies on the application of the single symmetric function max pooling. In reality, a set of optimization functions are used by the network to choose points which is more curious or instructive from the PC and encapsulate the selection reason, and the fully connected layers, used for shape classification or shape segmentation, integrate these learned ideal significances hooked on a global descriptor regarding the overall shape. After being trained on the Point Sampling Network-created plant dataset, the network can simultaneously realize semantic and leaf instance segmentation.

Seaweeds are good sources of natural anti-oxidant effect of bioactive compounds and can be used as anti-oxidant, controlling bacterial infection in seaweeds as well as pharmacological industries. Present study was analysis of bioactive compounds extracted from Ulva lactuca L. HPLC purified protein by the GC-MS study was used to evaluate anti-bacterial and antioxidant effect for controlling bacterial infection in human pathogenic bacteria. ULP protein extract will be novel bioactive compounds with anti-oxidant and radical scavenging possessions divulge the unexploited things of seaweed could be used in the medicinal and food industries. [ABSTRACT FROM AUTHOR]

Background: Rodents and shrew living in the vicinity of human beings perform significant role to harbor different spe­cies of ectoparasites and thus act as the reservoir host for the spread of diseases to human and animals. Methods: This study was undertaken to determine the species composition of the medically important ectoparasites present in the scrub typhus affected places of Vellore District using wonder and Sherman traps for trapping of live Ro­dent/Shrew during September 2017 to August 2018. Results: Rodent/shrew hosts belonged to two Families, three sub families and five genera and five different species. These animals carried 23 species of ectoparasites including 17 trombiculid mites, two non-trombiculid mites, two ticks and two fleas. A total of 940 chigger mites were collected which belonged to three Tribes, six Genera, two Subgenera and 17 species. Adult mites collected belonged to two families, three genera and three species. Ticks collected were classified under one family, two genera and two species. Fleas fall under one family, one subfamily, one tribe, one ge­nus and two species. Conclusion: This is the first record of 14 species of chigger mites and three species of adult mites from Vellore District, Tamil Nadu. Surveillance of these keystone ectoparasites helped to identify the medically important disease vectors causing acari-borne zoonotic diseases.

This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes. When individual models are used for classification, they are often prone to misclassification since they may not be able to learn the characteristics of all the classes adequately. This is due to the fact that each model may learn the characteristics of specific classes more efficiently than the other classes. We propose an ensemble model that leverages the predictions of three pre-trained models, DenseNet201, InceptionV3, and ResNet50 with accuracies of 94.54%, 88.38%, and 90.58%, respectively. The predictions of the base learners are combined using two methods: model averaging and weighted averaging. The performances of the models are evaluated, and the model averaging ensemble has an accuracy of 92.96% whereas the weighted average ensemble has an accuracy of 95.00%. The weighted average ensemble outperforms the model average ensemble and all individual models. The results from the evaluation demonstrate that utilizing an ensemble of base learners can successfully classify features that were incorrectly learned by individual base learners.

The present investigation explore the biosynthesis of copper oxide nanoparticles (CuO NPs) by simple Co-precipitation method using Eudrilus eugeniae vermi wash and its applications to the seed germination of green gram (Vigna radiata). UV–Visible Spectroscopy and Fourier Transform Infrared Spectroscopy (FTIR) confirm the formation of CuO and its interaction with the functional group of enzymes present in the vermin wash. X-ray Diffraction, Scanning Electron Microscopy; Energy Dispersive X-ray analysis reveals the crystalline structure morphology, and elements of the CuO NPs. In order to evaluate the bio-potential properties of CuO NPs, a seed germination studies were carried out with green gram (V. radiata) with different concentrations of CuO NPs, and the result reveals that CuO NPs show a positive influence on the germination of green gram.

Background & objectives: Scrub typhus is caused by Orientia tsutsugamushi and transmitted by larval trombiculid chigger mites. Rodents and shrews are hosts for chigger mites in the transmission to humans. Wonder and Sherman traps are widely used for trapping rodents and shrews for the collection of ectoparasitic chigger mites. A comparative trapping study was undertaken in different villages in Vellore district, Tamil Nadu and Thiruvananthapuram district, Kerala, India to evaluate the efficiency of traps for trapping rodents & shrews. Methods: Wonder and Sherman traps were set in outdoors and indoors to capture the rodents. Wonder Traps, large wired cage, and Sherman Traps are foldable. These traps were employed for the rodents/ shrew surveillance to gather chigger vector mites. Identification of rodents, chigger mites and Trap index were observed. Results: Z Test comparing the capture rates showed wonder trap (P

In this paper, a selective image encryption algorithm based on square-wave shuffling in the orthogonal polynomials domain is proposed. This algorithm is adaptive to the user device's computing power, as it can operate in the spatial/frequency/hybrid domain. Simultaneous confusion and diffusion is performed on important regions, while in unimportant regions, shuffling is conducted; chaotic maps are used for both. To obtain the encrypted image, a new shuffling method, called square-wave shuffling, is proposed and applied. Extensive experiments have been conducted in PCs and in smart-phones with the Android programming environment. Its suitability to these devices is verified, and the superiority of the proposed method in comparison to other contemporary methods is also reported. [ABSTRACT FROM AUTHOR]

Autonomous vehicles require in-depth knowledge of their surroundings, making path segmentation and object detection crucial for determining the feasible region for path planning. Uniform characteristics of a road portion can be denoted by segmentations. Currently, road segmentation techniques mostly depend on the quality of camera images under different lighting conditions. However, Light Detection and Ranging (LiDAR) sensors can provide extremely precise 3D geometry information about the surroundings, leading to increased accuracy with increased memory consumption and computational overhead. This paper introduces a novel methodology which combines LiDAR and camera data for road detection, bridging the gap between 3D LiDAR Point Clouds (PCs). The assignment of semantic labels to 3D points is essential in various fields, including remote sensing, autonomous vehicles, and computer vision. This research discusses how to select the most relevant geometric features for path planning and improve autonomous navigation. An automatic framework for Semantic Segmentation (SS) is introduced, consisting of four processes: selecting neighborhoods, extracting classification features, and selecting features. The aim is to make the various components usable for end users without specialized knowledge by considering simplicity, effectiveness, and reproducibility. Through an extensive evaluation of different neighborhoods, geometric features, feature selection methods, classifiers, and benchmark datasets, the outcomes show that selecting the appropriate neighborhoods significantly develops 3D path segmentation. Additionally, selecting the right feature subsets can reduce computation time, memory usage, and enhance the quality of the results.

In this paper, a new color image retrieval technique is proposed with local features given by Scale Invariant Feature Transform (SIFT) key points that are described in the integer-natured, computationally light Orthogonal Polynomials Transform (OPT) domain. The transform's point spread operators are derived from a generating function, modification to which has been proposed for alleviating computational complexity further. The expressive power of the transform coefficients has been exploited for forming the descriptors of SIFT key points of a given image. The key point descriptors, OPT-SIFT so formed have good expressive power, despite being shorter in length and having a reduced computational complexity. A retrieval technique has been proposed based on OPT-SIFT features. The proposed retrieval technique has been experimented with images from standard databases such COIL-100 and Corel and the results demonstrate the superiority of the proposed descriptors when compared to other descriptors. [ABSTRACT FROM AUTHOR]