Khac Tuan Nguyen - Academia.edu (original) (raw)

Papers by Khac Tuan Nguyen

2022 IEEE Ninth International Conference on Communications and Electronics (ICCE)

$Research paper thumbnail of SHREC\u2719 Track: Extended 2D Scene Image-Based 3D Scene Retrieval$

In the months following our SHREC 2018-2D Scene Image-Based 3D Scene Retrieval (Scene IBR2018) tr... more In the months following our SHREC 2018-2D Scene Image-Based 3D Scene Retrieval (Scene IBR2018) track, we have extended the number of the scene categories fro the initial 10 classes in the Scene IBR2018 benchmark to 30 classes, resulting in a new benchmark Scene IBR2019 which has 30,000 scene images and 3,000 3D scene models. For that reason, we seek to further evaluate the performance of existing and 2D scene image-based 3D scene retrieval algorithms using this extended and more comprehensive new benchmark. Three groups from the Netherlands, the United States and Vietnam participated and collectively submitted eight runs. This report documents the evaluation of each method based on seven performances metrics, offers an in-depth discussion as well as analysis on the methods employed and discusses future directions that have the potential to address this task. Again, deep learning techniques have demonstrated notable performance in terms of both accuracy and scalability when applied t...

This paper uses both parametric and non-parametric approaches to estimate technical, allocative, ... more This paper uses both parametric and non-parametric approaches to estimate technical, allocative, and economic efficiencies for the agriculture production in sixty provinces of Vietnam in the period 1990-2005. Under different technology specifications, both approaches show that the average technical, allocative, and economic efficiency estimates were not high, and there would be a large room for the studied provinces to improve their agricultural production efficiency. To examine consistency of the estimates from two approaches under different specifications of returns to scale, we use Spearman rank test, and the results indicate that parametric and non-parametric approaches provide different estimates.

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020

Traffic flow analysis is essential for intelligent transportation systems. In this paper, we intr... more Traffic flow analysis is essential for intelligent transportation systems. In this paper, we introduce our Intelligent Traffic Analysis Software Kit (iTASK) to tackle three challenging problems: vehicle flow counting, vehicle re-identification, and abnormal event detection. For the first problem, we propose to real-time track vehicles moving along the desired direction in corresponding motionof-interests (MOIs). For the second problem, we consider each vehicle as a document with multiple semantic words (i.e., vehicle attributes) and transform the given problem to classical document retrieval. For the last problem, we propose to forward and backward refine anomaly detection using GAN-based future prediction and backward tracking completely stalled vehicle or sudden-change direction, respectively. Experiments on the datasets of traffic flow analysis from AI City Challenge 2020 show our competitive results, namely, S1 score of 0.8297 for vehicle flow counting in Track 1, mAP score of 0.3882 for vehicle re-identification in Track 2, and S4 score of 0.9059 for anomaly detection in Track 4. All data and source code are publicly available on our project page.

Meccanica, 2021

This paper shows a new study on the effect of various dry and isotropic friction levels on the pr... more This paper shows a new study on the effect of various dry and isotropic friction levels on the progression and dynamic response of a vibro-impact locomotion system. An experimental vibro-impact self-propelled apparatus, which is able to vary the friction force while remaining the total weight of the system, was practically implemented. A new dimensionless model was developed based on the validated mathematical model, allowing to examine the effects of the excitation force and the friction force independently. The experimental data revealed that, the force ratio between the excitation magnitude and friction level would not be totally correct to represent the excitation effects in dimensionless modeling the system. The level of friction force may have a significant effect not only on how fast the system move, but also on which direction of the progression. Bifurcation analysis and basin of attraction were calculated to scrutinize the effect of friction on the scaled model. The results showed that various friction would lead to either period-1 or chaotic motion of the system. The new findings would be useful for further studies on the design and operation of vibro-impact driven locomotion systems and capsule robots.

Lecture Notes in Mechanical Engineering, 2021

This paper presents experimental study on a vibro-impact driven and self-propelled locomotion sys... more This paper presents experimental study on a vibro-impact driven and self-propelled locomotion system under anisotropic friction, where the forward friction is larger than backward friction. The friction force was considered as a combination of two parts: one fixed value and an additional and adjustable value. An experimental apparatus was made to provide the preset value of friction, and an ability to vary the ratio between the friction force in forward direction and that in backward direction while keeping the weight of the whole system unchanged. Four experimental sets were implemented, giving a deep insight of the system responses, both in progression velocity of the system and in the relative motions of the two masses. The results revealed that, the system is able to move forward even when the forward friction is larger than the backward friction. Moreover, a larger level of preset friction resulted in faster movement of the system. The experimental results would be useful for further studies on the design and operation of vibration-driven locomotion systems without external propulsion components.

In this work, we propose Context-based Instance Segmentation for video object segmentation in two... more In this work, we propose Context-based Instance Segmentation for video object segmentation in two passes. Namely, in the first pass, we estimate the main properties of each instance (i.e., human/non-human, rigid/deformable, known/unknown category) by propagating its initial mask to other frames. We employ Instance Re-Identification Flow in this pass. The result of the first pass helps our system to automatically select the appropriate scheme for instance segmentation in the second pass. In the second pass, we process human and non-human instances separately. For human instance, we employ Mask R-CNN to extract human segments, OpenPose to merge fragments (in a frame), and object flow to correct and refine the result across frames. For non-human instance, if the instance has a wide variation in its appearance and it belongs to known categories (which can be inferred from the initial mask), we use Mask-RCNN for instance segmentation. If the instance is nearly rigid, we synthesize images...

In the months following our SHREC 2018 2D Scene Image-Based 3D Scene Retrieval (SceneIBR2018) tra... more In the months following our SHREC 2018 2D Scene Image-Based 3D Scene Retrieval (SceneIBR2018) track, we have extended the number of the scene categories from the initial 10 classes in the SceneIBR2018 benchmark to 30 classes, resulting in a new benchmark SceneIBR2019 which has 30,000 scene images and 3,000 3D scene models. For that reason, we seek to further evaluate the performance of existing and new 2D scene image-based 3D scene retrieval algorithms using this extended and more comprehensive new benchmark. Three groups from the Netherlands, the United States and Vietnam participated and collectively submitted eight runs. This report documents the evaluation of each method based on seven performance metrics, offers an indepth discussion as well as analysis on the methods employed and discusses future directions that have the potential to address this task. Again, deep learning techniques have demonstrated notable performance in terms of both accuracy and scalability when applied t...

Sketch-based 3D scene retrieval is to retrieve 3D scene models given a u ser’s hand-drawn 2D scen... more Sketch-based 3D scene retrieval is to retrieve 3D scene models given a u ser’s hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To bo ost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC’18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of th scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchm ark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objectiv e of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieva l algorithms using this extended and more comprehensive ...

Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D s... more Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To boost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC'18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of the scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchmark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objective of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieval algorithms using this extended and more comprehensive new benchmark. In this track, two groups from USA and Vietnam have successfully submitted 4 runs. Based on 7 commonly used retrieval metrics, we evaluate their retrieval performance. We have also conducted a comprehensive analysis and discussion of these methods and proposed several future research directions to deal with this challenging research topic. Deep learning techniques have been proved their great potentials again in dealing with this challenging retrieval task, in terms of both retrieval accuracy and scalability to a larger dataset. We hope this publicly available benchmark, together with its evaluation results and source code, will further enrich and promote 2D scene sketch-based 3D scene retrieval research area and its corresponding applications.

Sketch-based 3D model retrieval has the intuitiveness advantage over other types of retrieval sch... more Sketch-based 3D model retrieval has the intuitiveness advantage over other types of retrieval schemes. Currently, there is a lot of research in sketch-based 3D model retrieval, which usually targets the problem of retrieving a list of candidate 3D models using a single sketch as input. 2D scene sketch-based 3D scene retrieval is a brand new research topic in the field of 3D object retrieval. Unlike traditional sketch-based 3D model retrieval which ideally assumes that a query sketch contains only a single object, this is a new 3D model retrieval topic within the context of a 2D scene sketch which contains several objects that may overlap with each other and thus be occluded and also have relative location configurations. It is challenging due to the semantic gap existing between the iconic 2D representation of sketches and more accurate 3D representation of 3D models. But it also has vast applications such as 3D scene reconstruction, autonomous driving cars, 3D geometry video retrieval, and 3D AR/VR Entertainment. Therefore, this research topic deserves our further exploration. To promote this interesting research, we organize this SHREC track and build the first 2D scene sketch-based 3D scene retrieval benchmark by collecting 3D scenes from Google 3D Warehouse and utilizing our previously proposed 2D scene sketch dataset Scene250. The objective of this track is to evaluate the performance of different 2D scene sketch-based 3D scene retrieval algorithms using a 2D sketch query dataset and a 3D Warehouse model dataset. The benchmark contains 250 scene sketches and 1000 3D scene models, and both are equally classified into 10 classes. In this track, six groups from five countries (China, Chile, USA, UK, and Vietnam) have registered for the track, while due to many challenges involved, only 3 groups have successfully submitted 8 runs. The retrieval performance of submitted results has been evaluated using 7 commonly used retrieval performance metrics. We also conduct a thorough analysis and discussion on those metho [...]

2D scene image-based 3D scene retrieval is a new research topic in the field of 3D object retriev... more 2D scene image-based 3D scene retrieval is a new research topic in the field of 3D object retrieval. Given a 2D scene image, it is to search for relevant 3D scenes from a dataset. It has an intuitive and convenient framework which allows users to learn, search, and utilize the retrieved results for vast related applications, such as automatic 3D content generation for 3D movie, game and animation production, robotic vision, and consumer electronics apps development, and autonomous vehicles. To advance this promising research, we organize this SHREC track and build the first 2D scene image-based 3D scene retrieval benchmark by collecting 2D images from ImageNet and 3D scenes from Google 3D Warehouse. The benchmark contains uniformly classified 10,000 2D scene images and 1,000 3D scene models of ten (10) categories. In this track, seven (7) groups from five countries (China, Chile, USA, UK, and Vietnam) have registered for the track, while due to many challenges involved, only three (3) groups have successfully submitted ten (10) runs of five methods. To have a comprehensive comparison, seven (7) commonly-used retrieval performance metrics have been used to evaluate their retrieval performance. We also suggest several future research directions for this research topic. We wish this publicly available [ARYLL18] benchmark, comparative evaluation results and corresponding evaluation code, will further enrich and boost the research of 2D scene image-based 3D scene retrieval and its applications.

Traffic flow analysis is essential for intelligent transportation systems. In this paper, we prop... more Traffic flow analysis is essential for intelligent transportation systems. In this paper, we propose methods for two challenging problems in traffic flow analysis: vehicle re-identification and abnormal event detection. For the first problem, we propose to combine learned high-level features for vehicle instance representation with hand-crafted local features for spatial verification. For the second problem, we propose to use multiple adaptive vehicle detectors for anomaly proposal and use heuristics properties extracted from anomaly proposals to determine anomaly events. Experiments on the datasets of traffic flow analysis from AI City Challenge 2019 show that our methods achieve mAP of 0.4008 for vehicle re-identification in Track 2, and can detect abnormal events with very high accuracy (F1 = 0.9429) in Track 3.

Future Data and Security Engineering, 2017

As inertial sensors are low-cost, easy-to-use, and can be integrated in wearable devices, they ca... more As inertial sensors are low-cost, easy-to-use, and can be integrated in wearable devices, they can be used to establish as a new modality for user authentication in the smart environment in which computing systems can recognize persons implicitly by their walking patterns. This motivates our proposal to use multi-region size Convolutional Neural Network to recognize users from their gait patterns recorded from accelerometers and gyroscopes in mobile and wearable devices.

2021 International Conference on Advanced Technologies for Communications (ATC), 2021

In this paper, we study the multipath routing problem in MPTCP-enable software-defined networks. ... more In this paper, we study the multipath routing problem in MPTCP-enable software-defined networks. We have proposed an efficient multipath routing algorithm for creating bandwidth-abundant and flexible future software-defined core networks. Our proposed algorithm will figure out k-least-jointed shortest paths by using conventional k-shortest path algorithm, e.g., Yen's algorithm, incorporating with a greedy algorithm for the path selection. Thanks to that, the algorithm can help to exploit the use of MPTCP while reducing the bottleneck problem that may occur with shared links in the networks. In order to verify the efficiency of our proposal, we have implemented an MPTCP-enable software-defined testbed based on mininet, Ryu controller, and Kali Linux OS and perform numerical experiments to compare the network performance of our approach to traditional MPTCP as well as conventional TCP (without MPTCP). The obtained results confirmed that, by reducing the number of jointed links in the set of selected k-shortest paths, our solution outperforms the traditional MPTCP as well as that of conventional TCP networks.

Proceedings of the American Mathematical Society, 1999

We prove a complex function field analogue of Szpiro’s conjecture for hyperelliptic curves and so... more We prove a complex function field analogue of Szpiro’s conjecture for hyperelliptic curves and some applications. The cases of function fields of positive characteristic and number fields are discussed briefly.