Information reliability in complex multitask networks

The emergence of distributed and complex networks has altered the field of information and data processing in the past few years. In distributed networks, the connected neighboring nodes can cooperate and share information with each other in order to solve particular tasks. However, in many applications the agents might be reluctant to share their true data with all their neighbors due to privacy and security constraints. In this paper, we study the performance of multitask distributed networks where sharing genuine information is subject to a cost. We formulate an information credibility model which results in the probability of sharing genuine information at each time instant according to the cost. Each agent then shares its true information with only a subset of its neighbors while sending fabricated data to the rest according to this probability. This behavior can affect the performance of the whole network in an adverse manner especially in cases where the cost is high. To overcome this problem, we propose an adaptive reputation protocol which enables the agents to evaluate the behavior of their neighbors over time and select the most reputable subset of neighbors to share genuine information with. We provide an extensive simulation-based analysis to compare the performance of the proposed method with several other distributed learning strategies. The results show that the proposed method outperforms the other learning strategies and enables the network to have a superior performance especially when the cost of sharing genuine information is high

Cooperative particle filtering for tracking ERP subcomponents from multichannel EEG

In this study, we propose a novel method to investigate P300 variability over different trials. The method incorporates spatial correlation between EEG channels to form a cooperative coupled particle filtering method that tracks the P300 subcomponents, P3a and P3b, over trials. Using state space systems, the amplitude, latency, and width of each subcomponent are modeled as the main underlying parameters. With four electrodes, two coupled Rao-Blackwellised particle filter pairs are used to recursively estimate the system state over trials. A number of physiological constraints are also imposed to avoid generating invalid particles in the estimation process. Motivated by the bilateral symmetry of ERPs over the brain, the channels further share their estimates with their neighbors and combine the received information to obtain a more accurate and robust solution. The proposed algorithm is capable of estimating the P300 subcomponents in single trials and outperforms its non-cooperative counterpart

Development of an application for indoor navigation and deviation detection

LAUREA MAGISTRAL

Slides with English text that are explained in Persian

The common pattern of presentation in the Iranian medical community is lengthy English text in slides that are presented orally in Farsi, both in conferences and classrooms. In this paper, we aim to further explore this phenomenon based on a theory in the domain of cognitive science named the cognitive load theory (CLT). According to Atkinson and Shiffrin's model introduced in 1968, human memory consists of three parts: sensory memory, working memory, and long-term memory. Information first enters the sensory memory, and if received adequate attention and reaches the level of consciousness, it enters the working memory, which, unlike the other two memories, i.e, sensory and long-term memory, has a limited capacity (1). Interestingly, working memory has two separate and independent channels for processing visual and auditory information with a limited and predetermined capacity (dual-channel theory). As a result, the speed of learning in humans restricts (2). In 1988, Sweller proposed a theory of learning called the CLT, in which the three key components of the cognitive structure, i.e. memory systems, learning processes, and types of the cognitive load imposed on the working memory, were merged. According to this theory, because of the limited capacity of the working memory, any factor that imposes an excessive load on this memory will disrupt the learning process (2). Here three types of loads are introduced: 1. Intrinsic load is related to the task. The more complex the information that must be processed by the working memory, the greater the load imposes. – Cont