RESUMO
A series of novel photo- and ionochromic N-acylated 2-(aminomethylene)benzo[b]thiophene-3(2Ð)-ones with a terminal phenanthroline receptor substituent was synthesized. Upon irradiation in acetonitrile or DMSO with light of 436 nm, they underwent Z-E isomerization of the C=C bond, followed by very fast NâO migration of the acyl group and the formation of nonemissive O-acylated isomers. These isomers were isolated preparatively and fully characterized by IR, 1H, and 13C NMR spectroscopy as well as HRMS and XRD methods. The reverse thermal reaction was catalyzed by protonic acids. N-Acylated compounds exclusively with Fe2+ formed nonfluorescent complexes with a contrast naked-eye effect: a color change of the solutions from yellow to dark orange. Subsequent selective interaction with AcO- led to the restoration of the initial absorption and emission properties. Thus, the obtained compounds represent dual-mode "on-off-on" switches of optical and fluorescent properties under sequential exposure to light and H+ or sequential addition of Fe2+ and AcO- ions.
RESUMO
In recent years, there has been an increasing demand to digitize and electronically access historical records. Optical character recognition (OCR) is typically applied to scanned historical archives to transcribe them from document images into machine-readable texts. Many libraries offer special stationary equipment for scanning historical documents. However, to digitize these records without removing them from where they are archived, portable devices that combine scanning and OCR capabilities are required. An existing end-to-end OCR software called anyOCR achieves high recognition accuracy for historical documents. However, it is unsuitable for portable devices, as it exhibits high computational complexity resulting in long runtime and high power consumption. Therefore, we have designed and implemented a configurable hardware-software programmable SoC called iDocChip that makes use of anyOCR techniques to achieve high accuracy. As a low-power and energy-efficient system with real-time capabilities, the iDocChip delivers the required portability. In this paper, we present the hybrid CPU-FPGA architecture of iDocChip along with the optimized software implementations of the anyOCR. We demonstrate our results on multiple platforms with respect to runtime and power consumption. The iDocChip system outperforms the existing anyOCR by 44× while achieving 2201× higher energy efficiency and a 3.8% increase in recognition accuracy.
RESUMO
2-Benzo[b]thienyl fulgides and fulgimides containing bulky diphenylmethylene substituents were synthesized in the form of their ring-opened E- or Z-isomers. In contrast to the majority of known fulgides/fulgimides, that form colored ring-closed structures under UV irradiation, the obtained compounds undergo an irreversible transformation leading to decoloration of their solutions. This rearrangement with the formation of the dihydronaphthalene core appeared to be by 2-3 orders of magnitude more efficient than for the known diphenylmethylene(aryl(hetaryl))fulgides. The molecular structures of E- and Z-isomers and of products of the photoinduced rearrangement completed by 1,5-H shift reaction, 3a,4-dihydronaphtho[2,3-c]furans(pyrroles) C, were established based on the data of 1H and 13C NMR spectroscopy and X-ray diffraction studies.
RESUMO
The estimation of human hand pose has become the basis for many vital applications where the user depends mainly on the hand pose as a system input. Virtual reality (VR) headset, shadow dexterous hand and in-air signature verification are a few examples of applications that require to track the hand movements in real-time. The state-of-the-art 3D hand pose estimation methods are based on the Convolutional Neural Network (CNN). These methods are implemented on Graphics Processing Units (GPUs) mainly due to their extensive computational requirements. However, GPUs are not suitable for the practical application scenarios, where the low power consumption is crucial. Furthermore, the difficulty of embedding a bulky GPU into a small device prevents the portability of such applications on mobile devices. The goal of this work is to provide an energy efficient solution for an existing depth camera based hand pose estimation algorithm. First, we compress the deep neural network model by applying the dynamic quantization techniques on different layers to achieve maximum compression without compromising accuracy. Afterwards, we design a custom hardware architecture. For our device we selected the FPGA as a target platform because FPGAs provide high energy efficiency and can be integrated in portable devices. Our solution implemented on Xilinx UltraScale+ MPSoC FPGA is 4.2× faster and 577.3× more energy efficient than the original implementation of the hand pose estimation algorithm on NVIDIA GeForce GTX 1070.
