The automatic recognition of <I>Prep C</I> sequences in Italian

In liver stereotactic body radiotherapy (SBRT) using fiducial markers, the accuracy of automatic image recognition of fiducial markers is important, and the imaging dose cannot be neglected in image-guided radiotherapy. Optimal imaging parameters of fiducial markers were investigated for automatic image recognition and imaging dose. We investigated automatic recognition with fiducial markers of different shapes and sizes. In addition, the optimum imaging conditions were examined based on the automatic recognition when the presence or absence of a filter, focal spot size, and phantom thickness were altered using the fiducial markers with a high automatic recognition. The results for different shapes and sizes of fiducial markers showed that larger markers were recognized more automatically, whereas shorter markers were recognized in the correct position. By using the filter, we were able to reduce the imaging dose by one third or one half compared to the case without the filter. The results for the focal spot size showed that using a larger size resulted in higher automatic recognition accuracy than using a smaller size. For the relationship between the automatically recognized imaging conditions and the air kerma when the phantom thickness was altered, it was necessary to keep the tube current-time product constant and increase the tube voltage in order to avoid poor recognition accuracy. The parameters we proposed are effective in shortening the treatment time and reducing the imaging dose because they allow us to acquire images with low doses and high accuracy of automatic recognition.

Introduction. Lost voice function compensation determines the personal and social life of laryngectomees. Automatic speech recognition and synthesis methods are widely used as apps for additional and alternative communication. One of the urgent tasks in clinical practice is voice restoration effectiveness evaluation. Aim. To evaluate the effectiveness of voice rehabilitation results in laryngectomized patients using the automatic assessment of speech intelligibility. Materials and methods. 3 groups of 30 laryngectomized patients depending on the method of voice rehabilitation (esophageal voice, tracheoesophageal voice, electrolarynx), and 14 patients after various surgical interventions on the larynx were included. All patients underwent pseudo-voice recording for further assessment of phrasal intelligibility using the automatic assessment software module, as well as by a trained and untrained listener. Results and discussion. Comparative analysis shows a minimum intelligibility level in automatic recognition, apparently due to semantic and contextual recognition even in untrained listener. Alaryngeal speech demonstrates worse recognition, compared to organ-preserving operations on the larynx, in untrained listeners and in automatic recognition. Experienced listeners demonstrate a consistently high level of recognition of all types of substitute speech. Conclusion. Objectification of the intelligibility assessment of substitute speech using automatic recognition systems allows leveling semantic and contextual recognition during assessment by both trained and untrained listeners. Automatic speech recognition and synthesys systems have application prospects in rehabilitation medicine, in particular, in patients with head and neck cancer.

The material of ACSR in transmission line is prone to local damage, which leads to broken strand defect and reducing power consumption safety. Therefore, an automatic recognition method of broken strand defect image of transmission line based on deep transfer learning is designed to improve the automatic recognition effect of broken strand defect image. The multi-scale algorithm is used to enhance the image. In the feature extraction part of the depth transfer learning framework in the confusion domain, the multi-source domain transfer and dual flow fusion algorithm are used to extract the features of the enhanced image, and the Euclidean distance between the feature vector and the template image feature vector is used to correct the image features; using the corrected image feature training network propagated to the automatic defect recognition part and the domain classification part, the loss function and back propagation algorithm are used to reduce the loss of feature extraction and automatic defect recognition part, and the optimal results of automatic defect recognition and domain classification are obtained. The experimental results show that the method can enhance the image effectively with high definition. At different image angles, the recognition accuracy of this method is as high as 0.96, which has better automatic recognition effect of defect image.

Exploring the effect of illumination on automatic expression recognition using the ICT-3DRFE database

The practical application of data mining technology aims to provide guarantee for better fault prediction of electromechanical equipment and safe and stable operation of expressways. In order to improve the accuracy of electromechanical equipment health status recognition, an automatic health status recognition model based on data mining was constructed. Firstly, the health status data of electromechanical equipment is collected, and the wavelet transform is used to denoise the health status data of the equipment. Then, the Elman neural network in data mining technology is introduced to design the automatic health status recognition model of electromechanical equipment, and the parameters of the Elman neural network are optimized. Finally, through the simulation test of automatic health status recognition of electromechanical equipment, the results show that, the automatic recognition effect of this model is good, and the error rate and rejection rate of the health status of mechanical and electrical equipment are lower than other models, which verifies the superiority of the automatic recognition of the health status of mechanical and electrical equipment of this model. This paper analyzed the application value of data mining technolog, and the demand for fault prediction of electromechanical equipment on expressways is explained.

Forensic scientists often need to identify deceased people. The identification process mainly consists of the analysis of the DNA, dental records, and physical appearance. In humanitarian emergencies, the antemortem documentation needed for forensic analyses may be limited. In this context, face recognition plays a relevant role since antemortem pictures of missing persons are commonly made available by their families. Therefore, automatic recognition systems could be of paramount importance for reducing the search time in databases of face images and for providing a second opinion to the scientists. However, there are only few preliminary studies on automatic face recognition methods for forensic applications, and none of the works in the literature consider problems related to humanitarian emergencies. In this paper, we propose the first study on automatic face recognition for humanitarian emergencies. Specifically, we propose a recognition methodology and we analyze the accuracy of different biometric methods based on deep learning strategies for a real study case. In particular, the considered data regard recent deaths of migrants in Mediterranean Sea. The obtained results are satisfactory, and suggest that automatic recognition methods based on deep learning strategies could be effectively adopted as support tools for forensic identification.

One of the main challenges in facial expression recognition is illumination invariance. Our long-term goal is to develop a system for automatic facial expression recognition that is robust to light variations. In this paper, we introduce a novel 3D Relightable Facial Expression (ICT-3DRFE) database that enables experimentation in the fields of both computer graphics and computer vision. The database contains 3D models for 23 subjects and 15 expressions, as well as photometric information that allow for photorealistic rendering. It is also facial action units annotated, using FACS standards. Using the ICT-3DRFE database we create an image set of different expressions/illuminations to study the effect of illumination on automatic expression recognition. We compared the output scores from automatic recognition with expert FACS annotations and found that they agree when the illumination is uniform. Our results show that the output distribution of the automatic recognition can change significantly with light variations and sometimes causes the discrimination of two different expressions to be diminished. We propose a ratio-based light transfer method, to factor out unwanted illuminations from given images and show that it reduces the effect of illumination on expression recognition.

This article addresses the growing demand for efficient and high-quality manufacturing processes, highlighting the importance of the “Zero Defects Manufacturing” concept. It is explained how a standard tool such as a Coordinate Measuring Machine (CMM) — a current and standard method for dimensional inspection — contributes to ensure product quality in such and more rigorous procedure. Subsequently, the paper explains and compare the automatic recognition of Elementary Functional Geometries with the standard method, exploring differences and analyzing the advantages of automatic recognition in terms of efficiency and precision. The article then emphasizes the importance of recognizing elementary geometries and their tolerancing, including the recognition of flat, spherical, cylindrical, and conical surfaces. The primary focus of the work is to adapt partial derivatives to the equations of Gaussian and mean curvatures, highlighting their potential as tools for automatic geometric shape recognition. The article concludes by presenting decision criteria for different elementary geometries, emphasizing their relevance for implementing an automatic recognition algorithm for these geometries.

Given the current COVID-19 pandemic, medical research today focuses on epidemic diseases. Innovative technology is incorporated in most medical applications, emphasizing the automatic recognition of physical and emotional states. Most research is concerned with the automatic identification of symptoms displayed by patients through analyzing their body language. The development of technologies for recognizing and interpreting arm and leg gestures, facial features, and body postures is still in its early stage. More extensive research is needed using artificial intelligence (AI) techniques in disease detection. This paper presents a comprehensive survey of the research performed on body language processing. Upon defining and explaining the different types of body language, we justify the use of automatic recognition and its application in healthcare. We briefly describe the automatic recognition framework using AI to recognize various body language elements and discuss automatic gesture recognition approaches that help better identify the external symptoms of epidemic and pandemic diseases. From this study, we found that since there are studies that have proven that the body has a language called body language, it has proven that language can be analyzed and understood by machine learning (ML). Since diseases also show clear and different symptoms in the body, the body language here will be affected and have special features related to a particular disease. From this examination, we discovered that it is possible to specialize the features and language changes of each disease in the body. Hence, ML can understand and detect diseases such as pandemic and epidemic diseases and others.

Automatic recognition experiments were performed to compare overall spectral shape versus formants as speaker-independent acoustic parameters for vowel identity. Stimuli consisted of four repetitions of 11 vowels spoken by 17 female speakers and 12 male speakers (29*11*4 = 1276 total stimuli). Formants were computed automatically by peak picking of 12th-order LP model spectra. Spectral shape was represented using three methods: (1) by a cosine basis vector expansion of the power spectrum: (2) as the output of a 16-channel, 1/3-oct filter bank; and (3) as the output of a 16-channel mel-spaced filter bank. Automatic recognition was based on maximum likelihood estimation in a multidimensional space. For all cases considered, the representations based on spectal shape resulted in significantly higher recognition accuracy than for recognition based on only three formants. For example, using the entire database of all speakers and 11 vowels, recognition based on spectral shape was about 85% vs 69% for three formants. If the data were restricted to female speakers and the seven vowels /a,i,u,æ,ɝ,ɪ,ɛ/, recognition was about 97% based on spectral shape versus 84% for formants. These results indicate that, at least for automatic recognition of vowels, spectral peak detection is neither necessary nor sufficient. [Work supported by NSF.]

There is important information in the geological section map of metro project for construction safety risk identification. Therefore geological section automatic recognition is a core process for risk identification automation, however, legends in the geological section map automatic recognition by computer is a foundation work. This paper proposes the categories of legends, the challenges and probabilities of legends recognition, and presents the methodology and detailed algorithms of legend of "clay" and "silty clay" recognition as example. At last an automatic legends recognition application example on the project of Pangxiejia Station of Wuhan Metro Line 2 is presented and geological stratum information recognition algorithms of the station exterior is proposed.

The spatial resolution is increasingly high as development of optical remote sensing, and more and more optical sensors can achieve the detection ability of sub meter, which lays down the data foundation for automatic recognition of ship targets. However, mature technology is lacked to identify the ship models automatically with remote sensing images. In this study, an automatic recognition method for ship targets is proposed based on the local invariant feature extraction algorithm SIFT (Scale Invariant Feature Transform), which is consist of feature extraction and description, feature matching and target recognition. The model of unknown target is identified based on the target library using the matching difference of targets with the same model and different models. The experiment results show that this automatic recognition flow is effective to identify the ship targets of interest based on the target library, and the total correct recognition rate is 92%. This method provides a new flow for automatic model recognition of ship targets, and has considerable potential for wide applications.

The promise of a powerful computing device to help people in productivity as well as in recreation can only be realized with proper human-machine communication. Automatic recognition and understanding of spoken language is the first step toward natural human-machine interaction. Research in this field has produced remarkable results, leading to many exciting expectations and new challenges. We summarize the development of the spoken language technology from both a vertical (chronology) and a horizontal (spectrum of technical approaches) perspective. We highlight the introduction of statistical methods in dealing with language-related problems, as this represents a paradigm shift in the research field of spoken language processing. Statistical methods are designed to allow the machine to learn structural regularities in the speech signal, directly from data, for the purpose of automatic speech recognition and understanding. Research results in spoken language processing have led to a number of successful applications, ranging from dictation software for personal computers and telephone-call processing systems for automatic call routing, to automatic sub-captioning for television broadcasts. We analyze the technical successes that support these applications. Along with an assessment of the state of the art in this broad technical field, we also discuss the limitations of the current technology, and point out the challenges that are ahead. This paper presents an accurate overview of spoken language technology as a basis to inspire future advances.

Application of artificial intelligence based on pattern recognition in music entertainment environment and automatic music recognition

Automatic sound recognition for human body acoustic signals has attracted wide interests in recent years. However, the power of automatic sound recognition largely depends on the choice of features representing the acoustic signal. Recently, the time-frequency features and cepstral features are the most commonly utilized features in automatic recognition. The aim of this paper is to compare the time-frequency analysis versus cepstral analysis to find the best feature extraction technique. The one that has the greatest influence on the recognition and validation of diagnosed respiratory diseases in infants and children. This paper proves that the cepstral analysis of features result in better recognition accuracy, and the Mel-Frequency Cepstral Coefficients (MFCC) has the highest influence on recognition accuracy up to 94%, and more, versus the time-frequency features and linear cepstral technique. The used database was collected from infants and young children till the age of 12 years. This database comprised 492 disease of 3 different categories specifically rattle, stridor and wheeze.