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Since the invention of the microphone by Barina in 1876, there have been numerous applications of audio processing, such as phonographs, broadcasting stations, and public address systems, which merely capture and amplify sound and play it back. Nowadays, audio processing involves analysis and noise-filtering techniques. There are various methods for noise filtering, each employing unique algorithms, but they all require two or more microphones for signal processing and analysis. For instance, on mobile phones, two microphones located in different positions are utilized for active noise cancellation (one for primary audio capture and the other for capturing ambient noise). However, a drawback is that when the sound source is distant, it may lead to poor audio capture. To capture sound from distant sources, alternative methods, like blind signal separation and beamforming, are necessary. This paper proposes employing a beamforming algorithm with two microphones to enhance speech and implementing this algorithm on an embedded system. However, prior to beamforming, it is imperative to accurately detect the direction of the sound source to process and analyze the audio from that direction. [ABSTRACT FROM AUTHOR]

In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people's activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2. For mixed source separation, in the first experiment, the average signal-to-interference ratio (SIR) at 1 m and 2 m distances was 16.72 and 15.76, respectively. In the second experiment, when evaluated using speech recognition, the algorithm improved speech recognition accuracy to 95%. [ABSTRACT FROM AUTHOR]

With the rapid advancement of modern hardware technology, breakthroughs have been made in many areas of artificial intelligence research, leading to the direction of machine replacement or assistance in various fields. However, most artificial intelligence or deep learning techniques require large amounts of training data and are typically applicable to a single task objective. Acquiring such large training datasets can be particularly challenging, especially in domains like medical imaging. In the field of image processing, few-shot image segmentation is an area of active research. Recent studies have employed deep learning and meta-learning approaches to enable models to segment objects in images with only a small amount of training data, allowing them to quickly adapt to new task objectives. This paper proposes a network architecture for meta-learning few-shot image segmentation, utilizing a meta-learning classification weight transfer network to generate masks for few-shot image segmentation. The architecture leverages pre-trained classification weight transfers to generate informative prior masks and employs pre-trained feature extraction architecture for feature extraction of query and support images. Furthermore, it utilizes a Feature Enrichment Module to adaptively propagate information from finer features to coarser features in a top-down manner for query image feature extraction. Finally, a classification module is employed for query image segmentation prediction. Experimental results demonstrate that compared to the baseline using the mean Intersection over Union (mIOU) as the evaluation metric, the accuracy increases by 1.7% in the one-shot experiment and by 2.6% in the five-shot experiment. Thus, compared to the baseline, the proposed architecture with meta-learning classification weight transfer network for mask generation exhibits superior performance in few-shot image segmentation. [ABSTRACT FROM AUTHOR]

AimTo evaluate the effect of the shunting branch of the portal vein (PV) (left or right) and the initial stent position (optimal or suboptimal) of a transjugular intrahepatic portosystemic shunt (TIPS).MethodsWe retrospectively reviewed 307 consecutive cirrhotic patients who underwent TIPS placement for variceal bleeding from March 2001 to July 2010 at our center. The left PV was used in 221 patients and the right PV in the remaining 86 patients. And, 224 and 83 patients have optimal stent position and sub-optimal stent positions, respectively. The patients were followed until October 2011 or their death. Hepatic encephalopathy, shunt dysfunction, and survival were evaluated as outcomes. The difference between the groups was compared by Kaplan-Meier analysis. A Cox regression model was employed to evaluate the predictors.ResultsAmong the patients who underwent TIPS to the left PV, the risk of hepatic encephalopathy (P = 0.002) and mortality were lower (P < 0.001) compared to those to the right PV. Patients who underwent TIPS with optimal initial stent position had a higher primary patency (P < 0.001) and better survival (P = 0.006) than those with suboptimal initial stent position. The shunting branch of the portal vein and the initial stent position were independent predictors of hepatic encephalopathy and shunt dysfunction after TIPS, respectively. And, both were independent predictors of survival.ConclusionTIPS placed to the left portal vein with optimal stent position may reduce the risk of hepatic encephalopathy and improve the primary patency rates, thereby prolonging survival.