Dynamic FOV visible light communications receiver for dense optical networks

This study explores how the field-of-view (FOV) of a visible light communications (VLCs) receiver can be manipulated to realise the best signal-to-noise ratio (SNR) while supporting device mobility and optimal access point (AP) selection. The authors propose a dynamic FOV receiver that changes its aperture according to receiver velocity, location, and device orientation. The D-FOV technique is evaluated through modelling, analysis, and experimentation in an indoor environment comprised of 15 VLC APs. The proposed approach is also realised as an algorithm that is studied through analysis and simulation. The results of the study indicate the efficacy of the approach including a 3X increase in predicted SNR over static FOV approaches based on measured received signal strength in the testbed. Additionally, the collected data reveal that D-FOV increases effectiveness in the presence of noise. Finally, the study describes the tradeoffs among the number of VLC sources, FOV, user device velocity, and SNR as a performance metric.Accepted manuscrip

A Brief Comparison of K-means and Agglomerative Hierarchical Clustering Algorithms on Small Datasets

In this work, the agglomerative hierarchical clustering and K-means clustering algorithms are implemented on small datasets. Considering that the selection of the similarity measure is a vital factor in data clustering, two measures are used in this study - cosine similarity measure and Euclidean distance - along with two evaluation metrics - entropy and purity - to assess the clustering quality. The datasets used in this work are taken from UCI machine learning depository. The experimental results indicate that k-means clustering outperformed hierarchical clustering in terms of entropy and purity using cosine similarity measure. However, hierarchical clustering outperformed k-means clustering using Euclidean distance. It is noted that performance of clustering algorithm is highly dependent on the similarity measure. Moreover, as the number of clusters gets reasonably increased, the clustering algorithms’ performance gets higher

Plane waves in noncommutative fluids

We study the dynamics of the noncommutative fuid in the Snyder space perturbatively at the first order in powers of the noncommutative parameter. The linearized noncommutative fluid dynamics is described by a system of coupled linear partial differential equations in which the variables are the fluid density and the fluid potentials. We show that these equations admit a set of solutions that are monocromatic plane waves for the fluid density and two of the potentials and a linear function for the third potential. The energy-momentum tensor of the plane waves is calculated.Comment: 11 pages. Version published as a Lette

Effect of Aqueous Extract of Cathedral Cactus (Euphorbia trigona Mill) on Larvae of Anopheles arabiensis (Diptera: Culicidae)

Abstract: Mosquitoes are considered as vector of malaria disease and some other endemic diseases in the world. There are some methods already been used for controlling mosquito; of which is using natural products. This study was conducted at Laboratories of Faculty of Engineering and Technology, University of Gezira, to evaluate the effect of cortex, spine and pith parts of cactus (Euphorbia trigona) on Anopheles mosquito larvae. The plant parts were collected from Wad Medani City, whereas, the mosquito larvae were collected from the breeding sites at Tayba village, Gezira State, Sudan. The plant parts (cortex, spines and pith) were shade dried away from the direct sunlight, grounded and then kept separately in small plastic sacks. From each plant part, a concentration of 1200 mg/L was used. The standards of WHO for testing toxicity of the toxic compound against mosquito larvae was followed. The mortality in Anopheles larvae were 48%, 37% and 62%, respectively, for trigona cortex, spine and pith. The results also showed that, the three used parts have a varied great impact on the survived larvae (morphological changes of skin color was in 82%, disconnecting of digestive tract was in 48%, and separation of some body parts was in 32%, after 48 hours of applying it). The study recommends adding these cactus parts as potential natural products for Anopheles larval control, and also running more sensitive tests to measure the environmental impact of these products, especially on human and on the aquatic faun

ANNz2: Photometric Redshift and Probability Distribution Function Estimation using Machine Learning

We present ANNz2, a new implementation of the public software for photometric redshift (photo-z) estimation of Collister & Lahav, which now includes generation of full probability distribution functions (PDFs). ANNz2 utilizes multiple machine learning methods, such as artificial neural networks and boosted decision/regression trees. The objective of the algorithm is to optimize the performance of the photo-z estimation, to properly derive the associated uncertainties, and to produce both single-value solutions and PDFs. In addition, estimators are made available, which mitigate possible problems of non-representative or incomplete spectroscopic training samples. ANNz2 has already been used as part of the first weak lensing analysis of the Dark Energy Survey, and is included in the experiment's first public data release. Here we illustrate the functionality of the code using data from the tenth data release of the Sloan Digital Sky Survey and the Baryon Oscillation Spectroscopic Survey. The code is available for download at http://github.com/IftachSadeh/ANNZ

An application of multi-level DC-link converter for optimised permutation control of PV sources under partial shading

The paper describes application of a multi-level DC-link converter in overcoming the problem of partially shaded series-connected PV sources. The converter control engages a permutation algorithm which enables each PV source of the string to produce the maximum power. The main features of the system are: (i) a continual operation of all PV sources, shaded and non-shaded, at their maximum power points, (ii) delivery of all extracted power from PV sources to the load and (ii) generation of multi-level output voltage waveform with a low total harmonic distortion

Mangrove litter production and seasonality of dominant species in Zanzibar, Tanzania

This study is aimed at examining the litter production and seasonality of Avicennia marina, Bruguiera gymnorhiza, and Rhizophora mucronata. Litter was collected using nylon litter traps of 1 mm2 mesh size in the Uzi-Nyeke mixed mangroves, Zanzibar, over a period of 2 years. Contents were sorted, dried, weighed, and the average daily litter production for each component was calculated. A distinct seasonality and species variation were found in all mangrove litter components. The average annual litterfall rate was higher in B. gymnorhiza, followed by R. mucronata and A. marina (3.0, 2.8, and 2.0 ton dry wt. ha-1year–1 respectively). Leaf fraction was the main component of litter in all species, but fruit and flower for R. mucronata also had a considerable contribution to the total litterfall. The presented patterns of litter production are associated with average temperature and wind speed which are both strongly correlated with litter seasonality. Our data contributes to the body of knowledge on  patterns of litter production and the ecological integrity of mangrove forests in Zanzibar.Keywords: Litterfall, mangrove species, seasonal pattern

Boolean logic algebra driven similarity measure for text based applications

In Information Retrieval (IR), Data Mining (DM), and Machine Learning (ML), similarity measures have been widely used for text clustering and classification. The similarity measure is the cornerstone upon which the performance of most DM and ML algorithms is completely dependent. Thus, till now, the endeavor in literature for an effective and efficient similarity measure is still immature. Some recently-proposed similarity measures were effective, but have a complex design and suffer from inefficiencies. This work, therefore, develops an effective and efficient similarity measure of a simplistic design for text-based applications. The measure developed in this work is driven by Boolean logic algebra basics (BLAB-SM), which aims at effectively reaching the desired accuracy at the fastest run time as compared to the recently developed state-of-the-art measures. Using the term frequency–inverse document frequency (TF-IDF) schema, the K-nearest neighbor (KNN), and the K-means clustering algorithm, a comprehensive evaluation is presented. The evaluation has been experimentally performed for BLAB-SM against seven similarity measures on two most-popular datasets, Reuters-21 and Web-KB. The experimental results illustrate that BLAB-SM is not only more efficient but also significantly more effective than state-of-the-art similarity measures on both classification and clustering tasks