A nonsymmetrical salamo-like fluorescence chemical sensor for selective identification of Cu2+ and B4O72- ions and practical applications.

An innovative salamo-like fluorescent chemical sensor H2L, has been prepared that can be utilized to selectively detect Cu2+ and B4O72- ions. Cu2+ ions can bind to oxime state nitrogen and phenol state oxygen atoms in the chemosensor H2L, triggering the LMCT effect leading to fluorescence enhancement. The crystal structure of the copper(II) complex, named as [Cu(L)], has been achieved via X-ray crystallography, and the sensing mechanism has been confirmed by further theoretical calculations with DFT. Besides, the sensor H2L recognizes B4O72- ions causing an ICT effect resulting in bright blue fluorescence. Moreover, the sensor has relatively high selectivity and sensitivity for Cu2+ and B4O72- ions, and the detection limits are 1.02 × 10-7 and 2.06 × 10-7 M, respectively. In addition, the good biocompatibility and excellent water solubility of the sensor H2L make it very advantageous in practical applications, using H2L powder for fingerprint visualization, using H2L to identify the phenomenon of B4O72- ions emitting bright blue fluorescence, making it an ink that can print encrypted messages on A4 paper, in addition to this, based on H2L, the real water sample was tested for Cu2+ ion recognition, and finally the test strip experiment was carried out.

Optimizing the Deep Neural Networks by Layer-Wise Refined Pruning and the Acceleration on FPGA.

To accelerate the practical applications of artificial intelligence, this paper proposes a high efficient layer-wise refined pruning method for deep neural networks at the software level and accelerates the inference process at the hardware level on a field-programmable gate array (FPGA). The refined pruning operation is based on the channel-wise importance indexes of each layer and the layer-wise input sparsity of convolutional layers. The method utilizes the characteristics of the native networks without introducing any extra workloads to the training phase. In addition, the operation is easy to be extended to various state-of-the-art deep neural networks. The effectiveness of the method is verified on ResNet architecture and VGG networks in terms of dataset CIFAR10, CIFAR100, and ImageNet100. Experimental results show that in terms of ResNet50 on CIFAR10 and ResNet101 on CIFAR100, more than 85% of parameters and Floating-Point Operations are pruned with only 0.35% and 0.40% accuracy loss, respectively. As for the VGG network, 87.05% of parameters and 75.78% of Floating-Point Operations are pruned with only 0.74% accuracy loss for VGG13BN on CIFAR10. Furthermore, we accelerate the networks at the hardware level on the FPGA platform by utilizing the tool Vitis AI. For two threads mode in FPGA, the throughput/fps of the pruned VGG13BN and ResNet101 achieves 151.99 fps and 124.31 fps, respectively, and the pruned networks achieve about 4.3× and 1.8× speed up for VGG13BN and ResNet101, respectively, compared with the original networks on FPGA.

Taking advantage of hybrid bioinspired intelligent algorithm with decoupled extended Kalman filter for optimizing growing and pruning radial basis function network.

The growing and pruning radial basis function (GAP-RBF) network is a promising sequential learning algorithm for prediction analysis, but the parameter selection of such a network is usually a non-convex problem and makes it difficult to handle. In this paper, a hybrid bioinspired intelligent algorithm is proposed to optimize GAP-RBF. Specifically, the excellent local convergence of particle swarm optimization (PSO) and the extensive search ability of genetic algorithm (GA) are both considered to optimize the weights and bias term of GAP-RBF. Meanwhile, a competitive mechanism is proposed to make the hybrid algorithm choose the appropriate individuals for effective search and further improve its optimization ability. Moreover, a decoupled extended Kalman filter (DEKF) method is introduced in this study to reduce the size of error covariance matrix and decrease the computational complexity for performing real-time predictions. In the experiments, three classic forecasting issues including abalone age, Boston house price and auto MPG are adopted for extensive test, and the experimental results show that our method performs better than PSO and GA these two single bioinspired optimization algorithms. What is more, our method via DEKF achieves the better results in comparison with the state-of-art sequential learning algorithms, such as GAP-RBF, minimal resource allocation network, resource allocation network using an extended Kalman filter and resource allocation network.