Estimating Waterbird Abundance on Catfish Aquaculture Ponds Using an Unmanned Aerial System

In this study, we examined the use of an unmanned aerial system (UAS) to monitor fish-eating birds on catfish (Ictalurus spp.) aquaculture facilities in Mississippi, USA. We tested 2 automated computer algorithms to identify bird species using mosaicked imagery taken from a UAS platform. One algorithm identified birds based on color alone (color segmentation), and the other algorithm used shape recognition (template matching), and the results of each algorithm were compared directly to manual counts of the same imagery. We captured digital imagery of great egrets (Ardea alba), great blue herons (A. herodias), and double-crested cormorants (Phalacrocorax auritus) on aquaculture facilities in Mississippi. When all species were combined, template matching algorithm produced an average accuracy of 0.80 (SD = 0.58), and color segmentation algorithm produced an average accuracy of 0.67 (SD = 0.67), but each was highly dependent on weather, image quality, habitat characteristics, and characteristics of the birds themselves. Egrets were successfully counted using both color segmentation and template matching. Template matching performed best for great blue herons compared to color segmentation, and neither algorithm performed well for cormorants. Although the computer-guided identification in this study was highly variable, UAS show promise as an alternative monitoring tool for birds at aquaculture facilities

Dataset for Controllable factors affecting accuracy and precision of human identification of animals from drone imagery

Dataset from the results of an experiment to determine how three controllable factors, flight altitude, camera angle, and time of day, affect human identification and counts of animals from drone images to inform best practices to survey animal communities with drones. We used a drone (unoccupied aircraft system, or UAS) to survey known numbers of eight animal decoy species, representing a range of body sizes and colors, at four GSD (ground sampling distance) values (0.35, 0.70, 1.06, 1.41 cm/pixel) representing equivalent flight altitudes (15.2, 30.5, 45.7, 61.0 m) at two camera angles (45° and 90°) and across a range of times of day (morning to late afternoon). Expert human observers identified and counted animals in drone images to determine how the three controllable factors affected accuracy and precision. Observer precision was high and unaffected by tested factors. However, results for observer accuracy revealed an interaction among all three controllable factors. Increasing flight altitude resulted in decreased accuracy in animal counts overall; however, accuracy was best at midday compared to morning and afternoon hours, when decoy and structure shadows were present or more pronounced. Surprisingly, the 45° camera enhanced accuracy compared to 90°, but only when animals were most difficult to identify and count, such as at higher flight altitudes or during the early morning and late afternoon. We provide recommendations based on our results to design future surveys to improve human accuracy in identifying and counting animals from drone images for monitoring animal populations and communities

Remote detection of invasive alien species

The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail

Despeckling of Carotid Artery Ultrasound Images with a Calculus Approach

&lt;P&gt;Background: Carotid artery images indicate any presence of plaque content, which may lead to atherosclerosis and stroke. Early identification of the disease is possible by taking B-mode ultrasound images in the carotid artery. Speckle is the inherent noise content in the ultrasound images, which essentially needs to be minimized. &lt;/P&gt;&lt;P&gt; Objective: The objective of the proposed method is to convert the multiplicative speckle noise into additive, after which the frequency transformations can be applied. &lt;/P&gt;&lt;P&gt; Method: The method uses simple differentiation and integral calculus and is named variable gradient summation. It differs from the conventional homomorphic filter, by preserving the edge features to a great extent and better denoising. The additive image is subjected to wavelet decomposition and further speckle filtering with three different filters Non Local Means (NLM), Vectorial Total Variation (VTV) and Block Matching and 3D filtering (BM3D) algorithms. By this approach, the components dependent on the image are identified and the unwanted noise content existing in the high frequency portion of the image is removed. &lt;/P&gt;&lt;P&gt; Results &amp; Conclusion: Experiments conducted on a set of 300 B-mode ultrasound carotid artery images and the simulation results prove that the proposed method of denoising gives enhanced results as compared to the conventional process in terms of the performance evaluation methods like peak signal to noise ratio, mean square error, mean absolute error, root mean square error, structural similarity, quality factor, correlation and image enhancement factor.&lt;/P&gt; </jats:sec

Carotid artery ultrasound image analysis: A review of the literature

Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima–media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima–media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning–based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.</jats:p

Approaches to interpret the outcomes of a network meta-analysis on comparative efficacy of different targeted therapies plus fulvestrant for advanced breast cancer following progression on prior endocrine therapy

Rama Jayaraj,1 Chellan Kumarasamy,2 Shanthi Sabarimurugan,3 Suja Samiappan41College of Health and Human Sciences, Charles Darwin University, Casuarina, Northern Territory 0909, Australia; 2University of Adelaide, Adelaide, South Australia 5005, Australia; 3School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India; 4Department of Biochemistry, Bharathiar University, Coimbatore, Tamil Nadu, IndiaZhang and colleagues have conducted a network meta-analysis regarding fulvestrant combined targeted therapies for breast cancer, which has been published in the Cancer Management and Research journal.1 The study itself is interesting in its approach.View the original paper by Zhang and colleagues

A Novel and Efficient square root Computation Quantum Circuit for Floating-point Standard

It is imperative that quantum computing devices perform floating-point arithmetic operations. This paper presents a circuit design for floating-point square root operations designed using classical Babylonian algorithm. The proposed Babylonian square root, is accomplished using Clifford+T operations. This work focuses on realizing the square root circuit by employing the bit Restoring and bit Non-restoring division algorithms as two different approaches. The multiplier of the proposed circuit uses an improved structure of Toom-cook 2.5 multiplier by optimizing the T-gate count of the multiplier. It is determined from the analysis that the proposed square root circuit employing slow-division algorithms results in a T-count reduction of 80.51% and 72.65% over the existing work. The proposed circuit saves a significant number of ancillary qubits, resulting in a qubit cost savings of 61.67 % When compared to the existing work.peerReviewe