With the massive increase of similar electronic devices, the requirements for the capacity, timeliness and accuracy of electronic target intelligence processing are also getting higher and higher. The defects existing...
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The identification of traffic signs, also known as traffic sign recognition or TSR, is one of the most important aspects of research that has to be done before fully autonomous driving systems can be developed. When i...
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Multipliers are the utmost commonly used elements in today’s digital electronics. In digital signal processing systems, hardware multiplication is critical for obtaining high data throughput. Based on the increasing ...
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Alzheimer's disease is one of the commonly occurring disease in which the common cause of dementia called as memory loss will happen resiliently and damage the brain activity. It also produces cognitive abilities ...
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Automatic ancient coin classification based on patternrecognition is an open research problem. Ancient coins have significant value in cultural heritage and the originality of coins is imperative for their significan...
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This work proposes a pipeline that aims to recognize the products in a shelf, at the level of the single SKU (stock Keeping Unit), starting from a photo of that shelf. It is composed of a first neural network that det...
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ISBN:
(纸本)9783031064302;9783031064296
This work proposes a pipeline that aims to recognize the products in a shelf, at the level of the single SKU (stock Keeping Unit), starting from a photo of that shelf. It is composed of a first neural network that detects the individual products on the shelf and has been trained with the SKU110K dataset and a second network, designed and built within this work that associates to the single image created by the first network, an embedding vector, which describes its distinctive features. By obtaining this vector of the input image, it is possible to measure the similarity, by means of the cosine similarity, between this vector and all the embedding vectors in the comparison dataset. The vector with the highest cosine similarity is associated to an image labeled with the EAN (European Article Number) code and, therefore, this EAN will be that of the input image. Given the particular task, there are not currently any dataset able to meet our requirements as they have not such a granular level of detail (EAN labeled), so a new properly designed dataset is created to solve this task.
Fault detection and diagnostics are important steps in the predictive maintenance of industrial systems, especially faults in the mechanical parts most susceptible to fail, such as bearings and gears in rotating machi...
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ISBN:
(纸本)9781665496070
Fault detection and diagnostics are important steps in the predictive maintenance of industrial systems, especially faults in the mechanical parts most susceptible to fail, such as bearings and gears in rotating machines. These two components represent more than 50% of causes of the operational downtime. Therefore, the detection of their appearance allows anticipating the total failure of the machine and schedule in advance maintenance actions. However, in the presence of a combined gear and bearing faults, it is difficult to isolate their states. To remedy this situation, this paper proposes a data processing methodology that exploits the threephase current signals of the rotating machine and build a health indicator (HI) from each current phase that reveals the different health states. This indicator is constructed by extracting features from the collected raw data in frequency and time domains, and then they properly combined with a physical significance. After that, all health indicators (HIs) of the three phase current data are fed to a machinelearning model for an online patternrecognition of the bearing and gear states, including the combined faults. The proposed approach is demonstrated through a test bench that studies bearing and gear defects of a gearbox under different operating conditions.
Optical flow methods, which estimate a dense motion field starting from a sparse one, are playing an important role in many visual learning and recognition applications. The proposed system is based only on sparse opt...
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ISBN:
(纸本)9783031064333;9783031064326
Optical flow methods, which estimate a dense motion field starting from a sparse one, are playing an important role in many visual learning and recognition applications. The proposed system is based only on sparse optical flow and line detector. It is able to densify the starting optical flow, reaching good performances in objective and subjective manner, using common applications like clustering and standard KITTI evaluation kit. In particular, an appreciable improvement has been achieved in terms of quantity of motion vectors grows (up to 540%). Since often in smart cameras both optical flow and lines are available, the proposed approach avoids overloading the Engine Control Unit to transmit the entire image flow and allows reducing the power consumption, realizing a real-time robust system.
One of the applications of deep learning is deciphering the unscripted text over the walls and pillars of historical monuments is the major source of information extraction. This information gives us an idea about the...
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Digital images are more and more part of everyday life. Efficient compression methods are needed to reduce the disk-space usage for their storage and the bandwidth for their transmission while keeping the resolution a...
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ISBN:
(纸本)9783031064272;9783031064265
Digital images are more and more part of everyday life. Efficient compression methods are needed to reduce the disk-space usage for their storage and the bandwidth for their transmission while keeping the resolution and the visual quality of the reconstructed images as close to the original images as possible. Not all images have the same importance. The facial images are being extensively used in many applications (e.g., law enforcement, social networks) and require high efficient facial image compression schemes in order to not compromise face recognition and identification (e.g., for surveillance and security scenarios). For this reason, we propose a promising approach that consists of a custom loss that combines the two tasks of image compression and face recognition. The results show that our method compresses efficiently face images guaranteeing high perceptive quality and face verification accuracy.
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