Individuals with dysarthria exhibit irregular speech patterns that vary by disease, significantly reducing the accuracy of conventional speech-recognition systems. Previous studies have typically focused on a single disease group or used aggregated data without accounting for inter-disease variation, thereby limiting disease-specific insights. In this study, fluency metrics were extracted from a Korean dysarthric speech corpus across three disease groups (stroke, cerebral palsy, and peripheral neuropathy) and the diseases were classified based on these features. The performance of the disease-specific speech-recognition models was evaluated using the weighted character error rate (Weighted-CER). Results showed that classification based on fluency metrics achieved 99% accuracy. The disease-specific models improved the CER by up to 18.34 and 1.05 percentage points compared with the Whisper–Small model and a model trained on the entire dataset, respectively. In terms of Weighted-CER, the error rate decreased by up to 15.27 and 1.49 percentage points, respectively. These findings indicate that disease-specific models can meaningfully enhance speech recognition and underscore the importance of developing speech-recognition systems that can adapt to individual speech characteristics in patients with dysarthria.

Shock waves caused by earthquakes can be devastating. Generating realistic earthquake-caused ground motion waveforms help reducing losses in lives and properties, yet generative models for the task tend to generate subpar waveforms. We present High-fidelity Earthquake Groundmotion Generation System (HEGGS) and demonstrate its superior performance using earthquakes from North American, East Asian, and European regions. HEGGS exploits the intrinsic characteristics of earthquake dataset and learns the waveforms using an end-to-end differentiable generator containing conditional latent diffusion model and hi-fidelity waveform construction model. We show the learning efficiency of HEGGS by training it on a single GPU machine and validate its performance using earthquake databases from North America, East Asia, and Europe, using diverse criteria from waveform generation tasks and seismology. Once trained, HEGGS can generate three dimensional E-N-Z seismic waveforms with accurate P/S phase arrivals, envelope correlation, signal-to-noise ratio, GMPE analysis, frequency content analysis, and section plot analysis.

The increasing integration of diverse co-processors and peripherals within mobile Arm System-on-Chips (SoCs) presents significant challenges for secure and efficient device I/O. Existing approaches relying on memory encryption introduce substantial performance and power overheads, which are exacerbated by the need for real-time data processing and strict power efficiency requirements in mobile platforms. These issues hinder the wider adoption of Arm Confidential Compute Architecture (CCA), which aims to provide robust security guarantees. To address these challenges, we present Portal, a secure and efficient device I/O interface for Arm CCA on mobile Arm SoCs. Portal achieves secure I/O through strict memory isolation without the need for memory encryption. By leveraging the memory isolation mechanism in Arm CCA, Portal enforces hardware-level access control, ensuring that only designated Realm virtual machines and peripherals can access the Portal-protected plaintext memory regions. This design eliminates the overhead associated with encryption, supports dynamic peripheral integration, and maintains robust security guarantees. The evaluation results demonstrate that Portal incurs a minimal one-time overhead of 9.8%, while enhancing scalability and power efficiency, making it a pivotal solution for fostering the adoption of the upcoming Arm CCA in mobile and resource-constrained environments.

Current-efficient, fast-transient reconfigurable low-dropout regulator (LDO) is proposed for the wireless sensor network (WSN) system. The proposed LDO is designed and simulated in a 65 nm CMOS process showing the 3 key features: 1) a reconfigurable LDO architecture to achieve both low quiescent current (IQ) and wide bandwidth by adaptively adjusting to different load current conditions, 2) ultra-low-IQ and high PSR regulator for light-load efficient operation utilizing the gain boosting scheme within the flipped voltage follower loop, and 3) fast transient regulator for robust heavy-load operation with level-shifted impedance attenuation buffer (IAB) to reduce the supply ripples generated by wireless transceiver. The proposed LDO shows the state-of-the-art 93.8% current efficiency in the load condition of 1 μA, and achieves 0.672 ns response time even with 10 ns load transition.

Real-time lung electrical impedance tomography (EIT) IC with SNR equalization is implemented in 110-nm CMOS process. The fast-settling high-pass filter (FS-HPF) and fast-settling low-pass filter (FS-LPF) is proposed to reduce the settling-time which takes over 90% of entire EIT operating time. For the FS-LPF, voltage-controlled pseudo-resistor (VCPR) and current DAC-based voltage control circuit are proposed. For accurate image reconstruction, adaptive current control (ACC) scheme is implemented for SNR equalization of sensing electrodes. As a result, 35-μs settling-time is achieved for 100-kHz carrier frequency satisfying 30-fps operation on single receiver channel. The simulation results show that the SNR equalization can reduce the center of mass (COM) error of reconstructed image by 72.8% with ACC.