A joint motion & disparity motion estimation technique for 3D integral video compression using evolutionary strategy

3D imaging techniques have the potential to establish a future mass-market in the fields of entertainment and communications. Integral imaging, which can capture true 3D color images with only one camera, has been seen as the right technology to offer stress-free viewing to audiences of more than one person. Just like any digital video, 3D video sequences must also be compressed in order to make it suitable for consumer domain applications. However, ordinary compression techniques found in state-of-the-art video coding standards such as H.264, MPEG-4 and MPEG-2 are not capable of producing enough compression while preserving the 3D clues. Fortunately, a huge amount of redundancies can be found in an integral video sequence in terms of motion and disparity. This paper discusses a novel approach to use both motion and disparity information to compress 3D integral video sequences. We propose to decompose the integral video sequence down to viewpoint video sequences and jointly exploit motion and disparity redundancies to maximize the compression. We further propose an optimization technique based on evolutionary strategies to minimize the computational complexity of the joint motion disparity estimation. Experimental results demonstrate that Joint Motion and Disparity Estimation can achieve over 1 dB objective quality gain over normal motion estimation. Once combined with Evolutionary strategy, this can achieve up to 94% computational cost saving

Motion and disparity estimation with self adapted evolutionary strategy in 3D video coding

Real world information, obtained by humans is three dimensional (3-D). In experimental user-trials, subjective assessments have clearly demonstrated the increased impact of 3-D pictures compared to conventional flat-picture techniques. It is reasonable, therefore, that we humans want an imaging system that produces pictures that are as natural and real as things we see and experience every day. Three-dimensional imaging and hence, 3-D television (3DTV) are very promising approaches expected to satisfy these desires. Integral imaging, which can capture true 3D color images with only one camera, has been seen as the right technology to offer stress-free viewing to audiences of more than one person. In this paper, we propose a novel approach to use Evolutionary Strategy (ES) for joint motion and disparity estimation to compress 3D integral video sequences. We propose to decompose the integral video sequence down to viewpoint video sequences and jointly exploit motion and disparity redundancies to maximize the compression using a self adapted ES. A half pixel refinement algorithm is then applied by interpolating macro blocks in the previous frame to further improve the video quality. Experimental results demonstrate that the proposed adaptable ES with Half Pixel Joint Motion and Disparity Estimation can up to 1.5 dB objective quality gain without any additional computational cost over our previous algorithm.1Furthermore, the proposed technique get similar objective quality compared to the full search algorithm by reducing the computational cost up to 90%

An evolutionary strategy based motion estimation algorithm for H.264 video codecs

In this paper, we propose a new motion estimation algorithm based on evolutionary strategy (ES) for the H.264 video codec applied to monoscopic video. The proposed technique applies in macroblock basis and performs a parallel local search for the motion vector associated with the minimum motion compensated residue. For this purpose (/spl mu/+/spl lambda/)-ES is used with heuristically and randomly generated population of initial motion vectors. Experimental results show that the proposed scheme can reduce the computational complexity up to 50% of the motion estimation algorithm used in the H.264 reference codec at the same picture quality. Therefore, the proposed algorithm provides a significant improvement in motion estimation in the H.264 video codec

Predicting fraud in mobile money transfer using case-based reasoning

This paper proposes an improved CBR approach for the identification of money transfer fraud in Mobile Money Transfer (MMT) environments. Standard CBR capability is augmented by machine learning techniques to assign parameter weights in the sample dataset and automate k-value random selection in k-NN classification to improve CBR performance. The CBR system observes users’ transaction behaviour within the MMT service and tries to detect abnormal patterns in the transaction flows. To capture user behaviour effectively, the CBR system classifies the log information into five contexts and then combines them into a single dimension, instead of using the conventional approach where the transaction amount, time dimensions or features dimension are used individually. The applicability of the proposed augmented CBR system is evaluated using simulation data. From the results, both dimensions show good performance with the context of information weighted CBR system outperforming the individual features approach

Lime and Mango Juice as Coagulants for Soft Cheese Made from Fresh or Reconstituted Milk

In order to simplify the cheese-making process and find suitable alternatives to sodom apple as coagulant, cheese was prepared from fresh and reconstituted milk using lime and mango juice as coagulants. Treatments correspond to fresh milk coagulated with lime (FML), reconstituted milk coagulated with lime (RML), fresh milk coagulated with mango (FMM), reconstituted milk coagulated with mango (RMM). Chemical composition of cheese varied significantly across the treatments. Total solids was 49.9, 43.4, 49.6 and 42.1%; protein was 20.6, 18.1, 21.6 and18.9%; while fat content was 22.4, 19.7, 21.6 and 17.9% for FML, RML, FMM and RMM respectively. Protein and fat content of cheese made from fresh milk was higher than that from reconstituted milk. Mango-precipitated cheese had higher protein than lime-precipitated cheese while lime-precipitated cheese had higher fat content than mango-precipitated cheese. Cheese yield varied from 11- 16% with lime-precipitated cheese having higher yields than mango-precipitated cheese, and fresh milk yielding more cheese than reconstituted milk. Acceptability scores were 7.1, 7.1, 7.1 and 6.6 for FML, RML, FMM and RMM respectively on a scale of 1-9. Except for RMM which had a significantly lower score, there were no significant differences in acceptability for FML, RML and FMM. When compared with wara cheese purchased from the open market, lime and mango-precipitated cheese generally had better consumer acceptance than wara (acceptability score of 6.7).   These results show that lime and mango juice can serve as suitable alternatives to sodom apple juice as coagulants for fresh cheese. Reconstituted milk can also be used for making cheese where availability of fresh milk is limited. Keywords: fresh cheese, coagulants, lime, mango, mil

Testing the transport-induced environmental Kuznets curve hypothesis: The role of air and railway transport

The airline and railway industry contribute immensely to economic development, however, its role in environmental pollution requires attention. Here, this study builds on the theoretical pedigree of the environmental Kuznets curve (EKC) hypothesis to explore the contribution of the air and railway transportation sector and urbanization to the emission-growth argument. We utilized annual time-frequency data from 1995 to 2014 for a panel of top 10 air passenger carrier countries using robust panel estimators that control for cross-section dependence. The empirical analysis shows a positive significant relationship between emissions and economic growth, thus, economic growth is emission-embedded with limited green growth. The existence of the EKC phenomenon is affirmed for the investigated blocs — where economic growth is prioritized over environmental quality. Additional, while air transportation drives pollution, railway transportation and urbanization decline emission over the sampled period. The results underscore the need for clean and environmentally friendly energy sources for air sector operations

Determinants of modality of management of acute kidney injury in children seen at a tertiary hospital in Nigeria

Background: The cost of taking care of children with acute kidney injury (AKI) is enormous and beyond the reach of many caregivers in  sub-Saharan Africa which are largely resource poor. It is therefore imperative to determine those who may benefit from conservative management which is comparatively cheaper to the renal replacement therapy (RRT).Objectives: To determine the clinical characteristics of children who were offered conservative and renal replacement therapy and evolve the most statistically significant eligibility criteria. Methods: A descriptive  crosssectional study of children presenting with AKI admitted into the Emergency Paediatric Unit (EPU) of the University of Ilorin Teaching Hospital (UITH) between January 2008 to December 2012 was carried out. Demographic, clinical, and laboratory data were collected. A serialblood chemistry and urine analysis were also obtained. A total of 22 cases of acute kidney injury were seen within the period. Fourteen were conservatively managed while eight underwent sessions of dialysis.Results: The age range for those who had conservative managementwas 4-17 years with a mean ±SD of 8.11±3.91 years while the corresponding value in those with renal replacement therapy was 1.5-16years with a mean ±SD of 9.68±5.54years. There was no statisticalsignificant difference in the highest serum potassium, urea andcreatinine. However, the lowest urine output was significantly differentamong the two groups (p< 0.05).Conclusion: Urine output could be used as an eligibility criterion todetermine children with AKI who will require renal replacement therapy or benefit from a trial of conservative management.  Keyword: Acute kidney injury; conservative management; dialysi

The criticality of ICT-trade nexus on economic and inclusive growth

© 2020 Commonwealth Secretariat. This paper contributes to the ICT-growth and trade-growth literature by investigating the ICT-trade nexus on economic and inclusive growth. That is, does ICT adoption enhance or distort the impact of trade on growth? With data on 53 African countries from 2005 to 2015 using mobile phones and fixed telephone subscriptions as indicators of ICT, findings provide evidence that (1) trade is a significant and positive predictor of growth, (2) the impact of trade on growth differs significantly across Africa’s sub-regions, (3) the effect of ICT adoption differs significantly across sub-regions, (4) ICT innovation enhances the impact of trade on growth, and (5) the ICT-trade nexus differ significantly across sub-regions. The study submits that these variables are critical drivers of economic and inclusive growth in Africa. However, the lack of consistency of the results across the sub-regions suggests that the level of ICT is still undeveloped. Policy implications are discussed

An investigation into the anthropogenic nexus among consumption of energy, tourism and economic growth: Do economic policy uncertainties matter?

Global warming has been a pressing issue for the past decade as various economic activities have been flagged and are expected to reduce emissions. While previous studies have examined the energy consumption-emissions-economic growth nexus in significant detail, attention is yet to be given to the role of economic policy uncertainties and human activities such as tourism in a carbon function. Thus, this study aims to investigate the long run relationship between energy consumption, tourists’ arrivals, economic policy uncertainty and ecological footprint in the top ten earners from international tourism over the period 1995 to 2015. The FMOLS and DOLS estimation techniques and the Dumitrescu and Hurlin Causality tests were used in the study. Empirical results suggest that economic policy uncertainties in addition to tourism and energy consumption are drivers of environmental degradation. However, the contribution of energy consumption to ecological footprint is significantly moderated by economic policy uncertainties such that a 1% increase in the latter reduces environmental damage by 0.71%. This study suggests that policy uncertainties matter a great deal for energy and environmental policies. Also, green economic growth is possible if the proper implementation of environmental protection policies can restrict the harmful impact of economic activities on the quality of the environment. Based on the empirical findings, vital energy policy recommendations are suggested

Sign and human action detection using deep learning

Human beings usually rely on communication to express their feeling, and ideas and solve disputes among them. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, or even vocals. It is usually essential for all the communicating parties to be fully conversant with a common language that they are using. However, this hasn’t been the case between speech impaired people who use sign language and the regular people in the society who use spoken languages. Different studies have pointed out a significant gap between these people and the regular people, limiting the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language. This is in an attempt to narrow this communication gap between the speech-impaired people and the regular people in the community. Two models were developed in the research, which includes CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training, and testing accuracies of 98.8% and 97.4%, respectively. The model also achieved average weighted precession, and recall was also 97% and 96%, respectively. On the other hand, the LSTM model’s performance was quite poor, with maximum training and testing, the achieved performance is 49.4% and 48.7% respectively. The research concluded that the CNN model was the best for recognizing and determining British sign language