A Vision Based Method to Distinguish and Recognize Static and Dynamic Gesture

AbstractVision based gesture recognizing acquired large improvements these years, as bonds of research achievements in labs become practical in factories. However, most of these findings are still restricted to either dynamic gesture or static posture, while the combination of them is always neglected but turns out to be common in practical. In this paper, we consider a vision based system that can interpret both dynamic and static gestures in real-time. Haar-like features and ada boost classifier are used to identify hands in images. Test results showed that when given suitable settings, the method of distinction runs successfully

Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

In this paper, we propose a new unsupervised domain adaptation (DA) method called layer-adapted implicit distribution alignment networks (LIDAN) to address the challenge of cross-corpus speech emotion recognition (SER). LIDAN extends our previous ICASSP work, deep implicit distribution alignment networks (DIDAN), whose key contribution lies in the introduction of a novel regularization term called implicit distribution alignment (IDA). This term allows DIDAN trained on source (training) speech samples to remain applicable to predicting emotion labels for target (testing) speech samples, regardless of corpus variance in cross-corpus SER. To further enhance this method, we extend IDA to layer-adapted IDA (LIDA), resulting in LIDAN. This layer-adpated extention consists of three modified IDA terms that consider emotion labels at different levels of granularity. These terms are strategically arranged within different fully connected layers in LIDAN, aligning with the increasing emotion-discriminative abilities with respect to the layer depth. This arrangement enables LIDAN to more effectively learn emotion-discriminative and corpus-invariant features for SER across various corpora compared to DIDAN. It is also worthy to mention that unlike most existing methods that rely on estimating statistical moments to describe pre-assumed explicit distributions, both IDA and LIDA take a different approach. They utilize an idea of target sample reconstruction to directly bridge the feature distribution gap without making assumptions about their distribution type. As a result, DIDAN and LIDAN can be viewed as implicit cross-corpus SER methods. To evaluate LIDAN, we conducted extensive cross-corpus SER experiments on EmoDB, eNTERFACE, and CASIA corpora. The experimental results demonstrate that LIDAN surpasses recent state-of-the-art explicit unsupervised DA methods in tackling cross-corpus SER tasks

Learning Local to Global Feature Aggregation for Speech Emotion Recognition

Transformer has emerged in speech emotion recognition (SER) at present. However, its equal patch division not only damages frequency information but also ignores local emotion correlations across frames, which are key cues to represent emotion. To handle the issue, we propose a Local to Global Feature Aggregation learning (LGFA) for SER, which can aggregate longterm emotion correlations at different scales both inside frames and segments with entire frequency information to enhance the emotion discrimination of utterance-level speech features. For this purpose, we nest a Frame Transformer inside a Segment Transformer. Firstly, Frame Transformer is designed to excavate local emotion correlations between frames for frame embeddings. Then, the frame embeddings and their corresponding segment features are aggregated as different-level complements to be fed into Segment Transformer for learning utterance-level global emotion features. Experimental results show that the performance of LGFA is superior to the state-of-the-art methods.Comment: This paper has been accepted on INTERSPEECH 202

Use of a novel valve stent for transcatheter pulmonary valve replacement: An animal study

ObjectiveThe goal of this study was to evaluate valvular functionality after transcatheter pulmonary valve replacement in sheep using a novel pulmonary valve stent.MethodsFresh porcine pericardium cross-linked with 0.6% glutaraldehyde was treated with L-glutamine to eliminate glutaraldehyde toxicity and sutured onto a valve ring before mounting on a nitinol stent to construct the pulmonary valve stent. Percutaneous femoral vein transcatheter pulmonary valve replacement was performed with the newly constructed valve stent. Pulmonary valve stents were implanted in 10 healthy sheep (6 males and 4 females) weighing an average of 25.7 ± 4.1 kg. Color Doppler echocardiography, 64-row computed tomography, and direct catheter examination were used to assess valvular function.ResultsImplantation was successful in 8 sheep. Shortly after surgery, all artificial valve stents exhibited normal open and close functionality and no stenosis or insufficiency. Heart rate was slightly elevated at this time, while all other hemodynamic parameters were normal. Six-month follow-up revealed no evidence of valve stent dislocation and normal valvular and cardiac functionality. There was no evidence of stent fracture. Repeated valve stent implantation was well tolerated as indicated by good valvular functionality 2 months postdelivery.ConclusionThe novel pulmonary valve stent described herein can be delivered via percutaneous femoral vein transcatheter implantation and is highly efficacious at 6 months postdelivery. Furthermore, repeated valve stent replacement was successful

The effects of fermentation and adsorption using lactic acid bacteria culture broth on the feed quality of rice straw

To improve the nutritional value and the palatability of air-dried rice straw, culture broth of the lactic acid bacteria community SFC-2 was used to examine the effects of two different treatments, fermentation and adsorption. Air-dried and chopped rice straw was treated with either fermentation for 30 d after adding 1.5 L nutrient solution (50 mL inocula L–1, 1.2×1012 CFU mL–1 inocula) kg–1 straw dry matter, or spraying a large amount of culture broth (1.5 L kg–1 straw dry matter, 1.5×1011 CFU mL–1 culture broth) on the straw and allowing it to adsorb for 30 min. The feed quality and aerobic stability of the resulting forage were examined. Both treatments improved the feed quality of rice straw, and adsorption was better than fermentation for preserving nutrients and improving digestibility, as evidenced by higher dry matter (DM) and crude protein (CP) concentrations, lower neutral detergent fiber (NDF), acid detergent fiber (ADF) and NH3-N concentrations, as well as higher lactic acid production and in vitro digestibility of DM (IVDMD). The aerobic stability of the adsorbed straw and the fermented straw was 392 and 480 h, respectively. After being exposed to air, chemical components and microbial community of the fermented straw were more stable than the adsorbed straw