1,721,113 research outputs found
Analysis of asynchronous cognitive radio system with imperfect sensing and bursty primary user traffic
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.This paper presents a theoretical analysis of the spectrum utilization levels in a cognitive radio system. We assume that the traffic of the primary network is bursty and asynchronous with the secondary network, which performs imperfect spectrum sensing. Collisions of the primary and the secondary packets are assumed to result in increased packet error probabilities. We present primary and secondary utilization levels under optimized secondary transmission periods for varying primary traffic characteristics and secondary sensing performance levels. The results are also validated by extensive Monte Carlo simulations. We find that an asynchronous cognitive radio network with imperfect spectrum sensing is feasible when optimized transmission periods are used. The effects of primary traffic’s burst pattern and secondary sensing performance are discussed.European Commission ; TÜBİTAKPost prin
Appearance based occlusion aware 3D object tracking
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.Object tracking is an important element of computer vision algorithms. This problem is difficult due to occlusion, illumination changes and shadows. We propose an appearance based occlusion-aware method for object tracking. Proposed method is based on particle filter tracking in a multi-camera environment. In this method, observations involving both position and appearance information are evaluated depending on whether the corresponding objects are involved in occlusion or not. Tracking is done using state vector in 3D coordinates and probabilities that objects are occluded is estimated to elevate tracking performance. Particles are graded according to their positions and appearances by taking occlusions into account. Weighting particles in terms of position information allows particles to imitate object position and motion. Appearance information help recognize objects after occlusion and track objects when position information is not available. In case of occlusion, particles are weighted according to occlusion probability in order not to make them affected by possibly false measurements. Appearance information is updated by time to account for appearance changes. Appearance is not updated if the object is involved in occlusion. Experiments with PETS and EPFL datasets revealed the success of proposed method and that our method can be applied to different camera configurations
Object tracking in the presence of occlusions using multiple cameras: a sensor network approach
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.This article describes a sensor network approach to tracking a single object in the presence of static and moving occluders using a network of cameras. To conserve communication bandwidth and energy, we combine a task-driven approach with camera subset selection. In the task-driven approach, each camera first performs simple local processing to detect the horizontal position of the object in the image. This information is then sent to a cluster head to track the object. We assume the locations of the static occluders to be known, but only prior statistics on the positions of the moving occluders are available. A noisy perspective camera measurement model is introduced, where occlusions are captured through occlusion indicator functions. An auxiliary particle filter that incorporates the occluder information is used to track the object. The camera subset selection algorithm uses the minimum mean square error of the best linear estimate of the object position as a metric, and tracking is performed using only the selected subset of cameras.Using simulations and preselected subsets of cameras, we investigate (i) the dependency of the tracker performance on the accuracy of the moving occluder priors, (ii) the trade-off between the number of cameras and the occluder prior accuracy required to achieve a prescribed tracker performance, and (iii) the importance of having occluder priors to the tracker performance as the number of occluders increases. We find that computing moving occluder priors may not be worthwhile, unless it can be obtained cheaply and to high accuracy. We also investigate the effect of dynamically selecting the subset of camera nodes used in tracking on the tracking performance. We show through simulations that a greedy selection algorithm performs close to the brute-force method and outperforms other heuristics, and the performance achieved by greedily selecting a small fraction of the cameras is close to that of using all the cameras.DARPA Microsystems Technology Office ; NSF ; CNS ; ARO ; DoD Multidisciplinary University Research Initiative
Bispectrum estimation using a MISO autoregressive model
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.Bispectra are third-order statistics that have been used extensively in analyzing nonlinear and non-Gaussian data. Bispectrum of a process can be computed as the Fourier transform of its bicumulant sequence. It is in general hard to obtain reliable bicumulant samples at high lags since they suffer from large estimation variance. This paper proposes a novel approach for estimating bispectrum from a small set of given low lag bicumulant samples. The proposed approach employs an underlying MISO system composed of stable and causal autoregressive components. We provide an algorithm to compute the parameters of such a system from the given bicumulant samples. Experimental results show that our approach is capable of representing non-polynomial spectra with a stable underlying system model, which results in better bispectrum estimation than the leading algorithm in the literature
On sensor fusion for head tracking in augmented reality applications
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.The paper presents a simple setup consisting of a camera and an accelerometer located on a head mounted display, and investigates the performance of head tracking for augmented reality applications using this setup. The information from the visual and inertial sensors is fused in an extended Kalman filter (EKF) tracker. The performance of treating accelerometer measurements as control inputs is compared to treating both camera and accelerometer measurements as measurements, i.e., fusing them in the measurement update stage of the EKF simultaneously. It is concluded via simulations that treating accelerometer measurements as control inputs performs practically as good as treating both measurements as measurements, while providing a lower complexity tracker.TÜBİTA
On the feasibility of synchronous, retransmission-based cognitive networks
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.This paper presents a new, joint Markov chain model for the primary and secondary traffic in a cognitive radio network to assess the feasibility of opportunistic spectrum access for different operational scenarios, validated by extensive realistic Monte-Carlo simulations. Enhancing on the current literature, the generalized model allows for the consideration of a retransmission-based traffic as well as the presence of transmission queues for the primary user. The generalized model also allows for the investigation of the system behavior in the presence of imperfect sensing at the secondary users. The presented model is applicable for all primary user traffic models that use discrete-time Markov chains. An infinitely backlogged secondary user network is considered to investigate the maximum possible network utilization. The paper concludes that a percentile channel occupation of the primary user on its own is not always a sufficient metric to assess whether secondary transmission is feasible. It is shown that the interplay between primary network traffic characteristics, as well as the sensing frequency and probabilities of missed detection and false alarm for the secondary network detector are of primal importance for such feasibility claims. The results reveal that the preferred sensing frequency for the secondary users is a function of the primary user traffic pattern and that the commonly used frequency of sensing every transmission slot is not always optimal. The results also show that when sensing frequency is low, secondary utilization decreases with more bursty primary traffic, whereas the impact on primary utilization becomes less.TÜBİTA
Energy sensing strategy optimization for opportunistic spectrum access
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.This paper introduces a correlator-based energy sensing strategy for opportunistic spectrum access in a slow, flat-fading channel. The correlator provides weighted energy accumulation in time. We assume that the noise variance is known and the primary user (PU) traffic follows a two state Markov chain with known idle and busy rates. Using Chebyshev bounds on missed detection and false alarm probabilities, we find that the optimal weighting function is an increasing function of time and its shape is dependent on the PU traffic characteristics and SNR. We show that the traditional flat-integration based energy collection method is suboptimal both in the error probability and channel utilization sense.European Commission ; Argela Technologies ; Türk Teleko
Effect of camera-IMU displacement calibration error on tracking performance
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.Due to their complementary properties, inertial measurement units (IMU) and cameras are used in ego-motion tracking applications. For this, the relative rotation and displacement between the camera and IMU reference frames has to be known. There are established methods for the accurate estimation of the relative orientation, however, accurate estimation of the displacement is still a challenging problem. When this is not possible, one might resort to the alternative approach of fusing camera and gyroscope data only, as this does not require the displacement information. To be able to asses such alternatives, this paper presents a systematic methodology based on realistic simulations to analyze the effect of the camera - IMU displacement calibration error on tracking performance, and discusses in detailed simulations the dependency of tracker performance metrics on the camera - IMU displacement's magnitude and calibration error.TÜBİTA
Recovery of temporal synchronization error through online 3D tracking with two cameras
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.Multiple object tracking within a network of cameras with overlapping fields of views has gained interest. The acquisition of images in an asynchronous manner hinders the practical implementation of such systems. Most of the previous work reported tests over short intervals, leaving the performance degradation due to asynchronous image acquisition unknown. In this work, we propose an online method to recover the synchronization error while tracking objects. The recovered error is fed back to trackers so as to restore their performance. The time synchronization error is measured by the mismatch in the epipolar constraint between the two cameras. We show that successful recovery of the synchronization error is possible when its product with the object motion speeds are within some limits
Rf energy harvesting and transfer for spectrum sharing cellular iot communications in 5g systems
No full textThis paper proposes an energy and spectrum efficient IoT network for 5G systems where spectrum is shared with the cellular system for spectrum efficiency and energy harvesting and energy transfer are utilized for energy efficiency. The IoT network, which consists of sensor nodes and a cluster head with a reliable energy source, reuses part of the cellular band whenever the cellular network does not utilize it. The cluster head performs spectrum sensing, random scheduling of the sensor nodes, and schedules some idle time for energy transfer. The sensor nodes harvest RF energy from the cellular traffic and the transferred energy from the cluster head. Provided the sensor nodes have sufficient energy, they transmit collected sensory data when scheduled. The inter-play between the cellular and IoT network introduces trade-offs between the spectrum availability, energy availability, information and energy transfer. This paper shows that for the same cellular traffic level, as the number of sensor nodes in the network increases, the IoT network utilization increases resulting in a multi-user gain thanks to the broadcast nature of the energy transfer. The results offer insights into different operational regimes and exposes what type of IoT applications may be feasible with such networks.TÜBİTAK ; Argela of Argela 5G Center of Excellence Collaborations Progra
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