Image Classification with CondenseNeXt for ARM-Based Computing Platforms

In this paper, we demonstrate the implementation of our ultra-efficient deep convolutional neural network architecture: CondenseNeXt on NXP BlueBox, an autonomous driving development platform developed for self-driving vehicles. We show that CondenseNeXt is remarkably efficient in terms of FLOPs, designed for ARM-based embedded computing platforms with limited computational resources and can perform image classification without the need of a CUDA enabled GPU. CondenseNeXt utilizes the state-of-the-art depthwise separable convolution and model compression techniques to achieve a remarkable computational efficiency. Extensive analyses are conducted on CIFAR-10, CIFAR-100 and ImageNet datasets to verify the performance of CondenseNeXt Convolutional Neural Network (CNN) architecture. It achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error). CondenseNeXt achieves final trained model size improvement of 2.9+ MB and up to 59.98% reduction in forward FLOPs compared to CondenseNet and can perform image classification on ARM-Based computing platforms without needing a CUDA enabled GPU support, with outstanding efficiency.Comment: 6 pages, 7 figures, conference, published IEEE Conference pape

Think beyond ascites

In children with gross, persistent ascites wherein clinical scenario is not agreeable to common conditions, one needs to revise the diagnosis and rule out the surgical cause for abdominal distension mimicking ascites. We are reporting here, a case of two year old female child who presented with abdominal distension, clinically suggestive of ascites and subsequently diagnosed to have a large chylous mesenteric cyst which was determined on biochemical investigations, imaging and confirmed on surgical intervention. She was managed surgically with successful outcome

Egocentric Audio-Visual Noise Suppression

This paper studies audio-visual suppression for egocentric videos -- where the speaker is not captured in the video. Instead, potential noise sources are visible on screen with the camera emulating the off-screen speaker's view of the outside world. This setting is different from prior work in audio-visual speech enhancement that relies on lip and facial visuals. In this paper, we first demonstrate that egocentric visual information is helpful for noise suppression. We compare object recognition and action classification based visual feature extractors, and investigate methods to align audio and visual representations. Then, we examine different fusion strategies for the aligned features, and locations within the noise suppression model to incorporate visual information. Experiments demonstrate that visual features are most helpful when used to generate additive correction masks. Finally, in order to ensure that the visual features are discriminative with respect to different noise types, we introduce a multi-task learning framework that jointly optimizes audio-visual noise suppression and video based acoustic event detection. This proposed multi-task framework outperforms the audio only baseline on all metrics, including a 0.16 PESQ improvement. Extensive ablations reveal the improved performance of the proposed model with multiple active distractors, over all noise types and across different SNRs.Comment: Under Review at ICASSP 202

Image Classification with CondenseNeXt for ARM-Based Computing Platforms

In this paper, we demonstrate the implementation of our ultra-efficient deep convolutional neural network architecture: CondenseNeXt on NXP BlueBox, an autonomous driving development platform developed for self-driving vehicles. We show that CondenseNeXt is remarkably efficient in terms of FLOPs, designed for ARM-based embedded computing platforms with limited computational resources and can perform image classification without the need of a CUDA enabled GPU. CondenseNeXt utilizes the state-of-the-art depthwise separable convolution and model compression techniques to achieve a remarkable computational efficiency. Extensive analyses are conducted on CIFAR-10, CIFAR-100 and ImageNet datasets to verify the performance of Con-denseNeXt Convolutional Neural Network (CNN) architecture. It achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error). CondenseNeXt achieves final trained model size improvement of 2.9+ MB and up to 59.98% reduction in forward FLOPs compared to CondenseNet and can perform image classification on ARM-Based computing platforms without needing a CUDA enabled GPU support, with outstanding efficiency

SCA: Streaming Cross-attention Alignment for Echo Cancellation

End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation. However, most state-of-the-art methods for echo cancellation are either classical DSP-based or hybrid DSP-ML algorithms. Components such as the delay estimator and adaptive linear filter are based on traditional signal processing concepts, and deep learning algorithms typically only serve to replace the non-linear residual echo suppressor. This paper introduces an end-to-end echo cancellation network with a streaming cross-attention alignment (SCA). Our proposed method can handle unaligned inputs without requiring external alignment and generate high-quality speech without echoes. At the same time, the end-to-end algorithm simplifies the current echo cancellation pipeline for time-variant echo path cases. We test our proposed method on the ICASSP2022 and Interspeech2021 Microsoft deep echo cancellation challenge evaluation dataset, where our method outperforms some of the other hybrid and end-to-end methods

CondenseNeXtV2: Light-Weight Modern Image Classifier Utilizing Self-Querying Augmentation Policies

Artificial Intelligence (AI) combines computer science and robust datasets to mimic natural intelligence demonstrated by human beings to aid in problem-solving and decision-making involving consciousness up to a certain extent. From Apple’s virtual personal assistant, Siri, to Tesla’s self-driving cars, research and development in the field of AI is progressing rapidly along with privacy concerns surrounding the usage and storage of user data on external servers which has further fueled the need of modern ultra-efficient AI networks and algorithms. The scope of the work presented within this paper focuses on introducing a modern image classifier which is a light-weight and ultra-efficient CNN intended to be deployed on local embedded systems, also known as edge devices, for general-purpose usage. This work is an extension of the award-winning paper entitled ‘CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems’ published for the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). The proposed neural network dubbed CondenseNeXtV2 utilizes a new self-querying augmentation policy technique on the target dataset along with adaption to the latest version of PyTorch framework and activation functions resulting in improved efficiency in image classification computation and accuracy. Finally, we deploy the trained weights of CondenseNeXtV2 on NXP BlueBox which is an edge device designed to serve as a development platform for self-driving cars, and conclusions will be extrapolated accordingly

Directional Source Separation for Robust Speech Recognition on Smart Glasses

Modern smart glasses leverage advanced audio sensing and machine learning technologies to offer real-time transcribing and captioning services, considerably enriching human experiences in daily communications. However, such systems frequently encounter challenges related to environmental noises, resulting in degradation to speech recognition and speaker change detection. To improve voice quality, this work investigates directional source separation using the multi-microphone array. We first explore multiple beamformers to assist source separation modeling by strengthening the directional properties of speech signals. In addition to relying on predetermined beamformers, we investigate neural beamforming in multi-channel source separation, demonstrating that automatic learning directional characteristics effectively improves separation quality. We further compare the ASR performance leveraging separated outputs to noisy inputs. Our results show that directional source separation benefits ASR for the wearer but not for the conversation partner. Lastly, we perform the joint training of the directional source separation and ASR model, achieving the best overall ASR performance.Comment: Submitted to ICASSP 202

Pea ferritin stability under gastric pH conditions determines the mechanism of iron uptake in Caco-2 cells

Background: Iron deficiency is an enduring global health problem that requires new remedial approaches. Iron absorption from soybean-derived ferritin, an ∼550-kDa iron storage protein, is comparable to bioavailable ferrous sulfate (FeSO4). However, the absorption of ferritin is reported to involve an endocytic mechanism, independent of divalent metal ion transporter 1 (DMT-1), the transporter for nonheme iron. Objective: Our overall aim was to examine the potential of purified ferritin from peas (Pisum sativum) as a food supplement by measuring its stability under gastric pH treatment and the mechanisms of iron uptake into Caco-2 cells. Methods: Caco-2 cells were treated with native or gastric pH–treated pea ferritin in combination with dietary modulators of nonheme iron uptake, small interfering RNA targeting DMT-1, or chemical inhibitors of endocytosis. Cellular ferritin formation, a surrogate measure of iron uptake, and internalization of pea ferritin with the use of specific antibodies were measured. The production of reactive oxygen species (ROS) in response to equimolar concentrations of native pea ferritin and FeSO4 was also compared. Results: Pea ferritin exposed to gastric pH treatment was degraded, and the released iron was transported into Caco-2 cells by DMT-1. Inhibitors of DMT-1 and nonheme iron absorption reduced iron uptake by 26–40%. Conversely, in the absence of gastric pH treatment, the iron uptake of native pea ferritin was unaffected by inhibitors of nonheme iron absorption, and the protein was observed to be internalized in Caco-2 cells. Chlorpromazine (clathrin-mediated endocytosis inhibitor) reduced the native pea ferritin content within cells by ∼30%, which confirmed that the native pea ferritin was transported into cells via a clathrin-mediated endocytic pathway. In addition, 60% less ROS production resulted from native pea ferritin in comparison to FeSO4. Conclusion: With consideration that nonheme dietary inhibitors display no effect on iron uptake and the low oxidative potential relative to FeSO4, intact pea ferritin appears to be a promising iron supplement