Chien Van Trinh

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Compressive Sensing (CS) is an emerging new sampling technique which helps to break through the Nyquist sampling frequency for sparse signals. This paper addresses improving one of its recovery algorithms known as the Block Compressive Sensing with Smooth Projected Landweber (BCS-SPL). For reducing the blocking artifacts in BCS-SPL, the Wiener filter has(More)
Block-based compressive sensing is attractive for sensing natural images and video because it makes large-sized image/video tractable. However, its reconstruction performance is yet to be improved much. This paper proposes a new block-based compressive video sensing recovery scheme which can reconstruct video sequences with high quality. It generates(More)
Due to unlimited increase of cars and transportation systems, a real time embedded system called Automatic Number Plate Recognition (ANPR) is very important for humans to detect and manage. This paper presents results of developing and deploying an ANPR applied to electronic tolling collection (ETC) systems in Vietnam with some special issues. Our model is(More)
Compressive Sensing (CS) is a novel sampling framework which is more efficient than the Nyquist sampling for sparse signals. A major challenge in CS is its quality improvement of recovered signal when noise exists. To reduce noise in the recovered images, filters are usually employed. This paper focuses on improving the quality of CS recoveries by applying(More)
In this paper, we present an implementation of a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system based on the multi-core Texas Instrument (TI) C64x+ digital signal processor (DSP). The system is implemented by employing real time data exchange (RTDX) and serial rapid input/output (SRIO) techniques for(More)
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