Indoor visible light communication localization system utilizing received signal strength indication technique and trilateration method

Visible light communication (VLC) based on light-emitting diodes (LEDs) technology not only provides higher data rate for indoor wireless communications and offering room illumination but also has the potential for indoor localization. VLC-based indoor positioning using the received optical power levels from emitting LEDs is investigated. We consider both scenarios of line-of-sight (LOS) and LOS with non-LOS (LOSNLOS) positioning. The performance of the proposed system is evaluated under both noisy and noiseless channel as is the impact of different location codes on positioning error. The analytical model of the system with noise and the corresponding numerical evaluation for a range of signal-to-noise ratio (SNR) are presented. The results show that an accuracy of 12 dB

Effect of two types of graphene nanoplatelets on the physico–mechanical properties of linear low–density polyethylene composites

The influence of two types of graphene nanoplatelets (GNPs) on the physico-mechanical properties of linear low-density polyethylene (LLDPE) was investigated. The addition of these two types of GNPs – designated as grades C and M – enhanced the thermal conductivity of the LLDPE, with a more pronounced improvement resulting from the M-GNPs compared to C-GNPs. Improvement in electrical conductivity and decomposition temperature was also noticed with the addition of GNPs. In contrast to the thermal conductivity, C-GNPs resulted in greater improvements in the electrical conductivity and thermal decomposition temperature. These differences can be attributed to differences in the surface area and dispersion of the two types of GNPs

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy

“THE EFFECT OF THE PERCEIVED USEFULNESS OF INSTAGRAM BEAUTY INFLUENCER ON THE ATTITUDE AND PURCHASING INTENTION OF YOUNG FEMALES”

There is an increasing interest about the topic of eWOM among marketing scholars. However, most of the studies focused on western contexts and there is a lack of studies about the impact of eWOM on consumers’ attitude and intentions in the Gulf region and Qatar specifically. Hence, this study aims to focus on a specific social media channel; Instagram, and examine the effect of influencers’ recommendations on women consumption of cosmetics products in Qatar. Two hundred and seven women completed an online questionnaire. PROCESS has been used to analyze data. Results indicate that, unlike what has been established in other contexts, the perceived usefulness of influencers and trust did not have a significant effect on brand attitude and only positively affected Purchase Intention. Therefore, this study adds the to the existing body of knowledge in this area and could guide social media managers use of Instagram influencers and help marketing professionals develop more efficient online advertising strategies

Recognition of off-line printed Arabic text using Hidden Markov Models.

yesThis paper describes a technique for automatic recognition of off-line printed Arabic text using Hidden Markov Models. In this work different sizes of overlapping and non-overlapping hierarchical windows are used to generate 16 features from each vertical sliding strip. Eight different Arabic fonts were used for testing (viz. Arial, Tahoma, Akhbar, Thuluth, Naskh, Simplified Arabic, Andalus, and Traditional Arabic). It was experimentally proven that different fonts have their highest recognition rates at different numbers of states (5 or 7) and codebook sizes (128 or 256). Arabic text is cursive, and each character may have up to four different shapes based on its location in a word. This research work considered each shape as a different class, resulting in a total of 126 classes (compared to 28 Arabic letters). The achieved average recognition rates were between 98.08% and 99.89% for the eight experimental fonts. The main contributions of this work are the novel hierarchical sliding window technique using only 16 features for each sliding window, considering each shape of Arabic characters as a separate class, bypassing the need for segmenting Arabic text, and its applicability to other languages

Evaluation Techniques for the Corrosion Resistance of Self-Healing Coatings

Self-healing coatings, materials that autonomically repair damage, are a method of extending the life of corrosion prevention coatings. The different types of self-healing coatings are briefly outlined. A review of the evaluation methods of the performance of self-healing coatings using electrochemical, surface and microscopy techniques are provided. Both global and local evaluation techniques are reviewed with emphasis on the most used electrochemical techniques as well as suggestions for alternative electrochemical techniques for self-healing coating evaluation

Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches

To understand the real world using various types of data, Artificial Intelligence (AI) is the most used technique nowadays. While finding the pattern within the analyzed data represents the main task. This is performed by extracting representative features step, which is proceeded using the statistical algorithms or using some specific filters. However, the selection of useful features from large-scale data represented a crucial challenge. Now, with the development of convolution neural networks (CNNs), the feature extraction operation has become more automatic and easier. CNNs allow to work on large-scale size of data, as well as cover different scenarios for a specific task. For computer vision tasks, convolutional networks are used to extract features also for the other parts of a deep learning model. The selection of a suitable network for feature extraction or the other parts of a DL model is not random work. So, the implementation of such a model can be related to the target task as well as the computational complexity of it. Many networks have been proposed and become the famous networks used for any DL models in any AI task. These networks are exploited for feature extraction or at the beginning of any DL model which is named backbones. A backbone is a known network trained in many other tasks before and demonstrates its effectiveness. In this paper, an overview of the existing backbones, e.g. VGGs, ResNets, DenseNet, etc, is given with a detailed description. Also, a couple of computer vision tasks are discussed by providing a review of each task regarding the backbones used. In addition, a comparison in terms of performance is also provided, based on the backbone used for each task

Point of view: human development and impact for sustainability—‘A new pipeline theory in academia’

The role of academia in society is due for a comprehensive re-evaluation. Utilising the metaphor of a ‘network of pipelines’, this paper identifies two critical dimensions: a ‘people pipeline’, which includes a diverse range of students from various demographics—be they gender-based, socioeconomic or ethnic—and an ‘experience pipeline’ focused on the quality of teaching and learning. These components must be strategically synchronised to maximise societal and environmental impact. Drawing an analogy with engineered irrigation systems that require optimal operation and maintenance, the paper argues that academia should adopt a similarly meticulous approach. To this end, the article advocates for aligning educational endeavours more closely with Sustainable Development Goals, emphasising the need for a multi-layered, context-sensitive strategy