Evaluation of performance information downloading of (optical – radio frequency) hybrid system network

In this study the evaluation performance of (Optical light– radio Frequency) hybrid network system. We determine first the coverage area of optical system Li-Fi (light-fidelity) and radio frequency system (Wi-Fi) as well as the data rate downloading file size of them for single mobile user case. Secondly, we study the hybrid network system Li-Fi/Wi-Fi in an indoor for single mobile user case and the user mobility at the region of the coverage area. Throughput versus distance relationships has studied also for Li-Fi and Wi-Fi system in order to assess the performance of these system. The effect of the numbers of LED light sources Li-Fi hotspotsand the velocity of user are introduced in the take in account coverage regionareconsidered on the assessperformance of the hybrid networks systems Li-Fi/Wi-Fi. The evaluate performance of the hybrid network system hotspots Li-Fi/Wi-Fi for the information rate downloading size on the mobility have been conducted. The results show that the performance of hybrid network system hotspots Li-Fi/Wi-Fi have a significant effect because of the velocity of user. As well as, the results show that the performance of the hybrid network system hotspots Li-Fi/Wi-Fi increased when the number of light sources spotlights in the room are increases

A Software-Defined Multi-Element VLC Architecture

In the modern era of radio frequency (RF) spectrum crunch, visible light communication (VLC) is a recent and promising alternative technology that operates at the visible light spectrum. Thanks to its unlicensed and large bandwidth, VLC can deliver high throughput, better energy efficiency, and low cost data communications. In this article, a hybrid RF/VLC architecture is considered that can simultaneously provide light- ing and communication coverage across a room. Considered architecture involves a novel multi-element hemispherical bulb design, which can transmit multiple data streams over light emitting diode (LED) modules. Simulations considering various VLC transmitter configurations and topologies show that good link quality and high spatial reuse can be maintained in typical indoor communication scenarios

Scalability and Performance of Microservices Architectures.

Annotation- The inevitability of continuous evolution and seamless integration of dynamic alterations remains a paramount consideration in the realm of software engineering This concern is particularly pronounced within the context of contemporary microservices architectures embedded in heterogeneous and decentralized systems composed of numerous interdependent components A pivotal focal point within such a software design paradigm is to sustain optimal performance quality by ensuring harmonious collaboration among autonomous facets within an intricate framework The challenge of microservices evolution has predominantly revolved around upholding the harmonization of diverse microservices versions during updates all while curbing the computational overhead associated with such validation This study leverages previous research outcomes and tackles the evolution predicament by introducing an innovative formal model coupled with a fresh exposition of microservices RESTful APIs The amalgamation of Formal Concept Analysis and the Liskov Substitution Principle plays a pivotal role in this proposed solution A series of compatibility constraints is delineated and subjected to validation through a controlled experiment employing a representative microservices syste

Symbiots: Conceptual interventions into energy systems

Symbiots set out to examine values such as ease-of-use, comfort, and rationality assumed within conventions of ‘good design’, in order to expose issues related to energy consumption and current human- (versus eco-) centered design paradigms. Exploring re-interpretations of graphical patterns, architectural configurations and electrical infrastructure typical in Swedish cities, Symbiots takes the form of a photo series in the genre of contemporary hyper-real art photography. Painting a vivid picture of alternatives to current local priorities around energy consumption, the three design concepts depicted are strangely familiar, alternatively humorous and sinister

Robust Multilingual Part-of-Speech Tagging via Adversarial Training

Adversarial training (AT) is a powerful regularization method for neural networks, aiming to achieve robustness to input perturbations. Yet, the specific effects of the robustness obtained from AT are still unclear in the context of natural language processing. In this paper, we propose and analyze a neural POS tagging model that exploits AT. In our experiments on the Penn Treebank WSJ corpus and the Universal Dependencies (UD) dataset (27 languages), we find that AT not only improves the overall tagging accuracy, but also 1) prevents over-fitting well in low resource languages and 2) boosts tagging accuracy for rare / unseen words. We also demonstrate that 3) the improved tagging performance by AT contributes to the downstream task of dependency parsing, and that 4) AT helps the model to learn cleaner word representations. 5) The proposed AT model is generally effective in different sequence labeling tasks. These positive results motivate further use of AT for natural language tasks.Comment: NAACL 201

Factors of the Use of AI Technology Influencing Community Security in UAE

This paper presents a study on addressing 27 factors of the use of AI technology influencing community security in UAE. The factors are categorised in five groups namely AI Ethics; Compatibility; Complexity; Management support; and Staff Capability. This study used a questionnaire survey with the Abu Dhabi Police department as a case study for community security. The survey managed to 138 valid responses and analysed descriptively. In deciding the level of influence. It was found that 16 of the factors are having very high influence while the others are having high influence. In ranking analysis, it was found that the highest rank of AI technology's factors influence community security in each group is for compatibility (COMPA5), which underscores the harmony between an individual's skills and the employed AI technologies; complexity (COMPLEX2), highlighting the efficiency of AI-driven systems, particularly in rapid knowledge acquisition through online conferencing; management support (MS4), spotlighting the proactive endorsement and direction from organizational management in AI security technology implementation; ethics of AI (ETH3), accentuating the individual's commitment to ethical considerations while employing AI security technologies; and staff capability (SC2), which reflects the individual's proficiency and competence in effectively harnessing AI technologies for enhanced community security measures. Collectively, these factors shed light on the multifaceted ways AI technologies impact and shape the realm of UAE community security

Resource Optimization in Visible Light Communication Using Internet of Things

In the modern day, there is a serious spectrum crunch in the legacy radio frequency (RF) band, for which visible light communication (VLC) can be a promising option. VLC is a short-range wireless communication variant which uses the visible light spectrum. In this thesis, we are using a VLC-based architecture for providing scalable communications to Internet-of-Things (IoT) devices where a multi-element hemispherical bulb is used that can transmit data streams from multiple light emitting diode (LED) boards. The essence of this architecture is that it uses a Line-of-Sight (LoS) alignment protocol that handles the hand-off issue created by the movement of receivers inside a room. We start by proposing an optimization problem aiming to minimize the total consumed energy emitted by each LED taking into consideration the LEDs\u27 power budget, users\u27 perceived quality-of-service, LED-user associations, and illumination uniformity constraints. Then, because of the non-convexity of the problem, we propose to solve it in two stages: (1) We design an efficient algorithm for LED-user association for fixed LED powers, and (2) using the LED-user association, we find an approximate solution based on Taylor series to optimize the LEDs\u27 power. We devise two heuristic solutions based on this approach. The first heuristic solution, called the Low Complexity Two Stages Solution (TSS), optimizes the association between the LEDs and the mobile users before and then the power of each LED is optimized. In the second heuristic, named the Maximum Uniformity Approach, we try to improve the illumination uniformity first and then adjust the power values for each LED so that they do not go above a certain value. Finally, we illustrate the performance of our method via simulations

Transport of quantum excitations coupled to spatially extended nonlinear many-body systems

The role of noise in the transport properties of quantum excitations is a topic of great importance in many fields, from organic semiconductors for technological applications to light-harvesting complexes in photosynthesis. In this paper we study a semi-classical model where a tight-binding Hamiltonian is fully coupled to an underlying spatially extended nonlinear chain of atoms. We show that the transport properties of a quantum excitation are subtly modulated by (i) the specific type (local vs non-local) of exciton-phonon coupling and by (ii) nonlinear effects of the underlying lattice. We report a non-monotonic dependence of the exciton diffusion coefficient on temperature, in agreement with earlier predictions, as a direct consequence of the lattice-induced fluctuations in the hopping rates due to long-wavelength vibrational modes. A standard measure of transport efficiency confirms that both nonlinearity in the underlying lattice and off-diagonal exciton-phonon coupling promote transport efficiency at high temperatures, preventing the Zeno-like quench observed in other models lacking an explicit noise-providing dynamical system

UniCat: Crafting a Stronger Fusion Baseline for Multimodal Re-Identification

Multimodal Re-Identification (ReID) is a popular retrieval task that aims to re-identify objects across diverse data streams, prompting many researchers to integrate multiple modalities into a unified representation. While such fusion promises a holistic view, our investigations shed light on potential pitfalls. We uncover that prevailing late-fusion techniques often produce suboptimal latent representations when compared to methods that train modalities in isolation. We argue that this effect is largely due to the inadvertent relaxation of the training objectives on individual modalities when using fusion, what others have termed modality laziness. We present a nuanced point-of-view that this relaxation can lead to certain modalities failing to fully harness available task-relevant information, and yet, offers a protective veil to noisy modalities, preventing them from overfitting to task-irrelevant data. Our findings also show that unimodal concatenation (UniCat) and other late-fusion ensembling of unimodal backbones, when paired with best-known training techniques, exceed the current state-of-the-art performance across several multimodal ReID benchmarks. By unveiling the double-edged sword of "modality laziness", we motivate future research in balancing local modality strengths with global representations.Comment: Accepted NeurIPS 2023 UniReps, 9 pages, 4 table