Mishra, Deepak

An always-on intelligent system comprising of an image sensor requires continuous functioning of each pixel. This includes sensing the illumination content of the scene and also the conversion of the analog values into their digital representations. Therefore, power consumption during analog to digital conversion and computational cost at the image sensor module become critical while designing a system that is always-on and incorporates intelligence near the sensor module. This work focuses on the inherent property of the ADC for converting the analog pixel values to digital values by taking a defined number of analog-to-digital converter (ADC) cycles. The design factors considered are 1) Power saving due to reduced ADC conversion cycles for each pixel; 2) The reduced bit-precision of the processing unit to reduce hardware cost; 3) The dataflow design through hls4ml, which produces parallel computational modes for low latency CNN architectures. The proposed work implements two lightweight CNN models with reduced parameters as compared to the original architectural models of VGG16 (like) and SqueezeNet (like) which are trained in Qkeras and deployed on Zynq UltraScale+ MPSoC board. In addition, the design pipeline is validated on the MobileNetV2 and GhostNet architectures to demonstrate its generalization ability. A detailed analysis shows that limiting the number of ADC bits from 8 to 4 reduces the mean accuracy merely from 50.3 to 49.17 for VGG16 (like) and 67.83 to 67.80 for SqueezeNet (like) model, however, the readout power is significantly reduced from 140.45 mW to 7.7 mW for STL-10 dataset with 96×96 image resolution. Additional experiments are conducted with CIFAR-10 and mini-ImageNet datasets for classification and with Oxford-IIIT Pet Dataset for segmentation. The proposed work, thus, provides empirical evidence that a reasonable performance for intelligent vision tasks with power saving can be achieved by tuning CNN models to work with reduced ADC bit precision.

System heterogeneity in Federated Learning (FL) is commonly dealt with knowledge distillation by combining the clients' knowledge via distillation into a global model. However, such knowledge transfer to the global model is often limited by distillation efficiency and unavailability of the client data. Most of the existing approaches require proxy data on the server side for distillation, which often becomes a bottleneck. To circumvent these limitations, we propose a novel FL framework, FedAgPD (Aggregation-Assisted Proxyless Distillation for Heterogeneous Federated Learning) that comprises of deep mutual learning (DML) at client end, and global aggregation followed by noise engineered data-free distillation at the server end. DML enables server side global aggregation which otherwise is infeasible due to different client model architectures. The aggregation results in knowledge integration which is further boosted by the subsequent distillation. We further introduce the idea of selective mutual learning where only those clients perform DML that are not limited by computational capacity. This reduces the overall computational burden without any compromise in the performance. We conduct rigorous experiments on various publicly available datasets and observe a remarkable improvement in the performance over the existing heterogeneous FL methods. For example, for CIFAR100 dataset, FedAgPD shows almost two times better performance as compared to the best baseline. Moreover, we compared FedAgPD with recent homogeneous methods and observed a competitive performance. The results provide evidence for the utility and effectiveness of our approach and open up a new direction for heterogeneous FL. Code for FedAgPD is available at https://github.com/nirbhay-design/FedAgPD

Self-supervised learning (SSL) has emerged as a promising paradigm for medical image analysis by harnessing unannotated data. Despite their potential, the existing SSL approaches overlook the high anatomical similarity inherent in medical images. This makes it challenging for SSL methods to capture diverse semantic content in medical images consistently. This work introduces a novel and generalized solution that implicitly exploits anatomical similarities by integrating codebooks in SSL. The codebook serves as a concise and informative dictionary of visual patterns, which not only aids in capturing nuanced anatomical details but also facilitates the creation of robust and generalized feature representations. In this context, we propose CoBooM, a novel framework for self-supervised medical image learning by integrating continuous and discrete representations. The continuous component ensures the preservation of fine-grained details, while the discrete aspect facilitates coarse-grained feature extraction through the structured embedding space. To understand the effectiveness of CoBooM, we conduct a comprehensive evaluation of various medical datasets encompassing chest X-rays and fundus images. The experimental results reveal a significant performance gain in classification and segmentation tasks.

Sonography plays a critical role in diagnosing various health conditions, but operator-dependent limitations impact imaging quality and consistency. Robotic scanning has emerged as a solution to enhance imaging consistency while reducing operator dependency. This paper presents a compliant soft robotic gripper designed to assist in sonography procedures using hyperelastic materials. The gripper design features four channels with five inflatable strips per channel constructed with Polydimethylsiloxane (PDMS), allowing for spatial bending when pressurized. Fabrication involves CAD modelling, 3D printing, and silicon casting method-based curing and finally sealed with fabric and silicon. Experimental results demonstrate the gripper's capability, with a single channel achieving a deflection of approximately 57 degrees. Further optimization of channel length, number of channels, and number of strip configurations and their design is required through analytical or simulation work. The study showcases the feasibility of integrating soft robotics into medical imaging, potentially revolutionizing sonography practices and patient care.

Self-supervised learning (SSL) is potentially useful in reducing the need for manual annotation and making deep learning models accessible for medical image analysis tasks. By leveraging the representations learned from unlabeled data, self-supervised models perform well on tasks that require little to no fine-tuning. However, for medical images, like chest X-rays, characterized by complex anatomical structures and diverse clinical conditions, a need arises for representation learning techniques that encode fine-grained details while preserving the broader contextual information. In this context, we introduce MLVICX (Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning), an approach to capture rich representations in the form of embeddings from chest X-ray images. Central to our approach is a novel multi-level variance and covariance exploration strategy that effectively enables the model to detect diagnostically meaningful patterns while reducing redundancy. MLVICX promotes the retention of critical medical insights by adapting global and local contextual details and enhancing the variance and covariance of the learned embeddings. We demonstrate the performance of MLVICX in advancing self-supervised chest X-ray representation learning through comprehensive experiments. The performance enhancements we observe across various downstream tasks highlight the significance of the proposed approach in enhancing the utility of chest X-ray embeddings for precision medical diagnosis and comprehensive image analysis. For pertaining, we used the NIH-Chest X-ray dataset, while for downstream tasks, we utilized NIH-Chest X-ray, Vinbig-CXR, RSNA pneumonia, and SIIM-ACR Pneumothorax datasets. Overall, we observe up to 3% performance gain over SOTA SSL approaches in various downstream tasks. Additionally, to demonstrate the generalizability of the proposed method, we conducted additional experiments on fundus images and observed superior performance on multiple datasets. Codes are available at https://github.com/azad6629/mlvicx/ GitHub.

Bio-signals such as electroencephalogram (EEG) contain low activity regions often called B-noise and high activity regions called active potentials. The high activity regions are more important as compared to their counterpart. In addition, the signals are considerably sparse in the low activity regions. Thus a full n-bit conversion of low activity samples into digital domain increases readout power and reduces data acquisition rate of analog to digital converter (ADC). To alleviate these problems, a reconfigurable cyclic ADC is presented in this paper. Input range and conversion cycles of the proposed ADC are varied according to the samples of the neural signal. The high activity region samples are resolved using conventional n-bits, however, the low activity region is resolved using less number of bits. This saves readout power and also reduces the digital data content. The proposed ADC is designed and fabricated in UMC 180 nm CMOS technology. The ADC operates at a sampling rate of 200 kS/s and consumes 61.8 μW of power. The chip occupies an area of 0.031 mm2. Using reconfiguration, the power saving of 28.6% is achieved compared to the conventional n-bit full conversion.