Home Automation Using Augmented Reality (HAAR)

Wireless Personal Communications - The development of Smart Home Controllers has seen rapid growth in recent years, especially for smart devices, that can utilize the Internet of Things (IoT)....     

Hybrid image steganography method using Lempel Ziv Welch and genetic algorithms for hiding confidential data

Multidimensional Systems and Signal Processing - Digital images are commonly used in steganography due to the popularity of digital image transfer and exchange through the Internet. However, the...     

ENROUTE: An Entropy Aware Routing Scheme for Information-Centric Networks (ICN)

With the exponential growth of end users and web data, the internet is undergoing the change of paradigm from a user-centric model to a content-centric one, popularly known as information-centric networks (ICN). Current ICN research evolves around three key-issues namely (i) content request searching, (ii) content routing, and (iii) in-network caching scheme to deliver the requested content to the end user. This would improve the user experience to obtain requested content because it lowers the download delay and provides higher throughput. Existing researches have mainly focused on on-path congestion or expected delivery time of a content to determine the optimized path towards custodian. However, it ignores the cumulative effect of the link-state parameters and the state of the cache, and consequently it leads to degrade the delay performance. In order to overcome this shortfall, we consider both the congestion of a link and the state of on-path caches to determine the best possible routes. We introduce a generic term entropy to quantify the effects of link congestion and state of on-path caches. Thereafter, we develop a novel entropy dependent algorithm namely ENROUTE for searching of content request triggered by any user, routing of this content, and caching for the delivery this requested content to the user. The entropy value of an intra-domain node indicates how many popular contents are already cached in the node, which, in turn, signifies the degree of enrichment of that node with the popular contents. On the other hand, the entropy for a link indicates how much the link is congested with the traversal of contents. In order to have reduced delay, we enhance the entropy of caches in nodes, and also use path with low entropy for downloading contents. We evaluate the performance of our proposed ENROUTE algorithm against state-of-the-art schemes for various network parameters and observe an improvement of 29–52% in delay, 12–39% in hit rate, and 4–39% in throughput.

     

Correction to: Enhanced TOA Estimation Using OFDM over Wide-Band Transmission Based on a Simulated Model

Wireless Personal Communications -     

Ti3AlC2 coated D-shaped fiber saturable absorber for Q-switched pulse generation

In this letter, a titanium aluminum carbide (Ti3AlC2) coated D-shaped fiber is proposed and demonstrated as a new saturable absorber (SA) for Q-switched laser pulse generation. In preparing the SA, the Ti3AlC2 powder is dispersed in liquid polyvinyl alcohol (PVA) before the solution is dropped and left to dry onto a polished surface of D-shape fiber. The SA is added to an erbium-doped fiber laser (EDFL) cavity to modulate the cavity loss for Q-switching. The Q-switched laser is obtained at 1 561 nm. The pulse width of the pulses can be varied between 7.4 µs and 5.1 µs with a corresponding repetition rate range from 41.26 kHz to 54.35 kHz, when the pump power is increased from 42.2 mW to 71.5 mW. At 71.5 mW pump, the pulse energy is obtained at 70.3 nJ. The signal-to-noise ratio (SNR) of the fundamental frequency is recorded at 67 dB, which indicates the stability of the laser.     

Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm

IoT is one of the most important technologies that are used over the past few years, where everything is connected to the Internet; it is used in many fields; one of these fields is healthcare system that includes mobile health and remote patient monitoring (patients with kidney, heart disease, cancer, blood pressure, diabetes, respiratory disease and stroke). Integration of IoT and cloud computing can improve the performance of healthcare system and the development of the innovative applications in future. One of the major problems that cannot be ignored in cloud computing system is load balancing. Solving that problem leads to reduce the response time, power consumption, cost and increase server availability. This paper is comprised of two parts which are creating and implementing healthcare system by using IoT, and solving the problem of load balancing of the cloud computing by using intelligent algorithm called sparrow search algorithm (SSA). The SSA is used to select the best virtual machine (VM) among a group of VMs depending on the its fitness value; also many and varied tasks are scheduled with priority and assign to the best VMs depending on the its instruction millions (IM), where the task that has high IM is assigned to the best VM that has high fitness value. The outcomes demonstrated that the proposed method focuses to reduce the latency and packet loss while maximizing the throughput in healthcare systems; also the SSA has proved its robustness, efficiency and success when compared to other methods in terms of reducing makespan time, total processing time and provides load balancing among VMs, where the value of makespan time, processing time and degree of imbalance has decreased into (23.05), (899.8979) and (177.7675), respectively, in case of applying 500 tasks.

     

Financial assessment of health and safety programmes in the workplace

In evaluating health and safety improvements for performance improvement, it is necessary to account for both the contributions of a healthy workforce and the resources required supporting it.

The Economic Assessment of the Work Environment (EAWE) is a financial framework that helps management forecast the financial benefits of health and safety implementations. The five-step process comprises (1) a health assessment to identify critical elements in the work environment, (2) an action plan to address gaps, (3) performance targets based on internal goals and external benchmarks, (4) transformation of the expected improvements in health and safety into expected performance outcomes, and (5) implementation in stages, starting from individual jobs to entire organisation.

EAWE offers a dynamic framework for corporate decision-makers when evaluating health and safety programmes. Further research should explore the bounds of EAWE across different types of organisations and the evolution of performance over time.     

Solid biofuels in Mexico: a sustainable alternative to satisfy the increasing demand for heat and power

Bioenergy is the largest renewable energy source in Mexico with an estimated 4–9% of total current energy demand. There are large uncertainties and contrasting estimates regarding its current extent and end-uses, particularly with traditional uses. However, a large potential exists to improve the efficiency of existing uses and, at the same time, to diversify the use of SBF in the industrial and power sectors. This paper aims at: providing the first updated and comprehensive estimate of current SBF demand in Mexico including traditional and modern uses; providing a consistent estimate of actual SBF supply potential; estimating the total potential substitution of fossil fuels that could be achieved by SBF considering an integrated “modernization scenario”; and finally describing the main barriers limiting SBF to fully triggering its potential. Results show that current SBF consumption reached 481 PJ/yr in 2015; SBF supply potential reaches 3622 PJ/yr, out of which 883 PJ/yr could be used to substitute up to 29% of current demand of FF, mitigating 66 MtCO_2e/yr of greenhouse gas (GHG) emissions, or near 88 MtCO_2e/yr if mitigation from traditional uses is added.     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.