We use intelligent sensors for automatic identification to improve the decision-making of a dynamic scheduling tool. The regulatory complexity, the personalized production, and traceability requirements make QC laboratories an interesting use case. The proposed platform is applied to an industrial use-case in analytical Quality Control (QC) laboratories. Automatic identification intelligent sensors are used to improve the decision-making of a dynamic scheduling tool. The proposal is supported by an industrial implementation, which integrates intelligent sensors and real-time decision-making, using a combination of PLC and PC Platforms in a three-level architecture: cloud-fog-edge. This paper proposes the simultaneous integration of information from sensors and business data. The simultaneous integration of information from sensors with business data and how to acquire valuable information can be challenging. This requires an understanding of authenticity from the consumer perspective. Beyond systems that help in the detection and delivery of an authentic product, the wine industry can also use marketing tools to convey an authentic image and target consumers in an engaging way. Traceability systems can track and record movements in the supply chain, and by using various devices (e.g., tags, seals, smart phones), can even transmit information to the consumer to provide confidence in the authenticity of a wine. Analytical approaches in combination with chemometrics can authenticate wine by identifying and modeling specific chemical markers or spectral fingerprints. Authentication methods and traceability systems designed to protect wine provenance and quality have therefore gained the interest of both researchers and the wine industry. Wine is considered as a luxury product, which makes it highly susceptible to fraud and adulteration. Wine has been a part of human history for millennia and is an economically important global industry. Experiments have been conducted to corroborate the efficiency of the proposed method. Finally, a trained back-propagation neural network is used to perform the barcode recognition task. Secondly, the proposed system has to segment the barcode. The first step the system has to perform is to locate the position and orientation of the barcode in the required material document image. The paper presents an effective method to utilize the specific graphic features of barcodes for positioning and recognition purposes even in case of distorted barcodes. The back-propagation neural network (BPNN) is selected as a powerful tool to perform the recognition process. This paper proposes a smart barcode detection and recognition system (SBDR) based on fast hierarchical Hough transform (HHT). Moreover, an automated system is required to find, locate and decode barcodes on various document images even with low resolution compared with laser barcode readers (about 15,000 dots per inch) and can handle damaged bar codes. a robot with visual capability is required to play an important role in such a system. For the purpose of in-store or inspection automation, the human operator needs to be removed from the process, i.e. This may result in inconvenience in inspection automation because the human operator has to manipulate either the sensor or the objects. That is, unlike traditional camera-based picturing, the distance between the laser reader (sensor) and the target object is close to zero when the reader is applied. However, there is a major constraint when this tool is used. It is well known that in many stores laser bar-code readers are used at check-out counters. Barcodes have been widely used in many industrial products for automatic identification in data collection and inventory control purposes.
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