A new and facile approach to selectively functionalize the internal and external surfaces of porous silicon (pSi) for drug delivery applications is reported. To provide a surface that is suitable for sustained drug release of the hydrophobic cancer chemotherapy drug camptothecin (CPT), the internal surfaces of pSi films were first modified with 1-dodecene. To further modify the external surface of the pSi samples, an interlayer was applied by silanization with (3-aminopropyl)triethoxysilane (APTES) following air plasma treatment. In addition, copolymers of N-(2-hydroxypropyl) acrylamide (HPAm) and N-benzophenone acrylamide (BPAm) were grafted onto the external pSi surfaces by spin-coating and UV crosslinking. Each modification step was verified using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy, water contact angle (WCA) measurements, X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM). In order to confirm that the air plasma treatment and silanization step only occurred on the top surface of pSi samples, confocal microscopy was employed after fluorescein isothiocyanate (FITC) conjugation. Drug release studies carried out over 17 h in PBS demonstrated that the modified pSi reservoirs released CPT continuously, while showing excellent stability. Furthermore, protein adsorption and cell attachment studies demonstrated the ability of the graft polymer layer to reduce both significantly. In combination with the biocompatible pSi substrate material, the facile modification strategy described in this study provides access to new multifunctional drug delivery systems (DDS) for applications in cancer therapy.

Accurate endpoint detection is crucial for speech recognition accuracy. A novel approach that finds robust features for endpoint detection based on the empirical mode decomposition (EMD) algorithm and spectral entropy in a noisy environment is proposed. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions (IMFs), which is a zero-mean AM-FM component. Then spectral entropy can be used to extract the desired feature for IMF components. In order to show the effectiveness of the proposed method, we present examples showing that the new measure is more effective than traditional measures. The experiments show that the proposed algorithm can suppress different noise types with different SNR, and the algorithm is robust in the real signal tests.

Tetrolets are Haar-type wavelets whose supports are tetrominoes which are shapes made by connecting four equal-sized squares. Firstly, the magnitude of polarization image and angle of polarization image can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions using tetrolet transform. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the larger value of region energy information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused tetrolet coefficients. Experimental results show that the proposed method can detect image features more effectively and the fused image has better subjective visual effect.

This paper presents a method for robot path planning based on ant colony optimization algorithm, in order to resolve the weakness of ant colony algorithm such as slow convergence rate and easy to fall into local optimum and traps. This method uses anti-potential field to make the robot escape from them smoothly, and at the end of each cycle, uses the way of judge first and then hybridization to optimize the algorithm. Finally, the simulation results show that the performance of the algorithm has been improved, and proved that the optimization algorithm is valid and feasible.