Research
Research
Hardware Security Research
Identified the drawback of the existing solution for detecting Hardware Trojan without golden chips. Currently developing software toolchain for runtime Hardware Trojan detection and tolerance without golden chips.
Supervisor: Asst. Prof. Tamzidul Hoque, University of Kansas
Pattern Recognition using Neuromorphic Computing
Rouhan Noor, Kazi Mejbaul Islam et. al.
Advisor: Associate Professor Monjur Morshed,
Neuromorphic computing has been emerged as the contender of cognitive computing and next stage of artificial intelligence by introducing more brain like hardware architecture to accelerate cognitive computing. Proposed use of memristive device, phase change memory, spintronics are the forerunner of this next generation of artificial intelligence and neuromorphic computing hardware. SNN is the next generation neural network which opens exciting research avenue for low power spike event based computation. In this work we used recently developed SNN algorithm and framework named Spike Layer Error Reassignment in Time (SLAYER) framework for pattern recognition of Bengali handwritten digit using recently developed NumtaDB dataset (85000+) which is developed for Intel Loihi neuromorphic chip. You can check at this Link.
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense.
Sayeed Shafayet Chowdhury*, Rouhan Noor*, Kazi Mejbaul Islam*
*Authors have equal contribution in this work.
Autonomous aerial surveillance using drone feed is an interesting and challenging research domain. Along with surveillance, an autonomous device should be capable of detecting device malfunction or abnormality in real time. However, the inherent uncertainty embedded within the type and level of abnormality makes supervised techniques unsuitable since the adversary may present a unique anomaly for intrusion. To counter this, in this paper, we propose an unsupervised ensemble anomaly detection system to detect device anomaly of an unmanned drone analyzing multimodal data like images and IMU (Inertial Measurement Unit) sensor data synergistically. We have proposed AngleNet and used autoencoder to analyze image and IMU data, respectively and later ensembled the two pipelines for predicting degree of abnormality of the device. Furthermore, we have applied adversarial attack to test the robustness of the proposed approach and integrated defense mechanism. The proposed method performs satisfactorily on the IEEE SP Cup-2020 dataset with an accuracy of 97.8%. This work appeared in 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). Link.
Handwritten Bangla Numeral Recognition Using Ensembling of Convolutional Neural Network
Rouhan Noor, Kazi Mejbaul Islam, Md. Jakaria Rahimi
Despite being one of the major languages in the world, research regarding Bengali handwritten numeral recognition (BHNR) isn't enough in comparison with the other prominent languages. Existing methods mostly rely on feature extraction and some older machine learning algorithms. Recent bloom in machine learning due to deep neural network especially using Convolutional Neural Network (CNN) showing promising results in this field with better accuracy. Some recent works show very good accuracy only in recognizing plain simple digits but perform poor in challenging scenario because of lack of large and versatile training dataset. In this work, we've ensembled our best performing proposed CNN models to recognize numerals with high degree of accuracy beyond 96% even in most challenging noisy conditions. Initially 72000+ specimens from NumtaDB (85000+) have been used for training and 17000+ specimens have been used as test dataset. The improvement in performance in challenging scenarios has been observed, when various noisy training specimens have been augmented to create a training dataset of size about 114000 specimens. The performance of our proposed model has been compared with other existing works also and presented here. These finding are based on Computer Vision Challenge on Bengali HandWritten Digit Recognition (2018) competition submissions. This work appeared in 2018 21st International Conference on Computer and Information Technology (ICCIT). Link.
Unsupervised Abnormality Detection Using Heterogenous Autonomous System
Kazi Mejbaul Islam, Rouhan Noor et. al.
Due to the rise of autonomous vehicles like drones and cars anomaly detection for better and robust surveillance becomes prominent for real-time recognition of normal and abnormal states. But the whole system fails if the unmanned device is unable to detect its own device's anomaly in real-time. Considering the scenario, we can make use of various data of autonomous vehicles like images, video streams, and other digital or analog sensor data to detect device anomaly. In this paper, we have demonstrated a heterogeneous system that estimates the degree of an anomaly in unmanned surveillance drone by inspecting IMU (Inertial Measurement Unit) sensor data and real-time image in an unsupervised approach. We've used AngleNet for detecting images taken in an abnormal state. On top of that, an autoencoder fed by the IMU data has been ensembled with AngleNet for evaluating the final degree of the anomaly. This proposed method is based on the result of the IEEE SP Cup 2020 which achieved 97.3 percent accuracy on the provided dataset. Besides, this approach has been evaluated on an in-house setup for substantiating its robustness. This work appeared in 2020 IEEE REGION 10 CONFERENCE (TENCON). Link.
A Deep Convolutional Neural Network for Bangla Handwritten Numeral Recognition
Kazi Mejbaul Islam, Rouhan Noor et. al.
Despite being one of the major languages in the world, research regarding Bengali handwritten numeral recognition (BHNR) isn't enough in comparison with the other prominent languages. Existing methods mostly rely on feature extraction and some older machine learning algorithms. Recent bloom in machine learning due to deep neural network especially using Convolutional Neural Network (CNN) showing promising results in this field with better accuracy. Some recent works show very good accuracy only in recognizing plain simple digits but perform poor in challenging scenario because of lack of large and versatile training dataset. In this work, we propose a method where our proposed CNN model which recognizes numerals with high degree of accuracy beyond 96%, even in most challenging noisy conditions. Initially 72000+ specimens were used from NumtaDB (85000+) dataset for training and 1700+ specimens were used as test dataset. The improvement in performance in challenging scenarios is observed, when training specimens are augmented to create a training dataset of size about 114000 specimens. The performance of our proposed model also compared with other existing works and presented here. These findings are based on Computer Vision Challenge on Bengali HandWritten Digit Recognition (2018) competition submissions. This work appeared in 2018 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). Link.