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This research suggests using triple-layer remote phosphor (TRP) to improve the color quality and luminous flux coming out of white LEDs (WLEDs). The TRP structure contains three different phosphor layers: beneath is the yellow phosphor YAG:Ce3+, a red phosphor LiInO2:Eu3+ above, and a green phosphor Zn2GeO4:Mn2+ in within. Controlling the red-light composition with a red phosphor layer LiInO2:Eu3+ leads to a higher color rendering index (CRI). Thus, increase the luminous efficacy (LE) of WLEDs using a green phosphor layer of CaLaB3O7:Ce3+,Mn2+ to monitor green light composition. Additionally, when increasing these two phosphor concentrations, the yellow concentration YAG:Ce3+ decreases to keep the average correlated color temperatures (ACCTs) within 6000K - 8500K. Besides CRI and LE, controlling green and red phosphor concentration is also a way to analyze the color quality scale (CQS). The study result shows that the higher the LiInO2:Eu3+ concentration, the greater the CRI value. However, CRI significantly reduces when increasing Zn2GeO4:Mn2+ concentration. Meanwhile, CQS can raise a substantial amount in the concentration range of 10%-14% LiInO2:Eu3+, flout LiInO2:Eu3+ concentration. Peculiarly, parallel with the improvement of CRI and CQS, LE also grows over 40% due to a decrease in backscattering light and added green light. This research may be valuable for manufactures for improving the color quality and luminous flux of WLEDs. [ABSTRACT FROM AUTHOR]

With the fast development in recent years of micro PC, applications that require of using computer vision techniques have become more and more popular. This paper describes a thoroughly research on four different face detectors using the two most popular computer vision libraries (i.e., OpenCV and Dlib), and compares the detecting speed of those detectors. The face detectors, which are examined in all experiments, include two classic detectors (Haar and HOG based face detector) and two newer, deep neural networks based detectors. Also, four different Raspberry Pi models including Raspberry Pi Zero W, Pi 2, Pi 3, and Pi 4 are put into the experiments of face detecting speed. The results show that the Raspberry Pi 2, Pi 3, and Pi 4 are capable of doing real-time face detection tasks using Haar or HOG based face detector, and all four Raspberry Pi models can be used in practical applications where face detection is required. For other detectors based on deep neural networks, although are not suitable for face related real-time tasks, they are still effectively used in many other face related computer vision tasks. [ABSTRACT FROM AUTHOR]