Vinoth Punniyamoorthy

Although solutions to the challenge of binaural artificial recreation of audio spatialisation exist in the Computer Music domain, a review of the area suggests that a comprehensive, generic, accurate and efficient toolset is required; hence this paper will deal with automated sound creation using sound synthesis. The entire setup was implemented using Csound. Csound implements many synthesis techniques unavailable through other means, such as user-controlled physical modeling. A number of Csound opcodes is presented to satiate this necessity. It provides a platform for creating customized instruments to produce the required sounds and a player that renders the sound according to the requirements. It provides a powerful base for performing signal processing functions like envelope shaping, filtering and mixing (additive and subtractive synthesis of sounds with disparate parameters). The final output is the product of the filter output that produces the sound of required frequencies and envelope shaping function that controls the amplitude. The utmost use of this research work is the optimum reduction in human interference without compensating with any of the efficient results obtained.

Cloud-native architectures deployed on Amazon Web Services (AWS) increasingly rely on microservices and Internet of Things (IoT) platforms to support scalable, real-time, and data-intensive applications. While these paradigms improve agility and elasticity, they introduce significant challenges in secure data integration, governance, and regulatory compliance due to highly distributed data flows and expanded attack surfaces. This paper presents a secure cloud-native data integration architecture on AWS that unifies microservices-based application workloads, IoT data ingestion pipelines, and database-level protection using Oracle 19c Transparent Data Encryption (TDE). The proposed architecture maps established design patterns such as service meshes, edge and fog computing, and streaming-based ETL to AWS-native services, enabling encrypted data exchange in transit and robust protection of data at rest without application modifications. A prototype implementation using Amazon EKS, AWS IoT Core, AWS Glue, and Oracle 19c demonstrates scalable ingestion, low-latency processing, and minimal performance overhead from encryption. Experimental results validate that the architecture achieves improved scalability and tail-latency characteristics while maintaining strong data-at-rest security and compliance readiness. The paper further identifies research gaps in AI-driven automation, unified observability, and adaptive security frameworks for future cloud-native and multi-cloud data integration environments.

Financial technology platforms process massive volumes of digital transactions across mobile banking, payment gateways, online commerce systems, and automated financial services. Detecting fraudulent activity within these high-velocity environments requires scalable data processing pipelines, robust feature engineering strategies, and machine learning models capable of making real-time decisions. This paper presents a cloud-based framework for fraud detection that emphasizes the design of feature engineering workflows and end-to-end machine learning pipelines. The proposed approach integrates streaming data ingestion, distributed feature computation, and scalable model inference using modern cloud infrastructure. Key feature categories including behavioral aggregates, temporal velocity metrics, contextual device attributes, and historical transaction profiles are analyzed to improve model performance in highly imbalanced fraud detection scenarios. Using benchmark-style experiments and representative financial transaction datasets, the study evaluates multiple machine learning models including Random Forest, gradient boosting (XGBoost), and deep neural networks. Experimental results show that feature engineering quality plays a critical role in improving detection accuracy, while gradient boosting models provide an effective balance between predictive performance and inference latency. The paper also discusses architectural considerations for feature stores, trainingserving consistency, and scalable deployment on cloud platforms. The findings highlight how well-designed feature pipelines and ML workflows can significantly enhance the reliability and scalability of fraud detection systems in modern FinTech environments.

Enterprises increasingly operate across multiple cloud ecosystems to support large-scale analytics, artificial intelligence (AI), and data-driven decision making. However, the rapid growth of specialized cloud services has created architectural fragmentation, where data ingestion pipelines, analytics workloads, machine learning platforms, and business intelligence tools operate across heterogeneous environments. This paper proposes a reference architecture for unified multi-cloud AI data platforms that integrates data ingestion, governed storage, analytics processing, and AI/ML services into a cohesive enterprise framework. The architecture is designed to support interoperability across major cloud ecosystems including AWS, Azure, Google Cloud Platform, BigQuery, and Snowflake while maintaining governance, portability, and operational consistency. The study analyzes architectural layers for data integration, lakehouse and warehouse storage models, and consumption patterns for AI services and business intelligence applications. It also introduces practical evaluation metrics for platform performance, data freshness, portability overhead, and cost efficiency in multi-cloud environments. Through a comparative analysis of leading cloud data platforms, the paper highlights how hybrid deployment strategies can balance platform-native capabilities with enterprise governance requirements. The findings demonstrate that effective enterprise data platforms do not rely on a single cloud provider but instead leverage unified architectural principles, governed data products, and reusable analytics pipelines to enable scalable AI-driven analytics across domains such as healthcare, FinTech, and retail. The proposed reference architecture provides guidance for organizations seeking to design resilient, interoperable, and governance-aware multi-cloud data platforms for enterprise AI workloads.

Replication is fundamental to achieving availability and durability in cloud storage systems, yet the operational characteristics of failover under controlled disruptions remain insufficiently quantified in reproducible settings. This paper presents an empirical evaluation of replication and failover behavior in Amazon Web Services (AWS) using systematic synthetic failure injection. We measure Recovery Time Objective (RTO), Recovery Point Objective (RPO), tail recovery latency (P95/P99), replication lag distributions, latency amplification, and throughput degradation under zone-level and region-level failure scenarios. Results demonstrate that synchronous multi-availability-zone replication achieves low and stable RTO with negligible RPO, whereas asynchronous cross-region replication introduces measurable lag and significantly amplified tail recovery. By decomposing failover into detection, control-plane redirection, and client stabilization phases, the study provides a structured methodology for analyzing recovery dynamics and offers evidence-based guidance for replication strategy selection in stateful cloud workloads.

Public cloud platforms provide multiple storage abstractions, including object storage, block storage, and managed file systems, each designed to balance scalability, durability, and operational flexibility. Despite their widespread adoption, selecting an appropriate storage model for data-intensive workloads remains a non-trivial architectural decision due to differences in latency, throughput, and I/O behavior. This paper presents a systematic empirical evaluation of these storage abstractions under controlled experimental conditions to provide evidence based guidance for workload-driven storage selection. We benchmark sequential, random, and mixed I/O workloads, measuring sustained throughput, input/output operations per second (IOPS), latency characteristics, and performance stability. The results reveal distinct performance profiles across storage models. Block storage consistently delivers high IOPS and low latency, making it suitable for transactional and latency-sensitive workloads. Object storage demonstrates strong performance for large sequential transfers and bulk data movement. Managed file storage provides shared access semantics with moderate performance trade-offs. Rather than advocating a universally superior storage solution, the findings emphasize aligning storage selection with dominant workload characteristics. By quantifying observable performance differences across abstractions, this study provides practical architectural insights for analytics systems, distributed applications, and cloud-native data platforms.

End-to-end autonomous steering models map raw sensory observations directly to continuous control actions, reducing the hand-engineering burden of modular autonomy stacks. However, two practical barriers limit deployability: (i) steering prediction is inherently temporal and frame-wise convolutional neural networks (CNNs) often exhibit oscillations and degraded recovery under covariate shift, and (ii) centralized training requires aggregating large-scale driving data that are distributed, bandwidth-intensive, and privacy-sensitive. This paper presents a federated temporal autonomous steering framework combining CNN-based spatial perception with Long Short-Term Memory (LSTM) sequence modeling under Federated Learning. Multiple clients (simulated vehicles) train locally on heterogeneous, non-identically distributed (non-IID) driving data and periodically synchronize with a server via Federated Averaging. We formalize the temporal imitation learning objective, present a sequence-aware model architecture and preprocessing pipeline, and define a federated optimization protocol with communication and non-IID considerations. Extensive experiments compare centralized CNN, centralized CNN-LSTM, and federated CNN-LSTM baselines under steering imbalance and distribution shifts induced by curvature bias and lighting perturbations. Results show that temporal modeling reduces steering oscillation variance and improves closed-loop stability, while federated training achieves comparable accuracy to centralized training with privacy-preserving data locality. We also analyze convergence behavior under heterogeneous clients, communication cost per round, and ablations on sequence length, local epochs, and client sampling. The proposed framework provides a reproducible foundation for distributed, privacy-preserving end-to-end control.