1,721,010 research outputs found
Delta Activation Layer exploits temporal sparsity for efficient embedded video processing
No full textThis work is partially funded by research and innovation projects TEMPO (ECSEL JU under grant agreement No 826655), ANDANTE (ECSEL JU under grant agreement No 876925) and DAIS (KDT JU under grant agreement No 101007273), SunRISE (EUREKA cluster PENTA2018e-17004-SunRISE) and Comp4Drones (ECSEL JU grant agreement No. 826610). The JU receives support from the European Union's Horizon 2020 research and innovation programme and Sweden, Spain, Portugal, Belgium, Germany, Slovenia, Czech Republic, Netherlands, Denmark, Norway and Turkey
Aircraft Marshaling Signals Dataset of FMCW Radar and Event-Based Camera for Sensor Fusion
No full textDataset Introduction
The advent of neural networks capable of learning salient features from variance in the radar data has expanded the breadth of radar applications, often as an alternative sensor or a complementary modality to camera vision. Gesture recognition for command control is the most commonly explored application. Nevertheless, more suitable benchmarking datasets are needed to assess and compare the merits of the different proposed solutions. Furthermore, most current publicly available radar datasets used in gesture recognition provide little diversity, do not provide access to raw ADC data, and are not significantly challenging. To address these shortcomings, we created and made available a new dataset that combines two synchronized modalities: radar and dynamic vision camera of 10 aircraft marshalling signals at several distances and angles, recorded from 13 people. Moreover, we propose a sparse encoding of the time domain (ADC) signals that achieve a dramatic data rate reduction (>76%) while retaining the efficacy of the downstream FFT processing (<2% accuracy loss on recognition tasks). Finally, we demonstrate early sensor fusion results based on compressed radar data encoding in range-Doppler maps with dynamic vision data. This approach achieves higher accuracy than either modality alone.
Dataset Structure
The dataset has a common directory structure which contains additional information about the captures.
dataset_dir///--/ofxRadar8Ghz_yyyy-mm-dd_HH-MM-SS.rad
Identifiers
stage [train, test].
room: [conference_room, foyer, open_space].
person: [0-9]. Note that 0 stands for no person, and 1 for an unlabeled, random person (only present in test).
gesture: ['none', 'emergency_stop', 'move_ahead', 'move_back_v1', 'move_back_v2', 'slow_down' 'start_engines', 'stop_engines', 'straight_ahead', 'turn_left', 'turn_right'].
distance: ['xxx', '100', '150', '200', '250', '300', '350', '400', '450'] (in cm). Note that xxx is used for none gestures when there is no person present in front of the radar (i.e. background samples), or when a person is walking infront of the radar with varying distances but performing no gesture.If you use this dataset, please also cite our accompanying paper:
@inproceedings{mueller2023aircraft, title={Aircraft Marshalling Signals Dataset of Radar and Event-Based Camera for Sensor Fusion}, author={M\"uller, Leon and Sifalakis, Manolis and Eissa, Sherif and Yousefzadeh, Amirreza and Detterer, Paul and Stuijk, Sander, and Corradi, Federico}, journal={IEEE Radar Conference, San Antonio, TX}, volume={}, number={1}, pages={1--15}, year={2023}, publisher={IEE}
mu Brain: An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks
No full textEmpirical study on the efficiency of Spiking Neural Networks with axonal delays, and algorithm-hardware benchmarking
Full text linkThe role of axonal synaptic delays in the efficacy and performance of artificial neural networks has been largely unexplored. In step-based analog-valued neural network models (ANNs), the concept is almost absent. In their spiking neuroscience-inspired counterparts, there is hardly a systematic account of their effects on model performance in terms of accuracy and number of synaptic operations. This paper proposes a methodology for accounting for axonal delays in the training loop of deep Spiking Neural Networks (SNNs), intending to efficiently solve machine learning tasks on data with rich temporal dependencies. We then conduct an empirical study of the effects of axonal delays on model performance during inference for the Adding task [1]-[3], a benchmark for sequential regression, and for the Spiking Heidelberg Digits dataset (SHD) [4], commonly used for evaluating event-driven models. Quantitative results on the SHD show that SNNs incorporating axonal delays instead of explicit recurrent synapses achieve state-of-the-art, over 90% test accuracy while needing less than half trainable synapses. Additionally, we estimate the required memory in terms of total parameters and energy consumption of accomodating such delay-trained models on a modern neuromorphic accelerator [5], [6]. These estimations are based on the number of synaptic operations and the reference GF-22nm FDX CMOS technology. As a result, we demonstrate that a reduced parameterization, which incorporates axonal delays, leads to approximately 90% energy and memory reduction in digital hardware implementations for a similar performance in the aforementioned task.</p
Radar-Based Hand Gesture Recognition Using Spiking Neural Networks
Full text linkWe propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike trains. The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). The readout spike signal from the SNN is then used as input for different classifiers for comparison, including logistic regression, random forest, and support vector machine (SVM). Using liquid state machines of less than 1000 neurons, we achieve better than state-of-the-art results on two publicly available reference datasets, reaching over 98% accuracy on 10-fold cross-validation for both data sets
Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design
No full textThis work is partially funded by research and innovation projects ANDANTE (ECSEL JU under grant agreement No 876925), DAIS (KDT JU under grant agreement No 101007273) and MemScale (Horizon EU under grant agreement 871371). The JU receives support from the European Union's Horizon 2020 research and innovation programme and Sweden, Spain, Portugal, Belgium, Germany, Slovenia, Czech Republic, Netherlands, Denmark, Norway and Turkey
Adaptation and awareness for autonomic systems
No full textEThOS - Electronic Theses Online ServiceGBUnited Kingdo
SENeCA: Scalable Energy-efficient Neuromorphic Computer Architecture
No full textThis work is partially funded by research and innovation projects TEMPO (ECSEL JU under grant agreement No 826655), ANDANTE (ECSEL JU under grant agreement No 876925) and DAIS (KDT JU under grant agreement No 101007273). The JU receives support from the European Union's Horizon 2020 research and innovation programme and Sweden, Spain, Portugal, Belgium, Germany, Slovenia, Czech Republic, Netherlands, Denmark, Norway and Turkey
Machine Learning of Ultrasound Data: Cardiovascular Parameters Detection Using Carotid Artery Measurements
No full textBackground & ObjectiveCardiovascular diseases (CVDs) are the leading cause for death globally nowadays. Pulse wave velocity (PWV), a marker of arterial stiffness, is an important predictor of CVD risk. In precedent work, carotid artery data was collected with ultrasound to estimate the PWV with a digital signal processing (DSP) pipeline. As a potential alternative to the DSP-based approach, this thesis studies the applicability of machine learning(ML) for the estimation of carotid artery motion (diameter, distension, etc.) and explores to what extent neural networks can exploit the ultrasound data to extract relevant biomarker information.Methods:This thesis proposes a ML pipeline that processes the ultrasound data in a different perspective than the DSP approach. The ML pipeline consists of four modules (neural networks & post-processing) to: 1) segmentation based on cardiac cycle (CC), 2) detect the region of interest (ROI) of artery in the ultrasound data, 3) tracking the artery diameter and 4) post processing to estimate cardiac parameters e.g. pulse arrival time (PAT), an essential part of PWV estimation. Exploiting the features of the artery-lumen structure and time-evolving characteristics of collected ultrasound signal, the designed ML pipeline can acquire cardiac markers spatially and temporally with irregular kernels and sliding mechanism, decompose the complicated estimation into compact sub-modules.Results:The results show that the ML approach can successfully estimate the artery diameters and reserve important waveform features (max-slope moment) of the artery diameter, and can infer the CC markers without ECG data as a segmenting event for heart cycles. Thus, the PAT can be computed as the time difference of the max-slope moments of inferred artery diameter and detected CC markers. According to the numerical results, the PAT can be estimated on the average for an ultrasound data recording (120s), and the correlation coefficient of label PAT (computed from estimated parameters of DSP pipeline and ECG data) and estimated PAT (ML pipeline) is 0.8250. This indicates a good correlation and hence proves the effectiveness of the mean PAT estimation.Conclusion:In conclusion, the proposed ML pipeline can effectively estimate mean PAT, and demonstrate the feasibility to estimate PWV as a relevant cardiovascular marker using only ultrasound data of carotid arteries. Apart from the PAT, the heart rate can also be possibly tracked via intermediate results of the ML pipeline (CC markers). From the future perspective, the potential of phase information in the raw ultrasound data and further optimization are worth exploring, and the extension to hardware (e.g. chips, embedded system) can be implemented as a practical application.Electrical Engineerin
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