Practical classification of different moving targets using automotive radar and deep neural networks

In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed

From single neurons to social brains

The manufacture of stone tools is an integral part of the human evolutionary trajectory. However, very little research is directed towards the social and cognitive context of the process of manufacture. This article aims to redress this balance by using insights from contemporary neuroscience. Addressing successively more inclusive levels of analysis, we will argue that the relevant unit of analysis when examining the interface between archaeology and neuroscience is not the individual neuron, nor even necessarily the individual brain, but instead the socio-cognitive context in which brains develop and tools are manufactured and used. This context is inextricably linked to the development of unique ontogenetic scheduling, as evidenced by the fossil record of evolving hominin lineages

Automatic Recognition of Light Microscope Pollen Images

This paper is a progress report on a project aimed at the realization of a low-cost, automatic, trainable system "AutoStage" for recognition and counting of pollen. Previous work on image feature selection and classification has been extended by design and integration of an XY stage to allow slides to be scanned, an auto focus system, and segmentation software. The results of a series of classification tests are reported, and verified by comparison with classification performance by expert palynologists. A number of technical issues are addressed, including pollen slide preparation and slide sampling protocols