![]() Step 3: Browse the Content of a Leanback-Enabled Android App.Step 2: Understand the Anatomy of a Leanback-Enabled Android App for Amazon Fire TV.How to Build an Android App for Fire TV.Deep Linking to Featured Content from the Fire TV UI.Introduction to Linear Television Integration.AV Synchronization in Android Applications.Voice-enabling Transport Controls with Media Session API.Overview for Voice-enabling Your App and Content.Developing for Amazon Fire TV Devices Running Fire OS 7.Developing for Amazon Fire TV Devices Running Fire OS 6.Fire TV Streaming Media Player Specifications. ![]() Submitting Your Fire TV App to the Appstore.Fire TV Development Versus Android TV Development.Fire App Builder: A Toolkit for Building Fire TV Apps.
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![]() A multi-view approach based on the likelihood ratio tests (LRT) detector was proposed in and its adaptive counterpart was presented in for effective detection of low-signature targets in the presence of rough surface clutter. A real-time three-dimensional (3-D) model of the rough surface scattering was proposed in, which can be subtracted from the FL-GPR measurements to reduce the clutter. In, localized clutter outside of the region of interest was suppressed prior to sparse reconstruction. Coherent integration of measurements corresponding to multiple radar platform positions was demonstrated in for rough surface clutter suppression, whereas a nonlinear combining approach that exploits a similarity measure was developed in to adaptively mitigate imaging artifacts. Frequency subband processing was exploited in to obtain the best contrast between target and clutter signals, whereas recursive side-lobe minimization algorithm for reconstructing FL-GPR images with reduced clutter was proposed in. In, an ambiguity function based detector was proposed which exploits time-frequency characterization of target and clutter scattering for performance enhancement. Rough surface clutter suppression for array-based FL-GPR imaging was addressed in. |
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