10,440 research outputs found

    An overview of data acquisition, signal coding and data analysis techniques for MST radars

    Get PDF
    An overview is given of the data acquisition, signal processing, and data analysis techniques that are currently in use with high power MST/ST (mesosphere stratosphere troposphere/stratosphere troposphere) radars. This review supplements the works of Rastogi (1983) and Farley (1984) presented at previous MAP workshops. A general description is given of data acquisition and signal processing operations and they are characterized on the basis of their disparate time scales. Then signal coding, a brief description of frequently used codes, and their limitations are discussed, and finally, several aspects of statistical data processing such as signal statistics, power spectrum and autocovariance analysis, outlier removal techniques are discussed

    Frequency Analysis of Gradient Estimators in Volume Rendering

    Get PDF
    Gradient information is used in volume rendering to classify and color samples along a ray. In this paper, we present an analysis of the theoretically ideal gradient estimator and compare it to some commonly used gradient estimators. A new method is presented to calculate the gradient at arbitrary sample positions, using the derivative of the interpolation filter as the basis for the new gradient filter. As an example, we will discuss the use of the derivative of the cubic spline. Comparisons with several other methods are demonstrated. Computational efficiency can be realized since parts of the interpolation computation can be leveraged in the gradient estimatio

    EC-GSM-IoT Network Synchronization with Support for Large Frequency Offsets

    Full text link
    EDGE-based EC-GSM-IoT is a promising candidate for the billion-device cellular IoT (cIoT), providing similar coverage and battery life as NB-IoT. The goal of 20 dB coverage extension compared to EDGE poses significant challenges for the initial network synchronization, which has to be performed well below the thermal noise floor, down to an SNR of -8.5 dB. We present a low-complexity synchronization algorithm supporting up to 50 kHz initial frequency offset, thus enabling the use of a low-cost +/-25 ppm oscillator. The proposed algorithm does not only fulfill the 3GPP requirements, but surpasses them by 3 dB, enabling communication with an SNR of -11.5 dB or a maximum coupling loss of up to 170.5 dB.Comment: Wireless Communications and Networking Conference (WCNC), 201

    Good Features to Correlate for Visual Tracking

    Full text link
    During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin
    • …
    corecore