Adaptive multilayer management remotely operated underwater vehicle

В статье рассматривается самоорганизующаяся адаптивная система управления телеуправляемыми необитаемыми подводными аппаратами. Адаптивная нейронная система многослойного управления построена по принципу декомпозиции мультичастотного набора входных сигналов, формируемых в адаптивном джойстике управления. Декомпозиция наборов последовательностей управляющих сигналов проходит процедуру трешолдинга, разделения по оценкам спектра мультичастотного сигнала управления. Каскадный алгоритм построен по принципу интерполяции и децимации коэффициентов фильтра. Трешолдинг реализуется свёрткой форматного кадра управляющего сигнала с коэффициентами всплеск формирующего фильтра в базисе всплесков Добеши. Интерполяция коэффициентов фильтра происходит сдвигом частоты, децимация схлопыванием коэффициентов фильтра. Спектральные оценки, построенные по среднеквадратическому значению спектра, укладываются в спектральный радиус нормированного сигнала и формируют матрицу математического ожидания адаптивного сигнала управления. Реакции пилота телеуправляемого необитаемого подводного аппарата формируют управляющие сигналы в трёх плоскостях с заданными скоростями и моментами. Трешолдинг в базисе всплесков позволяет формировать сигналы управления с оптимальной крутизной выходной характеристики, что позволяет отказаться от необходимой ручной регулировки мощностей движителей двигательно-рулевого комплекса, при реализации полуавтоматичеcкого и автоматического управления. Обратная связь системы управления по наблюдаемой динамике позволяет реализовать функцию автопилота, с учётом заданных критериев качества. The article discusses the self-organizing adaptive system management remotely operated underwater vehicle. Adaptive neural system of multilayer control is built on the principle of decomposition of multi-frequency set of input signals generated in adaptive joystick of management. The decomposition of the sets of control signal sequences undergoes the procedure of tresholding, separation by estimates of the spectrum of the multi-frequency control signal. The cascade algorithm is based on the principle of interpolation and decimation of filter coefficients. Tresholding is implemented by convolving the format frame of the control signal with wavelet coefficients of the forming filter in the basis of Dobeshi wavelet. Interpolation of filter coefficients occurs by frequency shift, decimation by collapse of filter coefficients. Spectral estimates based on the standard value of the spectrum fit into the spectral radius of the normalized signal and form a matrix of mathematical expectation of the adaptive control signal. The reactions of the pilot of a remotely operated underwater vehicle form control signals in three planes with given speeds and moments. Tresholding in the basis of wavelets allows you to generate control signals with an optimal slope of the output characteristic, which allows you to abandon the necessary manual adjustment of the powers of the propulsion engines of the engine-steering system, when implementing semi-automatic and automatic control. Feedback of the control system according to the observed dynamics allows implementing the autopilot function, taking into account the specified quality criteria.

Social Distancing for Covid-19 Monitoring System

The battle of corona-virus and mankind is possible to be tackled as long as we maintain the basic norm of social distancing and wearing masks amongst ourselves as it is through our droplets from the respiratory tract that the virus spreads. With the increasing demand for man-force and people requiring to go to their workplaces post lockdown, it is very necessary that we save each other from the virus. In this project, we will go through a detailed explanation of how we can use Python, AI and Deep Learning to monitor social distancing at public places and workplaces are keeping a safe distance from each other by analyzing real-time video streams from the camera and also detect facial mask monitoring using OpenCV and Python. To ensure if people are following social distancing protocols in public places and workplaces, we wanted to develop a tool that can monitor if people are keeping a safe distance from one another, wearing masks or not by processing real-time video footage from the camera. People at workplaces, factories, shops can integrate this tool into their security camera systems and can monitor whether people are keeping a safe distance from each other or not along with that we detect facial mask monitoring using Python with help of haar-cascade algorithm to see whether a person is wearing a mask or not. We are also planning to include thermal screening detection to measure the temperature of the subjects, a dashboard which will display a live report of corona cases around the world. We will also include an alert system that will send a notification to the authorities if the social distancing is not followed or if the temperature exceeds the threshold. The authorities can take suitable measures to isolate the subject and thus prevent the spread of Covid-19.

Face Mask and Social Distancing Detection Using ML Technique

Detecting Mask and Social Distance is our main motive in this project.Face detection plays important roles in detecting face mask. Face detection means detecting or searching for a face in an image or video. For face and mask detection we use viola jones algorithm or Haar cascade algorithm using Open CV. For social distancing we use YOLO algorithm. We have created a system which detect the face and then, it will detect nose and mouth to confirm that the person wear mask or not.

Human Authentication using Face, Voice and Fingerprint Biometrics

Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this project, we present a multimodal biometric system that is based on the fusion of face, voice and fingerprint biometrics. For face recognition, we employ Haar Cascade Algorithm, while minutiae extraction is used for fingerprint recognition and we will be having a stored code word for the voice authentication, if any of these two authentication becomes true, the system consider the person as authorized person. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.

Improved Performance of Trash Detection and Human Target Detection Systems using Robot Operating System (ROS)

In a visual-based real detection system using computer vision, the most important thing that must be considered is the computation time. In general, a detection system has a heavy algorithm that puts a strain on the performance of a computer system, especially if the computer has to handle two or more different detection processes. This paper presents an effort to improve the performance of the trash detection system and the target partner detection system of a trash bin robot with social interaction capabilities. The trash detection system uses a combination of the Haar Cascade algorithm, Histogram of Oriented Gradient (HOG) and Gray-Level Coocurrence Matrix (GLCM). Meanwhile, the target partner detection system uses a combination of Depth and Histogram of Oriented Gradient (HOG) algorithms. Robotic Operating System (ROS) is used to make each system in separate modules which aim to utilize all available computer system resources while reducing computation time. As a result, the performance obtained by using the ROS platform is a trash detection system capable of running at a speed of 7.003 fps. Meanwhile, the human target detection system is capable of running at a speed of 8,515 fps. In line with the increase in fps, the accuracy also increases to 77%, precision increases to 87,80%, recall increases to 82,75%, and F1-score increases to 85,20% in trash detection, and the human target detection system has also improved accuracy to 81%, %, precision increases to 91,46%, recall increases to 86,20%, and F1-score increases to 88,42%.

Novel Vehicle Detection in Real Time Road Traffic Density Using Haar Cascade Comparing with KNN Algorithm based on Accuracy and Time Mean Speed

Aim: The main objective of the paper is to detect objects in iconic real time traffic density videos from CCTVs and Cameras using Haar Cascade algorithm and to compare algorithms with K-Nearest Neighbour algorithm (KNN). In this case we tried improving the rate of accuracy in predicting the traffic density. Materials and methods: Haar Cascade algorithm is applied on 5 realistic videos and which consists of more than 250 frames. For the same we evaluated the Accuracy and Precision values. Harr-like function displays the vehicle’s visual structure, and the AdaBoost machine learning algorithm was used to create a classifier by combining individual classifiers. The significance value achieved for finding the accuracy and precision was 0.445 and 0.754 respectively. Results and Discussions: Detection of vehicles in high speed videos is performed by using Haar Cascade which has mean accuracy with 85.22% and mean precision with 90.63% and 60% of mean accuracy and 58.53% mean precision in KNN classifiers. Conclusion: The performance of the Haar Cascade appears to be better than KNN in terms of both Accuracy and Precision.

STUDENT ATTENDANCE SYSTEM BASED ON THE FACE RECOGNITION

Attendance system is very important in schools and colleges' The student attendance program has many problems such as it may not be accurate and critical to maintain. Therefore, an existing system that uses a face recognition system increases accuracy and also requires less time than other methods. There are many systems available such as face recognition using IoT, PIR sensors and so on. With face recognition, hardware devices are helpful. But the challenge is to keep all the nerves properly without getting hurt. After learning all the techniques and techniques we try to use the system with Haar Cascade Algorithm with the highest accuracy among them all. It can take pictures from 50- 70cm. We create a graphical interface that takes pictures, builds a database and trains the database with a single click. After seeing the face it will show the student's name and roll number. That information is stored on an automatic attendance sheet by time and date.

Implementasi Algoritma Haar Cascade pada Aplikasi Pengenalan Wajah

The large number of citizens in an organization makes the development of an attendance system or citizen detection in a place important in the running of work activities in the organization. Utilization of an IP Camera which is only used for regular monitoring without further detection of the needs of citizens in the organization made the development of personnel detection developed for monitoring the presence of personnel. With the development of a face detection system, it is hoped that the facial algorithm development system will be developed using an IP Camera. Face detection has been developed which has many and special features which aim to determine whether or not a face has been detected in an image. With image management that is developed in face detection, detection will be faster and more accurate because the color is processed into gray degrees so that there are fewer color pixels than those with colors. By using the Python programming language and an image detection library called OpenCV, less code will be designed. This study uses the Viola Jones method, which is a fast and accurate face detection method developed by Paul Viola and Michael Jones. In this study, the Viola Jones method uses the Haar Cascade algorithm which functions as a detection feature in the system and is combined with the internal image process and the AdaBoost Learning and Cascade Classifier so that the detected face object will easily classify whether the object is a face or not. In this case the Cascade Classfier used in this study is the face and eyes. The development of this algorithm is carried out for face detection and recognition. The detection is done by taking pictures with the process taken using a webcam. The system will take several pictures and then the image data will be stored in a folder called dataSet. After that, all data is trained so that it can be recognized by the system. With retrieval, detection and recognition limitations that can only be taken from a distance of less than three meters, face detection on the IP Camera can still read objects other than faces. With recognition and accuracy on the webcam camera, about 80,5% this system can be developed with the Haar Cascade algorithm and the Haar Cascade algorithm precisely to be applied to the development of faced detection and face recognition. By developing the Haar Cascade algorithm for face detection, problems and utilization of an organization's data can be more easily detected and used by IP cameras that can support the performance process of face detection and recognition