Manoranjan Paul
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View article: A Review of Graphene-Integrated Biosensors for Non-Invasive Biochemical Monitoring in Health Applications
A Review of Graphene-Integrated Biosensors for Non-Invasive Biochemical Monitoring in Health Applications Open
This review explores the transformative potential of graphene-based, non-invasive biochemical sensors in the context of real-time health monitoring and personalised medicine. Traditional diagnostic methods often involve invasive procedures…
View article: Transformer-Guided Noise Detection and Correction in Remote Sensing Data for Enhanced Soil Organic Carbon Estimation
Transformer-Guided Noise Detection and Correction in Remote Sensing Data for Enhanced Soil Organic Carbon Estimation Open
Soil organic carbon (SOC) is a critical indicator of soil health, directly influencing crop productivity, soil structure, and environmental sustainability. Existing SOC estimation techniques using satellite reflectance data are effective f…
View article: Novel quantum circuit for image compression utilizing modified Toffoli gate and quantized transformed coefficient alongside a novel reset gate
Novel quantum circuit for image compression utilizing modified Toffoli gate and quantized transformed coefficient alongside a novel reset gate Open
Quantum image computing has emerged as a groundbreaking field, revolutionizing how we store and process data at speeds incomparable to classical methods. Nevertheless, as image sizes expand, so does the complexity of qubit connections, pos…
View article: PALQA: A Novel Parameterized Position-Aware Lossy Quantum Autoencoder using LSB Control Qubit for Efficient Image Compression
PALQA: A Novel Parameterized Position-Aware Lossy Quantum Autoencoder using LSB Control Qubit for Efficient Image Compression Open
With the growing interest in quantum computing, quantum image processing technology has become a vital research field due to its versatile applications and ability to outperform classical computing. A quantum autoencoder approach has been …
View article: Using Machine Learning to Enhance PostQuantum Cryptographic Algorithms
Using Machine Learning to Enhance PostQuantum Cryptographic Algorithms Open
The incoming need for defense against quantum computer attacks has motivated researchers to prioritize post-quantum cryptography (PQC) because this approach develops encryption which quantum computers cannot break. A great number of PQC al…
View article: Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques
Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques Open
Soil colour is a key indicator of soil health and the associated properties. In agriculture, soil colour provides farmers and advises with a visual guide to interpret soil functions and performance. Munsell colour charts have been used to …
View article: Deep Learning-Based Adaptive Downsampling of Hyperspectral Bands for Soil Organic Carbon Estimation
Deep Learning-Based Adaptive Downsampling of Hyperspectral Bands for Soil Organic Carbon Estimation Open
Accurate estimation of soil organic carbon (SOC) is critical for assessing soil health and guiding sustainable land management. Hyperspectral sensing has emerged as an approach for SOC analysis due to its ability to capture detailed spectr…
View article: An Overview of Quantum Circuit Design Focusing on Compression and Representation
An Overview of Quantum Circuit Design Focusing on Compression and Representation Open
Quantum image computing has attracted attention due to its vast storage capacity and faster image data processing, leveraging unique properties such as parallelism, superposition, and entanglement, surpassing classical computers. Although …
View article: BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation
BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation Open
Hyperspectral band selection algorithms are crucial for processing high-dimensional data, which enables dimensionality reduction, improves data analysis, and enhances computational efficiency. Among these, attention-based algorithms have g…
View article: An Overview of the Quantum Circuit Design Focusing on Compression and Representation
An Overview of the Quantum Circuit Design Focusing on Compression and Representation Open
Quantum image computing has attracted attention due to its vast storage and faster processing of image data than classical computers. Although classical computing power has grown substantially in the last decade, its abilities have plateau…
View article: Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds
Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds Open
With the great progress of 3D sensing and acquisition technology, the volume of point cloud data has grown dramatically, which urges the development of efficient point cloud compression methods. In this paper, we focus on the task of learn…
View article: Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentation
Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentation Open
Previous studies have demonstrated the effectiveness of point-based neural models on the point cloud analysis task. However, there remains a crucial issue on producing the efficient input embedding for raw point coordinates. Moreover, anot…
View article: Predicting Heart Failure with Attention Learning Techniques Utilizing Cardiovascular Data
Predicting Heart Failure with Attention Learning Techniques Utilizing Cardiovascular Data Open
Cardiovascular diseases (CVDs) encompass a group of disorders affecting the heart and blood vessels, including conditions such as coronary artery disease, heart failure, stroke, and hypertension. In cardiovascular diseases, heart failure i…
View article: Improved Video-Based Point Cloud Compression via Segmentation
Improved Video-Based Point Cloud Compression via Segmentation Open
A point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such…
View article: Improved video-based point cloud compression via segmentation
Improved video-based point cloud compression via segmentation Open
Point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such a…
View article: FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation
FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation Open
Conventional feature selection algorithms applied to Pseudo Time-Series (PTS) data, which consists of observations arranged in sequential order without adhering to a conventional temporal dimension, often exhibit impractical computational …
View article: Efficient Dynamic Point Cloud Compression Through Adaptive Hierarchical Partitioning
Efficient Dynamic Point Cloud Compression Through Adaptive Hierarchical Partitioning Open
Video-based Point Cloud Compression (V-PCC) is a dynamic point cloud coding standard that compresses 3D point clouds by projecting them into 2D frames, involving computationally intensive steps that can hinder real-time applications in aug…
View article: A Density-Aware Point Cloud Geometry Compression Leveraging Cluster- Centric Processing
A Density-Aware Point Cloud Geometry Compression Leveraging Cluster- Centric Processing Open
Recent years have encountered a noticeable expansion of point cloud-based 3D applications that compels the necessity of high-efficiency point cloud compression. Preserving the local density of point cloud is crucial for compression, howeve…
View article: An efficient video coding with object-bounded motion estimation using cuboid-based variable-sized block partitioning
An efficient video coding with object-bounded motion estimation using cuboid-based variable-sized block partitioning Open
Block-based motion modelling has been widely used in video coding where a frame is divided into fixed-sized blocks that are motion compensated independently. This often leads to coding inefficiency as fixed-sized blocks hardly align with t…
View article: Decision Support Systems (DSSs) ‘In the Wild’: The Factors That Influence Users’ Acceptance of DSSs in Naturalistic Settings
Decision Support Systems (DSSs) ‘In the Wild’: The Factors That Influence Users’ Acceptance of DSSs in Naturalistic Settings Open
A richer approach to studying Decision Support System (DSS) interactions is required to understand and predict the nature of actual use in the workplace. We used questionnaire and interview techniques to examine workers’ experiences relati…
View article: Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models
Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models Open
Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed …
View article: Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks Open
Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to distinguish…
View article: Efficient quantum image representation and compression circuit using zero-discarded state preparation approach
Efficient quantum image representation and compression circuit using zero-discarded state preparation approach Open
Quantum image computing draws a lot of attention due to storing and processing image data faster than classical. With increasing the image size, the number of connections also increases, leading to the circuit complex. Therefore, efficient…