Bubble-Proof Algorithm for Wave Union TDCs

Nowadays state-of-the-art time-to-digital converters (TDCs) are commonly implemented in field-programmable gate array (FPGA) devices using different variations of the wave union method. To take full advantage of this method many design challenges need to be overcome, one of which is an efficient data encoding. In this work, we describe in detail an effective algorithm to decode raw output data from a newly designed multisampling wave union TDC. The algorithm is able to correct bubble errors and detect any number of transitions, which occur in the wave union TDC output code. This allows us to reach a mean resolution as high as 0.39 ps and a single shot precision of 2.33 ps in the Xilinx Kintex-7 FPGA chip. The presented algorithm can be used for any kind of wave union TDCs and is intended for partial hardware implementation.

Artificial intelligence in a rugged design based on multi-bit rules

In this paper, the technologies for training large artificial neural networks are considered: the first technology is based on the use of multilayer “deep” neural networks; the second technology involves the use of a “wide” single-layer network of neurons giving 256 private binary solutions. A list of attacks aimed at the simplest one-bit neural network decision rule is given: knowledge extraction attacks and software data modification attacks; their content is considered. All single-bit decision rules are unsafe for applying. It is necessary to use other decision rules. The security of applying neural network decision rules in relation to deliberate hacker attacks is significantly reduced if you use a decision rule of a large number of output bits. The most important property of neural network transducers is that when it is trained using 20 examples of the “Friend” image, the “Friend” output code of 256 bits long is correctly reproduced with a confidence level of 0.95. This means that the entropy of the “Friend” output codes is close to zero. A well-trained neural network virtually eliminates the ambiguity of the “Friend” image data. On the contrary, for the “Foe” images, their initial natural entropy is enhanced by the neural network. The considered works made it possible to create a draft of the second national standard for automatic training of networks of quadratic neurons with multilevel quantizers.

Early Detection of Mold-Contaminated Peanuts Using Machine Learning and Deep Features Based on Optical Coherence Tomography

Fungal infection is a pre-harvest and post-harvest crisis for farmers of peanuts. In environments with temperatures around 28 °C to 30 °C or relative humidity of approximately 90%, mold-contaminated peanuts have a considerable likelihood to be infected with Aflatoxins. Aflatoxins are known to be highly carcinogenic, posing danger to humans and livestock. In this work, we proposed a new approach for detection of mold-contaminated peanuts at an early stage. The approach employs the optical coherence tomography (OCT) imaging technique and an error-correcting output code (ECOC) based Support Vector Machine (SVM) trained on features extracted using a pre-trained Deep Convolutional Neural Network (DCNN). To this end, mold-contaminated and uncontaminated peanuts were scanned to create a data set of OCT images used for training and evaluation of the ECOC-SVM model. Results showed that the proposed approach is capable of detecting mold-contaminated peanuts with respective accuracies of approximately 85% and 96% after incubation periods of 48 and 96 h.

Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses

In recent years, online and offline teaching activities have been combined by the Small Private Online Course (SPOC) teaching activities, which can achieve a better teaching result. Therefore, colleges around the world have widely carried out SPOC-based blending teaching. Particularly in this year’s epidemic, the online education platform has accumulated lots of education data. In this paper, we collected the student behavior log data during the blending teaching process of the “College Information Technology Fundamentals” course of three colleges to conduct student learning behavior analysis and learning outcome prediction. Firstly, data collection and preprocessing are carried out; cluster analysis is performed by using k-means algorithms. Four typical learning behavior patterns have been obtained from previous research, and these patterns were analyzed in terms of teaching videos, quizzes, and platform visits. Secondly, a multiclass classification framework, which combines a feature selection method based on genetic algorithm (GA) with the error correcting output code (ECOC) method, is designed for training the classification model to achieve the prediction of grade levels of students. The experimental results show that the multiclass classification method proposed in this paper can effectively predict the grade of performance, with an average accuracy rate of over 75%. The research results help to implement personalized teaching for students with different grades and learning patterns.

Increasing the information capacity of a fiber-optic multi-sensor converter of binary mechanical signals into electrical signals

The design and operation principle of a multi-sensor Converter of binary mechanical signals into electrical signals based on a partitioned fiber-optic digital-to-analog Converter with a parallel structure is considered. The digital-to-analog Converter is made from a set of simple and technological (three to five digit) fiber-optic digital-to-analog sections. The advantages of the optical scheme of the proposed. Converter in terms of metrological and energy characteristics in comparison with single multi-bit converters are justified. It is shown that by increasing the number of digital-analog sections, it is possible to repeatedly increase the information capacity of a multi-sensor Converter without tightening the requirements for its manufacturing technology and element base. A mathematical model of the proposed Converter is developed that reflects the features of its operation in the mode of sequential time conversion of the input code vectors of individual fiber-optic sections into electrical analogues and the formation of the resulting output code vector.

Detection of Tampering by Image Resizing Using Local Tchebichef Moments

There are many image resizing techniques, which include scaling, scale-and-stretch, seam carving, and so on. They have their own advantages and are suitable for different application scenarios. Therefore, a universal detection of tampering by image resizing is more practical. By preliminary experiments, we found that no matter which image resizing technique is adopted, it will destroy local texture and spatial correlations among adjacent pixels to some extent. Due to the excellent performance of local Tchebichef moments (LTM) in texture classification, we are motivated to present a detection method of tampering by image resizing using LTM in this paper. The tampered images are obtained by removing the pixels from original images using image resizing (scaling, scale-and-stretch and seam carving). Firstly, the residual is obtained by image pre-processing. Then, the histogram features of LTM are extracted from the residual. Finally, an error-correcting output code strategy is adopted by ensemble learning, which turns a multi-class classification problem into binary classification sub-problems. Experimental results show that the proposed approach can obtain an acceptable detection accuracies for the three content-aware image re-targeting techniques.

An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC

The ability to classify rooms in a home is one of many attributes that are desired for social robots. In this paper, we address the problem of indoor room classification via several convolutional neural network (CNN) architectures, i.e., VGG16, VGG19, & Inception V3. The main objective is to recognize five indoor classes (bathroom, bedroom, dining room, kitchen, and living room) from a Places dataset. We considered 11600 images per class and subsequently fine-tuned the networks. The simulation studies suggest that cleaning the disparate data produced much better results in all the examined CNN architectures. We report that VGG16 & VGG19 fine-tuned models with training on all layers produced the best validation accuracy, with 93.29% and 93.61% on clean data, respectively. We also propose and examine a combination model of CNN and a multi-binary classifier referred to as error correcting output code (ECOC) with the clean data. The highest validation accuracy of 15 binary classifiers reached up to 98.5%, where the average of all classifiers was 95.37%. CNN and CNN-ECOC, and an alternative form called CNN-ECOC Regression, were evaluated in real-time implementation on a NAO humanoid robot. The results show the superiority of the combination model of CNN and ECOC over the conventional CNN. The implications and the challenges of real-time experiments are also discussed in the paper.