Publications | Duc Ngoc Minh Dang, Ph.D.

2024

CroSSHeteroFL: Cross-Stratified Sampling Composition-Fitting to Federated Learning for Heterogeneous Clients

Vo Phuc Tinh, Hoang Hai Son, Duc Ngoc Minh Dang, and 7 more authors

IEEE Access, 2024
SwinTExCo: Exemplar-based video colorization using Swin Transformer

Duong Thanh Tran, Nguyen Doan Hieu Nguyen, Trung Thanh Pham, and 5 more authors

Expert Systems with Applications, 2024

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Video colorization represents a compelling domain within the field of Computer Vision. The traditional approach in this field relies on Convolutional Neural Networks (CNNs) to extract features from each video frame and employs a recurrent network to learn information between video frames. While demonstrating considerable success in colorization, most traditional CNNs suffer from a limited receptive field size, capturing local information within a fixed-sized window. Consequently, they struggle to directly grasp long-range dependencies or pixel relationships that span large image or video frame areas. To address this limitation, recent advancements in the field have leveraged Vision Transformer (ViT) and their variants to enhance performance. This article introduces Swin Transformer Exemplar-based Video Colorization (SwinTExCo), an end-to-end model for the video colorization process that incorporates the Swin Transformer architecture as the backbone. The experimental results demonstrate that our proposed method outperforms many other state-of-the-art methods in both quantitative and qualitative metrics. The achievements of this research have significant implications for the domain of documentary and history video restoration, contributing to the broader goal of preserving cultural heritage and facilitating a deeper understanding of historical events through enhanced audiovisual materials.
CLIP-Prefix for Image Captioning and an Experiment on Blind Image Guessing

Triet Minh Huynh, Duy Linh Nguyen, Thanh Tri Nguyen, and 3 more authors

In Industrial Networks and Intelligent Systems, 2024

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Image caption generation resides at the intersection of computer vision and natural language processing, with its primary goal being the creation of descriptive and coherent textual narratives that faithfully depict the content of an image. This paper presents two models that leverage CLIP as the image encoder and fine-tune GPT-2 for caption generation on the Flickr30k and Flickr8k datasets. The first model utilizes a straightforward mapping network and outperforms the original architecture with a BLEU-1 score of 0.700, BLEU-4 score of 0.257, and ROUGE score of 0.569 on the Flickr8k dataset. The second model constitutes a new architecture exploring the boundaries of minimal visual information required for captioning. It incorporates CLIP’s text encoder to produce input for the generator, while the image embedding serves solely as a validation mechanism. Despite its relatively lower performance, with a BLEU-1 score of 0.546, BLEU-4 score of 0.108, and ROUGE score of 0.444 on the Flickr8k dataset, this model demonstrates the decoder’s ability to create captions based on keyword descriptions alone, without direct access to the context vector.
Performance Comparison in Traffic Sign Recognition Using Deep Learning

Binh Dang Hai, Huu Duc Nguyen, Thanh Nam Vo, and 3 more authors

In Industrial Networks and Intelligent Systems, 2024

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In recent years, along with the increase in private cars, traffic signs have increased in quantity, demanding greater attentiveness from drivers. Many studies have been conducted on Traffic Sign Recognition (TSR) to enhance road safety and driver assistance systems by enabling vehicles to autonomously detect and interpret traffic signs, providing crucial information to drivers in real-time. This paper examines various traffic sign detection and classification models, which give practitioners and researchers valuable insights into selecting optimal solutions tailored to the needs of real-world applications. Notably, the investigation highlights YOLOv8 as a leading detection model, displaying exceptional results with an mAP of 99.4%. YOLOv8 provides various model sizes allowing for adaptation to specific real-time processing scenarios. On the other hand, the LeNet model is a standout performer in the classification domain, consistently achieving a remarkable accuracy of 98.2% while using only 0.4 million parameters. The LeNet architecture ensures accurate and rapid traffic sign classification, making it an appealing choice for applications where resource efficiency is critical.
Deep Learning-Based Automated Cashier System for Bakeries

Nam Van Hai Phan, Tha Thanh Le, Tuan Phu Phan, and 4 more authors

In Proceedings of the 2024 9th International Conference on Intelligent Information Technology, 2024

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The application of image recognition in the bakery business has paved the way for automatic payment systems, a significant advancement in the field of computer vision. This article delves into an exploration of advanced image recognition models to meticulously assess their effectiveness, speed, and suitability for seamless integration into specialized automatic payment systems tailored for bakeries. Specifically, YOLOX, YOLOv8, Faster R-CNN, and RetinaNet, each with different versions and backbones, are considered for evaluation based on their speed and performance. Notably, this study introduces a streamlined process for rapidly creating custom datasets for object detection research and evaluates models across these datasets. The insights and analyses derived from this study provide valuable perspectives for optimizing processes and enhancing the overall performance of automatic payment systems within bakeries.
Innovative Multi-Modal Control for Surveillance Spider Robot: An Integration of Voice and Hand Gesture Recognition

Dang Khoa Phan, Phuong-Nam Tran, Nhat Truong Pham, and 2 more authors

In Proceedings of the 2024 9th International Conference on Intelligent Information Technology, 2024

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The spider robot is designed to take on challenging tasks in hazardous conditions. It can move across challenging terrain like walls and rough surfaces, and effectively find lost objects. In this paper, an innovative multi-modal control approach was developed for the Surveillance Spider Robot (SSR) application, integrating voice recognition and hand gesture recognition as control commands. SSR, a six-legged robot, was designed using a Raspberry Pi 4B embedded device, Arduino Uno kit, RC Servo motors (MG996R), 18650 batteries, mini USB microphone (MI-350), Pi camera V1 (OV5647) and PWM generator (PCA9685). The robot can be controlled through voice or hand gesture recognition captured via camera and microphone. SSR is capable of performing ten specific tasks based on these control commands, including forward movement, backward movement, left turns, right turns, complete turns, movements with higher or lower centers of gravity, slow movement, body-hopping, and stopping. The performance evaluation of voice and hand gesture recognition suggested that SSR can be used in real-world applications with an accuracy that exceeds 90% for the ten specific tasks.
Vietnamese Traffic Sign Recognition Using Deep Learning

Dinh Thuan Nguyen, Minh Khanh Phan, Phuong-Nam Tran, and 1 more author

In Proceedings of the 2024 9th International Conference on Intelligent Information Technology, 2024

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Recognizing the importance of road safety, traffic signs are designed in a variety of shapes, colors, and images to guide and alert drivers. Overlooking these signs, particularly the crucial ones, due to various factors, can lead to legal violations or even catastrophic accidents, impacting multiple aspects of our lives. Consequently, the use of computer systems for traffic sign recognition is urgently needed for the safety of commuters. This not only plays a pivotal role in improving our continually evolving technology but also has substantial value in academic research. Due to the complexity of terrain and the influence of weather on outcomes, Traffic Sign Recognition (TSR) remains a high-priority challenge. This article focuses on the creation of a Vietnamese traffic sign dataset comprising 46 classes. Subsequently, we implement various detection and recognition models using this dataset to assess their performance.
Deep Learning Based Attendance Check System At FPT University

Chi Hung Le, Hung Phat Ly, Tien Duc Nguyen, and 3 more authors

In Proceedings of the 2024 9th International Conference on Intelligent Information Technology, 2024

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In the era of Vietnam’s Industrial Revolution 4.0, technological advancements are crucial, especially in computing and telecommunications. Digital images and videos have become indispensable tools in the modern information age. Human faces hold significant value as biometric identifiers in image and video databases used for surveillance systems. The foundation of model-automated attendance systems utilizing facial recognition is based on taking a face image captured by the camera as input and producing a text recording of the person’s presence. Our study focuses on developing an automatic attendance system based on face recognition using deep learning techniques for real-time recognition. The system includes image capture from a webcam, face detection, dataset management, face recognition, and attendance recording. We investigated and compared three different face recognition models: the VGG-Face model, the Siamese network model, and the Inception-ResNet-v2 model pre-trained. The Siamese Network model achieved the highest accuracy of 94.1% on the LFW dataset. On the other hand, we also trained this enhanced Siamese Network model on the VN-Celeb dataset specifically for testing on our system to be used for FPT University students - Vietnamese students. This enhanced Siamese Network model is integrated into the system, achieving 94.4% accuracy on the FPT University student dataset. This system streamlines attendance recording by eliminating the need for manual attendance taking, thereby minimizing human error and saving time.
MERSA: Multimodal Emotion Recognition with Self-Align Embedding

Quan Bao Le, Kiet Tuan Trinh, Nguyen Dinh Hung Son, and 3 more authors

In 2024 International Conference on Information Networking (ICOIN), 2024
Priority-Based Uplink Raw Slot Utilization in the IEEE 802.11ah Networks

Duc Ngoc Minh Dang, Anh Khoa Tran, and Nhat Truong Pham

In AETA 2022—Recent Advances in Electrical Engineering and Related Sciences: Theory and Application, 2024

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The IEEE 802.11ah standard allows an Access Point (AP) to connect up to 8192 stations at a transmission range of up to 1 km. The goal of the IEEE 802.11ah standard is to maintain wide connectivity and energy efficiency. Some stations are allocated to RAW slots, but they do not have uplink data packets to transmit results in low channel efficiency. The new MAC protocol (PUT-MAC) allows stations to use adjacent RAW slots in a priority manner to improve channel access usage efficiency. The stations use different Arbitration Inter Frame Space and contention window values to contend channel in the same RAW slot. The paper conducts simulations to compare the network performance of the PUT-MAC protocol with the IEEE 802.11ah.
Speech Emotion Recognition: A Brief Review of Multi-modal Multi-task Learning Approaches

Nhat Truong Pham, Anh-Tuan Tran, Bich Ngoc Hong Pham, and 3 more authors

In AETA 2022—Recent Advances in Electrical Engineering and Related Sciences: Theory and Application, 2024

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Speech emotion recognition (SER) has become an attention-grabbing topic in recent years thanks to the development of deep learning in the field of speech processing. However, it is difficult to recognize an accurate emotional state from only speech signals. Therefore, researchers have investigated multi-modalities such as speech, visual, and text inputs to improve the emotional recognition rate of speech. In addition, to enhance the generalized deep learning models for SER, multi-task learning (MTL) strategies have also been applied in the past decade. In this paper, a brief and comprehensive review of multi-modal multi-task learning (3MTL) approaches for recognizing emotional states from speech signals is presented, including multi-modal SER, multi-task learning SER, and multi-modal multi-task learning SER. This paper also discusses about some problems that still need to be solved in 3MTL SER and gives some suggestions for the future.

2023

Vitexco: Exemplar-based Video Colorization using Vision Transformer

Duong Thanh Tran, Phuong-Nam Tran, Nguyen Doan Hieu Nguyen, and 3 more authors

In 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), 2023
RBBA: ResNet - BERT - Bahdanau Attention for Image Caption Generator

Duc-Hieu Hoang, Anh-Khoa Tran, Duc Ngoc Minh Dang, and 3 more authors

In 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), 2023
Comparative analysis of multi-loss functions for enhanced multi-modal speech emotion recognition

Phuong-Nam Tran, Thuy-Duong Thi Vu, Nhat Truong Pham, and 2 more authors

In 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), 2023
SER-Fuse: An Emotion Recognition Application Utilizing Multi-Modal, Multi-Lingual, and Multi-Feature Fusion

Nhat Truong Pham, Le Thi Phan, Duc Ngoc Minh Dang, and 1 more author

In Proceedings of the 12th International Symposium on Information and Communication Technology, 2023

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Speech emotion recognition (SER) is a crucial aspect of affective computing and human-computer interaction, yet effectively identifying emotions in different speakers and languages remains challenging. This paper introduces SER-Fuse, a multi-modal SER application that is designed to address the complexities of multiple speakers and languages. Our approach leverages diverse audio/speech embeddings and text embeddings to extract optimal features for multi-modal SER. We subsequently employ multi-feature fusion to integrate embedding features across modalities and languages. Experimental results archived on the English-Chinese emotional speech (ECES) dataset reveal that SER-Fuse attains competitive performance in the multi-lingual approach compared to the single-lingual approaches. Furthermore, we provide the implementation of SER-Fuse for download at https://github.com/nhattruongpham/SER-Fuse to support reproducibility and local deployment.
Performance Analysis of Distributed Learning in Edge Computing on Handwritten Digits Dataset

Tinh Phuc Vo, Viet Anh Nguyen, Xuyen Bao Le Nguyen, and 2 more authors

In Industrial Networks and Intelligent Systems, 2023

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Deep learning models often consist of millions or even billions of parameters, making it challenging to deploy them on devices with limited resources. Therefore, this study presents scenarios to assess the computational capability of edge devices to provide an evaluation of the learning performance of distributed learning methods. It focuses on using Deep Neural Network and the handwritten digit dataset (MNIST) in edge computing to evaluate the performance of distributed learning methods (no-offloading, full-offloading, split computing, and federated computing) in both ideal and realistic conditions. The performance evaluations are based on Precision, Recall, Accuracy, F1-score, and Estimated time complexity. The findings indicate that the full-offloading method achieved the highest performance in ideal conditions. However, in realistic situations, the split computing and federated computing methods performed better than the others.
Multi-modal Speech Emotion Recognition: Improving Accuracy Through Fusion of VGGish and BERT Features with Multi-head Attention

Phuong-Nam Tran, Thuy-Duong Thi Vu, Duc Ngoc Minh Dang, and 2 more authors

In Industrial Networks and Intelligent Systems, 2023

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Recent research has shown that multi-modal learning is a successful method for enhancing classification performance by mixing several forms of input, notably in speech-emotion recognition (SER) tasks. However, the difference between the modalities may affect SER performance. To overcome this problem, a novel approach for multi-modal SER called 3M-SER is proposed in this paper. The 3M-SER leverages multi-head attention to fuse information from multiple feature embeddings, including audio and text features. The 3M-SER approach is based on the SERVER approach but includes an additional fusion module that improves the integration of text and audio features, leading to improved classification performance. To further enhance the correlation between the modalities, a LayerNorm is applied to audio features prior to fusion. Our approach achieved an unweighted accuracy (UA) and weighted accuracy (WA) of 79.96% and 80.66%, respectively, on the IEMOCAP benchmark dataset. This indicates that the proposed approach is better than SERVER and recent methods with similar approaches. In addition, it highlights the effectiveness of incorporating an extra fusion module in multi-modal learning.
Hybrid data augmentation and deep attention-based dilated convolutional-recurrent neural networks for speech emotion recognition

Nhat Truong Pham, Duc Ngoc Minh Dang, Ngoc Duy Nguyen, and 5 more authors

Expert Systems with Applications, Nov 2023
A novel solution for energy-saving and lifetime-maximizing of LoRa wireless mesh networks

Hoang Hai Son, Vo Phuc Tinh, Duc Ngoc Minh Dang, and 8 more authors

Journal of Information and Telecommunication, Aug 2023
Speech emotion recognition using overlapping sliding window and Shapley additive explainable deep neural network

Nhat Truong Pham, Sy Dzung Nguyen, Vu Song Thuy Nguyen, and 2 more authors

Journal of Information and Telecommunication, Mar 2023
Design and implementation of a VoIP PBX integrated Vietnamese virtual assistant: a case study

Hai Son Hoang, Anh Khoa Tran, Thanh Phong Doan, and 3 more authors

Journal of Information and Telecommunication, Mar 2023
SERVER: Multi-modal Speech Emotion Recognition using Transformer-based and Vision-based Embeddings

Nhat Truong Pham, Duc Ngoc Minh Dang, Bich Ngoc Hong Pham, and 1 more author

In Proceedings of the 2023 8th International Conference on Intelligent Information Technology, Feb 2023
Uplink registration-based MAC protocol for IEEE 802.11ah networks

Duc Ngoc Minh Dang, and Nhat Truong Pham

In Proceedings of the 2023 8th International Conference on Intelligent Information Technology, Feb 2023
Safety Is Our Friend: A Federated Learning Framework Toward Driver’s State and Behavior Detection

Anh Khoa Tran, Nguyen Dang Trac, Vo Phuc Tinh, and 4 more authors

IEEE Transactions on Computational Social Systems, Feb 2023

2022

Key Information Extraction from Mobile-Captured Vietnamese Receipt Images using Graph Neural Networks Approach

V.D. Pham, L.Q. Nguyen, N.T. Pham, and 3 more authors

Proceedings of 2022 6th International Conference on Green Technology and Sustainable Development, GTSD 2022, Feb 2022
Safety Message Broadcast Reliability Enhancement MAC protocol in VANETs

D.N.M. Dang, A.K. Tran, N.T. Pham, and 2 more authors

ICCE 2022 - 2022 IEEE 9th International Conference on Communications and Electronics, Feb 2022
Space-Frequency Diversity based MAC protocol for IEEE 802.11 ah networks

Duc Ngoc Minh Dang, Van Thau Tran, Hoang Lam Nguyen, and 3 more authors

In 2022 International Conference on Advanced Technologies for Communications (ATC), Oct 2022
Stabilizing and Enhancing Frequency Control of Power System Using Decentralized Observer-Based Sliding Mode Control

A.-T. Tran, N.T. Pham, V. Van Huynh, and 1 more author

Journal of Control, Automation and Electrical Systems, Oct 2022
Vietnamese Scene Text Detection and Recognition using Deep Learning: An Empirical Study

N.T. Pham, V.D. Pham, Q. Nguyen-Van, and 3 more authors

Proceedings of 2022 6th International Conference on Green Technology and Sustainable Development, GTSD 2022, Oct 2022
Efficient Resource Allocation using Federated Learning in Cellular Networks

Son Cao Nguyen, Minh Hoang, Tinh Phuc Vo, and 1 more author

In Proceedings of the 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval, Jun 2022

2021

Cross-Modal Deep Neural Networks based Smartphone Authentication for Intelligent Things System

Anh Khoa Tran, Dinh Nguyen The Truong, and Duc Ngoc Minh Dang

In Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval, Aug 2021
Design of a Soil Moisture Sensor for Application in a Smart Watering System

Anh Khoa Tran, Nguyen Minh Trong, Le Mai Bao Nhu, and 3 more authors

In 2021 IEEE Sensors Applications Symposium (SAS), Aug 2021
Hybrid data augmentation and deep attention-based dilated convolutional-recurrent neural networks for speech emotion recognition

N.T. Pham, D.N.M. Dang, and S.D. Nguyen

arXiv, Aug 2021

2020

A Method upon Deep Learning for Speech Emotion Recognition

Nhat Truong Pham, Duc Ngoc Minh Dang, and Sy Dung Nguyen

Journal of Advanced Engineering and Computation, Dec 2020
Wireless sensor networks and machine learning meet climate change prediction

Anh Khoa Tran, Nguyen Quang Minh, Hoang Hai Son, and 6 more authors

International Journal of Communication Systems, Nov 2020
An adaptive and cooperative MAC protocol in vehicular ad hoc network: design and performance analysis

Quan Le Trung, Long Bao Le, Duc Ngoc Minh Dang, and 1 more author

International Journal of Ad Hoc and Ubiquitous Computing, Nov 2020
Extreme power saving directional MAC protocol in IEEE 802.11ah networks

Quynh T. Ngo, Duc Ngoc Minh Dang, and Quan Le-Trung

IET Networks, Jul 2020
Designing Efficient Smart Home Management with IoT Smart Lighting: A Case Study

Anh Khoa Tran, Le Mai Bao Nhu, Hoang Hai Son, and 7 more authors

Wireless Communications and Mobile Computing, Nov 2020
Waste Management System Using IoT-Based Machine Learning in University

Anh Khoa Tran, Cao Hoang Phuc, Pham Duc Lam, and 7 more authors

Wireless Communications and Mobile Computing, Feb 2020

2019

A Novel Directional MAC in Restricted Access Window for IEEE 802.11ah Networks

Quynh Tu Ngo, Duc Ngoc Minh Dang, Quan Le-Trung, and 1 more author

In 2019 26th International Conference on Telecommunications (ICT), Apr 2019
High Throughput and Low Complexity Implementation for Uplink Scheme of 5G Technology

Pham Chi Bao, Dang Van Xuan Huong, Duc Ngoc Minh Dang, and 2 more authors

In 2019 26th International Conference on Telecommunications (ICT), Apr 2019

2018

Flying Height Optimization for Unmanned Aerial Vehicles in Cellular - Flying Adhoc Network

Quynh Ngo, Duc Ngoc Minh Dang, and Khoa Anh Tran

Journal of Advanced Engineering and Computation, Dec 2018
Enhanced Self-sorting Based MAC Protocol for Vehicular Ad-Hoc Networks

Q.T. Ngo, and D.N.M. Dang

Lecture Notes in Electrical Engineering, Dec 2018

2017

Analytical Study of the IEEE 1609.4 MAC in Vehicular Ad Hoc Networks

Duc Ngoc Minh Dang, Quynh Tu Ngo, Hanh Ngoc Dang, and 1 more author

In Lecture Notes in Electrical Engineering, Nov 2017
Directional Multi-channel MAC for VANETs

Duc Ngoc Minh Dang, Q. T. Ngo, H. N. Dang, and 2 more authors

Nov 2017

2016

A Dynamic Cooperative MAC Protocol for Vehicular Ad-hoc Networks

Quynh Tu Ngo, and Duc Ngoc Minh Dang

In AETA 2016: Recent Advances in Electrical Engineering and Related Sciences, Dec 2016
Multi-channel MAC Protocols and Power Control for Wireless Ad hoc Networks

Duc Ngoc Minh Dang, and Choong Seon Hong

May 2016
A Multi-channel MAC Protocol with Power Control for Wireless Ad Hoc Networks

Duc Ngoc Minh Dang, Quynh Tu Ngo, Hanh Ngoc Dang, and 1 more author

In AETA 2015: Recent Advances in Electrical Engineering and Related Sciences, May 2016
A QoE-based caching algorithm for HTTP adaptive streaming contents in radio access networks

Phuong L. Vo, Long Van Nguyen, Tuan-Anh Le, and 1 more author

In 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE), Jul 2016
An Efficient Multi-channel MAC protocol for Wireless Ad hoc Networks

Duc Ngoc Minh Dang, VanDung Nguyen, Huong Tra Le, and 2 more authors

Ad Hoc Networks, Jul 2016
OMF-MAC: An Opportunistic Matched Filter-Based MAC in Cognitive Radio Networks

Thant Zin Oo, Nguyen H. Tran, Duc Ngoc Minh Dang, and 3 more authors

IEEE Transactions on Vehicular Technology, Apr 2016

2015

A Cooperative — Efficient — Reliable MAC protocol for Vehicular Ad hoc Networks

Duc Ngoc Minh Dang, Hanh Ngoc Dang, Phuong Luu Vo, and 1 more author

In 2015 International Conference on Advanced Technologies for Communications (ATC), Oct 2015
A coalitional game approach for fractional cooperative caching in content-oriented networks

P.L. Vo, D.N.M. Dang, S. Lee, and 2 more authors

Computer Networks, Oct 2015
A hybrid multi-channel MAC protocol for wireless ad hoc networks

D.N.M. Dang, C.S. Hong, and S. Lee

Wireless Networks, Oct 2015
Application of the Lowest-ID Algorithm in Cluster-based TDMA System for VANETs

V. Nguyen, O. T. T. Kim, D. N. M. Dang, and 3 more authors

Oct 2015
Multi-channel MAC protocol with Directional Antennas in wireless ad hoc networks

Duc Ngoc Minh Dang, Huong Tra Le, Hyo Sung Kang, and 2 more authors

In 2015 International Conference on Information Networking (ICOIN), Jan 2015

2014

A Reliable Multi-hop Safety Message Broadcast in Vehicular Ad hoc Networks

Duc Ngoc Minh Dang, VanDung Nguyen, Pham Chuan, and 2 more authors

Jan 2014
An Efficient and Reliable MAC in VANETs

Duc Ngoc Minh Dang, Choong Seon Hong, Sungwon Lee, and 1 more author

IEEE Communications Letters, Apr 2014
HER-MAC: A Hybrid Efficient and Reliable MAC for Vehicular Ad hoc Networks

Duc Ngoc Minh Dang, H N Dang, V D Nguyen, and 2 more authors

Apr 2014
Mitigating starvation in wireless ad hoc networks: Multi-channel MAC and power control

D.N. Minh Dang, P.L. Vo, C.S. Hong, and 1 more author

Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2014, Apr 2014
Performance Analysis of the IEEE 802.11p under Finite Traffic Conditions

Duc Ngoc Minh Dang, Hanh Ngoc Dang, The Cuong Do, and 1 more author

In AETA 2013: Recent Advances in Electrical Engineering and Related Sciences, Apr 2014
Power control under QoS and interference constraint in femtocell cognitive networks

C.T. Do, D.N.M. Dang, T. LeAnh, and 3 more authors

International Conference on Information Networking, Apr 2014
e-VeMAC: An Enhanced Vehicular MAC Protocol to Mitigate the Exposed Terminal Problem

V. Nguyen, Duc Ngoc Minh Dang, S. Jang, and 2 more authors

Apr 2014

2013

An Enhanced Multi-channel MAC for Vehicular Ad Hoc Networks

Duc Ngoc Minh Dang, H. N. Dang, C. T. Do, and 2 more authors

Apr 2013
An efficient and reliable MAC for Vehicular Ad Hoc Networks

D.N.M. Dang, H.N. Dang, C.T. Do, and 1 more author

15th Asia-Pacific Network Operations and Management Symposium: "Integrated Management of Network Virtualization", APNOMS 2013, Apr 2013

2012

A SINR-based MAC protocol for wireless ad hoc networks

D.N.M. Dang, C.S. Hong, S. Lee, and 1 more author

IEEE Communications Letters, Apr 2012
A SINR-based Transmission Power Control for MAC Protocol in Wireless Ad hoc Networks

N. M. D. Duc, C. S. Hong, and Ieee

Apr 2012
An Energy Efficient Multi-channel MAC Protocol for Wireless Ad hoc Networks

Duc Ngoc Minh Dang, Mui Van Nguyen, Choong Seon Hong, and 2 more authors

GLOBECOM 2012, Apr 2012
An enhanced multi-channel MAC protocol for wireless Ad hoc networks

D.N.M. Dang, N.T. Quang, C.S. Hong, and 1 more author

14th Asia-Pacific Network Operations and Management Symposium: "Management in the Big Data and IoT Era", APNOMS 2012 - Final Program, Apr 2012
H-MMAC: A Hybrid Multi-channel MAC Protocol for Wireless Ad hoc Networks

N. M. D. Duc, C. S. Hong, and Ieee

Apr 2012