The emergence of sophisticated variant botnet attacks like Okiru poses significant security challenges to the proliferating IoT devices. This study proposes a reinforcement learning (RL)-based defense system for IoT networks. Our system employs an RL agent to analyze network packets in real-time and implement adaptive security measures. By leveraging AI techniques, this research offers a proactive approach to addressing complex cyber threats in IoT environments.

Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications of ASR is Speech-To-Text (STT) technology, which simplifies user workflows by transcribing spoken words into text. In the medical field, STT has the potential to significantly reduce the workload of clinicians who rely on typists to transcribe their voice recordings. However, developing an STT model for the medical domain is challenging due to the lack of sufficient speech and text datasets. To address this issue, we propose a medical-domain text correction method that modifies the output text of a general STT system using the Vision Language Pre-training (VLP) method. VLP combines textual and visual information to correct text based on image knowledge. Our extensive experiments demonstrate that the proposed method offers quantitatively and clinically significant improvements in STT performance in the medical field. We further show that multi-modal understanding of image and text information outperforms single-modal understanding using only text information.

Recently, deep learning approaches for MR motion artifact correction have been extensively studied. Although these approaches have shown high performance and lower computational complexity compared to classical methods, most of them require supervised training using paired artifact-free and artifact-corrupted images, which may prohibit its use in many important clinical applications. For example, transient severe motion (TSM) due to acute transient dyspnea in Gd-EOB-DTPA-enhanced MR is difficult to control and model for paired data generation. To address this issue, here we propose a novel unpaired deep learning scheme that does not require matched motion-free and motion artifact images. Specifically, the first step of our method is k -space random subsampling along the phase encoding direction that can remove some outliers probabilistically. In the second step, the neural network reconstructs fully sampled resolution image from a downsampled k -space data, and motion artifacts can be reduced in this step. Last, the aggregation step through averaging can further improve the results from the reconstruction network. We verify that our method can be applied for artifact correction from simulated motion as well as real motion from TSM successfully from both single and multi-coil data with and without k -space raw data, outperforming existing state-of-the-art deep learning methods.

This paper presents 16-bit DSP processor and system for baseband and voiceband processing of IS-136 digital cellular telephony that is North America TDMA standard. The DSP, named MIGHTI, that is designed for mobile communication applications, has some special instructions that allow instruction pipelining for the compound operations. MIGHTI includes a low-power 16 K-word flexible port fullcustom memory. With MIGHTI, IS-136 baseband and voiceband processing that normally requires 45 MIPS in a conventional DSP, is performed in just 36 MIPS. The system developed for baseband and voiceband processing does meet the requirements of the IS-136 standard.