Amirhossein Shantia

Amirhossein ShantiaUniversity of Groningen | RUG · Institute of Cognitive Engineering and Artificial Intelligence

1.45
· Master of Science
  • About
    Introduction
    Amirhossein Shantia currently works at the Institute of Cognitive Engineering and Artificial Intelligence, University of Groningen. Amirhossein does research in Artificial Intelligence and Artificial Neural Network. Their current project is 'PhD project: Continuous learning in robot navigation using virtual categorization and reinforcement learning, including robotic arm.'
    Current Institution
    Institute of Cognitive Engineering and Artificial Intelligence
    Groningen
    Current position
    Lecturer, and PhD student
    13
    Research items
    849
    Reads
    33
    Citations
    Research Experience
    Dec 2011 - Jan 2017
    Lecturer, and PhD student
    University of Groningen · Institute of Cognitive Engineering and Artificial Intelligence
    Groningen, Netherlands
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    Lambert Schomaker
    Thomas Kopinski
    Samineh Bagheri
    Deo Prakash
    Olarik Surinta
    Kristian Dilov
    Parth Tiwary
    Pourya Shahverdi
    Amirhosein Toosi
    Bowornrat Sriman
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    Lambert Schomaker
    Mahmood Fathy
    Marco A. Wiering
    Ahmad Khonsari
    Tijn van der Zant
    Bart Verheij
    Thomas Kopinski
    Samineh Bagheri
    Francesco Bidoia
    Matthia Sabatelli
    Awards & Achievements (1)
    Dec 2016
    Best Paper Award, SSCI Conference 2016
    Award
    Projects
    Projects (1)
    Project
    http://www.rug.nl/research/alice/autonomus-perceptive-systems/research-and-projects/phd-project_-continuous-learning-in-robot-navigation-using-virtual-categorization-and-reinforcement-learning?lang=en
    Research
    Research Items (13)
    In this paper we propose a new approach to Deep Neural Networks (DNNs) based on the particular needs of navigation tasks. To investigate these needs we created a labelled image dataset of a test environment and we compare classical computer vision approaches with the state of the art in image classification. Based on these results we have developed a new DNN architecture that outperforms previous architectures in recognizing locations, relying on the geometrical features of the images. In particular we show the negative effects of scale, rotation, and position invariance properties of the current state of the art DNNs on the task. We finally show the results of our proposed architecture that preserves the geometrical properties. Our experiments show that our method outperforms the state of the art image classification networks in recognizing locations.
    In this paper we propose a new approach to Deep Neural Networks (DNNs) based on the particular needs of navigation tasks. To investigate these needs we created a labeled image dataset of a test environment and we compare classical computer vision approaches with the state of the art in image classification. Based on these results we have developed a new DNN architecture that outperforms previous architectures in recognizing locations, relying on the geometrical features of the images. In particular we show the negative effects of scale, rotation, and position invariance properties of the current state of the art DNNs on the task. We finally show the results of our proposed architecture that preserves the geometrical properties. Our experiments show that our method outperforms the state of the art image classification networks in recognizing locations.
    A robot's local navigation is often done through forward simulation of robot velocities and measuring the possible trajectories against safety, distance to the final goal and the generated path of a global path planner. Then, the computed velocities vector for the winning trajectory is executed on the robot. This process is done continuously through the whole navigation process and requires an extensive amount of processing. This only allows for a very limited sampling space. In this paper, we propose a novel approach to automatically detect the type of surrounding environment based on navigation complexity using unsupervised clustering, and limit the local controller's sampling space. The experimental results in 3D simulation and using a real mobile robot show that we can increase the navigation performance by at least thirty percent while reducing the number of failures due to collision or lack of sampling.
    Robotic mapping and localization methods are mostly dominated by using a combination of spatial alignment of sensory inputs, loop closure detection, and a global fine-tuning step. This requires either expensive depth sensing systems, or fast computational hardware at run-time to produce a 2D or 3D map of the environment. In a similar context, deep neural networks are used extensively in scene recognition applications, but are not yet applied to localization and mapping problems. In this paper, we adopt a novel approach by using denoising autoencoders and image information for tackling robot localization problems. We use semi-supervised learning with location values that are provided by traditional mapping methods. After training, our method requires much less run-time computations, and therefore can perform real-time localization on normal processing units. We compare the effects of different feature vectors such as plain images, the scale invariant feature transform and histograms of oriented gradients on the localization precision. The best system can localize with an average positional error of ten centimeters and an angular error of four degrees in 3D simulation.
    Recognizing the semantic content of an image is a challenging problem in computer vision. Many researchers attempt to apply local image descriptors to extract features from an image, but choosing the best type of feature to use is still an open problem. Some of these systems are only trained once using a fixed descriptor, like the Scale Invariant Feature Transform (SIFT). In most cases these algorithms show good performance, but they do not learn from their mistakes once training is completed. In this paper a continuous deep neural network feedback system is proposed which consists of an adaptive neural network feature descriptor, the bag of visual words approach and a neural classifier. Two initialization methods for the neural network feature descriptor were compared, one where it was trained on SIFT descriptor output and one where it was randomly initialized. After initial training, the system propagates the classification error from the neural network classifier through the entire pipeline, updating not only the classifier itself, but also the type of features to extract. Results show that for both initialization methods the feedback system increased accuracy substantially when regular training was not able to increase it any further. The proposed neural-SIFT feature descriptor performs better than the SIFT descriptor itself even with a limited number of training instances. Initializing on an existing feature descriptor is beneficial when not a lot of training samples are available. However, when there are a lot of training samples the system is able to construct a well-performing descriptor, solely based on classifier feedback.
    Real Time Strategy Games are one of the most popular game schemes in PC markets and offer a dynamic environment that involves several interacting agents. The core strategies that need to be developed in these games are unit micro management, building order, resource management, and the game main tactic. Unfortunately, current games only use scripted and fixed behaviors for their artificial intelligence (AI), and the player can easily learn the counter measures to defeat the AI. In this paper, we describe a system based on neural networks that controls a set of units of the same type in the popular game StarCraft. Using the neural networks, the units will either choose a unit to attack or evade from the battlefield. The system uses reinforcement learning combined with neural networks using online Sarsa and neural-fitted Sarsa, both with a short term memory reward function. We also present an incremental learning method for training the units for larger scenarios involving more units using trained neural networks on smaller scenarios. Additionally, we developed a novel sensing system to feed the environment data to the neural networks using separate vision grids. The simulation results show superior performance against the human-made AI scripts in StarCraft.
    High performance, reliability, transient and permanentfault-tolerance, and low energy consumption are majorobjectives of Networks-on-Chip (NoCs). Since,different applications impose various communicationrequirements in NoCs, a number of research studieshave revealed that the performance advantages ofrouting schemes are more noticeable on powerconsumption under different traffic patterns. However,the power consumption issues of NoCs have not beenthoroughly investigated in the presence of faultyregions. To the best of our knowledge, this research isthe first attempt to examine the effects of most populartraffic patterns (i.e., Uniform, Local, and Hot-Spot) onpower consumption of NoCs in the presence ofpermanent faults.
    Fault-tolerance and network routing have been among the most widely studied topics in the research of parallel processing and computer networking. A fault- tolerant routing algorithm should guarantee the delivery of messages in the presence of faulty components. In this paper, we present a comparative performance study of nine prominent fault-tolerant routings in 2D wormhole-switched tori. These networks carry the software-based routing scheme which has been suggested as an instance of a fault-tolerant method widely used in the literature to achieve high adaptivity and support inter-processor communications in parallel computer networks due to its ability to preserve both communication performance and fault-tolerant demands in such systems. The performance measures studied are the throughput, average message latency, power, and average usage of virtual channels per node. Results obtained through simulation suggest two classes of presented routing schemes as high performance candidates in most faulty networks.
    One of the fundamental problems in parallel computing is how to efficiently perform routing in a faulty network each component of which fails with some probability. This paper presents a comparative performance study of ten prominent adaptive fault- tolerant routing algorithms in wormhole-switched 2-D mesh interconnect networks. These networks carry a routing scheme suggested by Boppana and Chalasani (1) as an instance of a fault-tolerant method. The suggested scheme is widely used in the literature to achieve high adaptivity and support inter-processor communications in parallel computer systems due to its ability to preserve both communication performance and fault-tolerant demands in these networks. The performance measures studied are the throughput, average message latency and average usage of virtual channels per node. Results obtained through simulation suggest two classes of presented routing schemes as high performance candidate in most faulty networks.
    One of the fundamental problems in parallel computing is how to efficiently perform routing in a faulty network each component of which fails with some probability. This paper presents a comparative performance study of ten prominent adaptive fault-tolerant routing algorithms in wormhole-switched 2-D mesh interconnect networks. These networks carry a routing scheme suggested by Boppana and Chalasani [1] as an instance of a fault-tolerant method. The suggested scheme is widely used in the literature to achieve high adaptivity and support inter-processor communications in parallel computer systems due to its ability to preserve both communication performance and fault-tolerant demands in these networks. The performance measures studied are the throughput, average message latency and average usage of virtual channels per node. Results obtained through simulation suggest two classes of presented routing schemes as high performance candidate in most faulty networks.
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