This communication proposes a grounded immittance function simulator that, depending on the proper choice of the passive components, can simulate parallel-type impedances of the R-L, R-C, and L-C forms. Only two grounded passive components and two voltage differencing buffered amplifiers (VDBAs) are used to implement the suggested circuit. All three simulated equivalent elements, namely Req, Leq, and Ceq, can be electronically adjusted through the VDBA's transconductance gain. The impact of the non-ideality of the VDBA device on the developed simulator is examined in detail. The voltage-mode bandpass filter has been implemented using the suggested active LC parallel impedance simulator to show that it performs as predicted. To prove the theory, the proposed circuit is simulated using the PSPICE tool. The findings of the experimental measures are also presented to demonstrate the circuit's feasibility.

This paper presents a technique to control the inventory level on the warehouse automation by using continuous reviewing and forecasting system. The product demand that stored in the warehouse depends on orders from customers. For each product, the demand follows a Poisson process and the delivery lead-time is known. The demand forecast can predict the trend of order and redefine the reorder point (RP) of inventory that linked to automatic warehouse control system. The automatic warehouse plays an important role in the computer integration manufacturing system (CIMS). The functions of CIMS include production planning, material requirement planning, work order generation, process control, quality control, shipping planning, warehouse and inventory management and material cost accounting. [1] This paper focuses on implementing an automatic warehouse control and inventory management demand forecasting system.

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This paper proposes a method based on the spectra response of IR detectors mounted on thermographic camera for emissivity measurement at various target surface temperatures, while the reflected temperature istaken into account, and also studies on the effect of surface roughness on the emissivity value. The emissivity (ε8-14μm) of general engineering material such as iron, stainless steel, brass, copper and aluminum obtained in this paper are in agreement with other literatures. Finally, results found that the roughness and emissivity of equipment increases with increasing of the operating time.

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Automatic fish recognition using deep learning and computer or machine vision is a key part of making the fish industry more productive through automation. An automatic sorting system will help to tackle the challenges of increasing food demand and the threat of food scarcity in the future due to the continuing growth of the world population and the impact of global warming and climate change. As far as the authors know, there has been no published work so far to detect and classify moving fish for the fish culture industry, especially for automatic sorting purposes based on the fish species using deep learning and machine vision. This paper proposes an approach based on the recognition algorithm YOLOv4, optimized with a unique labeling technique. The proposed method was tested with videos of real fish running on a conveyor, which were put randomly in position and order at a speed of 505.08 m/h and could obtain an accuracy of 98.15%. This study with a simple but effective method is expected to be a guide for automatically detecting, classifying, and sorting fish.

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