Characteristics of direct human impacts on the rivers Karun and Dez in lowland south-west Iran and their interactions with earth surface movements

Two of the primary external factors influencing the variability of major river systems, over river reach scales, are human activities and tectonics. Based on the rivers Karun and Dez in south-west Iran, this paper presents an analysis of the geomorphological responses of these major rivers to ancient human modifications and tectonics. Direct human modifications can be distinguished by both modern constructions and ancient remnants of former constructions that can leave a subtle legacy in a suite of river characteristics. For example, the ruins of major dams are characterised by a legacy of channel widening to 100's up to c. 1000 m within upstream zones that can stretch to channel distances of many kilometres upstream of former dam sites, whilst the legacy of major, ancient, anthropogenic river channel straightening can also be distinguished by very low channel sinuosities over long lengths of the river course. Tectonic movements in the region are mainly associated with young and emerging folds with NW–SE and N–S trends and with a long structural lineament oriented E–W. These earth surface movements can be shown to interact with both modern and ancient human impacts over similar timescales, with the types of modification and earth surface motion being distinguishable. This paper examines the geomorphological evidence and outlines the processes involved in the evolution of these interactions through time. The analysis shows how interactions between earth surface movements and major dams are slight, especially after ancient dam collapse. By contrast, interactions between earth surface movements and major anthropogenic river channel straightening are shown to be a key factor in the persistence of long, near-straight river courses. Additionally, it is suggested that artificial river development, with very limited river channel lateral migration, may promote incision across an active fold at unusually long distances from the fold “core” and may promote markedly increased sinuosity across a structural lineament

Design of high‐speed software defined radar with GPU accelerator

Software defined radar (SDRadar) systems have become an important area for future radar development and are based on similar concepts to Software defined radio (SDR). Most of the processing like filtering, frequency conversion and signal generation are implemented in software. Currently, radar systems tend to have complex signal processing and operate at wider bandwidth, which means that limits on the available computational power must be considered when designing a SDRadar system. This paper presents a feasible solution to this potential limitation by accelerating the signal processing using a GPU to enable the development of a high speed SDRadar system. The developed system overcomes the limitation on the processing speed by CPU-only, and has been tested on three different SDR devices. Results show that, with GPU accelerator, the processing rate can achieve up to 80 MHz compared to 20 MHz with the CPU-only. The high speed processing makes it possible to run in real-time and process full bandwidth across the WiFi signal acquired by multiple channels. The gains made through porting the processing to the GPU moves the technology towards real-world application in various scenarios ranging from healthcare to IoT, and other applications that required significant computational processing

Occupancy Detection and People Counting Using WiFi Passive Radar

Occupancy detection and people counting technologies have important uses in many scenarios ranging from management of human resources, optimising energy use in intelligent buildings and improving public services in future smart cities. Wi-Fi based sensing approaches for these applications have attracted significant attention in recent years because of their ubiquitous nature, and ability to preserve the privacy of individuals being counted. In this paper, we present a Passive Wi-Fi Radar (PWR) technique for occupancy detection and people counting. Unlike systems which exploit the Wi-Fi Received Signal Strength (RSS) and Channel State Information (CSI), PWR systems can directly be applied in any environment covered by an existing WiFi local area network without special modifications to the Wi-Fi access point. Specifically, we apply Cross Ambiguity Function (CAF) processing to generate Range-Doppler maps, then we use Time-Frequency transforms to generate Doppler spectrograms, and finally employ a CLEAN algorithm to remove the direct signal interference. A Convolutional Neural Network (CNN) and sliding-window based feature selection scheme is then used for classification. Experimental results collected from a typical office environment are used to validate the proposed PWR system for accurately determining room occupancy, and correctly predict the number of people when using four test subjects in experimental measurements

Attention‐enhanced Alexnet for improved radar micro‐Doppler signature classification

Abstract This work introduces an attention mechanism that can be integrated into any standard convolution neural network to improve model sensitivity and prediction accuracy with minimal computational overhead. The attention mechanism is introduced in a lightweight network – Alexnet and its classification performance for human micro‐Doppler signatures is evaluated. The Alexnet model trained with an attention module can implicitly highlight the salient regions in the radar signatures while suppressing the irrelevant background regions and consistently improving network predictions. Network visualizations are provided through class activation mapping, providing better insights into how the predictions are made. The visualizations demonstrate how the attention mechanism focusses on the region of interest in the radar signatures

The spatiotemporal spread of human migrations during the European Holocene

The European continent was subject to two major migrations of peoples during the Holocene: the northwestward movement of Anatolian farmer populations during the Neolithic and the westward movement of Yamnaya steppe peoples during the Bronze Age. These movements changed the genetic composition of the continent’s inhabitants. The Holocene was also characterized by major changes in vegetation composition, which altered the environment occupied by the original hunter-gatherer populations. We aim to test to what extent vegetation change through time is associated with changes in population composition as a consequence of these migrations, or with changes in climate. Using ancient DNA in combination with geostatistical techniques, we produce detailed maps of ancient population movements, which allow us to visualize how these migrations unfolded through time and space. We find that the spread of Neolithic farmer ancestry had a two-pronged wavefront, in agreement with similar findings on the cultural spread of farming from radiocarbon-dated archaeological sites. This movement, however, did not have a strong association with changes in the vegetational landscape. In contrast, the Yamnaya migration speed was at least twice as fast, and coincided with a reduction in the amount of broad-leaf forest and an increase in the amount of pasture and natural grasslands in the continent. We demonstrate the utility of integrating ancient genomes with archaeometric datasets in a spatiotemporal statistical framework, which we foresee will enable future studies of ancient populations movements, and their putative effects on local fauna and flora

OPERAnet:A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors

This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-based sensors. The dataset consists of RF data including Channel State Information (CSI) extracted from a WiFi Network Interface Card (NIC), Passive WiFi Radar (PWR) built upon a Software Defined Radio (SDR) platform, and Ultra-Wideband (UWB) signals acquired via commercial off-the-shelf hardware. It also consists of vision/Infra-red based data acquired from Kinect sensors. Approximately 8 hours of annotated measurements are provided, which are collected across two rooms from 6 participants performing 6 daily activities. This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities. Furthermore, it can potentially be used to passively track a human in an indoor environment. Such datasets are key tools required for the development of new algorithms and methods in the context of smart homes, elderly care, and surveillance applications.Comment: 17 pages, 7 figure

Radar Micro-Doppler Signature Classification using Dynamic Time Warping

This paper describes the first feasibility study using dynamic time warping (DTW) to classify the micro-Doppler signature (μ-DS ) for radar automatic target recognition (ATR). Real radar data has been used in the testing, and the performance of the DTW