Quantum Inspired Broadband Photonic Absorber: Potential Application as Solar Sail along with Mobility Analysis

Electromagnetic wideband absorption is still perceived as a critical and formidable challenge to address with an unambiguous photonic absorber. Subwavelength metamaterial (MM) unit cells with unique and controlled features have recently gained considerable interest. However, meta-atoms, generated using a quantum-inspired pattern distribution, are underwhelming in existing literature to design photonic absorbers and their potential application to manufacture solar sails is still quite uncommon. In this article, to create a flexible, polarization-insensitive, ultrathin, and broadband MM absorber, quantum interference pattern-inspired design is utilized. Herein, a novel approach to fabricating solar sails for the space exploration incorporates the proposed broadband photonic absorber rather than conventional reflectors. The quantum-inspired meta-absorber (QIMA) exhibits an absorption of over 91% for the visible domain, i.e., 380–800 nm under a conventional plane-polarized source. It is shown in the study that broadband absorbers are almost equivalent to excellent reflectors to design the solar sails in terms of the time-averaged force calculated by utilizing the Maxwell stress tensor method. Thus, the QIMA has the potential to be a viable alternative to reflectors in the design of futuristic solar sails for space exploration. The interference theory model is also utilized to assure the dependability of calculated data, and additionally, the standard AM1.5 solar spectrum is utilized to demonstrate the QIMA's solar-harvesting potentiality.     

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.     

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.     

Intelligently tuned wavelet parameters for GPS/INS error estimation

This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of th...     

A new document representation using term frequency and vectorized graph connectionists with application to document retrieval

This paper presents a new document representation with vectorized multiple features including term frequency and term-connection-frequency. A document is represented by undirected and directed graph, respectively. Then terms and vectorized graph connectionists are extracted from the graphs by employing several feature extraction methods. This hybrid document feature representation more accurately reflects the underlying semantics that are difficult to achieve from the currently used term histograms, and it facilitates the matching of complex graph. In application level, we develop a document retrieval system based on self-organizing map (SOM) to speed up the retrieval process. We perform extensive experimental verification, and the results suggest that the proposed method is computationally efficient and accurate for document retrieval.     

Replica Placement Strategies in Data Grid

Replication is a technique used in Data Grid environments that helps to reduce access latency and network bandwidth utilization. Replication also increases data availability thereby enhancing system reliability. The research addresses the problem of replication in Data Grid environment by investigating a set of highly decentralized dynamic replica placement algorithms. Replica placement algorithms are based on heuristics that consider both network latency and user requests to select the best candidate sites to place replicas. Due to dynamic nature of Grid, the candidate site holds replicas currently may not be the best sites to fetch replicas in subsequent periods. Therefore, a replica maintenance algorithm is proposed to relocate replicas to different sites if the performance metric degrades significantly. The study of our replica placement algorithms is carried out using a model of the EU Data Grid Testbed 1 [Bell et al. Comput. Appl., 17(4), 2003] sites and their associated network geometry. We validate our replica placement algorithms with total file transfer times, the number of local file accesses, and the number of remote file accesses.     

Pressure measurements in laboratory-scale blast wave flow fields

The present study examines the effects that temporal and spatial averagings due to finite size and finite response time of pressure transducers have on the pressure measurements in blast wave flow fields generated by milligram charges of silver azide. In such applications, the characteristic time and length scales of the physical process are of the same order of magnitude as the temporal and spatial characteristics of the transducer. The measured pressure values will then be spatially and temporally averaged, and important parameters for the assessment of blast effects may not be properly represented in the measured trace. In this study, face-on and side-on pressure transducer setups are considered. In the experiments, face-on and side-on readings at the same distance from the charge as well as time-resolved optical visualization of the whole flow field are obtained simultaneously for the same explosive event. The procedure of data extraction from the experimental pressure traces is revisited and discussed in detail. In the numerical modeling part of the study, numerical blast flow fields are generated using an Euler flow solver. A numerical pressure transducer model is developed to qualitatively simulate the averaging effects. The experimental and numerical data show that the results of pressure measurements in experiments with small charges must be used with great caution. The effective averaging of the pressure signal may lead to a significant underestimation of blast wave intensities. The side-on setup is especially prone to this effect. The face-on setup provides results close to those obtained from optical records only if the pressure transducer is sufficiently remote from the charge.     

D.c. and a.c. electrical properties of vacuum evaporated thin SiO/GeO2 films

The d.c. and a.c. electrical properties were studied for various compositions of SiO/GeO₂ co-evaporated thin films carrying aluminium electrodes, in the temperature range 193–413 K. A.c. measurements were made over the frequency range 2x10²–10⁶Hz. The value of the d.c. activation energy was found to decrease with increasing GeO₂ content in the SiO. In the region of high applied field (above 10⁶ Vm^–1, the conduction mechanism is governed by Schottky emission at the blocking contact. The a.c. electrical conductivity, (), varies with frequency according to the relation () ^s, where the exponent s was found to be dependent on temperature and frequency. The a.c. conduction at low temperature was due to an electronic hopping process. The number of localized sites was estimated from the a.c. measurements for different compositions of SiO/GeO₂ using the models proposed by Elliott and by Pollak, and the values are compared. The Elliott model satisfactorily accounts for the observed a.c. electrical results. A correlation was found between activation energy, optical band gap, conductivity and number of localized sites for the various compositions of SiO/GeO₂ films. The relative dielectric constant, _r, and loss factor, tan , were found to increase with the increase of GeO₂ content in the films.     

Multiple expert classification: a new methodology for parallel decision fusion

Abstract. A new parallel hybrid decision fusion methodology is proposed. It is demonstrated that existing parallel multiple expert decision combination approaches can be divided into two broad categories based on the implicit decision emphasis implemented. The first category consists of methods implementing computationally intensive decision frameworks incorporating a priori information about the target task domain and the reliability of the participating experts, while the second category encompasses approaches implementing group consensus without assigning any importance to the reliability of the experts and ignoring other contextual information. The methodology proposed in this paper is a hybridisation of these two approaches and has shown significant performance enhancements in terms of higher overall recognition rates along with lower substitution rates. Detailed analysis using two different databases supports this claim. Received January 19, 1999 / Revised March 20, 2000     

Implications to stormwater management as a result of lot scale rainwater tank systems: a case study in Western Sydney, Australia

Rainwater tanks are increasingly adopted in Australia to reduce potable water demand and are perceived to reduce the volume of stormwater discharge from developments. This paper investigates the water balance of rainwater tanks, in particular the possible impacts these tanks could have in controlling the stormwater discharge volume. The study collected water quantity data from two sites in the Hawkesbury City Council area, New South Wales, Australia and utilised the collected data in a simple water balance model to assess the effectiveness of rainwater tanks in reducing the stormwater discharge volume. The results indicate that a significant reduction in discharge volume from a lot scale development can be achieved if the rainwater tank is connected to multiple end-uses, but is minimal when using irrigation alone. In addition, the commonly used volumetric runoff coefficient of 0.9 was found to over-estimate the runoff from the roof areas and to thereby under-estimate the available volume within the rainwater tanks for retention or detention. Also, sole reliance on the water in the rainwater tanks can make the users aware of their water use pattern and water availability, resulting in significant reductions in water use as the supply dwindles, through self-imposed water restrictions.