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This review aims to summarize and describe research on the topic of automatic group emotion recognition. In recent years, the topic of emotion analysis of groups or crowds has gained interest, with studies performing emotion detection in different contexts, using different datasets and modalities (such as images, video, audio, social media messages), and taking different approaches. Articles are included after an innovative search method, including Dense Query Extraction and automatic cross-referencing. Discussed are the types of groups and emotion models considered in automatic emotion recognition research, common datasets for all modalities, general approaches taken, and reported performances. These performances are discussed, followed by an analysis of the application possibilities of the discussed methods. To ensure clear, replicable, and comparable studies, we suggest research should test on multiple, common datasets and report on multiple metrics, when possible. Implementation details and code should be made available where possible. An area of interest for future work is to build systems with more real-world application possibilities, coping with changing group sizes, different emotional subgroups, and changing emotions over time, while having a higher robustness and working with datasets with reduced biases.

To develop and validate a tool for automatic selection of the slice of minimal hiatal dimensions (SMHD) and segmentation of the urogenital hiatus (UH) in transperineal ultrasound (TPUS) volumes.Manual selection of the SMHD and segmentation of the UH was performed in TPUS volumes of 116 women with symptomatic pelvic organ prolapse (POP). These data were used to train two deep-learning algorithms. The first algorithm was trained to provide an estimation of the position of the SMHD. Based on this estimation, a slice was selected and fed into the second algorithm, which performed automatic segmentation of the UH. From this segmentation, measurements of the UH area (UHA), anteroposterior diameter (APD) and coronal diameter (CD) were computed automatically. The mean absolute distance between manually and automatically selected SMHD, the overlap (dice similarity index (DSI)) between manual and automatic UH segmentation and the intraclass correlation coefficient (ICC) between manual and automatic UH measurements were assessed on a test set of 30 TPUS volumes.The mean absolute distance between manually and automatically selected SMHD was 0.20 cm. All DSI values between manual and automatic UH segmentations were above 0.85. The ICC values between manual and automatic UH measurements were 0.94 (95% CI, 0.87-0.97) for UHA, 0.92 (95% CI, 0.78-0.97) for APD and 0.82 (95% CI, 0.66-0.91) for CD, demonstrating excellent agreement.Our deep-learning algorithms allowed reliable automatic selection of the SMHD and UH segmentation in TPUS volumes of women with symptomatic POP. These algorithms can be implemented in the software of TPUS machines, thus reducing clinical analysis time and simplifying the examination of TPUS data for research and clinical purposes. © 2021 The Authors. Ultrasound in Obstetricsamp; Gynecology published by John Wileyamp; Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Low-power consumption and long-distance transmission are two problems that have to be solved by the application of broadband power line communication for the automatic meter reading system. To reduce the power consumption of the communication module, based on the analysis of the composition of the power consumption, some methods are proposed. From the communication chip level and the module circuit level, the design scheme of low-power consumption is given. To solve the problem of transmission distance, a frequency band of 2.44 MHz~5.6 MHz is used as the main working frequency band. The communication module supports multiple frequency bands. Using this feature, the optimal frequency band is adaptively selected for communication and automatic switching, which further improve the transmission distance. Field application shows that the above methods effectively decrease the power consumption of the communication module and extend the transmission distance.

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task. This paper seeks to help modellers by leveraging the Bayesian framework and the concept of automatic relevance determination (ARD), in order to automatically determine an optimal utility function specification from an exponentially large set of possible specifications in a purely data-driven manner. Based on recent advances in approximate Bayesian inference, a doubly stochastic variational inference is developed, which allows the proposed DCM-ARD model to scale to very large and high-dimensional datasets. Using semi-artificial choice data, the proposed approach is shown to very accurately recover the true utility function specifications that govern the observed choices. Moreover, when applied to real choice data, DCM-ARD is shown to be able discover high quality specifications that can outperform previous ones from the literature according to multiple criteria, thereby demonstrating its practical applicability., 21 pages, 2 figures, 11 tables

The day-to-day growth of online information necessitates intensive research in automatic text summarization (ATS). The ATS software produces summary text by extracting important information from the original text. With the help of summaries, users can easily read and understand the documents of interest. Most of the approaches for ATS used only local properties of text. Moreover, the numerous properties make the sentence selection difficult and complicated. So this article uses a graph based summarization to utilize structural and global properties of text. It introduces maximal clique based sentence selection (MCBSS) algorithm to select important and non-redundant sentences that cover all concepts of the input text for summary. The MCBSS algorithm finds novel information using maximal cliques (MCs). The experimental results of recall oriented understudy for gisting evaluation (ROUGE) on Timeline dataset show that the proposed work outperforms the existing graph algorithms Bushy Path (BP), Aggregate Similarity (AS), and TextRank (TR).

Objectives An accurate and rapid diagnosis is crucial for the appropriate treatment of pulmonary tuberculosis (TB). This study aims to develop an artificial intelligence (AI)–based fully automated CT image analysis system for detection, diagnosis, and burden quantification of pulmonary TB. Methods From December 2007 to September 2020, 892 chest CT scans from pathogen-confirmed TB patients were retrospectively included. A deep learning–based cascading framework was connected to create a processing pipeline. For training and validation of the model, 1921 lesions were manually labeled, classified according to six categories of critical imaging features, and visually scored regarding lesion involvement as the ground truth. A “TB score” was calculated based on a network-activation map to quantitively assess the disease burden. Independent testing datasets from two additional hospitals (dataset 2, n = 99; dataset 3, n = 86) and the NIH TB Portals (n = 171) were used to externally validate the performance of the AI model. Results CT scans of 526 participants (mean age, 48.5 ± 16.5 years; 206 women) were analyzed. The lung lesion detection subsystem yielded a mean average precision of the validation cohort of 0.68. The overall classification accuracy of six pulmonary critical imaging findings indicative of TB of the independent datasets was 81.08–91.05%. A moderate to strong correlation was demonstrated between the AI model–quantified TB score and the radiologist-estimated CT score. Conclusions The proposed end-to-end AI system based on chest CT can achieve human-level diagnostic performance for early detection and optimal clinical management of patients with pulmonary TB. Key Points • Deep learning allows automatic detection, diagnosis, and evaluation of pulmonary tuberculosis. • Artificial intelligence helps clinicians to assess patients with tuberculosis. • Pulmonary tuberculosis disease activity and treatment management can be improved. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08365-z.

With the maturity of synthetic aperture radar (SAR) technology, the problem of imbalanced data has gradually emerged. This problem makes it difficult for the automatic target recognition (ATR) model to properly learn the classification boundaries of majority and minority category target samples. In this article, we propose an ATR model with new architecture, called the cost-sensitive awareness-based automatic target recognition (CA-ATR) model, which provides an effective way of solving the problem of imbalanced data. Aimed at the two issues caused by imbalanced data on ATR models, the proposed method solves the problems from both the data and algorithm levels. At the data level, CA-ATR avoids adverse correlations among the target samples through different oversampling methods. By making the ATR model cost-sensitive, the proposed method also avoids the empirical risk preference of the ATR model for majority category target samples at the algorithm-level. At the same time, CA-ATR can autonomously learn different cost-sensitive awareness from different imbalanced data sets. The awareness enables the ATR model to more accurately learn the classification boundaries between target samples that belong in different categories. Several experimental results show the superiority of the proposed approach based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. Compared with other imbalanced learning methods, the proposed method is able to solve different types of imbalanced data problems.

Automatic target recognition has been one of the hottest research in synthetic aperture radar (SAR) data processing. Noticing that popular recognition methods cannot utilize multiple features of SAR complex data, a method fused scattering center feature and deep convolutional neural network (CNN) feature is proposed in this letter. This method contains three key parts, namely, scattering center extraction and reconstruction block, CNN feature extraction block, and final feature fusion and classification block. In this process, the scattering center feature and CNN feature are fused at the level of feature maps, which retain the space information of 2-D feature maps. What is more, the proposed half end-to-end strategy realizes the automatic update of weighting parameters in feature extraction network and subnetwork, which promotes a better recognition efficiency. Experimental results on measured SAR data show that the proposed method can achieve better accuracy than other single feature-based methods and feature fusion methods.

In order to achieve automatic pressure regulating and promote the field application of negative pressure irrigation, an automatic negative pressure control device based on a single-chip microcomputer was developed for regulating and recording data automatically. The results of laboratory and field tests showed that the pressure adjustment precision was 0.45%, 1.79%, 1.99% and 0.84% respectively. In a field test, the water infiltration rates were decreased from 7.20–9.72 mm d−1 on the first day to 1.6–2.5 mm d−1 in the following 30 d, the soil moisture content was the largest around the emitter for 0.24–0.27 g g−1 and lowest in the margin for about 0.07–0.08 g g−1 at different negative pressure. Both laboratory and in-field measurements demonstrated that the device functioned well in signal acquisition, data conversion, processing, transmission, storage and control. The result showed the device could supply a low-rate water continuously and keep soil wetted. It needed more than 10 d obtain soil moisture conditions suitable for crop growth in the field, thus it is advised that a positive pressure should be applied in the initial to generate a stable water distribution, and then adopt a suitable negative pressure to meet crop water requirements. The study confirmed the potential of the device to be used for regulating pressure and recording data accurately in the laboratory and field experiment, and helped ensure future research of similar irrigation systems in the field.

Automatic modulation classification (AMC) is a process of automatic detection of modulation format imposed on the received signal with no prior information (carrier, signal power, phase offset) of the signal, also known as blind classification. In this paper, we proposed a new AMC algorithm, by combining the synergy of the meta-heuristic technique with Gabor feature extraction mainly used in texture analysis. Gabor filters are used to extract the features that are further optimized using the cuckoo search algorithm to increase the efficiency of the classification procedure. The classification approach is applied on digitally modulated signals having phase-shift keying, frequency-shift keying, and quadrature amplitude modulation schemes of order 2-64 over the nonfading channel (AWGN) and fading channel (Rayleigh). Simulations and performance comparison with the existing literature validate that the proposed solution has better classification accuracy with lower sample size and lower signal-to-noise ratio.

Automatic exposure control (AEC) is used to optimize the X-ray tube output during computed tomography (CT) scans. However, calculation of the tube current by AEC can be affected when a patient is not aligned with the rotational center of the X-ray tube. An automatic couch height-positioning compensation mechanism provides a corrective function when the patient is off-center. In this study, we aimed to (a) evaluate the performance characteristics of the positioning compensation mechanism and (b) confirm whether our proposed compensation method can be properly applied to a noise-based AEC system even if the CT device is not equipped with a positioning compensation mechanism. An elliptical phantom was scanned at various table heights on systems without/with the positioning compensation mechanism. Expressions describing the offset from the gantry’s isocenter and adjusted standard deviation settings were derived and used in our proposed compensation method. A phantom was scanned at various table heights with our proposed compensation method, and volume CT dose index (CTDIvol) and image noise levels were obtained. An anthropomorphic chest phantom was also scanned using the proposed compensation method to verify its accuracy. When the positioning compensation mechanism was used, it yielded a constant CTDIvol and image noise levels at various table heights tested. A comparison between our proposed method and the positioning compensation mechanism for both the elliptical and chest phantoms yielded similar CTDIvol. Therefore, both automatic and manual positioning compensation methods are useful for avoiding AEC miscalculations in off-centered patient positioning cases.

Deep learning has attracted intensive attention in synthetic aperture radar (SAR) automatic target recognition (ATR). Usually, a considerable number of labeled samples are necessary to learn a deep model for obtaining good generalization capability. However, the process of sample labeling is time-consuming and costly. This letter proposes an active self-paced deep learning (ASPDL) approach to SAR ATR. In a nutshell, we first introduce the Bayesian inference into the process of deep model parameter optimization, aiming at learning a robust classification model in the case of a limited number of labeled samples. Next, a cost-effective sample selection strategy is presented to iteratively and actively select the informative samples from a pool of unlabeled samples for labeling. Concretely, high-confidence samples are actively selected through self-paced learning (SPL) way and automatically pseudo-labeled with the current classification model, whereas low-confidence samples are chosen through an active learning strategy and manually labeled. Finally, we update the parameters of the model by minimizing a dual-loss function using a new training set that is constructed by incorporating new labeled samples with original ones. Experiments on the moving and stationary target acquisition and recognition (MSTAR) benchmark data demonstrate that the proposed method can achieve better classification accuracy with relatively few labeled samples compared with some state-of-the-art methods.

Synthetic aperture radar automatic target recognition (SAR ATR) is a key technique of remote-sensing image recognition, which has many potential applications in the fields of military surveillance, national defense, civil application, and so on. With the development of science and technology, deep convolutional neural network (DCNN) has been widely applied for SAR ATR. However, it is difficult to use deep learning to train models with limited ray SAR images. To resolve this problem, we proposed an effectively lightweight attention mechanism CNN (AM-CNN) model for SAR ATR. Extensive experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set illustrate that the AM-CNN model can achieve a superior recognition performance, and the average recognition accuracy can reach 99.35% on the classification of 10 class targets. Compared with the traditional CNN and the state-of-the-art method, our model is significantly superior to improve performance and efficiency.

Background Proper use of three-dimensional (3D) models generated from medical imaging data in clinical preoperative planning, training and consultation is based on the preliminary proved accuracy of the replication of the patient anatomy. Therefore, this study investigated the dimensional accuracy of 3D reconstructions of the knee joint generated from computed tomography scans via automatic segmentation by comparing them with 3D models generated through manual segmentation. Methods Three unpaired, fresh-frozen right legs were investigated. Three-dimensional models of the femur and the tibia of each leg were manually segmented using a commercial software and compared in terms of geometrical accuracy with the 3D models automatically segmented using proprietary software. Bony landmarks were identified and used to calculate clinically relevant distances: femoral epicondylar distance; posterior femoral epicondylar distance; femoral trochlear groove length; tibial knee center tubercle distance (TKCTD). Pearson's correlation coefficient and Bland and Altman plots were used to evaluate the level of agreement between measured distances. Results Differences between parameters measured on 3D models manually and automatically segmented were below 1 mm (range: −0.06 to 0.72 mm), except for TKCTD (between 1.00 and 1.40 mm in two specimens). In addition, there was a significant strong correlation between measurements. Conclusions The results obtained are comparable to those reported in previous studies where accuracy of bone 3D reconstruction was investigated. Automatic segmentation techniques can be used to quickly reconstruct reliable 3D models of bone anatomy and these results may contribute to enhance the spread of this technology in preoperative and operative settings, where it has shown considerable potential.

A phoneme is the smallest perceptually distinct sound unit that can be distinguished among words in a particular language. Every language has its own set of phonemes, and all possible words can be considered as ordered sequences of phonemes.The total number of phonemes contained in a language is always very few in comparison to the size of the vocabulary supported by the language. These facts have made phoneme recognition an attractive proposition in the entire journey of the Automatic Speech Processing (ASP) till date. As a result, the classification and recognition of phonemes are considered as the primary tasks of automatic speech recognition (ASR) systems irrespective of application domain. The dynamic nature of phonemes and several sources of their variability create lots of barriers in accurate identification of phonemes from an acoustic signal. The contribution of Machine Learning (ML) based techniques in overcoming these obstructions in automatic phoneme recognition (APR) is remarkable. Nowadays with lot of data availability, ML based ASR is preferred because of its simplicity over acoustic-phonetic based methods. The ML based techniques do not follow the conventional method based on identification of acoustic properties. Rather, ML techniques build their own trained model (algorithm) using readily available data. They do so by finding out the hidden patterns in speech signals, and acquire predictive intelligence through learning. Therefore, ML techniques can be said to provide a more generalized model for phoneme classification. In this paper, we present a comprehensive survey of ML tools to build phoneme recognizers. We also highlight some applications of speech (especially phoneme) recognition which illustrate the current scope as well as future prospects of APR.

The diseases and injuries of the knee joint are the most common orthopedic disorders. Personalized knee models can be helpful in the process of early intervention and lasting treatment techniques development. Fully automatic reconstruction of knee joint anatomical structures from medical images (CT, MRI, ultrasound) remains a challenge. For this reason, most of state-of-the-art knee joint models contain simplifications such as representation of muscles and ligaments as line segments connecting two points which replace attachment areas. The paper presents algorithms for automatic detection of such points on knee CT images. This paper presents three approaches to automatic detection of ligaments and tendons attachment sites on the patients CT images: qualitative anatomical descriptions, analysis of bones curvature, and quantitative anatomical descriptions. Combinations of these approaches result in new automatic detection algorithms. Each algorithm exploits anatomical peculiarities of each attachment site, e.g., bone curvature and number of other attachments in a neighborhood of the site. The experimental dataset consisted of 26 anonymized CT sequences containing right and left knee joints in different resolutions. The proposed algorithms take into account bone surface curvatures and spatial differences in locations of medial and lateral parts of both knees. The algorithms for detection of quadriceps femoris, popliteus, biceps femoris tendons, and lateral collateral and medial collateral ligaments attachment sites are provided, as well as examples of their application. Two algorithms are validated by comparison with known statistics of ligaments lengths and also using ground truth annotations for anatomical landmarks approved by clinical experts. The algorithms simplify generation of patient-specific knee joint models demanded in personalized biomechanical models. The algorithms in the current implementation have two important limitations. First, the correctness of the produced results depends on the bones segmentation quality. Second, the presented algorithms detect a point of the attachment site, which is not necessarily its center. Therefore, manual correction of the attachment site location may be required for attachments with relatively large area.

Using X-ray to evaluate adolescent idiopathic scoliosis (AIS) conditions is the clinical gold standard, with potential radiation hazards. 3D ultrasound has demonstrated its validity and reliability of estimating X-ray Cobb angle (XCA) using spinous process angle (SPA), which can be automatically measured. While angle measurement with ultrasound using spine transverse process-related landmarks (UCA) shows better agreed with XCA, its automatic measurement is challenging and not available yet. This research aimed to analyze and measure scoliotic angles through a novel semi-automatic UCA method. 100 AIS subjects (age: 15.0 ± 1.9 years, gender: 19 M and 81 F, Cobb: 25.5 ± 9.6°) underwent both 3D ultrasound and X-ray scanning on the same day. Scoliotic angles with XCA and UCA methods were measured manually; and transverse process-related features were identified/drawn for the semi-automatic UCA method. The semi-automatic method measured the spinal curvature with pairs of thoracic transverse processes and lumbar lumps in respective regions. The new semi-automatic UCA method showed excellent correlations with manual XCA (R2 = 0.815: thoracic angles R2 = 0.857, lumbar angles R2 = 0.787); and excellent correlations with manual UCA (R2 = 0.866: thoracic angles R2 = 0.921, lumbar angles R2 = 0.780). The Bland–Altman plot also showed a good agreement against manual UCA/XCA. The MADs of semi-automatic UCA against XCA were less than 5°, which is clinically insignificant. The semi-automatic UCA method had demonstrated the possibilities of estimating manual XCA and UCA. Further advancement in image processing to detect the vertebral landmarks in ultrasound images could help building a fully automated measurement method. Level III.

The paper discusses a new approach to improving the accuracy of reproducing the task signal by a precision circular transfer table based on the implementation in the form of a mechatronic module. This design eliminates the disadvantages of the traditional design, such as backlash, failure when fixing the face-plate. The mechatronic module is a gearless connection between the face-plate and the electric motor. The new design required a new approach to the operation of the automatic tracking and stage positioning control system. In the process of stage positioning, thermal deformations occur, and therefore, a feature is the inclusion of an additional automatic control system for the cooling of an executive synchronous electric motor with permanent magnets in the rotor. The paper deals with the issue of the interrelationship between the positioning and tracking systems with the thermal deformation compensation system. Simulation of the operation of two systems has been carried out.

Up to now, automatic control of the shearer speed has been performed to keep the actual speed at an operator-specified level or to keep the actual power at a stable level without overheating or overturning. However, the problem of control of coal seam cutting by the upper drum of a shearer in the case of a variable angle of drum – coal seam contact has yet to be studied. The aim of this work is to develop a method for synthesizing a system of fuzzy automatic control of coal massif cutting by a shearer drum based on an information criterion for the power efficiency of coal cutting with cutters. In this work, based on an information criterion for the power efficiency of coal cutting with cutters, a fuzzy inference algorithm is constructed for a system of automatic control of coal massif cutting by a shearer drum. In doing so, the parameters of the output linguistic variable term membership functions of the system and fuzzy operations are determined according to the recommendations of the classical Mamdani fuzzy inference algorithm using substantiated fuzzy production rules. The fuzzy inference algorithm constructed in this work is tested for efficiency based on the fraction of effective control actions generated by the fuzzy automatic control system. Using simulation, the efficiency of drum rotation speed control with the use of the proposed fuzzy inference algorithm is compared with that with the use of an uncontrolled shearer cutting drive. The study of the generation of control actions involving the upper shearer drum rotation speed showed that effective control actions were generated in the overwhelming majority of cases (about 93%). The proposed method forms a theoretical basis for the solution of the important scientific and practical problem of upper shearer drum rotation speed control automation with the aim to reduce specific power consumption and the amount of chips.

Introduction (problem statement and relevance). The graphs, logic and game-tree structures methods have been used in mechanics. The purpose of modeling an automatic gearbox with graphs can be versatile, namely: determining the transmission ratio of individual gears, analyzing the speed and acceleration of individual rotating elements.The purpose of the study. The article presents the application of decision trees in the analysis of automatic gearboxes modeled with the Hsu graph.Methodology and research methods. The paper presents a method of generating game tree structures that allow to change the values of decision parameters in the issues of decision making and knowledge generation. Specifying the rank of importance, in which order you should change individual items to active, allows you to detect the so-called redundant or temporarily redundant components for a given gear currently under consideration.Scientific novelty and results. At each stage of optimization, a tree is generated, selecting the optimal decisions. Then, vertices can be added to the tree that represent the optimal responses of the system to changes in arithmetic construction parameters.Practical significance. The most important in this regard will be the selection of the optimal programming environment with the possibility of installing the program in laboratory

The article presents approaches to the automatic detection of sound repetitions and the measurement of sound coherence in Russian poetic text basing on the syllabocentric concept of the sound texture of the verse, which distinguishes the phonosyllabeme as its main operational unit and determines the main types of relations between the elements of repetition - equiphony and metaphony - and also allows describe the text in its unity of sound-segmental and rhythmic structure. The paper considers the linguistic prerequisites, main algorithms and methods, which form the basis of the PHONOTEXT computer program (web application), which automatically analyzes and visually forms the picture of the sound composition of the text presenting its quantitative characteristics. The basic operations of the program are described in detail, as well as the mathematical formulas used to identify the structure and carry out mathematical measurement of sound repetitions in Russian poetic speech. The article discusses the results of the programs use for the comparative study of poetry and prose, individual works and individual styles of Russian poets. In particular, the data obtained using the Phonotext program allows us to speak of a tendency towards syntagmatic consolidation of the phonosyllabeme and intensive granulation of the sound texture of the text in the individual style of Osip Mandelstam as the evidence of his striving for poetic morphologization of the elements of sound repetition, it also shows a tendency towards the use of the dominant sound theme as a means for cohesion of a complex syntactic and compositional whole, in contrast to Vladimir Nabokovs idiostyle, which is characterized by its orientation towards splitting up the elements of repetition, with a relatively chaotic interaction and spreading of consonances over the text and their low activity as a means of syntagmatic segmentation and consolidation of the text. The prospects for the development of the Phonotext program are described for identifying the relations of segment-sound and prosodic parallelism in speech (relations of equirhythmy and equiphony, including rhyme), which will provide an automatic presentation of a complete picture of the sound composition, the interaction of its main forms in the text and its further functional description.

The article continues the series of the author publications, which considers the main provisions of the “Clutch-to-Clutch Shifts” (CTC) technology — technology for automatic controlling the switching of automatic transmission (AT) stages, used in the global mass production of vehicles, hybrid and battery electric vehicles. It provides high-quality shifts, brought to the level of continuously variable transmissions. Development of power units in recent years has led to a rapid increase in the number of AT stages, which is accompanied by a complication of kinematic schemes, designs, control algorithms and, in general, an increase in the role of mechatronic control systems (MCS) in ensuring their high technical level. Effectiveness of the CTC technology depends on the perfection of the algorithms used. But their composition is not specified, which, with a large amount of information, makes it difficult for potential users to navigate in this set. In this paper, we propose (for the first time in the Russian language) the generalized structure of a set of standard algorithms necessary for the implementation of this technology in the AT MCS, which is developed on the basis of their selection from a pre-formed database of patents and publications on the theory and technology of AT control, following classification and grouping by functional purpose. For each item of the structure, there are links to patents with matching names. Detailed descriptions of several typical CTC algorithms in the AT MCS of General Motors patents are given.

In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to delineate tumor contours. To reduce the burden on radiation oncologists, an automatic segmentation of the tumor contours would prove useful. To the best of our knowledge, automatic tumor contour segmentation has rarely been applied to cervical cancer treatment. In this study, diffusion-weighted images (DWI) of 98 patients with cervical cancer were acquired. We trained an automatic tumor contour segmentation model using 2D U-Net and 3D U-Net to investigate the possibility of applying such a model to clinical practice. A total of 98 cases were employed for the training, and they were then predicted by swapping the training and test images. To predict tumor contours, six prediction images were obtained after six training sessions for one case. The six images were then summed and binarized to output a final image through automatic contour segmentation. For the evaluation, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) was applied to analyze the difference between tumor contour delineation by radiation oncologists and the output image. The DSC ranged from 0.13 to 0.93 (median 0.83, mean 0.77). The cases with DSC

Topic modeling is a probabilistic graphical model for discovering latent topics in text corpora by using multinomial distributions of topics over words. Topic labeling is used to assign meaningful labels for the discovered topics. In this paper, we present a new topic labeling method that uses automatic term recognition to discover and assign relevant labels for each topic, i.e., TLATR (Topic Labeling using Automatic Term Recognition). TLATR uses domain-specific multi-terms that appear in the set of documents belonging to a topic. The multi-term having the highest score as determined by the automatic term recognition algorithm is chosen as the label for that topic. To evaluate TLATR, we use two real, publicly available datasets that contain scientific articles’ abstracts. The topic label evaluation is done both automatically and using human annotators. For the automatic evaluation, we use Pointwise Mutual Information, Normalized Pointwise Mutual Information, and document similarity. For human evaluation, we employ the average rating method. Furthermore, we also evaluate the quality of the topic models using the Adjusted Rand Index. To prove that our novel method extracts relevant topic labels, we compare TLATR with two state-of-the-art methods, one supervised and one unsupervised, provided by the NETL Automatic Topic Labelling system. The experimental results show that our method outperforms or provides similar results with both NETL’s supervised and unsupervised approaches.

Essential voice tremor (EVT) is a voice disorder resulting from dyscoordination within the laryngeal musculature. A low-frequency fluctuations of fundamental voice frequency or the strength of excitation amplitude is the main consequence of the disorder. The automatic classification of healthy control and EVT is useful tool for the clinicians. A typical automatic EVT classification involves three steps. The first step is to compute the pitch contour from the speech. The second step is to compute the features from the pitch contour, and the final step is to use a classifier to classify the features into healthy or EVT. It is shown that a high-resolution pitch contour estimated from the glottal closure instants (GCIs) is useful for EVT classification. The HPRC estimation can be very poor in the presence of noise. Hence, a probabilistic source filter model based noise robust GCI detection is used for HPRC estimation. The Empirical mode decomposition based feature extraction is used followed by a support vector machine classifier. The EVT classification performance is evaluated using recordings from 45 subjects. The proposed method is found to perform better than the baseline techniques in eight different additive noise conditions with six SNR levels.

Background and purpose: Large radiotherapy (RT) planning imaging datasets with consistently contoured cardiovascular structures are essential for robust cardiac radiotoxicity research in thoracic cancers. This study aims to develop and validate a highly accurate automatic contouring model for the heart, cardiac chambers, and great vessels for RT planning computed tomography (CT) images that can be used for dose-volume parameter estimation. Materials and methods: A neural network model was trained using a dataset of 127 expertly contoured planning CT images from RT treatment of locally advanced non-small-cell lung cancer (NSCLC) patients. Evaluation of geometric accuracy and quality of dosimetric parameter estimation was performed on 50 independent scans with contrast and without contrast enhancement. The model was further evaluated regarding the clinical acceptability of the contours in 99 scans randomly sampled from the RTOG-0617 dataset by three experienced radiation oncologists. Results: Median surface dice at 3 mm tolerance for all dedicated thoracic structures was 90% in the test set. Median absolute difference between mean dose computed with model contours and expert contours was 0.45 Gy averaged over all structures. The mean clinical acceptability rate by majority vote in the RTOG-0617 scans was 91%. Conclusion: This model can be used to contour the heart, cardiac chambers, and great vessels in large datasets of RT planning thoracic CT images accurately, quickly, and consistently. Additionally, the model can be used as a time-saving tool for contouring in clinic practice. (c) 2021 The Authors. Published by Elsevier B.V. Radiotherapy and Oncology 165 (2021) 52-59 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Synthetic aperture radar (SAR) automatic target recognition (ATR) plays an important role in SAR image interpretation. However, at least hundreds of training samples are usually required for each target type in the existing SAR ATR algorithms. In this article, a novel few-shot learning framework named hybrid inference network (HIN) is proposed to tackle the problem of SAR target recognition with only a few training samples. The recognition procedure of HIN consists of two main stages. In the first stage, an embedding network is utilized to map the SAR images into an embedding space. In the second stage, a hybrid inference strategy that combines the inductive inference and the transductive inference is adopted to classify the samples in the embedding space. In the inductive inference section, each sample is recognized independently according to a metric based on Euclidean distance. In the transductive inference section, all samples are recognized as a whole according to their manifold structures by label propagation. Finally, in the hybrid inference section, the classification result is obtained by combining the above two inference methods. To train the framework more effectively, a novel loss function named enhanced hybrid loss is proposed to constrain samples to gain better interclass separability in the embedding space. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) benchmark data set illustrate that HIN performs well in few-shot SAR image classification.

Automatic Exposure Control (AEC) systems optimise radiation dose to the patient while providing adequate image quality. This study examined the effect that the increased localiser region of interest of a hybrid PET/CT has on the CTDIvol, focussing on the role of extraneous objects and patient attenuation profiles. A Siemens Biograph™ 16 Horizon PET/CT system and a Siemens Somatom Sensation 64, both employing the Siemens CAREDose 4D AEC system, were used for acquisition of a range of phantoms. The effect of patient miscentring and effect of the patient bed impinging on the localiser was established and modelled. For PA localiser scans, a non-linear relationship between miscentring and CTDIvol was observed, attributable to the presence of the patient bed being misinterpreted as the patient width. The model identified how the presence of the patient bed led to an increase in the CTDIvol significantly larger than expected (~12%, or 1 mSv), particularly prevalent for smaller patients.

Creating a coherent summary of the text is a challenging task in the field of Natural Language Processing (NLP). Various Automatic Text Summarization techniques have been developed for abstractive as well as extractive summarization. This study focuses on extractive summarization which is a process containing selected delineative paragraphs or sentences from the original text and combining these into smaller forms than the document(s) to generate a summary. The methods that have been used for extractive summarization are based on a graph-theoretic approach, machine learning, Latent Semantic Analysis (LSA), neural networks, cluster, and fuzzy logic. In this paper, a semantic graph-based approach SGATS (Semantic Graph-based approach for Automatic Text Summarization) is proposed to generate an extractive summary. The proposed approach constructs a semantic graph of the original Hindi text document by establishing a semantic relationship between sentences of the document using Hindi Wordnet ontology as a background knowledge source. Once the semantic graph is constructed, fourteen different graph theoretical measures are applied to rank the document sentences depending on their semantic scores. The proposed approach is applied to two data sets of different domains of Tourism and Health. The performance of the proposed approach is compared with the state-of-the-art TextRank algorithm and human-annotated summary. The performance of the proposed system is evaluated using widely accepted ROUGE measures. The outcomes exhibit that our proposed system produces better results than TextRank for health domain corpus and comparable results for tourism corpus. Further, correlation coefficient methods are applied to find a correlation between eight different graphical measures and it is observed that most of the graphical measures are highly correlated.

To propose a fully automated algorithm, which is implemented to segment subcutaneous adipose tissue (SAT) and internal adipose tissue (IAT) from the total adipose tissue for whole-body fat distribution analysis using proton density fat fraction (PDFF) magnetic resonance images. Adipose tissue segmentation was implemented using the U-Net deep neural network model. All datasets were collected using a 3.0 T magnetic resonance imaging (MRI) scanner for whole-body scan of 20 volunteers covering from neck to knee with about 160 images for each volunteer. PDFF images were reconstructed based on chemical-shift-encoded fat–water imaging. After selecting the representative PDFF images (total 906 images), the manual labeling of the SAT area was used for model training (504 images), validation (168 images), and testing (234 images). The automatic segmentation model was validated through three indices using the validation and test sets. The dice similarity coefficient, precision rate, and recall rate were 0.976 ± 0.048, 0.978 ± 0.048, and 0.978 ± 0.050, respectively, in both validation and test sets. The proposed algorithm can reliably and automatically segment SAT and IAT from whole-body MRI PDFF images. The proposed method provides a simple and automatic tool for whole-body fat distribution analysis to explore the relationship between fat deposition and metabolic-related chronic diseases.

The creation of a virtual educational environment, consisting of an information space that ensures the availability of unlimited educational material through communication means, a virtual or real communication channel between a student and a teacher, increases the role of self-education, the dominance of learning over teaching. Paraphrasing [1], we can say that physics is owned not by the one who knows the formulas and definitions, but by the one who, with their help, can solve physical problems (PP). Similarly to the words of E. Fermi [2]: "A person knows physics if he/she can solve problems." The ability to solve PP contributes to the concretization of students' knowledge; without it, there is a separation of theoretical, lecture, educational material from the main task of any learning process – the practical application of accumulated knowledge and skills. Solving of PP contributes to the development of mental activity, the formation of creativity, intelligence, observation, independence and accuracy, is one of the forms of repetition, control and assessment of knowledge. At the same time, it is the solution of problems that is the most difficult element of physical education, causing methodological, didactic, psychological, and mathematical difficulties. It is known, that along with the traditional methods of solving PP: arithmetic, algebraic, geometric, graphic, experimental, since the beginning of the 2000s, information technology, computer technology, and programs - answer books have been actively used. The traditionally difficult issue of solving problems in physics requires both the improvement of classical methods and the development of new software tools for solving problems. The paper discusses the possibilities of using computers to solve various types of physical problems, the use of a site, created by the author, with considered examples of solving more than 2500 problems in physics, a developed program for automatic solution of problems in physics (APS - automatic physics solver) during practical exercises.

The massive use of multimedia technologies has enabled the exploration of information in many data such as texts, audio, videos, and images. Computational methods are being developed for several purposes such as monitoring, security, business, and even health through the automatic diagnosis of diseases by medical images. Among these diseases, we have melanoma skin cancer. Melanoma is a skin cancer that causes a large number of fatalities worldwide. Several methods for the automatic diagnosis of melanoma in dermoscopic images have been developed. For these methods to be more efficient, it is essential to isolate the lesion region. This study used a melanoma segmentation method based on U-net and LinkNet deep learning networks combined with transfer learning and fine-tuning techniques. Additionally, we evaluate the model’s ability to learn to segment the disease or just the dataset by combining datasets. The experiments were carried out in three datasets (PH2, ISIC 2018, and DermIS) and obtained promising results, emphasizing the U-net that obtained an average Dice of 0.923 in the PH2 dataset, Dice = 0.893 in ISIC 2018, and Dice = 0.879 in the DermIS dataset.

Background: In this competing era, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching- Learning Process (TLP). An assessment is one way to identify the learner’s weak spot in the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. To generate questions is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Objective: The proposed system is to generate the test questions that are mapped with bloom’s taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest bloom’s cognitive levels. Methods: Proposed an Automatic Question Generation (AQG) system which autom Proposed an Automatic Question Generation (AQG) system which, automatically generates the assessment questions from the input file Dynamically.atically generates the assessment questions dynamically from the input file. Results and Conclusion: The outputs are dynamic to create a different set of questions at each execution. Here, the input paragraph is selected from the computer science domain and their output efficiency is measured using precision and recall.

PURPOSE Automatizing the scoring of the cytokinesis-blocked micronucleus assay spares a lot of valuable time. The dose-effect relationship can be applied reliably for dose estimation if the quality of the slides is the same from the perspective of the used image processing algorithm. This aspect brings in additional requirements against the quality of the slides compared to the conventional visual scoring. MATERIALS AND METHODS An add-in software was created to the non-fluorescent RS-MN automatic MN scoring system which is capable of measuring quantitatively the degree of typical anomalies. The image processing is less reliable when the presence of these anomalies is more frequent. The behavior of the designed sample quality parameters (SQPs) was tested on in vitro irradiated peripheral blood samples (0, 1, and 2 Gy) obtained from a healthy donor and also on samples from patients undergoing low dose-rate brachytherapy. RESULTS We examined 20 different SQPs and identified two that are independent and correlate significantly with the error of the fully automatic MN frequency. One is related to the size of the cells and the other reflects the homogeneity of the environment. An equation was established which presents a connection between the error of the auto MN frequency and the SQPs. By adding a fourth cleaning step to the conventional sample preparation and changing the pre-dripping temperature of the slide, the SQP can be modified, and consequently, the sample quality can be improved. The gain in accuracy is 54 ± 10 MN per 1000 binucleated cells, which corresponds to the effects of 0.5 Gy. Around the lowest limit of detection (

Purpose Manual delineation of a rectal tumor on a volumetric image is time‐consuming and subjective. Deep learning has been used to segment rectal tumors automatically on T2‐weighted images, but automatic segmentation on diffusion‐weighted imaging is challenged by noise, artifact, and low resolution. In this study, a volumetric U‐shaped neural network (U‐Net) is proposed to automatically segment rectal tumors on diffusion‐weighted images. Methods Three hundred patients of locally advanced rectal cancer were enrolled in this study and divided into a training group, a validation group, and a test group. The region of rectal tumor was delineated on the diffusion‐weighted images by experienced radiologists as the ground truth. A U‐Net was designed with a volumetric input of the diffusion‐weighted images and an output of segmentation with the same size. A semi‐automatic segmentation method was used for comparison by manually choosing a threshold of gray level and automatically selecting the largest connected region. Dice similarity coefficient (DSC) was calculated to evaluate the methods. Results On the test group, deep learning method (DSC = 0.675 ± 0.144, median DSC is 0.702, maximum DSC is 0.893, and minimum DSC is 0.297) showed higher segmentation accuracy than the semi‐automatic method (DSC = 0.614 ± 0.225, median DSC is 0.685, maximum DSC is 0.869, and minimum DSC is 0.047). Paired t‐test shows significant difference (T = 2.160, p = 0.035) in DSC between the deep learning method and the semi‐automatic method in the test group. Conclusion Volumetric U‐Net can automatically segment rectal tumor region on DWI images of locally advanced rectal cancer.

This work compares three different approaches to automatically segment the femoral artery from 2D ultrasound images. Two of the architectures follow a sequential structure, where each ultrasound image is considered a slice of the whole vessel volume, and its previous segmentation result will be part of the input, thus leading to a spatial prior. The Dice score on test data show a better performance on the baseline U-Net (0.819) compared to the sequential U-Net approaches (0.633, 0.725) for the femoral artery segmentation. This could be attributed to the misalignment of the slices being used in those networks. A possible improvement could be assumed in the implementation of a spatially calibrated and tracked ultrasound probe. Overall, these results indicate promising approaches for an automatic segmentation of the femoral artery using 2D ultrasound data.

Background Improvement of image quality in radiology, including the maxillofacial region, is important for diagnosis by enhancing the visual perception of the original image. One of the most used modification methods is sharpening, in which simultaneously with the improvement, due to edge enhancement, several artifacts appear. These might lead to misdiagnosis and, as a consequence, to improper treatment. The purpose of this study was to prove the feasibility and effectiveness of automatic sharpening detection based on neural networks. Methods The in-house created dataset contained 4290 X-ray slices from different datasets of cone beam computed tomography images were taken on 2 different devices: Ortophos 3D SL (Sirona Dental Systems GmbH, Bensheim, Germany) and Planmeca ProMax 3D (Planmeca, Helsinki, Finland). The selected slices were modified using the sharpening filter available in the software RadiAnt Dicom Viewer software (Medixant, Poland), version 5.5. The neural network known as "ResNet-50" was used, which has been previously trained on the ImageNet dataset. The input images and their corresponding sharpening maps were used to train the network. For the implementation, Keras with Tensorflow backend was used. The model was trained using NVIDIA GeForce GTX 1080 Ti GPU. Receiver Operating Characteristic (ROC) analysis was performed to calculate the detection accuracy using MedCalc Statistical Software version 14.8.1 (MedCalc Software Ltd, Ostend, Belgium). The study was approved by the Ethical Committee. Results For the test, 1200 different images with the filter and without modification were used. An analysis of the detection of three different levels of sharpening (1, 2, 3) showed sensitivity of 53%, 93.33%, 93% and specificity of 72.33%, 84%, 85.33%, respectively with an accuracy of 62.17%, 88.67% and 89% (p Conclusions This study showed a high performance in automatic sharpening detection of radiological images based on neural network technology. Further investigation of these capabilities, including their application to different types of radiological images, will significantly improve the level of diagnosis and appropriate treatment.

This work deals with the possibilities of contemporary automatic identification of objects. Automatic object identification can be done by two computational procedures, namely object detection and object recognition. This work deals with the automatic buildings detection, specifically. Presented detection is performed using the edge detectors, namely Prewwitt, Roberts, Canny and Sobel. The main goal of our work was to automate the device for the detection of hazardous substances in the air, as the detection of hazardous substances is realized by laser-based CBRN (Chemical, Biological, Radiological and Nuclear) stand-off detectors, which evaluate the measured data from the reflected laser beam. In this case, buildings are the most reflective surfaces. In order to detect a building, it is necessary to find a suitable edge detector to be used in further research and serve as a basis for software solution of automatic identification.

The subject of the research is automatic control system modeling features for tensioning of stands roughing group, which takes into account changes in the rolling speed at exit of the previous stand and entrance to the next stand. Control systems for high-speed rolling on section mills are the most critical systems, since the trouble-free operation of rolling mill largely depends on their work. Rolling speed control is understood to mean tension regulation in the roughing group of stands and stabilization of the rolling loop in the finishing groups. The influence of such technological factors as uneven heating of blanks, change in the crimping mode in stands, etc. leads to the appearance of tension or back-up forces, deviation of rolled loop from the specified values. Tension rolling, in contrast to loop rolling, is a stable rolling mode. However, (at significant values of tension in the rolled products) such a rolling mode leads to different thicknesses of the finished product. The loop rolling mode is an unstable mode and is impossible without automatic control systems. Both in the tension rolling mode and in the free rolling mode with a loop, it is necessary to study automatic control systems in order to determine the possibilities of compensating for disturbing influences and obtaining rolled products of the given accuracy. Therefore, the main task of the automatic control system is to maintain the rolling mode with the lowest possible tension. To achieve this goal, direct control of the tension of the rolled strip with modern technical means is rather difficult, and the operation of tension control systems is based on indirect methods of measuring it, and the study of the system efficiency is reduced to modeling the process itself. The developed model consists of three stands and two inter-stand spaces, since it takes into account changes in rolling speed at the exit of previous stand and the entrance to the next stand. It is due to this that adequate simulation results are obtained that are close to the real rolling process. Keywords: automation, rolls, stand, inter-stand spacing, modeling, loop tension, rolling mill, roughing group.

Context. The important task was solved during the scientific research related to the development of the methods for automatic synthesis of Petri nets while tuning up of the coordinating automatic control systems. The importance of development of these methods is due to the evolution of intelligent systems. These systems provide the automation of labor intensive processes in the particular case this is the tuning of the certain type of complex control systems. Objective. The purpose of the scientific work is to minimize the time and automation of process in tuning of the multilevel coordinating automatic control systems. Method. The principle of automatic synthesis of Petri nets and the implementation of certain algorithms for tuning complex control systems based on the functioning of an artificial neural network are proposed. The mathematical description of the method for changing the coefficients in neural connections of network in the synthesis of Petri nets is presented. Results. The experiments were conducted in the Matlab\Simulink 2012a environment. These experiments were bound to the joint functioning of an artificial neural network and Petri nets. The functioning of Petri nets was presented in the Matlab \ Simulink environment using Statflow diagrams. As a result of the experiments we have obtained the temporal characteristics of the functioning of artificial neural network providing the composition of Petri nets. The fundamental suitability of using artificial neural network to provide the automatic composition of Petri nets was determined on the basis of analysis of temporal characteristics. Conclusion. The problem linked to the development of system for the joint functioning of neural network and Petri nets for the formation of algorithms and sequential calculations was solved in this work. Thus the method of automatic synthesis of Petri nets and the method of developing of the certain algorithms based on the functioning of a neural network were further developed.

The aim of this study was to compare selected parameters of automatic milking in various European Union countries and United States recorded between 2018 and 2020. Statistical analysis showed highly significant effect of country on all tested milking parameters. It was noted that i.e. the average number of cows per one robot, depending on country, ranged between 51.49 (the Netherlands) and 60.03 (Germany). Cows were milked on the average 2.50 (France) – 2.83 (Latvia) per day, with milking speed ranging from 2.30 kg/min (Lithuania) to 2.99 kg/min (US). Daily milk yield obtained from one cow ranged from 25.12 kg (Lithuania) to 34.11 kg (US), while milk efficiency from 1.44 (Lithuania) to 1.77 kg/min (US). Results also showed that the daily milk yield from one robot ranged from 1 351 kg (Latvia) to 1 930 kg (US). The statistical differences between the milking parameters in the compared countries may be the result of the diversified genetic potential of milked cows and the diversity of the feed base.

The objective of the study was to evaluate the effect of hygiene measures in automatic milking units on the transmission of 3 mastitis pathogens considered to be mainly or partly transmitted from cow to cow during milking events. Two studies were conducted as within-herd experimental trials in 2 Danish commercial dairy herds (A and B) with automatic milking systems. Interventions to enhance hygiene were implemented on the automatic milking units. The 2 studies evaluated separate interventions. In herd A, the hygiene interventions were manual wash with the Lely foam unit and adjustments on the brush-mediated teat cleaning procedure. In herd B, the hygiene intervention included automatic disinfection spray on the upper surface of the brush motor and daily change of brushes. Composite milk samples were collected longitudinally at 3- or 4-wk intervals from all lactating cows. Additional milk samples were taken from cows entering or leaving the study groups. Milk samples were analyzed with quantitative PCR. A hidden Markov model implemented within a Bayesian framework was used to estimate the transmission probability. For analysis, 701 samples from 156 cows were used for herd A, and 1,349 samples from 390 cows were used for herd B. In the intervention group in herd B, transmission of Streptococcus agalactiae was reduced to 19% (95% posterior credibility interval: 0.00–64%) of the transmission in the control group, whereas transmission of Streptococcus dysgalactiae was reduced to 17% (95% posterior credibility interval: 0.00–85%) of transmission in the control group. This suggests that automatic spray on the upper surface of the brush motor with disinfectant along with daily change of brushes collectively reduced transmission of Strep. agalactiae and Strep. dysgalactiae. Results on Staphylococcus aureus in herd B and results on manual foam cleaning and brush-mediated teat cleaning adjustments in herd A were inconclusive.

The article continues the series of the author publications, which considers the main provisions of the “Clutch-to-Clutch Shifts” (CTC) technology — technology for automatic controlling the switching of automatic transmission (AT) stages, used in the global mass production of vehicles, hybrid and battery electric vehicles. It provides high-quality shifts, brought to the level of continuously variable transmissions. Development of power units in recent years has led to a rapid increase in the number of AT stages, which is accompanied by a complication of kinematic schemes, designs, control algorithms and, in general, an increase in the role of mechatronic control systems (MCS) in ensuring their high technical level. Effectiveness of the CTC technology depends on the perfection of the algorithms used. But their composition is not specified, which, with a large amount of information, makes it difficult for potential users to navigate in this set. In this paper, we propose (for the first time in the Russian language) the generalized structure of a set of standard algorithms necessary for the implementation of this technology in the AT MCS, which is developed on the basis of their selection from a pre-formed database of patents and publications on the theory and technology of AT control, following classification and grouping by functional purpose. For each item of the structure, there are links to patents with matching names. Detailed descriptions of several typical CTC algorithms in the AT MCS of General Motors patents are given.

Background: Active (new/enlarging) T2 lesion counts are routinely used in the clinical management of multiple sclerosis. Thus, automated tools able to accurately identify active T2 lesions would be of high interest to neuroradiologists for assisting in their clinical activity. Objective: To compare the accuracy in detecting active T2 lesions and of radiologically active patients based on different visual and automated methods. Methods: One hundred multiple sclerosis patients underwent two magnetic resonance imaging examinations within 12 months. Four approaches were assessed for detecting active T2 lesions: (1) conventional neuroradiological reports; (2) prospective visual analyses performed by an expert; (3) automated unsupervised tool; and (4) supervised convolutional neural network. As a gold standard, a reference outcome was created by the consensus of two observers. Results: The automated methods detected a higher number of active T2 lesions, and a higher number of active patients, but a higher number of false-positive active patients than visual methods. The convolutional neural network model was more sensitive in detecting active T2 lesions and active patients than the other automated method. Conclusion: Automated convolutional neural network models show potential as an aid to neuroradiological assessment in clinical practice, although visual supervision of the outcomes is still required.

Image representation is an active area of research with increasing applications in military and defense. Image representation aims at representing an image with lesser number of coefficients than the actual image, without affecting the image quality. It is the first step in image compression. Once the image is represented by using some set of coefficients, it is further encoded using various compression algorithms. This paper proposes an adaptive method for image representation, which uses Complex Wavelet transform and the concept of phase congruency, where the number of coefficients used for image representation depends on the information content in the input image. The efficiency of the proposed method has been assessed by comparing the number of coefficients used to represent the image using the proposed method with that used when Complex Wavelet transform is used for image representation. The resultant image quality is determined by computing the PSNR values and Normalized Cross Correlation. Experiments carried out show highly promising results, in terms of the reduction in the number of coefficients used for image representation and the quality of the resultant image.