Silent speech recognition has emerged as a promising approach for enabling hands-free and discreet interaction with head-worn devices. In this paper, we present QuietSync, a multimodal system that combines inertial measurement unit (IMU) and contact electrode (ExG) signals to achieve accurate silent speech recognition using off-the-shelf devices. QuietSync utilizes an IMU attached to the lower part of the headphones near the ear and strategically places ExG electrodes on the headphones, glasses (nose and behind the ear), and face (for VR applications) to capture subtle movements and muscle activity associated with silent speech production. We conducted a user study with 9 participants and successfully recognized 12 commands with an accuracy of 94.2%. Our system leverages the complementary nature of IMU and ExG signals to enhance the robustness and reliability of silent speech recognition. The IMU captures subtle movements of the jaw and facial muscles, while the ExG electrodes detect low-amplitude surface muscle activity associated with speech production. We show that our system is not affected by the length and speech mannerisms of the commands, and can be fine-tuned for users of varied native languages with only 5 samples. Our findings demonstrate the feasibility of using off-the-shelf head-worn devices to enable silent speech recognition, opening up new possibilities for seamless and discreet interaction with devices such as VR/AR headsets and earables. To the best of our knowledge, QuietSync is the first system to enable silent speech interaction for multiple form factors.

The microservice system architecture (MSA) outperforms the monolithic system architecture in terms of maintainability, extensibility, scalability, and fault tolerance. This is prompting a widescale migration of software systems from existing monolith systems to MSA. Most microservice systems utilize container technology for deployment. The fact that Kubernetes (K8s) provides a fully-fledged toolchain for managing container-based applications is prompting many organizations to adopt the K8s protocol for microservice system deployment and operations. Microservice monitoring is essential to the success of any service operation. The collection of logs and aggregation of metrics by most existing microservice monitoring systems is somewhat intrusive. Furthermore, the heterogeneity of Kubernetes technology means that most monitoring methods are inapplicable in situations where microservices are developed for a system using a variety of underlying languages and platforms. In the current study, we developed a monitoring mechanism that provides various metrics specific to microservice systems in a nonintrusive way. The proposed K8s-based microservice monitoring system, referred to as KMamiz (Kubernetes-based Microservice Analysis and Monitoring using Istio and Zipkin), enables the construction and visualization for service-level/endpoint-level dependency graphs and endpoint request chains, and the service cohesion/coupling analysis to enhance system quality for the development team.

Abstract Despite the growing importance of the microservice architecture (MSA), interactions among the various elements (e.g., services, endpoints, and versions) remain difficult to manage. This research devised a system, called version‐based microservice analysis, monitoring, and visualization (VMAMV), including multiple proposed methods to facilitate the testing, monitoring, and visualization of microservice systems by considering service versions and risks. VMAMV can generate version‐based service dependency graphs, provide graph search services, perform consumer‐driven contract testing, and conduct a risk analysis. Besides, VMAMV can also notify users of existing anomalies and potential risks to facilitate the development and operation of microservice systems. Experiments were conducted to demonstrate the efficacy of the VMAMV system in the detection of service anomalies, old patch versions, low‐usage service versions, the presentation of contract test results and service error chains, and the validity of risk analysis.

Recent studies have shown that integrating individualized templates into a template-matching target identification method could significantly improve the performance of a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). However, collecting the template (or calibration) data for each individual can be time-consuming and laborious. This issue can be alleviated by employing phase-coded visual stimuli because phase information could be discriminated by using templates synthesized from the template induced by a visual stimulus. Minimizing phase intervals between two adjacent visual stimuli could increase the number of stimuli without increasing the calibration cost. Nonetheless, no study has investigated the effects of the phase interval on the classification performance. This study compared the classification accuracy of SSVEPs with five different phase intervals (0.1 π, 0.2 π, 0.3 π, 0.4 π, and 0.5 π) using synthesized individual templates with task-related component analysis (TRCA)-based spatial filtering. From a public 12-class SSVEP dataset, phase-adjusted SSVEP data were created by adding time shifts according to the five phase intervals. The classification results showed that the accuracy was sufficiently high when the phase intervals were over 0.3 π, suggesting the use of up to six phase-shifted visual stimuli at a given frequency.

On the pathway from laboratory settings to real world environment, a major challenge on the development of a robust electroencephalogram (EEG)-based brain-computer interface (BCI) is to collect a significant amount of informative training data from each individual, which is labor intensive and time-consuming and thereby significantly hinders the applications of BCIs in real-world settings. A possible remedy for this problem is to leverage existing data from other subjects. However, substantial inter-subject variability of human EEG data could deteriorate more than improve the BCI performance. This study proposes a new transfer learning (TL)-based method that exploits a subject's pilot data to select auxiliary data from other subjects to enhance the performance of an EEG-based BCI for drowsiness detection. This method is based on our previous findings that the EEG correlates of drowsiness were stable within individuals across sessions and an individual's pilot data could be used as calibration/training data to build a robust drowsiness detector. Empirical results of this study suggested that the feasibility of leveraging existing BCI models built by other subjects' data and a relatively small amount of subject-specific pilot data to develop a BCI that can outperform the BCI based solely on the pilot data of the subject.

An augmented brain computer interface that can detect users' brain states in real-life situations has been developed using wireless EEG headsets, smart phones and ubiquitous computing services. This kind of wearable natural user interfaces will have a wide-range of potential applications in future smart environments. This paper describes its ubiquitous system architecture and introduces its enabling technologies, which include machine-to-machine publish/subscribe protocols, multi-tier fog/cloud computing infrastructure and a linked data web. Its real-time responsiveness and easiness-of-use will be demonstrated by playing a multi-player on-line BCI game EEG Tractor Beam at the Intelligent Environment Conference.

Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.