arXiv subjects

Mahdi H. Miraz

Publications and source records attributed to Mahdi H. Miraz.

At least 19 recordsLinked to original sources

arXiv2026

Decentralised Trust and Security Mechanisms for IoT Networks at the Edge: A Comprehensive Review

INTRODUCTION: The proliferation of the amalgamation of IoT and edge computing has increased the demand for decentralised trust and security mechanisms capable of operating across heterogeneous and resource-limited devices. Approaches such as federated learning, Zero Trust architectures, lightweight blockchain and distributed neural models offer alternatives to centralised control. OBJECTIVES: This review examines various state-of-the-art decentralised mechanisms and evaluates their effectiveness in terms of securing IoT networks at the edge. METHODS: Thirty recent studies were analysed to compare how decentralised architectures establish trust, support secure communication and enable intrusion and anomaly detection. Frameworks, such as DFGL-LZTA, SecFedDNN and COSIER were assessed. RESULTS: Decentralised designs enhance privacy, reduce single points of failure and improve adaptive threat response, though challenges remain in scalability, efficiency and interoperability. CONCLUSION: The study identifies key considerations and future research needs for building secure and resilient trust-aware IoT edge ecosystems.

cs.CR↗

arXiv2026

Optimising Blockchain Scalability for Real-Time IoT Applications

The convergence of blockchain and the Internet of Things (IoT) enables secure, decentralised, and verifiable data exchange across distributed smart environments. However, traditional blockchain frameworks suffer from inherent scalability constraints, limited throughput, and high latency, which conflict with the stringent real-time requirements of IoT applications such as industrial automation, intelligent healthcare, and smart transportation. These systems demand ultra-low latency, high transaction throughput, lightweight computation, and efficient resource utilisation. This review provides a comprehensive, structured analysis of state-of-the-art scalability solutions specifically adapted to blockchain-enabled IoT. The discussion encompasses Layer 1 enhancements, Layer 2 off-chain processing, sharding-based parallelisation, integration of edge and fog computing, and hybrid consensus mechanisms. For each approach, the review highlights operational principles, performance benefits, trade-offs in decentralisation and security, and suitability for latency-sensitive deployments. Furthermore, real-time quality-of-service considerations are examined to understand how scalability strategies impact system responsiveness, energy efficiency, and data integrity. Key open challenges, including the scalability-security trade-off, privacy preservation, interoperability, and sustainable resource management, have been identified as persistent barriers to large-scale adoption. Finally, the review outlines future research directions, emphasising adaptive and AI-driven consensus algorithms, quantum-safe cryptographic models, the convergence of blockchain with 5G/6G networks, and edge intelligence. By consolidating diverse technical insights and emerging trends, this work serves as a timely reference for developing scalable, secure, and sustainable blockchain architectures for real-time IoT applications.

cs.DC↗

arXiv2025

Deep Neural Watermarking for Robust Copyright Protection in 3D Point Clouds

The protection of intellectual property has become critical due to the rapid growth of three-dimensional content in digital media. Unlike traditional images or videos, 3D point clouds present unique challenges for copyright enforcement, as they are especially vulnerable to a range of geometric and non-geometric attacks that can easily degrade or remove conventional watermark signals. In this paper, we address these challenges by proposing a robust deep neural watermarking framework for 3D point cloud copyright protection and ownership verification. Our approach embeds binary watermarks into the singular values of 3D point cloud blocks using spectral decomposition, i.e. Singular Value Decomposition (SVD), and leverages the extraction capabilities of Deep Learning using PointNet++ neural network architecture. The network is trained to reliably extract watermarks even after the data undergoes various attacks such as rotation, scaling, noise, cropping and signal distortions. We validated our method using the publicly available ModelNet40 dataset, demonstrating that deep learning-based extraction significantly outperforms traditional SVD-based techniques under challenging conditions. Our experimental evaluation demonstrates that the deep learning-based extraction approach significantly outperforms existing SVD-based methods with deep learning achieving bitwise accuracy up to 0.83 and Intersection over Union (IoU) of 0.80, compared to SVD achieving a bitwise accuracy of 0.58 and IoU of 0.26 for the Crop (70%) attack, which is the most severe geometric distortion in our experiment. This demonstrates our method's ability to achieve superior watermark recovery and maintain high fidelity even under severe distortions.

cs.CV↗

arXiv2025

AstuteRAG-FQA: Task-Aware Retrieval-Augmented Generation Framework for Proprietary Data Challenges in Financial Question Answering

Retrieval-Augmented Generation (RAG) shows significant promise in knowledge-intensive tasks by improving domain specificity, enhancing temporal relevance, and reducing hallucinations. However, applying RAG to finance encounters critical challenges: restricted access to proprietary datasets, limited retrieval accuracy, regulatory constraints, and sensitive data interpretation. We introduce AstuteRAG-FQA, an adaptive RAG framework tailored for Financial Question Answering (FQA), leveraging task-aware prompt engineering to address these challenges. The framework uses a hybrid retrieval strategy integrating both open-source and proprietary financial data while maintaining strict security protocols and regulatory compliance. A dynamic prompt framework adapts in real time to query complexity, improving precision and contextual relevance. To systematically address diverse financial queries, we propose a four-tier task classification: explicit factual, implicit factual, interpretable rationale, and hidden rationale involving implicit causal reasoning. For each category, we identify key challenges, datasets, and optimization techniques within the retrieval and generation process. The framework incorporates multi-layered security mechanisms including differential privacy, data anonymization, and role-based access controls to protect sensitive financial information. Additionally, AstuteRAG-FQA implements real-time compliance monitoring through automated regulatory validation systems that verify responses against industry standards and legal obligations. We evaluate three data integration techniques - contextual embedding, small model augmentation, and targeted fine-tuning - analyzing their efficiency and feasibility across varied financial environments.

cs.LG↗

arXiv2025

6G communications through sub-Terahertz CMOS power amplifiers: Design challenges and trends

The fifth-generation (5G) network faces limitations in supporting emerging applications, such as artificial intelligence (AI), virtual reality (VR) and digital twins. To overcome these confines, sub-Terahertz (sub-THz) and Terahertz (THz) technologies are considered to be key enablers of effective 6G wireless communications, offering higher transmission speeds, longer range and wider bandwidth. Achieving these capabilities requires careful engineering of 6G transceivers, with a focus on efficient power amplifiers (PAs) in the front-end, which play a critical role in effectively amplifying and transmitting signals over long distances. Complimentary metal-oxidesemiconductor (CMOS) technology-based PA in sub-THz suffers severe parasitic and limited maximum frequency, however, this has eventually been solved by different design architectures and scaling down of CMOS technology to break through the frequency limitations. In this article, we reviewed the potentials and capabilities of CMOS technology for designing 6G hardware, identified the state-of-art PA designs in the sub-THz band and then examined as well as compared the designs to identify the suitable design strategies for better performance. The circuit optimisation techniques, such as coupled-line, passive gain boosting method, zero-degree power splitting, load-pull matching, diode and capacitor linearisation for better gain, saturated output power and power added efficiency, are considered for the PA design architectures at different sub-THz bands. Furthermore, these methods are summarised and discussed with their advantages and disadvantages in lieu with their performances. The PA design trends, challenges and future perspectives are also presented and discussed. Therefore, this comprehensive review article will serve as a comparative study and reference for future PA designs for radio frequency integrated circuits (RFIC).

cs.NI↗

arXiv2025

Low latency FPGA implementation of twisted Edward curve cryptography hardware accelerator over prime field

The performance of any elliptic curve cryptography hardware accelerator significantly relies on the efficiency of the underlying point multiplication (PM) architecture. This article presents a hardware implementation of field-programmable gate array (FPGA) based modular arithmetic, group operation, and point multiplication unit on the twisted Edwards curve (Edwards25519) over the 256-bit prime field. An original hardware architecture of a unified point operation module in projective coordinates that executes point addition and point doubling within a single module has been developed, taking only 646 clock cycles and ensuring a better security level than conventional approaches. The proposed point multiplication module consumes 1.4 ms time, operating at a maximal clock frequency of 117.8 MHz utilising 164,730 clock cycles having 183.38 kbps throughput on the Xilinx Virtex-5 FPGA platform for 256-bit length of key. The comparative assessment of latency and throughput across various related recent works indicates the effectiveness of our proposed PM architecture. Finally, this high throughput and low latency PM architecture will be a good candidate for rapid data encryption in high-speed wireless communication networks.

cs.CR↗

arXiv2025

The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA's benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats.

cs.CR↗

arXiv2025

Optimised Design of a Current Mirror in 150 nm GaAs Technology

The Current Mirror (CM) is a basic building block commonly used in analogue and mixed-signal integrated circuits. Its significance lies in its ability to replicate and precisely regulate the current, making it crucial in various applications such as amplifiers, filters and data converters. Recently, there has been a growing need for smaller and more energy-efficient Radio Frequency (RF) devices due to the advancements in wireless communication, the Internet of Things (IoT) and portable electronics. This research aims to propose an improved and optimised CM design focusing on compactness and energy-efficient operation. Through a comprehensive methodology involving transistor sizing, biasing techniques, load resistance selection, frequency response stabilisation and noise analysis, the proposed high swing CM design achieves a gain of at least 6.005 dB, a reduced power consumption of 91.17 mW, a wide bandwidth of 22.60 kHz and improved linearity as well as accuracy through precise current matching and minimised mismatch. This optimised CM design will further boost the realisation of compact and lower power RF devices, contributing to the advancement of analogue circuit design techniques and enhancing system performance, accuracy and reliability.

eess.SP↗

arXiv2024

Enhancing Depressive Post Detection in Bangla: A Comparative Study of TF-IDF, BERT and FastText Embeddings

Due to massive adoption of social media, detection of users' depression through social media analytics bears significant importance, particularly for underrepresented languages, such as Bangla. This study introduces a well-grounded approach to identify depressive social media posts in Bangla, by employing advanced natural language processing techniques. The dataset used in this work, annotated by domain experts, includes both depressive and non-depressive posts, ensuring high-quality data for model training and evaluation. To address the prevalent issue of class imbalance, we utilised random oversampling for the minority class, thereby enhancing the model's ability to accurately detect depressive posts. We explored various numerical representation techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT) embedding and FastText embedding, by integrating them with a deep learning-based Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model. The results obtained through extensive experimentation, indicate that the BERT approach performed better the others, achieving a F1-score of 84%. This indicates that BERT, in combination with the CNN-BiLSTM architecture, effectively recognises the nuances of Bangla texts relevant to depressive contents. Comparative analysis with the existing state-of-the-art methods demonstrates that our approach with BERT embedding performs better than others in terms of evaluation metrics and the reliability of dataset annotations. Our research significantly contribution to the development of reliable tools for detecting depressive posts in the Bangla language. By highlighting the efficacy of different embedding techniques and deep learning models, this study paves the way for improved mental health monitoring through social media platforms.

cs.CL↗

arXiv2024

Enhancing Robot Navigation Efficiency Using Cellular Automata with Active Cells

Enhancing robot navigation efficiency is a crucial objective in modern robotics. Robots relying on external navigation systems are often susceptible to electromagnetic interference (EMI) and encounter environmental disturbances, resulting in orientation errors within their surroundings. Therefore, the study employed an internal navigation system to enhance robot navigation efficacy under interference conditions, based on the analysis of the internal parameters and the external signals. This article presents details of the robot's autonomous operation, which allows for setting the robot's trajectory using an embedded map. The robot's navigation process involves counting the number of wheel revolutions as well as adjusting wheel orientation after each straight path section. In this article, an autonomous robot navigation system has been presented that leverages an embedded control navigation map utilising cellular automata with active cells which can effectively navigate in an environment containing various types of obstacles. By analysing the neighbouring cells of the active cell, the cellular environment determines which cell should become active during the robot's next movement step. This approach ensures the robot's independence from external control inputs. Furthermore, the accuracy and speed of the robot's movement have been further enhanced using a hexagonal mosaic for navigation surface mapping. This concept of utilising on cellular automata with active cells has been extended to the navigation of a group of robots on a shared navigation surface, taking into account the intersections of the robots' trajectories over time. To achieve this, a distance control module has been used that records the travelled trajectories in terms of wheel turns and revolutions.

cs.RO↗

arXiv2024

Exploring the Emerging Technologies within the Blockchain Landscape

Although blockchain technology was first introduced in 2008 and materialised in 2009, the early usage of blockchain were mainly limited to financial technologies, particularly cryptocurrencies. Later, blockchain became a widespread emerging technology, utilised in multifaceted sectors and applications. In fact, various new and innovative application of blockchain and distributed ledger technologies are still continuously being researched and explored. On the other hand, smart-contracts were first introduced in 1990s, however, it did not gain enough popularity until being integrated with blockchain technologies lately. The duo lately been seen as the key to many innovations in various industries and sectors. So, we took data from 1445 blockchain-related patent documents and tried to map out the historical and current trends in patenting activities in the blockchain field. This helps us get a better grasp of how blockchain technologies are evolving and being tracked. In addition to serving as an indicator of science and technology growth, patents are also used to judge the research potential and development of a particular technology.

cs.DC↗

arXiv2024

Design of a W-band High-PAE Class A&AB Power Amplifier in 150nm GaAs Technology

Nanometer scale power amplifiers (PA) at sub-THz suffer from severe parasitic effects that lead to experience limited maximum frequency and reduced power performance at the device transceiver front end. The integrated circuits researchers proposed different PA design architecture combinations at scaled down technologies to overcome these limitations. Although the designs meet the minimum requirements, the power added efficiency (PAE) of PA is still quite low. In this paper, a W-band single-ended common-source (CS) and cascode integrated 3-stage 2-way PA design is proposed. The design integrated different key design methodologies to mitigate the parasitic; such as combined Class AB and Class A stages for gain-boosting and efficiency enhancement, Wilkinson power combiner for higher output power, linearity, and bandwidth, and transmission line (TL)-based wide band matching network for better inter-stage matching and compact size. The proposed PA design is validated using UMS 150-nm GaAs pHEMT using advanced design system (ADS) simulator. The results show that the proposed PA achieved a gain of 20.1 dB, an output power of 17.2 dBm, a PAE of 33 % and a 21 GHz bandwidth at 90 GHz Sub-THz band. The PA layout consumes only 5.66 X 2.51 mm2 die space including pads. Our proposed PA design will boost the research on sub-THz integrated circuits research and will smooth the wide spread adoption of 6G in near future.

cs.NI↗

arXiv2024

Optimisation and Performance Computation of a Phase Frequency Detector Module for IoT Devices

The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequency detector (PFD) is a critical device to regulate and provide accurate frequency in IoT devices. Designing a PFD poses challenges in achieving precise phase detection, minimising dead zones, optimising power consumption, and ensuring robust performance across various operational frequencies, necessitating complex engineering and innovative solutions. This study delves into optimising a PFD circuit, designed using 90 nm standard CMOS technology, aiming to achieve superior operational frequencies. An efficient and high-frequency PFD design is crafted and analysed using cadence virtuoso. The study focused on investigating the impact of optimising PFD design. With the optimised PFD, an operational frequency of 5 GHz has been achieved, along with a power consumption of only 29 μW. The dead zone of the PFD was only 25 ps.

cs.NI↗

arXiv2024

Deep Learning Based Cyberbullying Detection in Bangla Language

The Internet is currently the largest platform for global communication including expressions of opinions, reviews, contents, images, videos and so forth. Moreover, social media has now become a very broad and highly engaging platform due to its immense popularity and swift adoption trend. Increased social networking, however, also has detrimental impacts on the society leading to a range of unwanted phenomena, such as online assault, intimidation, digital bullying, criminality and trolling. Hence, cyberbullying has become a pervasive and worrying problem that poses considerable psychological and emotional harm to the people, particularly amongst the teens and the young adults. In order to lessen its negative effects and provide victims with prompt support, a great deal of research to identify cyberbullying instances at various online platforms is emerging. In comparison to other languages, Bangla (also known as Bengali) has fewer research studies in this domain. This study demonstrates a deep learning strategy for identifying cyberbullying in Bengali, using a dataset of 12282 versatile comments from multiple social media sites. In this study, a two-layer bidirectional long short-term memory (Bi-LSTM) model has been built to identify cyberbullying, using a variety of optimisers as well as 5-fold cross validation. To evaluate the functionality and efficacy of the proposed system, rigorous assessment and validation procedures have been employed throughout the project. The results of this study reveals that the proposed model's accuracy, using momentum-based stochastic gradient descent (SGD) optimiser, is 94.46%. It also reflects a higher accuracy of 95.08% and a F1 score of 95.23% using Adam optimiser as well as a better accuracy of 94.31% in 5-fold cross validation.

cs.CL↗

arXiv2023

A Novel Defocus-Blur Region Detection Approach Based on DCT Feature and PCNN Structure

The motion or out-of-focus effect in digital images is the main reason for the blurred regions in defocused-blurred images. It may adversely affect various image features such as texture, pixel, and region. Therefore, it is important to detect in-focused objects in defocused-blurred images after the segmentation of blurred and non-blurred regions. The state-of-the-art techniques are prone to noisy pixels, and their local descriptors for developing segmentation metrics are also complex. To address these issues, this research, therefore, proposed a novel and hybrid-focused detection approach based on Discrete Cosine Transform (DCT) coefficients and PC Neural Net (PCNN) structure. The proposed approach partially resolves the limitations of the existing contrast schemes to detect in-focused smooth objects from the out-of-focused smooth regions in the defocus dataset. The visual and quantitative evaluation illustrates that the proposed approach outperformed in terms of accuracy and efficiency to referenced algorithms. The highest F-score of the proposed approach on Zhao's dataset is 0.7940 whereas on Shi's dataset is 0.9178.

cs.CV↗

arXiv2020

Integration of Blockchain and IoT: An Enhanced Security Perspective

Blockchain (BC), a by-product of Bitcoin cryptocurrency, has gained immense and wide scale popularity for its applicability in various diverse domains - especially in multifaceted non-monetary systems. By adopting cryptographic techniques such as hashing and asymmetric encryption - along with distributed consensus approach, a Blockchain based distributed ledger not only becomes highly secure but also immutable and thus eliminates the need for any third-party intermediators. On the contrary, innumerable IoT (Internet of Things) devices are increasingly being added to the network. This phenomenon poses higher risk in terms of security and privacy. It is thus extremely important to address the security aspects of the growing IoT ecosystem. This paper explores the applicability of BC for ensuring enhanced security and privacy in the IoT ecosystem. Recent research articles and projects or applications were surveyed to assess the implementation of BC for IoT Security and identify associated challenges and propose solutions for BC enabled enhanced security for the IoT ecosystem.

cs.CR↗

arXiv2020

Blockchain Enabled Smart Contract Based Applications: Deficiencies with the Software Development Life Cycle Models

With the recent popularity of Blockchain and other Distributed Ledger Technologies (DLT), blockchain enabled smart contract applications has attracted increased research focus. However, the immutability of the blocks, where the smart contracts are stored, causes conflicts with the traditional Software Development Life Cycle (SDLC) models usually followed by software engineers. This clearly shows the unsuitability of the application of SDLC in designing blockchain enabled smart contract based applications. This research article addresses this current problem by first exploring the six traditional SDLC models, clearly identifying the conflicts in a table with the application of smart contracts and advocates that there is an urgent need to develop new standard model(s) to address the arising issues. The concept of both block immutability and contract is introduced. This is further set in a historical context from legacy smart contracts and blockchain enabled smart contracts extending to the difference between "shallow smart contracts" and "deep smart contracts". To conclude, the traditional SDLC models are unsuitable for blockchain enabled smart contract-based applications.

cs.CY↗

arXiv2020

Analysis of Users' Behaviour and Adoption Trends of Social Media Payment Platforms

The recent proliferation of Electronic Commerce (E-commerce) has been further escalated by multifaceted emerging payment solutions such as cryptocurrencies, mobile, peer-to-peer (P2P) and social media payment platforms. While these technological advancements are gaining tremendous popularity, mostly for their ease of use, various impediments such as security and privacy concerns, societal and cultural norms etc. forbear the users' adoption trends to some extents. This article examines the current status of the social media payment platforms as well as the projection of future adoption trends. Our research underlines the motivations and obstacles to the adoption of social media platforms.

cs.CY↗