System for Automatic Recognition of Types of Sources of Regional Seismic Events

The scientific article is devoted to the issues of SDR system control. Software Defined Radio is a system designed for software control of information transmission processes in a radio communication channel. Recognition of digital modulation types is used, which automatically classifies the type of digital modulation of the received signal. The following issues are covered in the article: the analysis of existing approaches in the task of automatic recognition of types of digital modulation is carried out; the analysis and classification of informative features in the task of automatic recognition is carried out the following types of digital modulation: 2-PSK, 4-PSK, 8-PSK, 2-FSK, 8-QAM, 16-QAM, 64-QAM and OFDM. This article uses a neural network approach based on cumulative characteristics. To solve these problems, the methods of calculation and selection of informative cumulative features described in the developed model of the system of automatic recognition of digital modulation types by means of a neural network on cumulative features at a known value of carrier frequency are described. A model of the system of automatic recognition of digital modulation types at a known value is constructed carrier frequency. An algorithm for automatic recognition of digital modulation types has been developed using a multilayer neural network. The influence of noise in the communication channel on the probability of recognizing the types of digital modulation at a known value of the carrier frequency is investigated. It was found that regardless of the type of noise in the communication channel, the law of error distribution in IQ data becomes close to normal. This fact is one important argument for the use of cumulative features in the task of automatic recognition of types of digital modulation. Therefore, the task of automatic recognition of digital modulation types is quite relevant. Further research may be aimed at expanding the range of high-order cumulative features used, due to which it is possible to increase the probability of correct recognition of types of digital modulation, and solving the recognition problem at an unknown value of the frequency and initial phase of the carrier signal.

License plate recognition (LPR) system is an important system in our life. LPR is an image processing and a character recognition system that used to recognize any car from the others. An automatic license plate recognition system for the three different Iraqi car license plates was proposed in this paper. Differentiating between the three styles were done depending on the plate size. An optical character recognition (OCR) is used with correlation approach and templates matching for plate recognition by segmenting each number, character and word into sub images. The software used is MATLAB R2014a. The algorithm is successfully constructed with sample of images correctly identified.

With the increasing improvement of living standards, healthy long-distance running has become one of the current ways of aerobic physical exercise. Long-distance running is beneficial to many bodily functions such as neck, spine, and heart, but inappropriate long-distance running can easily damage the joints of the human body. In order to make more fitness athletes run with a healthy and reasonable posture, this paper studies the automatic recognition and correction system of running movements based on computer vision technology. Based on the analysis of human motion detection methods and human motion recognition methods, an automatic recognition and correction system for running actions is designed. In order to understand the accuracy of the system in the recognition of wrong actions, this paper compares the designed system with the traditional system. The results show that the system designed in this paper has good accuracy and can control the deviation within a reasonable range.

We present a system for automatic Chinese business card recognition. Chinese business card is characterized by its complex layout - Chinese-English mixed character sets and diverse typesetting. Our goal is to extract meaningful information item from the scanned image, such as "holder-name", "title", "address", etc. The automatic recognition task is divided into three stages: 1) document geometrical layout analysis; 2) bilingual character recognition; 3) text understanding and information extraction. The final results of this system are stored in database for later use. The whole system has been tested with card images scanned at 400 dpi and results are reported.

An automatic road sign recognition system based on a computational model of human recognition processing

Over the past two decades face recognition has gained immense interest and is one of the most interesting research areas. The reason for this could be the need for designing automatic recognition and surveillance systems or a human-computer interface. This involves knowledge and contribution not only from various fields of pattern recognition, machine learning, computer vision, and image processing but also in psychology and neuroscience. There are several challenging factors in face recognition like illumination, scale, expression, and pose which are addressed by several researchers to achieve a good recognition rate, but still, there is no robust technique that is available against uncontrolled factors from the environment, hardware, and software of the system. A facial recognition system is a technology that can recognize a human face by pinpointing and measuring facial features in an image. Face recognition allows access to buildings without the need for a key or even faster transits in airport security. But fraudsters can target this face recognition system like any other privacy technology with spoofing. Such a spoofing attack can be quite severe. Hackers will be able to gain access to secure facilities or buildings and homes resulting in the sabotage of critical and confidential data. In this work, a face recognition system with anti-spoofing features is proposed. ResNet50 neural network architecture is used for training the model for face recognition. For anti-spoofing, eye blink detection for photo attacks, and reflection of light for video attacks are incorporated. A prototype is built that implements these functions in hardware.

In recent years, with the popularity of the Internet, digital media has become an indispensable part of people's life. Image acquisition equipment is widely used in daily life and work. However, due to technical problems, it can not meet the performance requirements of users. At the same time, a large number of data and information are difficult to be effectively processed, which also reduces the image quality, reduces the storage space and increases the image distortion. Therefore, it is necessary to develop new algorithms to improve these defects. Therefore, this paper designs and develops an automatic digital media image recognition system based on mean clustering algorithm. Firstly, this paper describes the concept and characteristics of digital media. Then, this paper also studies the application of mean clustering algorithm. Based on this, an automatic digital media image recognition system is designed, and the performance of the system is tested by simulation. Finally, the test results show that the algorithm can accurately identify media images. In general, the performance of the digital media image automatic recognition system based on mean clustering algorithm can meet the needs of target users.

Automatic detonator code recognition via deep neural network

Optical Character Recognition (OCR) research includes computer vision, artificial intelligence, and pattern recognition. Character recognition has garnered a lot of attention in the last decade due to its broad variety of uses and applications, including multiple-choice test data, business documents (e.g., ID cards, bank notes, passports, etc.), and automatic number plate recognition. This paper introduces an automatic recognition system for printed numerals. The automatic reading system is based on extracting local statistical and geometrical features from the text image. Those features are represented by eight vectors extracted from each digit. Two of these features are local statistical (A, A th), and six are local geometrical (P1, P2, P3, P4, P5, and P6). Thus, the database created consists of 1120 statistical and geometrical features. For the purpose of recognition, the features of the test image are compared with the features of all the images saved in the database depending on the value of the Minimum Distance (MD). All digits (0–9) were identified with 100% accuracy. The average computational time required to recognize a numeral at any font size is 0.06879 seconds.

Human perception of sign language can serve as inspiration for the improvement of automatic recognition systems. Experiments with human signers show that sign language signs contain redundancy over time. In this article, experiments are conducted to investigate whether comparable redundancies also exist for an automatic sign language recognition system. Such redundancies could be exploited, for example, by reserving more processing resources for the more informative phases of a sign, or by discarding uninformative phases. In the experiments, an automatic system is trained and tested on isolated fragments of sign language signs. The stimuli used were similar to those of the human signer experiments, allowing us to compare the results. The experiments show that redundancy over time exists for the automatic recognizer. The central phase of a sign is the most informative phase, and the first half of a sign is sufficient to achieve a recognition performance similar to that of the entire sign. These findings concur with the results of the human signer studies. However, there are differences as well, most notably the fact that human signers score better on the early phases of a sign than the automatic system. The results can be used to improve the automatic recognizer, by using only the most informative phases of a sign as input.

<span lang="EN-US">Current Automatic Speaker Recognition (ASR) System has emerged as an important medium of confirmation of identity in many businesses, ecommerce applications, forensics and law enforcement as well. Specialists trained in criminological recognition can play out this undertaking far superior by looking at an arrangement of acoustic, prosodic, and semantic attributes which has been referred to as structured listening. An algorithmbased system has been developed in the recognition of forensic speakers by physics scientists and forensic linguists to reduce the probability of a contextual bias or pre-centric understanding of a reference model with the validity of an unknown audio sample and any suspicious individual. Many researchers are continuing to develop automatic algorithms in signal processing and machine learning so that improving performance can effectively introduce the speaker’s identity, where the automatic system performs equally with the human audience. In this paper, I examine the literature about the identification of speakers by machines and humans, emphasizing the key technical speaker pattern emerging for the automatic technology in the last decade. I focus on many aspects of automatic speaker recognition (ASR) systems, including speaker-specific features, speaker models, standard assessment data sets, and performance metrics</span>

Characterizing and detecting spontaneous speech: Application to speaker role recognition

Automatic license plate detection (ALPD) and recognition systems are becoming ever more widely used in the world. At present, most of the ALPD systems for Chinese license plates(LPs) are either slow or less accurate, which can affect the subsequent stage of license plate character recognition. In this paper we propose a fast and accurate ALPD system for images taken in the wild. We first design and train a YOLO-like [14] but simpler network for fast LP localization; then we develop another network to find the four corner points of the LP within the previously selected rectangular area; finally we use perspective projection to convert the LP to the front view for easy subsequent recognition. CCPD [2] dataset is used for training the networks. Experimental results show two distinctive advantages of the proposed method. First, the proposed method is both very accurate and robust. The recognition accuracy rate has reached 98.6% over a wide range of test images, especially those image types not contained in the training set. Secondly, the proposed method is very fast. The whole LP detection and recognition system can process images of size 720P at the rate of 33.4 frames per second, far exceeding previously reported performance in terms of both accuracy and speed. In addition, we find that many of the LP corner labels are not accurate yet the output from our method give very accurate LP corner coordinates. So we use our network to correct the LP labels in the CCPD dataset.

Automatic facial expression recognition systems can provide information about our emotions and how they change over time. However, based on different statistical methods the results of automatic systems have not yet been compared. In the current paper we evaluate the emotion detection between three different commercial systems (i.e. Affectiva, Kairos and Microsoft) when detecting dynamic and spontaneous facial expressions. Even if the study was performed on a limited sample of videos, the results show significant differences between the systems for the same video and per system across comparable facial expressions. Finally, we reflect on the implications according the generalization of the results provided by automatic emotion detection.

This paper presents an automatic fruit recognition system for classifying and identifying fruit types. The work exploits the fruit shape and color, to identify each image feature. The proposed system includes three phases namely: pre-processing, feature extraction, and classification phases. In the pre-processing phase, fruit images are resized to 90 x 90 pixels in order to reduce their color index. In feature extraction phase, the proposed system uses scale invariant feature transform (SIFT) and shape and color features to generate a feature vector for each image in the dataset. For classification phase, the proposed model applies K-Nearest Neighborhood (K-NN) algorithm classification, and support vector machine (SVM) algorithm of different kinds of fruits. A series of experiments were carried out using the proposed model on a dataset of 178 fruit images. The results of carrying out these experiments demonstrate that the proposed approach is capable of automatically recognize the fruit name with a high degree of accuracy.