Technical specialist helping companies to innovate | MBSE | MBD | ASIC/SoC/FPGA/ARM | AI | Wireless
#Engineers: Join us at the #IMS2024 workshop to learn a convolutional neural network (CNN) for channel estimation using OTA measurements through mmWave PAAM and AMD RFSoC-based 5G NR receiver in a CATR chamber. https://spr.ly/60495BGKB
#Engineers: Join us at the #IMS2024 workshop to learn a convolutional neural network (CNN) for channel estimation using OTA measurements through mmWave PAAM and AMD RFSoC-based 5G NR receiver in a CATR chamber. https://spr.ly/60455IqLx
Radar Signal Processing, Computational Electromagnetics, passionate about Electronic Warfare
Spiking Neural Network for LPI Radar Classification: the principle is very interesting, the time frequency image of the radar signal (LFM, Costas, ...) is converted into sipke trains, using LIF neurons according to code rate, this spiking structure interfaces with convolutional layers, max pooling layers and fully connected layers (just like DNN) to give out scores for the different classes (modulation types of the radar signals). It is interesting to note how a continuous time representation, output of the spiking network, interfaces with the features maps of the convolutional layers....
HPL:Neural network modeling and prediction of HfO2 thin film properties tuned by thermal annealing. The results showed that the three-hidden-layer back-propagation neural network (THL-BPNN) achieved stable and accurate fitting. https://lnkd.in/gjsfQzY5
Did you know that the flexibility and real-time processing of #FPGAs makes them strong candidates for implementing small-scale neural networks like autoencoders? In this video, Liquid Instruments engineer Jessica Patterson creates a pulsed radar signal and obscures it with noise. Then, she uses the Moku #NeuralNetwork to decode and reconstruct the original signal with an #autoencoder network in real time. Check it out: https://hubs.ly/Q02-qxzy0
Transformer Neural Networks (TNNs) are helping Motional’s onboard perception system predict future object movements and plan a safe course forward. Learn more about how Motional is pushing the boundaries of AV technology for a smoother, safer ride. https://lnkd.in/ee9f56AX
Together with Graphcore we introduce Unit-Scaled Maximal Update Parametrization (u-μP) — a new method designed to make it easier and more efficient to train and optimize neural networks, even as they grow in size. Key Benefits of u-μP: 1️⃣ Hyperparameter Transferability: Simplifies the work of tuning settings (hyperparameters) for different model sizes. 2️⃣ Stable Training: Enhances stability, allowing training in lower precisions like FP8, hence a significant potential training speedup and memory reduction. 🔗 Read the full research here: https://lnkd.in/et2q9bfj #writtenbyalephalpha
Heard about Neural Quantum Processor Ultra? Enjoy a powerful and cinematic experience. Combining 20 multilayer neural networks, the AI-powered processor intelligently analyzes images to recreate every detail in every pixel. Automatic brightness adjustment, contrast enhancement and other improvements will perfect the resolution of the content. LV: bit.ly/3yHWFzE EE: bit.ly/4bFvSCy LT: bit.ly/456C1VT
Ranu, Krishnan et al. have benchmarked a variety of equivariant graph neural network force fields for atomistic simulations in our latest featured #DigitalDiscovery article. Discover the behaviour and limitations of these models in real-world scenarios in their paper, available #openaccess here: https://lnkd.in/dbRAx8ec
I do not think that disparate systems actually exist. People say that HDC and Neural Networks are two wholly different processes. Then how come I can join them? People say Algebra and Geometry are two different processes. Then how come I can join them? People say Quantum mechanics and Relativistic mechanics are different processes. Then how come I can join them? It's all just compute, and compute is simply the process of turning noise into signal data. When someone or something is 'intelligent', it is efficient at turning noise into signal data for a given process. https://lnkd.in/gY-xrJzX
I'm happy to share that our paper, titled "Wavelet Convolution for Large Receptive Fields" [Finder, Amoyal, Treister & Freifeld], was accepted to #ECCV2024. In it, we show how wavelet transforms (WT) can be used to achieve CNNs with unprecedented receptive fields and an increased response to low frequencies in the input. These improvements translate to overall improved results in traditional benchmarks, robustness, scalability (w.r.t. the amount of data), and shape-bias. The paper is available at https://lnkd.in/eFqu_NSg The code is available at https://lnkd.in/ejVuWDDg
The first of our 3 recently-accepted #ECCV2024 papers, titled "Wavelet Convolutions for Large Receptive Fields", is now available: https://lnkd.in/dPvf9efM In recent years, there have been attempts to increase the kernel size of Convolutional Neural Nets (CNNs) to mimic the global receptive field of Vision Transformers' (ViTs) self-attention blocks. That approach, however, quickly hit an upper bound and saturated way before achieving a global receptive field. In this work, we demonstrate that by leveraging the Wavelet Transform (WT), it is, in fact, possible to obtain very large receptive fields without suffering from over-parameterization, e.g., for a k×k receptive field, the number of trainable parameters in the proposed method grows only logarithmically with k. The proposed layer, named WTConv, can be used as a drop-in replacement in existing architectures, results in an effective multi-frequency response, and scales gracefully with the size of the receptive field. We demonstrate the effectiveness of the WTConv layer within ConvNeXt and MobileNetV2 architectures for image classification, as well as backbones for downstream tasks, and show it yields additional properties such as robustness to image corruption and an increased response to shapes over textures. Shahaf Finder, Roy Amoyal #computervision #deeplearning #ECCV #wavelets
