Postharvest quality detection and grading are important factors to improve the commercial value of fruit. The natural biological characteristics bring challenges to the rapid and non-destructive evaluation of the internal quality of fruits with large size and thick peel. A lot of studies have tried to establish a feasible technique to achieve rapid, non-destructive, and accurate detection for this kind of fruit in commercial real-time grading. This article focuses on large-sized and thick-skinned fruits and comprehensively reviews the latest technical progress in the non-destructive detection of internal quality. It can provide a valuable reference for the development of postharvest processing technology for this kind of fruit.

Research Progress on Non-Destructive Detection of Internal Quality of Fruits with Large Size and Thick Peel: A Review

Modeling of laser optical detection system for detecting target’s sensitivity and analysis of the impact of optical heterodyne detection performance

Spatiotemporal characteristics of dynamic speckle from a 3D target in atmospheric turbulence

Three-dimensional reconstruction based on speckle projection method can acquire 3D data by only one speckle image of object, which is suitable for measuring moving or deforming object, and has been widely used in many field. However, conventional speckle 3D reconstruction systems often require speckle projection devices with complex structures, which are relatively large in volume and high in cost. In addition, the digital image correlation algorithms used to search corresponding points, which is time-consuming and not conducive to rapid acquisition of 3D data. In this paper, a low-cost laser speckle 3D reconstruction system is built by using semiconductor lasers. At the same time, by optimizing the image correction and sub-pixel matching optimization algorithms, the corresponding points searching can be done more quickly, which improves the efficiency and accuracy of speckle 3D reconstruction. The experiments proves that the low-cost laser speckle 3D reconstruction system presented can effectively realize fast 3D digital imaging and measurement, largely promote the time efficiency of 3D data calculation, and the measurement error can reach ±0.3mm, which can meet the requirements of miniaturization and low-cost applications with low accuracy requirements.

A low-cost system for fast 3D reconstruction based on laser speckle projection

采用GaN HEMT的可调窄脉冲激光器驱动电路

Review of laser speckle target detection technology

—Task scheduling poses a major challenge for cloud computing environments. Task scheduling ensures cost-effective task execution and improved resource utilization. It is classified as a NP-hard problem due to its nondeterministic polynomial time nature. This characteristic motivates researchers to employ meta-heuristic algorithms. The number of cloud users and computing capabilities is leading to increased concerns about energy consumption in cloud data centers. In order to leverage cloud resources in the most energy-efficient manner while delivering real-time services to users, a viable cloud task scheduling solution is necessary. This study proposes a new deadline-aware task scheduling algorithm for cloud environments based on the Firefly Optimization Algorithm (FOA). The suggested scheduling algorithm achieves a higher level of efficiency in multiple parameters, including execution time, waiting time, resource utilization, the percentage of missed tasks, power consumption, and makespan. According to simulation results, the proposed algorithm is more effective and superior to the CSO algorithm under HP2CN and NASA workload archives.

/pdf/deadline-aware-task-scheduling-for-cloud-computing-using-2fa9e9i5.pdf

Deadline-aware Task Scheduling for Cloud Computing using Firefly Optimization Algorithm

—The analysis of human movement has attracted the attention of many scholars of various disciplines today. The purpose of such systems is to perceive human behavior from a sequence of video images. They monitor the population to find common properties among pedestrians on the scene. In video surveillance, the main purpose of detecting specific or malicious events is to help security personnel. Different methods have been used to detect human behavior from images. This paper has used an efficient computational algorithm for detecting anomalies in video images based on the combined approach of the differential evolution algorithm and cellular neural network. In this method, the input image's gray-level image is first generated. Because it may be possible to identify several large areas in the image after the threshold, the largest white area is selected as the target area. The images are then used to remove noise, smooth the image, and fade the morphology. The results showed that the proposed method has higher speed and accuracy than other methods. The advantage of the algorithm is that it has a runtime of three seconds on a home computer, and the average sensitivity criterion is 98.6% (97.2%).

Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach

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Papers

Review of laser speckle target detection technology

Deadline-aware Task Scheduling for Cloud Computing using Firefly Optimization Algorithm

Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach