84 citations · 96 across the 14 of their papers we have counts for
18 papers
VoxelSynth3D: Interpretable Volumetric Image-Domain Metal Artifact Reduction with a Paired Synthetic CLINIC Benchmark
Amritesh Banerjee, Abdul Basit, Renil Renji Joseph +2
Metal artifacts in postoperative musculoskeletal CT obscure bone-implant and adjacent soft-tissue interfaces. Many metal artifact reduction (MAR) methods require unavailable raw pr…
ThinkNet: Evolutionary Deep Ensembles for Efficient Motor-Imagery EEG Classification
Abdul Basit, Saim Rehman, Muhammad Shafique
Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-…
EEG-Fusion: Failure-Informed Source-Free Expert Routing for Robust Motor Imagery EEG Decoding
Abdul Basit, Saim Rehman, Muhammad Shafique
Subject-independent motor-imagery (MI) EEG decoding can exhibit subject-level failures even when average performance appears acceptable: under subject shift, a decoder can become a…
MVB-Grasp: Minimum-Volume-Box Filtering of Diffusion-based Grasps for Frontal Manipulation
Bibek Poudel, Abdul Basit, Muhammad Shafique
State-of-the-art 6-DoF grasp generators excel on tabletop benchmarks with overhead cameras but struggle in frontal grasping scenarios on low-cost manipulators with constrained work…
Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models
Abdul Basit, Ashir Rashid, Muhammad Abdullah Hanif +1
Multiple Sclerosis (MS) is a chronic autoimmune disease that can significantly reduce the quality of life of a patient. Existing treatment options can only help slow down the progr…
Scale-Gest: Scalable Model-Space Synthesis and Runtime Selection for On-Device Gesture Detection
Abdul Basit, Saim Rehman, Muhammad Shafique
Realizing on-device ML-based gesture detection under tight real-time performance, energy and memory constraints is challenging, especially when considering mobile devices with vary…
Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models
Muhammad Shafique, Abdul Basit, Muhammad Abdullah Hanif +3
This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines hardware and software co-design of transformer blocks w…
PatchBlock: A Lightweight Defense Against Adversarial Patches for Embedded EdgeAI Devices
Nandish Chattopadhyay, Abdul Basit, Amira Guesmi +3
Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous driving and surveillance, which re…
CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning
Abdul Basit, Maha Nawaz, Saim Rehman +1
Efficient control of prosthetic limbs via non-invasive brain-computer interfaces (BCIs) requires advanced EEG processing, including pre-filtering, feature extraction, and action pr…
PennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generation
Abdul Basit, Minghao Shao, Muhammad Haider Asif +4
The growing demand for robust quantum programming frameworks has unveiled a critical limitation: current large language model (LLM) based quantum code assistants heavily rely on re…
MetaCipher: A Time-Persistent and Universal Multi-Agent Framework for Cipher-Based Jailbreak Attacks for LLMs
Boyuan Chen, Minghao Shao, Abdul Basit +2
As large language models (LLMs) grow more capable, they face growing vulnerability to sophisticated jailbreak attacks. While developers invest heavily in alignment finetuning and s…
QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges
Abdul Basit, Minghao Shao, Muhammad Haider Asif +4
Recent advances in Large Language Models (LLMs) have demonstrated strong potential in code generation, yet their effectiveness in quantum computing remains underexplored. This pape…
BRAVE: Brain-Controlled Prosthetic Arm with Voice Integration and Embodied Learning for Enhanced Mobility
Abdul Basit, Maha Nawaz, Muhammad Shafique
Non-invasive brain-computer interfaces (BCIs) have the potential to enable intuitive control of prosthetic limbs for individuals with upper limb amputations. However, existing EEG-…
A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG
Abdul Basit, Nouhaila Innan, Muhammad Haider Asif +4
Large Language Models (LLMs) offer powerful capabilities in code generation, natural language understanding, and domain-specific reasoning. Their application to quantum software de…
A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges
Nandish Chattopadhyay, Abdul Basit, Bassem Ouni +1
Adversarial attacks have emerged as a major challenge to the trustworthy deployment of machine learning models, particularly in computer vision applications. These attacks have a v…
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges
Minghao Shao, Abdul Basit, Ramesh Karri +1
Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. Thes…
MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Prosthetic Arm System
Maha Nawaz, Abdul Basit, Muhammad Shafique
Currently, individuals with arm mobility impairments (referred to as "patients") face limited technological solutions due to two key challenges: (1) non-invasive prosthetic devices…
MedAide: Leveraging Large Language Models for On-Premise Medical Assistance on Edge Devices
Abdul Basit, Khizar Hussain, Muhammad Abdullah Hanif +1
Large language models (LLMs) are revolutionizing various domains with their remarkable natural language processing (NLP) abilities. However, deploying LLMs in resource-constrained…