Fahimeh Farahnakian

University of Turku | UTU · Department of Information Technology

Dear Colleagues, Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications such as autonomous systems, remote sensing, video surveillance and military. This Special Issue aims to explore the developments in the field of mult...

Object detection as the main task of computer vision aims at locating and classifying interest objects in a scene. Most existing object detection methods utilize RGB images that are captured by cameras. However, RGB images cannot directly provide depth information that would help an object detector to achieve better performance in a complex environ...

Object detection is a fundamental computer vision task for many real-world applications. In the maritime environment, this task is challenging due to varying light, view distances, weather conditions, and sea waves. In addition, light reflection, camera motion and illumination changes may cause to false detections. To address this challenge, we pre...

Image fusion methods have gained a lot of attraction over the past few years in the field of sensor fusion. An efficient image fusion approach can obtain complementary information from various multi-modality images. In addition, the fused image is more robust to imperfect conditions such as mis-registration and noise. The aim of this paper is to ex...

Robust real-time object detection and tracking are challenging problems in autonomous transportation systems due to operation of algorithms in inherently uncertain and dynamic environments and rapid movement of objects. Therefore, tracking and detection algorithms must cooperate with each other to achieve smooth tracking of detected objects that la...

Identification of network attacks is a matter of great concern for network operators due to extensive the number of vulnerabilities in computer systems and creativity of the attackers. Anomaly-based Intrusion Detection Systems (IDSs) present a significant opportunity to identify possible incidents, logging information and reporting attempts. Howeve...

Resource management in cloud infrastructures is one of the most challenging problems due to the heterogeneity of resources, variability of the workload and scale of data centers. Efficient management of physical and virtual resources can be achieved considering performance requirements of hosted applications and infrastructure costs. In this paper,...

Dynamic Virtual Machine (VM) consolidation is one of the most promising solutions to reduce energy consumption and improve resource utilization in data centers. Since VM consolidation problem is strictly NP-hard, many heuristic algorithms have been proposed to tackle the problem. However, most of the existing works deal only with minimizing the num...

High energy consumption of cloud data centers is a matter of great concern. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy in data centers. A VM consolidation approach uses live migration of VMs so that some of the under-loaded Physical Machines (PMs) can be switched-off or put into a low-power mod...

Efficient energy use has become a critical issue for designing and managing of cloud data centers. Virtualization is a key technology for reducing energy cost and improving resource utilization in data centers. One of the challenges faced by virtualized data centers is to decide how to pack VMs on the least number of physical machines. This paper p...

As the scale of cloud data centers becomes larger and larger, the energy consumption of data centers also grows rapidly. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy by turning off idle or under-utilized Physical Machines (PMs) in data centers. In this paper, we present a multi-agent based archit...

In order to resource management in a large-scale data center, we present a hierarchical agent-based architecture. In this architecture, multi agents cooperate together to minimize the number of active physical machines according to the current resource requirements. We proposed a local agent in each physical machine (PM) to determine the PM’s statu...

As the scale of a cloud data center becomes larger and larger, the energy consumption of the data center also grows rapidly. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy by turning off unused Physical Machines (PMs) in data centers. In this paper, we present a distributed controller to perform dy...

Adaptive routing algorithms improve network performance by distributing traffic over the whole network. However, they require congestion information to facilitate load balancing. To provide local and global congestion information, we propose a learning method based on dual reinforcement learning approach. This information can be dynamically updated...

Dynamic consolidation techniques optimize resource utilization and reduce energy consumption in Cloud data centers. They should consider the variability of the workload to decide when idle or underutilized hosts switch to sleep mode in order to minimize energy consumption. In this paper, we propose a Reinforcement Learning-based Dynamic Consolidati...

As energy consumption of ICT infrastructures has increased considerably in the recent years, the research community and companies are working on energy-aware resource management strategies. In order to reduce energy cost, we propose a dynamic virtual machine consolidation algorithm to minimize the number of active physical servers on a data center....

Virtualization is a vital technology of cloud computing which enables the partition of a physical host into several Virtual Machines (VMs). The number of active hosts can be reduced according to the resources requirements using live migration in order to minimize the power consumption in this technology. However, the Service Level Agreement (SLA) i...

Network congestion has a negative impact on the performance of on-chip networks due to the increased packet latency. Many congestion-aware routing algorithms have been developed to alleviate traffic congestion over the network. In this paper, we propose a congestion-aware routing algorithm based on the Q-learning approach for avoiding congested are...

Many adaptive routing protocols have been developed for Networks -on-Chip to improve the network performance by traffic reduction. In this paper, we present an adaptive routing algorithm based upon the Q-routing, which distributes traffic by a learning method in the entire network. The learning method utilizes local and global traffic information a...

In this paper, we propose a congestion-aware routing algorithm based on Dual Reinforcement Q-routing. In this method, local and global congestion information of the network is provided for each router, utilizing learning packets. This information should be dynamically updated according to the changing traffic conditions in the network. For this pur...

The occurrence of congestion in on-chip networks can severely degrade the performance due to increased message latency. In mesh topology, minimal methods can propagate messages over two directions at each switch. When shortest paths are congested, sending more messages through them can deteriorate the congestion condition considerably. In this pape...

Network congestion can limit performance of NoC due to increased transmission latency and power consumption. Congestion-aware adaptive routing can greatly improve the network performance by balancing the traffic load over the network. In this paper, we present a reinforcement learning method, Q-learning, for NoC to alleviate congestion in the netwo...

Since the quality of data affects the success rate ofdata mining and learning algorithms, it is always attempted to identify and remove the irrelevant and redundant information in a dataset. Robotic soccer is a multi-agent system in which agents play in real-time, dynamic, complex and noisy environment. Many parameters affect the result of shooting...

Recently the reinforcement learning method is actively used in multi-agent systems. Because of this method played a significant role by handling the inherent complexity of such systems. Robotic soccer is a multi-agent system in which agents play in real-time, dynamic, complex and unknown environment. Since the main purpose of a soccer game is to sc...

The robotic soccer is one of the most complex multiagent systems in which agents play the role of soccer players. The characteristics of such systems are: realtime, noisy, collaborative and adversarial. Therefore, playing agents must be capable to making decisions. This paper describes the use of decision tree to kick and catch the ball for two sim...