Tuning the Solubility of Copper Complex in Atom Transfer Radical Self-Condensing Vinyl Polymerizations to Control Polymer Topology via One-Pot to the Synthesis of Hyperbranched Core Star Polymers

In this paper, we propose a simple one-pot methodology for proceeding from atom transfer reaction-induced conventional free radical polymerization (AT-FRP) to atom transfer self-condensing vinyl polymerization (AT-SCVP) through manipulation of the catalyst phase homogeneity (i.e., CuBr/2,2'-bipyridine (CuBr/Bpy)) in a mixture of styrene (St), 4-vinyl benzyl chloride (VBC), and ethyl 2-bromoisobutyrate. Tests of the solubilities of CuBr/Bpy and CuBr2/Bpy under various conditions revealed that both temperature and solvent polarity were factors affecting the solubility of these copper complexes. Accordingly, we obtained different polymer topologies when performing AT-SCVP in different single solvents. We investigated two different strategies to control the polymer topology in one-pot: varying temperature and varying solvent polarity. In both cases, different fractions of branching revealed the efficacy of varying the polymer topology. To diversify the functionality of the peripheral space, we performed chain extensions of the resulting hyperbranched poly(St-co-VBC) macroinitiator (name as: hbPSt MI) with either St or tBA (tert-butyl acrylate). The resulting hyperbranched core star polymer had high molecular weights (hbPSt-g-PSt: Mn = 25,000, Đ = 1.77; hbPSt-g-PtBA: Mn = 27,000, Đ = 1.98); hydrolysis of the tert-butyl groups of the later provided a hyperbranched core star polymer featuring hydrophilic poly(acrylic acid) segments

Computation-aware intra-mode decision for H.264 coding and transcoding

[[abstract]]been equipped with modern video codecs. Video communications, especially for encoding H.264 format bit-stream, however, are usually very power-consuming, leading to rather limited communication period for mobile devices powered by batteries. Computation-aware video coding can effectively extend the battery life. In this paper, we propose a computation-aware intra mode decision for H.264 coding and transcoding applications. The proposed algorithm optimizes the visual quality by adaptively adjusting the number of prediction modes in mode decision under a given computation constraint. We introduce a new concept of computation buffer and formulate the computation control of mode decision as a rate-distortion optimization problem of computation buffer control. Experimental results show that our proposed algorithm can effectively control the computational complexity while maintaining good RD-performance and satisfying the given computation constraint.[[fileno]]2030144030046[[department]]電機工程學

Rapid identification of Mycobacterium tuberculosis infection by a new array format-based surface plasmon resonance method

Tubercle bacillus [TB] is one of the most important chronic infectious diseases that cause millions of deaths annually. While conventional smear microscopy and culture methods are widely used for diagnosis of TB, the former is insensitive, and the latter takes up to 6 to 8 weeks to provide a result, limiting the value of these methods in aiding diagnosis and intermediate decisions on treatment. Therefore, a rapid detection method is essential for the diagnosis, prognosis assessment, and recurrence monitoring. A new surface plasmon resonance [SPR] biosensor based on an array format, which allowed immobilizing nine TB antigens onto the sensor chip, was constructed. Simultaneous determination of multiple TB antibodies in serum had been accomplished with this array-based SPR system. The results were compared with enzyme-linked immunosorbent assay, a conventional immunological method. Array-based SPR showed more advantages in providing label-free and real-time detection. Additionally, the high sensitivity and specificity for the detection of TB infection showed its potential for future development of biosensor arrays for TB diagnosis

Low-rank matrix recovery with structural incoherence for robust face recognition

We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standard face recog-nition algorithms such as Eigenfaces to recently proposed sparse representation-based classification (SRC) methods, most prior works did not consider possible contamination of data during training, and thus the associated performance might be degraded. Based on the recent success of low-rank matrix recovery, we propose a novel low-rank matrix ap-proximation algorithm with structural incoherence for ro-bust face recognition. Our method not only decomposes raw training data into a set of representative basis with corre-sponding sparse errors for better modeling the face images, we further advocate the structural incoherence between the basis learned from different classes. These basis are en-couraged to be as independent as possible due to the regu-larization on structural incoherence. We show that this pro-vides additional discriminating ability to the original low-rank models for improved performance. Experimental re-sults on public face databases verify the effectiveness and robustness of our method, which is also shown to outper-form state-of-the-art SRC based approaches. 1