Electromagnetic properties of a thermoplastic natural rubber (TPNR), a lithium–nickel–zinc (Li–Ni–Zn) ferrite and a TPNR–ferrite composite subjected to transverse electromagnetic (TEM) wave propagation were investigated. The incorporation of the ferrite into the matrix of the TPNR was found to reduce the dielectric loss but the magnetic loss increased. The absorption characteristics of all the samples subjected to a normal incidence of TEM wave were investigated based on a model of a single-layered plane wave absorber backed by a perfect conductor. It is evident from a computer simulation that the ferrite is a narrowband absorber, whereas the polymeric samples show broadband absorption characteristics. Minimal reflection of the microwave power or matching condition occurs when the thickness of the absorbers approximates an odd number multiple of a quarter of the propagating wavelength. This is discussed as due to cancellation of the incident and reflected waves at the surface of the absorbers. The Li–Ni–Zn ferrite exhibits another matching condition at low frequency when the magnitude of the complex relative dielectric permittivity (εr*) equals that of the complex relative magnetic permeability (μr*). The specular absorber method provides a simple theoretical graphic aid for determining the absorption characteristics and the location of the matching conditions in the frequency domain. The result for the ferrite sample was tested and confirmed directly from terminated one-port measurements.

No abstract

There is a need for automatic systems that can reliably detect, track and classify fish and other marine species in underwater videos without human intervention. Conventional computer vision techniques do not perform well in underwater conditions where the background is complex and the shape and textural features of fish are subtle. Data-driven classification models like neural networks require a huge amount of labelled data, otherwise they tend to over-fit to the training data and fail on unseen test data which is not involved in training. We present a state-of-the-art computer vision method for fine-grained fish species classification based on deep learning techniques. A cross-layer pooling algorithm using a pre-trained Convolutional Neural Network as a generalized feature detector is proposed, thus avoiding the need for a large amount of training data. Classification on test data is performed by a SVM on the features computed through the proposed method, resulting in classification accuracy of 94.3% for fish species from typical underwater video imagery captured off the coast of Western Australia. This research advocates that the development of automated classification systems which can identify fish from underwater video imagery is feasible and a cost-effective alternative to manual identification by humans.

Underwater video and digital still cameras are rapidly being adopted by marine scientists and managers as a tool for non‐destructively quantifying and measuring the relative abundance, cover and size of marine fauna and flora. Imagery recorded of fish can be time consuming and costly to process and analyze manually. For this reason, there is great interest in automatic classification, counting, and measurement of fish. Unconstrained underwater scenes are highly variable due to changes in light intensity, changes in fish orientation due to movement, a variety of background habitats which sometimes also move, and most importantly similarity in shape and patterns among fish of different species. This poses a great challenge for image/video processing techniques to accurately differentiate between classes or species of fish to perform automatic classification. We present a machine learning approach, which is suitable for solving this challenge. We demonstrate the use of a convolution neural network model in a hierarchical feature combination setup to learn species‐dependent visual features of fish that are unique, yet abstract and robust against environmental and intra‐and inter‐species variability. This approach avoids the need for explicitly extracting features from raw images of the fish using several fragmented image processing techniques. As a result, we achieve a single and generic trained architecture with favorable performance even for sample images of fish species that have not been used in training. Using the LifeCLEF14 and LifeCLEF15 benchmark fish datasets, we have demonstrated results with a correct classification rate of more than 90%.

No abstract

Abstract:In this study, in vitro cytotoxicity of nickel zinc (NiZn) ferrite nanoparticles against human colon cancer HT29, breast cancer MCF7, and liver cancer HepG2 cells was examined. The morphology, homogeneity, and elemental composition of NiZn ferrite nanoparticles were investigated by scanning electron microscopy, transmission electron microscopy, and energy dispersive X-ray spectroscopy, respectively. The exposure of cancer cells to NiZn ferrite nanoparticles (15.6-1,000 µg/mL; 72 hours) has resulted in a dose-dependent inhibition of cell growth determined by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. The quantification of caspase-3 and -9 activities and DNA fragmentation to assess the cell death pathway of the treated cells showed that both were s timulated when exposed to NiZn ferrite nanoparticles. Light microscopy examination of the cells exposed to NiZn ferrite nanoparticles demonstrated significant changes in cellular morphology. The HepG2 cells were most prone to apoptosis among the three cells lines examined, as the result of treatment with NiZn nanoparticles. In conclusion, NiZn ferrite nanoparticles are suggested to have potential cytotoxicity against cancer cells.

It is interesting to develop effective fish sampling techniques using underwater videos and image processing to automatically estimate and consequently monitor the fish biomass and assemblage in water bodies. Such approaches should be robust against substantial variations in scenes due to poor luminosity, orientation of fish, seabed structures, movement of aquatic plants in the background and image diversity in the shape and texture among fish of different species. Keeping this challenge in mind, we propose a unified approach to detect freely moving fish in unconstrained underwater environments using a Region-Based Convolutional Neural Network, a state-of-the-art machine learning technique used to solve generic object detection and localization problems. To train the neural network, we employ a novel approach to utilize motion information of fish in videos via background subtraction and optical flow, and subsequently combine the outcomes with the raw image to generate fish-dependent candidate regions. We use two benchmark datasets extracted from a large Fish4Knowledge underwater video repository, Complex Scenes dataset and the LifeCLEF 2015 fish dataset to validate the effectiveness of our hybrid approach. We achieve a detection accuracy (F-Score) of 87.44% and 80.02% respectively on these datasets, which advocate the utilization of our approach for fish detection task.

A facile thermal-treatment route was successfully used to synthesize ZnO nanosheets. Morphological, structural, and optical properties of obtained nanoparticles at different calcination temperatures were studied using various techniques. The FTIR, XRD, EDX, SEM and TEM images confirmed the formation of ZnO nanosheets through calcination in the temperature between 500 to 650°C. The SEM images showed a morphological structure of ZnO nanosheets, which inclined to crumble at higher calcination temperatures. The XRD and FTIR spectra revealed that the samples were amorphous at 30°C but transformed into a crystalline structure during calcination process. The average particle size and degree of crystallinity increased with increasing calcination temperature. The estimated average particle sizes from TEM images were about 23 and 38 nm for the lowest and highest calcination temperature i.e. 500 and 650°C, respectively. The optical properties were determined by UV–Vis reflection spectrophotometer and showed a decrease in the band gap with increasing calcination temperature.