Ubiquitous Emotion Recognition with Multimodal Mobile Interfaces

In 1997 Rosalind Picard introduced fundamental concepts of affect recognition. Since this time, multimodal interfaces such as Brain-computer interfaces (BCIs), RGB and depth cameras, physiological wearables, multimodal facial data and physiological data have been used to study human emotion. Much of the work in this field focuses on a single modality to recognize emotion. However, there is a wealth of information that is available for recognizing emotions when incorporating multimodal data. Considering this, the aim of this workshop is to look at current and future research activities and trends for ubiquitous emotion recognition through the fusion of data from various multimodal, mobile devices

Fourth Generation Wireless Systems: Requirements and Challenges for the Next Frontier

Fourth generation wireless systems (4G) are likely to reach the consumer market in another 4-5 years. 4G comes with the promise of increased bandwidth, higher speeds, greater interoperability across communication protocols, and user friendly, innovative, and secure applications. In this article, I list the requirements of the 4G systems by considering the needs of the users in the future. These requirements can be met if technical and business challenges can be overcome. Technical challenges include mobility management, quality of service, interoperability, high data rate, security, survivability, spectrum, intelligent mobile devices, middleware, and network access. I discuss the most plausible solutions to these technical challenges in this paper. Business-related challenges include billing, payment methods, pricing, size of investments, content provision and mediation, and the trade-off between richness and reach. If these technical and business challenges can be met, then 4G will become the next frontier in data and voice communication infrastructure

分散アンテナネットワークにおける広帯域MIMO伝送技術に関する研究

Tohoku University安達文幸課

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications

Automatic classification of adventitious respiratory sounds: a (un)solved problem?

(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers’ performance. (2) Methods: We conducted a set of experiments where we varied the durations of the other events on three tasks: crackle vs. wheeze vs. other (3 Class); crackle vs. other (2 Class Crackles); and wheeze vs. other (2 Class Wheezes). Four classifiers (linear discriminant analysis, support vector machines, boosted trees, and convolutional neural networks) were evaluated on those tasks using an open access respiratory sound database. (3) Results: While on the 3 Class task with fixed durations, the best classifier achieved an accuracy of 96.9%, the same classifier reached an accuracy of 81.8% on the more realistic 3 Class task with variable durations. (4) Conclusion: These results demonstrate the importance of experimental design on the assessment of the performance of automatic ARS classification algorithms. Furthermore, they also indicate, unlike what is stated in the literature, that the automatic classification of ARS is not a solved problem, as the algorithms’ performance decreases substantially under complex evaluation scenarios.publishe

Optimization of medical image steganography using n-decomposition genetic algorithm

Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications