This paper investigates low-energy consumption and low-power hardware models and processor architectures for performing the real-time recognition of objects in power-constrained autonomous systems and robots. Most recent developments show that convolutional deep neural networks are currently the state-of-the-art in terms of classification accuracy. In this article we propose to use of a different type of deep neural network-stacked autoencoders-and show that within a limited number of layers and nodes, for accommodating the use of low-power accelerators such as mobile GPUs and FPGAs, we are still able to achieve both classification levels not far from the state-of-the-art and a high number of processed frames per second. We present experiments using the color CIFAR-10 dataset. This enables the adaptation of the architecture to a live feed camera. Another novelty equally proposed for the first time in this work suggests that the training phase can also be performed in these low-power devices, instead of the usual approach that uses a desktop CPU or a GPU to perform this task and only runs the trained network later on the FPGA. This allows incorporating new functionalities as, for example, a robot performing runtime learning.

Based on this work, a medical prototype will be developed for early cataract detection, classification, and hardness estimation.

This paper addresses the use of computer-aided diagnosis (CAD) system for the cataract classification based on ultrasound technique. Ultrasound A-scan signals were acquired in 220 porcine lenses. B-mode and Nakagami images were constructed. Ninety-seven parameters were extracted from acoustical, spectral and image textural analyses and were subjected to feature selection by Principal Component Analysis (PCA). Bayes, K Nearest-Neighbors (KNN), Fisher Linear Discriminant (FLD) and Support Vector Machine (SVM) classifiers were tested. The classification of healthy and cataractous lenses shows a good performance for the four classifiers (F-measure ≥92.68%) with SVM showing the highest performance (90.62%) for initial versus severe cataract classification.