Demands Of Modern Applications Research Articles

Air-Cooled Heat Exchangers as a Heat Sink: Heat Transfer

This paper discusses heat transfer concepts in air-cooled heat exchangers (ACHEs) and how ACHEs work like the principle of Heat Sink. A heat exchanger used for transferring heat between source and a working fluid. There use is for cooling and heating processes and have a huge impact on different industries coolers are an environmentally friendly solution as they do not require an additional water supply due to water loss from drift and evaporation, making them suitable for locations where water as a cooling medium is not readily available . These devices utilize air as a cooling medium, transferring heat from a hot source to the environment. As technology advances and demands for higher performance and energy efficiency increase, research on air-cooled heat exchangers continues to evolve. This research area offers significant opportunities to contribute to advancements in various fields, improve energy efficiency, and address environmental concerns. By exploring innovative design approaches and leveraging emerging technologies, researchers can develop air-cooled heat exchangers that meet the evolving demands of modern applications

Research on the integrated application of machine learning in Unity

Abstract. In recent years, the introduction of Machine Learning Agents has enabled more individuals to apply machine learning within the renowned Unity game engine. As application development has become increasingly complex, traditional artificial intelligence (AI) struggles to keep pace with the growing demands of modern applications. However, machine learning offers new avenues for developing smarter, more dynamic game characters. This paper aims to provide a comprehensive review of the various applications of machine learning within the Unity ecosystem, including game AI, intelligent character behavior, and reinforcement learning. Through a literature review and case studies, this paper explores the specific applications of machine learning algorithms in the Unity engine and analyzes the current state of development in different fields. The research findings indicate that the application of machine learning in Unity exhibits numerous developmental trends and leaves ample room for further application development in its strong areas. This technology can be innovatively applied across many fields, from implementing advanced NPCs and third-person game agents to anti-cheat mechanisms, from image recognition-based health systems to the design of complex constrained environments. The integrated application model of machine learning and Unity3D will bring more innovation and possibilities to future application development.

Evolutionary optimisation of pixelated IFA inspired antennas

As wireless communication systems increasingly require compact and efficient antennas, conventional antenna design methods are proving difficult to meet the rigorous demands of modern applications. For this purpose, this study introduces a methodology which uses pixelated Inverted-F Antenna (IFA) inspired designs optimised through genetic algorithms to enhance performance in constrained spatial environments. As pixels the antenna features the exemplary use of Einstein Hat-shaped tiles, enabling the antenna to efficiently utilize space. A with the proposed method optimised antenna is compared to traditional IFA designs and shows improved properties like enhanced antenna gain and efficiency as well as smaller reflection coefficient offering a promising solution for future compact antenna systems in the Internet of Things and beyond. Finally, a prototype was manufactured and the scattering parameters and antenna gain were measured within an anechoic chamber.

Asynchronous Memory Access Unit: Exploiting Massive Parallelism for Far Memory Access

The growing memory demands of modern applications have driven the adoption of far memory technologies in data centers to provide cost-effective, high-capacity memory solutions. However, far memory presents new performance challenges because its access latencies are significantly longer and more variable than local DRAM. For applications to achieve acceptable performance on far memory, a high degree of memory-level parallelism (MLP) is needed to tolerate the long access latency. While modern out-of-order processors are capable of exploiting a certain degree of MLP, they are constrained by resource limitations and hardware complexity. The key obstacle is the synchronous memory access semantics of traditional load/store instructions, which occupy critical hardware resources for a long time. The longer far memory latencies exacerbate this limitation. This article proposes a set of Asynchronous Memory Access Instructions (AMI) and its supporting function unit, Asynchronous Memory Access Unit (AMU), inside contemporary Out-of-Order Core. AMI separates memory request issuing from response handling to reduce resource occupation. Additionally, AMU architecture supports up to several hundreds of asynchronous memory requests through re-purposing a portion of L2 Cache as scratchpad memory (SPM) to provide sufficient temporal storage. Together with a coroutine-based programming framework, this scheme can achieve significantly higher MLP for hiding far memory latencies. Evaluation with a cycle-accurate simulation shows AMI achieves 2.42× speedup on average for memory-bound benchmarks with 1μs additional far memory latency. Over 130 outstanding requests are supported with 26.86× speedup for GUPS (random access) with 5 μs latency. These demonstrate how the techniques tackle far memory performance impacts through explicit MLP expression and latency adaptation.

Research on UAV flight attitude control system based on simulink and genetic algorithm

With the evolution of networking and information technology, unmanned aerial vehicles (UAVs) have emerged as integral components in numerous fields, executing diverse and vital tasks. The accuracy and efficiency of UAV operations are greatly influenced by their attitude control systems, which dictate flight stability, speed, and functionality. However, traditional control methods for UAV flight stability and accuracy have become outdated, unable to meet the demands of modern applications. To address these challenges, this study introduces a novel attitude control approach based on genetic algorithms. This method utilizes the principles of natural selection and genetic traits to optimize the parameters of the attitude control system. The objective is to enhance the current control effectiveness, improve the UAV's maneuverability, and enable its versatile applications across multiple fields. Simulink tool simulations are conducted to evaluate the stability of the optimized results. This simulation tool allows for the replication of real-world flight conditions and the assessment of the attitude control system's performance under various scenarios. The results of these simulations provide valuable insights into the effectiveness of the genetic algorithm-based attitude control method. This study contributes to the advancement of UAV technology by improving the accuracy and stability of attitude control systems. It paves the way for more efficient and reliable UAV operations in various fields, from environmental monitoring to search-and-rescue missions, thus enhancing their operational capabilities and expanding their potential applications.

Integrative Resource Management in Multi Cloud Computing: A DRL Based Approach for multi-objective Optimization

INTRODUCTION: The multi-data canter architecture is being investigated as a significant development in meeting the increasing demands of modern applications and services. The study provides a toolset for creating and managing virtual machines (VMs) and physical hosts (PMs) in a virtualized cloud environment, as well as for simulating various scenarios based on real-world cloud usage trends.
 OBJECTIVES: To propose an optimized resource management model using the Enhanced Flower Pollination algorithm in a heterogeneous environment.
 METHODS: The combination of Q-learning with flower pollination raises the bar in resource allocation and job scheduling. The combination of these advanced methodologies enables our solution to handle complicated and dynamic scheduling settings quickly, making it suited for a wide range of practical applications. The algorithm finds the most promising option by using Q-values to drive the pollination process, enhancing efficiency and efficacy in discovering optimal solutions. An extensive testing using simulation on various datasets simulating real-world scenarios consistently demonstrates the suggested method's higher performance.
 RESULTS: In the end, the implementation is done on AWS clouds; the proposed methodology shows the excellent performance by improving energy efficiency, Co2 Reduction and cost having multi-cloud environment 
 CONCLUSION: The comprehensive results and evaluations of the proposed work demonstrate its effectiveness in achieving the desired goals. Through extensive experimentation on diverse datasets representing various real-world scenarios, the proposed work consistently outperforms existing state-of-the-art algorithms.

Design optimization for microstrip antennas based on polymethyl methacrylate (PMMA) substrate and carbon nanotube (CNT) conductive material in sub-6 Ghz band

Abstract Background The rapid expansion of modern smart applications, demanding faster data transfer and extensive bandwidth, has prompted the development of new-generation networks like 5G and 6G. These networks encompass additional frequency bands such as sub-6 GHz, millimeter waves, and terahertz bands to meet the growing bandwidth requirements. However, despite the substantial bandwidth available in these bands, several challenges must be addressed to overcome unfavorable propagation characteristics. Moreover, numerous applications necessitate wireless devices with antennas that exhibit high flexibility and exceptional radiation responses, particularly when subjected to bending effects. This requirement highlights the importance of polymers-based antennas that can adapt to changing conditions while maintaining optimal performance. The present comprehensive study delves into the performance evaluation of rectangular and circular microstrip antennas utilizing PMMA (polymethyl methacrylate) polymer substrate with varying thicknesses. Results Notably, CNTs (Carbon Nanotubes) are employed as an alternative to traditional copper for the conductive part and ground plane. Both PMMA-based antennas, integrated with CNTs, exhibit a compact footprint of 27.8 × 47.8 × 1.5 mm3 for the circular antenna and 22.8 × 39.5 × 1.5 mm3 for the rectangular antenna. Impressively, the realized gain of both antennas surpasses 5 dBi, demonstrating robust performance in both flat and bending scenarios across different substrate thicknesses. Conclusions The rectangular antenna achieves a bandwidth of approximately 200 MHz, while the circular microstrip antenna showcase annotable bandwidth of 500 MHz. These exceptional outcomes position the two microstrip antennas as highly suitable for a diverse range of emerging applications within the sub-6 GHz band (the frequency range below 6 GHz in the radio spectrum). Thus, the combination of PMMA substrate, CNTs and the compact form factor of the antennas presents a compelling solution for meeting the demands of modern applications requiring efficient wireless communication with enhanced performance and bandwidth.

Specifications of bioinspired material structural foam prosoma of Tachpleus gigas and Carcinocorpius rotundicaud using acoustic impulse response

Renewable material innovation has shifted from inorganic materials to biological materials. Especially due to the demands of modern applications in food, biomedicine, and environmental sustainability. The peculiarity of the horseshoe crab is in the prosoma with a hollow structure (foam). The development of structural specifications also leads to non-destructive modern measurements using acoustic response impulse. This study aimed to determine the specifications foam material (shape and size) of the prosoma Tachypleus gigas and Carcinoscorpius rotundicauda using acoustic impulse responses. The research includes the characterization of the components and structure of the foam material prosoma using acoustic impulse response The prosoma components of T. gigas and C. rotundicauda have high protein and crude fiber, as well as functional groups according to chitin. The weight of T. gigas and C. rotundicauda ranges 484.33 ± 5.13 g and 493.33 ±15.28 g and densities of 1206.45 ± 148.01 kg/m3 and 1264.60 ± 120.01 kg/m3, but have differences in pore diameter and strut, 431.39μm ± 166.22 and 153.30μm ± 33.62 for pore diameter and strut, 120.09μm ± 57.83 and 98.0881μm ± 26.75 and compressive strength of 220.68 kPa and 285.88 kPa, and elasticity of 17.85 and 38.12.

A New Edge Computing Architecture for IoT and Multimedia Data Management

The Internet of Things and multimedia devices generate a tremendous amount of data. The transfer of this data to the cloud is a challenging problem because of the congestion at the network level, and therefore processing time could be too long when we use a pure cloud computing strategy. On the other hand, new applications requiring the processing of large amounts of data in real time have gradually emerged, such as virtual reality and augmented reality. These new applications have gradually won over users and developed a demand for near real-time interaction of their applications, which has completely called into question the way we process and store data. To address these two problems of congestion and computing time, edge architecture has emerged with the goal of processing data as close as possible to users, and to ensure privacy protection and responsiveness in real-time. With the continuous increase in computing power, amounts of memory and data storage at the level of smartphone and connected objects, it is now possible to process data as close as possible to sensors or directly on users devices. The coupling of these two types of processing as close as possible to the data and to the user opens up new perspectives in terms of services. In this paper, we present a new distributed edge architecture aiming to process and store Internet of Things and multimedia data close to the data producer, offering fast response time (closer to real time) in order to meet the demands of modern applications. To do this, the processing at the level of the producers of data collaborate with the processing ready for the users, establishing a new paradigm of short supply circuit for data transmission inspired of short supply chains in agriculture. The removing of unnecessary intermediaries between the producer and the consumer of the data improves efficiency. We named this new paradigm the Short Supply Circuit Internet of Things (SSCIoT).

Research on Wireless Network Virtualization Positioning Technology Based on Next-Generation Agile IoT Technology

Wireless network virtualization (WNV) is an evolving technology that supports networking for future wireless networks and agile IoT applications that optimize wireless bandwidth, provide network protection, and increase network performance in general. However, the exponential growth and increased demand for IoT have posed considerable scalability problems, interoperability, and heterogeneity across diverse fields from intelligent cities to healthcare, urban computing, and tactile fast internet growth. In this paper, IoT-based WNV (IoT-WNV) technology has been used. The leading technologies that create a scalable and stable network, consistent with many IoT products, for effective wireless virtualization in next-generation agile IoT systems are the networking function virtualization (NFV), mobile edge computing, and software-defined networks (SDNs). The network function virtualization (NFV) feature improves network stability, while the SDN operates, optimizes, and configures network services dynamically, enabling dynamically-based network functionality and interconnectivity. This special problem involves virtualizing IoT system wireless sensor networks to speed up network resource management by analyzing data obtained from devices deployed in various applications. This technological combination will satisfy the demands of modern applications. IoT-WNV aims to improve wireless virtualization for the next-generation network by implementing human-smart technology for load balance, network management, and network security.

A Genetic-algorithm-based Approach to the Design of DCT Hardware Accelerators

As modern applications demand an unprecedented level of computational resources, traditional computing system design paradigms are no longer adequate to guarantee significant performance enhancement at an affordable cost. Approximate Computing (AxC) has been introduced as a potential candidate to achieve better computational performances by relaxing non-critical functional system specifications. In this article, we propose a systematic and high-abstraction-level approach allowing the automatic generation of near Pareto-optimal approximate configurations for a Discrete Cosine Transform (DCT) hardware accelerator. We obtain the approximate variants by using approximate operations, having configurable approximation degree, rather than full-precise ones. We use a genetic searching algorithm to find the appropriate tuning of the approximation degree, leading to optimal tradeoffs between accuracy and gains. Finally, to evaluate the actual HW gains, we synthesize non-dominated approximate DCT variants for two different target technologies, namely, Field Programmable Gate Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs). Experimental results show that the proposed approach allows performing a meaningful exploration of the design space to find the best tradeoffs in a reasonable time. Indeed, compared to the state-of-the-art work on approximate DCT, the proposed approach allows an 18% average energy improvement while providing at the same time image quality improvement.

Testing GraphQL APIs: Challenges, Tools and Best Practices

GraphQL has revolutionized API development by enabling precise and flexible data querying, offering developers control over the structure and scope of their requests. While this flexibility enhances efficiency, it also introduces unique challenges for testing. Unlike traditional REST APIs, GraphQL’s schema-driven and dynamic query structures demand a paradigm shift in validation methods. Key issues such as schema validation, nested data testing, query optimization, and security vulnerabilities require specialized testing strategies. This paper explores these challenges and evaluates modern tools such as Apollo Studio, GraphQL Playground, and Postman for GraphQL API testing. A comprehensive testing framework is proposed, addressing schema validation, query coverage, performance testing, and security enhancements. The paper concludes with actionable best practices for integrating GraphQL API testing into CI/CD pipelines, ensuring scalability, security, and reliability. By adopting these strategies, organizations can build resilient and efficient APIs that meet the demands of modern applications.

DRMaestro: orchestrating disaggregated resources on virtualized data-centers

Modern applications demand resources at an unprecedented level. In this sense, data-centers are required to scale efficiently to cope with such demand. Resource disaggregation has the potential to improve resource-efficiency by allowing the deployment of workloads in more flexible ways. Therefore, the industry is shifting towards disaggregated architectures, which enables new ways to structure hardware resources in data centers. However, determining the best performing resource provisioning is a complicated task. The optimality of resource allocation in a disaggregated data center depends on its topology and the workload collocation. This paper presents DRMaestro, a framework to orchestrate disaggregated resources transparently from the applications. DRMaestro uses a novel flow-network model to determine the optimal placement in multiple phases while employing best-efforts on preventing workload performance interference. We first evaluate the impact of disaggregation regarding the additional network requirements under higher network load. The results show that for some applications the impact is minimal, but other ones can suffer up to 80% slowdown in the data transfer part. After that, we evaluate DRMaestro via a real prototype on Kubernetes and a trace-driven simulation. The results show that DRMaestro can reduce the total job makespan with a speedup of up to ≈1.20x and decrease the QoS violation up to ≈2.64x comparing with another orchestrator that does not support resource disaggregation.

SEAMERS: A Semi-partitioned Energy-Aware scheduler for heterogeneous MulticorE Real-time Systems

Abstract Over the years, the nature of processing platforms is witnessing a significant shift in most of the battery supported real-time systems, which now support a combination of specialized multicores to meet the demands of modern applications. Devising energy-efficient schedulers has become a critical issue for such kinds of devices. Hence, this research presents a low-overhead heuristic strategy named SEAMERS, for DVFS based energy-aware scheduling for a set of real-time periodic tasks on a heterogeneous multicore platform. The presented strategy operates in four phases, namely Deadline Partitioning, Core Clustering, Task Allocation and Energy-Aware Scheduling. Our experimental analysis shows that the presented strategy improves upon the state-of-the-art in terms of energy savings (16% to 47% on average) and enables significant improvement in resource utilization.

A novel symmetric image cryptosystem resistant to noise perturbation based on S8 elliptic curve S-boxes and chaotic maps

The recent decade has seen a tremendous escalation of multimedia and its applications. These modern applications demand diverse security requirements and innovative security platforms. In this manuscript, we proposed an algorithm for image encryption applications. The core structure of this algorithm relies on confusion and diffusion operations. The confusion is mainly done through the application of the elliptic curve and S8 symmetric group. The proposed work incorporates three distinct chaotic maps. A detailed investigation is presented to analyze the behavior of chaos for secure communication. The chaotic sequences are then accordingly applied to the proposed algorithm. The modular approach followed in the design framework and integration of chaotic maps into the system makes the algorithm viable for a variety of image encryption applications. The resiliency of the algorithm can further be enhanced by increasing the number of rounds and S-boxes deployed. The statistical findings and simulation results imply that the algorithm is resistant to various attacks. Moreover, the algorithm satisfies all major performance and quality metrics. The encryption scheme can also resist channel noise as well as noise-induced by a malicious user. The decryption is successfully done for noisy data with minor distortions. The overall results determine that the proposed algorithm contains good cryptographic properties and low computational complexity makes it viable to low profile applications.

A Review on Variable Flux Machine Technology: Topologies, Control Strategies and Magnetic Materials

Modern applications demand challenging operation requirements from electrical machines. Variable-flux machine (VFM) concepts are found to offer new opportunities for improved machine design. This paper reviews the VFM technology. Three topologies, mainly the parallel hybrid VFMs, series hybrid VFMs, and variable-flux flux intensifying type VFMs are discussed in detail. The variable-flux flux Intensifying machines are found to be the state-of-the-art VFM technology available to-date. The paper also reviews the magnetization process in the VFMs and relates this with the B-H curve of the magnetic material. The conventional PM machine mathematical model is adapted to represent the operation of the VFMs. The different operational limitations of the VFMs are explained and the different modes of operation of the VFMs have been reviewed and compared. The magnetization state manipulation techniques are reviewed. The AC magnetization is found to be the more preferable approach to reduce weight and manufacturing complexity of the VFMs. The trapezoidal current pulse based approach is popular in AC magnetization state manipulation, however, new advanced forms of current vector trajectory control techniques offer comparatively better magnetic state manipulation and higher speed capability for remagnetization of the magnets in the VFMs. The paper also reviews the different magnetization state estimation techniques that are useful during control systems development for the VFMs. The magnetic material for the VFM design is reviewed and potential alternative magnetic material that can satisfy the requirements of the VFMs have been identified. Finally, the current status, challenges, and the potential of VFMs technology are discussed in the conclusion of the paper.

Comparative Study of NoSQL and Relational Database

Relational Database and NoSQL are competing types of database models. The former has been in existence since 1979 and the latter since the year 2000. The demands of modern applications especially in web 2.0, 3.0 and big data have made NoSQL a popular database of choice. Choosing an appropriate database model to use is an important decision that developers must make based on the features of a given database model. This paper compares the features of Relational Databases and NoSQL to establish which database is better at supporting demands of modern applications. The paper also brings out the challenges of NoSQL. Finally, the paper concludes by determining whether Relational Databases would completely be replaced by NoSQL database models. The findings revealed that, Relational Databases are based on ACID model which emphasizes better consistency, security and offers a standard query language. However, Relational Databases have poor scalability, weak performance, cost more, face availability challenges when supporting large number of users and handle limited volume of data. NoSQL, on the other hand is based on the BASE model, which emphasizes greater scalability and provides a flexible schema, offers better performance, mostly open source, cheap but, lacks a standard query language and does not provide adequate security mechanisms. Both databases will continue to exist alongside each other with none being better than the other. The choice of the database to use will depend on the nature of the application being developed. Each database type has its own challenges and strengths, with relational database lacking of support for unstructured data while NoSQL lacks standardization and has poor security. Modern applications in web 2.0, 3.0 and big data are well suited to use NoSQL but, there are still many applications that rely on Relational Databases.

Next Generation Virtual Memory Management

The goal of virtual memory is an abstraction of infinite and private memory for every process. Unfortunately, the insatiable memory demands of modern applications increasingly violate this abstraction by exposing capacity, bandwidth, and performance limitations of modern hardware. Furthermore, emerging memory technologies are likely to exacerbate this problem. For instance, non-volatile memory differs from DRAM due to its asymmetric read/write performance and thus will likely be an addition rather than a drop-in replacement for DRAM. This talk will describe these problems and recent architecture and software innovations that address of some of them. If adopted, these solutions will impose substantial challenges for operating system memory management, which has evolved very slowly over the past 30 years. I will draw lessons from the past 15 years of garbage collection advances to suggest some promising directions for innovation.

RACE

A wide range of applications, including bioinformatics, time series, and log analysis, depend on the identification of repetitions in very long sequences. The problem of finding maximal pairs subsumes most important types of repetition-finding tasks. Existing solutions require both the input sequence and its index (typically an order of magnitude larger than the input) to fit in memory. Moreover, they are serial algorithms with long execution time. Therefore, they are limited to small datasets, despite the fact that modern applications demand orders of magnitude longer sequences. In this paper we present RACE, a parallel system for finding maximal pairs in very long sequences. RACE supports parallel execution on stand-alone multicore systems, in addition to scaling to thousands of nodes on clusters or supercomputers. RACE does not require the input or the index to fit in memory; therefore, it supports very long sequences with limited memory. Moreover, it uses a novel array representation that allows for cache-efficient implementation. RACE is particularly suitable for the cloud (e.g., Amazon EC2) because, based on availability, it can scale elastically to more or fewer machines during its execution. Since scaling out introduces overheads, mainly due to load imbalance, we propose a cost model to estimate the expected speedup, based on statistics gathered through sampling. The model allows the user to select the appropriate combination of cloud resources based on the provider's prices and the required deadline. We conducted extensive experimental evaluation with large real datasets and large computing infrastructures. In contrast to existing methods, RACE can handle the entire human genome on a typical desktop computer with 16GB RAM. Moreover, for a problem that takes 10 hours of serial execution, RACE finishes in 28 seconds using 2,048 nodes on an IBM BlueGene/P supercomputer.

Recent advances in filter topologies and realizations for satellite communications

This paper presents an overview of recent advances in radio frequency and microwave filter topologies for satellite communication systems. Many types of filters have been developed during the last years in order to satisfy the demands of modern applications in both terrestrial systems and onboard spacecrafts, leading to a great variety of aspects such as transfer functions, resonator implementations or coupling structures. This paper revisits some of the last advances in this area, including the modeling and full-wave simulation. Some recent designs using dual-mode cavities along with other novel implementations in ridge waveguide will be shown.