Development of a highly precise place recognition module for effective human-robot interactions in changing lighting and viewpoint conditions

We present a highly precise and robust module for indoor place recognition, extending the work by Lemaignan et al. and Robert Jr. by giving the robot the ability to recognize its environment context. We developed a full end-to-end convolutional neural network architecture, using a pre-trained deep convolutional neural network and the explicit inductive bias transfer learning strategy. Experimental results based on the York University and Rzeszów University dataset show excellent performance values (over 94.75 and 97.95 percent accuracy) and a high level of robustness over changes in camera viewpoint and lighting conditions, outperforming current benchmarks. Furthermore, our architecture is 82.46 percent smaller than the current benchmark, making our module suitable for embedding into mobile robots and easily adoptable to other datasets without the need for heavy adjustments

Automated Defect Detection of Screws in the Manufacturing Industry Using Convolutional Neural Networks

Defect detection in industrial production processes is an important and necessary part of quality control. Many defects can occur during the manufacturing process, causing high manufacturing costs. Thus the inspection of screws, which represent an indispensable element of many mechanical components, is a critical process. To reduce manufacturing costs and increase efficiency, a reliable method for inspection is Deep Learning. It can help simplify the process of quality control and increase the velocity and volume of detected defects in screws. This approach uses a CNN model to classify non-defective and defective screws with different types of defects. Instead of manual quality control methods, which can be easily biased, our CNN approach is accurate, cost-efficient, and fast, with an accuracy of over 97 percent. With this approach corresponding to industrial production processes, different defects in screws and non-defective screws can be classified from images according to a real-world industrial inspection scenario

High-Performance Fake Voice Detection on Automatic Speaker Verification Systems for the Prevention of Cyber Fraud with Convolutional Neural Networks

This study proposes a highly effective data analytics approach to prevent cyber fraud on automatic speaker verification systems by classifying histograms of genuine and spoofed voice recordings. Our deep learning-based lightweight architecture advances the application of fake voice detection on embedded systems. It sets a new benchmark with a balanced accuracy of 95.64% and an equal error rate of 4.43%, contributing to adopting artificial intelligence technologies in organizational systems and technologies. As fake voice-related fraud causes monetary damage and serious privacy concerns for various applications, our approach improves the security of such services, being of high practical relevance. Furthermore, the post-hoc analysis of our results reveals that our model confirms image texture analysis-related findings of prior studies and discovers further voice signal features (i.e., textural and contextual) that can advance future work in this field

A Highly Effective Deep Learning Based Escape Route Recognition Module for Autonomous Robots in Crisis and Emergency Situations

Using convolutional neural networks we extend the work by Dugdale\u27s group on socially relevant multi-agent systems in crisis and emergency situations by giving the artificial agent the ability to precisely recognize escape signs, doors and stairs for escape route planning. We build an efficient recognition module consisting of three blocks of a depth-wise separable convolutional layer, a max-pooling layer, and a batch-normalization layer before dense, dropout and classifying the image. A rigorous evaluation based on the MCIndoor20000 dataset shows excellent performance values (e.g. over 99.81 percent accuracy). In addition, our module architecture is 78 times smaller than the MCIndoor20000 benchmark - making it suitable for embedding in operational drones and robots

DSPs as flexible Multimedia Accelerators

The increase of multimedia data processing requires immense processing power and transfer bandwidth as well as the consideration of real-time requirements. To reduce the CPU load the integration of flexible coprocessors seems to be a promising approach. This paper focuses on the integration of Digital Signal Processors (DSPs) as flexible multimedia accelerators into standard PC architectures running microkernel-based systems. Three components proved essential for multimedia acceleration: First, a data transfer mechanism capable of sustaining at least 30 Mbytes/s transfer rate, second, a DSP kernel with static scheduling and a very efficient context switch and third, a microkernel server running at the host which is responsible for data transfer between DSP and CPU and for calculating DSP schedules. 1. INTRODUCTION The increasing integration of multimedia data into applications requires immense processing power and transfer bandwidth as well as the consideration of real-time requiremen..

On the Integration of DSP Hardware into a Microkernel-based Operating System

Two technological developments reraise the debate about the use of DSPs as accelerators for certain applications vs. the employment of standard offthe -shelf microprocessors: ffl Tremendous development investments make general-purpose microprocessors even more powerful by the use of superscalar execution and sophisticated caching structures. ffl Microkernel-based operating system architectures permit very flexible decomposition of application systems due to their efficient message passing capabilities. Hence, this paper reports on two experiments. First, some elementary algorithms are run on a DSP and a modern superscalar microprocessor. Second, a DSP board is integrated into the message passing scheme of a modern microkernel-based operating system and a more complicated application is run on a DSP and evaluated. 1 Introduction Multimedia applications introduce at least two problems: ffl The need for high computing power and transfer bandwidth between CPU and peripheral devices. ..

Two-Level Classification of Chronic Stress Using Machine Learning on Resting-State EEG Recordings

While there are several works that diagnose acute stress using electroencephalographic recordings and machine learning, there are hardly any works that deal with chronic stress. Currently, chronic stress is mainly determined using questionnaires, which are, however, subjective in nature. While chronic stress has negative influences on health, it also greatly influences decision-making processes in humans. In this paper we propose a novel machine learning approach based on the fine-graded spectral analysis of resting-state EEG recordings, to diagnose chronic stress. By using this new machine learning approach, we achieve a very good balanced accuracy of 81.33%, outperforming the current benchmark by 10%. Our algorithm allows an objective assessment of chronic stress, is accurate, robust, fast and cost-efficient and substantially contributes to decision-making research, as well as Information Systems research in healthcare

Machine Learning Based Diagnosis of Binge Eating Disorder Using EEG Recordings

Binge eating disorder is the most common eating disorder and therefore an important health problem worldwide often resulting in obesity. Current investigations on binge eating disorder’s impact on the human brain regarding electroencephalography data are limited to traditional approaches. In this study we make use of a Machine Learning method both for distinguishing individuals affected by a BED and healthy individuals with an overall accuracy of 81.25% and highlighting low theta sub-band in the range of 4.5 – 6 Hz as the most important distinctive feature. Individuals with a BED show significantly higher theta activity. Using Machine learning approaches based on EEG data is a promising approach in order to facilitate disorder identification and to provide novel insights for health scientists