In the cases of severe paralysis in which the ability to control the body movements of a person is limited to the muscles around the eyes, eye movements or blinks are the only way for the person to communicate. Interfaces that assist in such communications often require More
In the cases of severe paralysis in which the ability to control the body movements of a person is limited to the muscles around the eyes, eye movements or blinks are the only way for the person to communicate. Interfaces that assist in such communications often require special hardware or reliance on active infrared illumination. In this paper, we propose a non-intrusive algorithm for eye gaze estimation that works with video input from an inexpensive camera and without special lighting. The main contribution of this paper is proposing a new geometrical model for eye region that only requires the image of one iris for gaze estimation. Essential parameters for this system are the best fitted ellipse of the iris and the pupil center. The algorithms used for both iris ellipse fitting and pupil center localization pose no pre-assumptions on the head pose. All in all, the achievement of this paper is the robustness of the proposed system to the head pose variations. The performance of the method has been evaluated on both synthetic and real images leading to errors of 2.12 and 3.48 degrees, respectively.
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This paper presents an automatic sound source localization approach based on combination of the basic time delay estimation sub method namely, Time Difference of Arrival (TDOA), and Steered Response Power (SRP) methods. The TDOA method is a fast but vulnerable approach More
This paper presents an automatic sound source localization approach based on combination of the basic time delay estimation sub method namely, Time Difference of Arrival (TDOA), and Steered Response Power (SRP) methods. The TDOA method is a fast but vulnerable approach to find sound source location in long distances and reverberant environments and so sensitive in noisy situations, on the other hand the conventional SRP method is time consuming but successful approach to accurately find sound source location in noisy and reverberant environment. Also another SRP based method namely SRP Phase Transform (SRP-PHAT) has been suggested for better noise robustness and more accuracy of sound source localization. In this paper, based on the combination of TDOA and SRP based methods, two approaches proposed for sound source localization. In the first proposed approach which is named Classical TDOA-SRP, the TDOA method is used to find approximate sound source direction and then SRP based methods used to find the accurate location of sound source in the Field of View (FOV) which is obtained through the TDOA method. In the second proposed approach which is named Optimal TDOA-SRP, for more reduction of computational time of processing of SRP based methods and better noise robustness, a new criteria has been proposed to find the effective FOV which is obtained through the TDOA method. Experiments carried out under different conditions confirm the validity of the purposed approaches.
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A closed-form solution for target localization based on the realistic distance-dependent noises in illuminator of opportunity passive radar and the reduction method of the bias which exists in the two-stage weighted least squares (2SWLS) method is proposed. 2SWLS is a c More
A closed-form solution for target localization based on the realistic distance-dependent noises in illuminator of opportunity passive radar and the reduction method of the bias which exists in the two-stage weighted least squares (2SWLS) method is proposed. 2SWLS is a classic method for time-of-arrival (TOA) and frequency-of-arrival (FOA) localization problem and has a couple of improved solutions over the years. The 2SWLS and its improved solutions have great localization performances in their established location scenarios on the basis of two approximations that setting the noise to a constant and ignoring the high-order terms of TOA and FOA measurement noises. It is these two approximations that lead to a sub-optimal solution with bias. The bias of 2SWLS has a significant influence on the target localization in illuminator of opportunity passive radar that has lower measurement accuracy and higher noises than active radar. Therefore, this paper starts by taking into consideration of the realistic distance-dependent characteristics of TOA/-FOA noises and improving 2SWLS method. Then, the bias of the improved 2SWLS method is analyzed and bias-reduced solution based on weighted least squares (WLS) is developed. Numerical simulations demonstrate that, compared to the existing improved solutions of the 2SWLS, the proposed method effectively reduces the bias and achieves higher localization accuracy.
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In this paper, the problem of joint energy efficient spectrum sensing and determining the mobile primary user location is proposed based on compressive sensing in cognitive sensor networks. By utilizing compressive sensing, the ratio of measurements for the sensing node More
In this paper, the problem of joint energy efficient spectrum sensing and determining the mobile primary user location is proposed based on compressive sensing in cognitive sensor networks. By utilizing compressive sensing, the ratio of measurements for the sensing nodes are considerably reduced. Therefore, energy consumption is improved significantly in spectrum sensing. The multi-antenna sensors is also considered to save more energy. On the other hand, multi-antenna sensor utilization is a proper solution instead of applying more sensors. The problem is formulated to maximize the network lifetime and find the mobile primary user position by sensors selection under the detection performance and accuracy of localization constraints. For this purpose, a cooperative game is proposed to study this problem. It is shown that with the proposed game, the network lifetime is maximized while the proper sensors which participate in spectrum sensing and primary user localization are determined. Simulation results show that the network lifetime is improved while the detection performance constraint is satisfied and the location of the primary user is determined with high accuracy.
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The Localization is the core element in Wireless Sensor Network WSN, especially for those nodes without GPS or BDS; leaning towards improvement, based on its effective and increased use in the past decade. Localization methods are thus very important for estimating the More
The Localization is the core element in Wireless Sensor Network WSN, especially for those nodes without GPS or BDS; leaning towards improvement, based on its effective and increased use in the past decade. Localization methods are thus very important for estimating the position of relative nodes in the network allowing a better and effective network for increasing the efficiency and thus increasing the lifeline of the network. Determining the current limitations in FA that are applied for solving different optimization problems is poor exploitation capability when the randomization factor is taken large during firefly changing position. This poor exploitation may lead to skip the most optimal solution even present in the vicinity of the current solution which results in poor local convergence rate that ultimately degrades the solution quality. This paper presents GEFIR (GenFire) algorithm to calculate position of unknown nodes for the fishermen in the ocean. The proposed approach calculates the position of unknown nodes, the proposed method effectively selects the anchor node in the cluster head to reduce the energy dissipation. Major benefits over other similar localization algorithms are a better positioning of nodes is provided and average localization error is reduced which eventually leads to better efficiency thus optimize the lifetime of the network for sailors. The obtained results depict that the proposed model surpasses the previous generation of localization algorithm in terms of energy dispersion and location estimation which is suitable for fishermen on the ocean bed.
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