An official website of the United States government
The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML-driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML-based algorithms for the sensing and perception of waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to a high chip area and power consumption. We address this issue by exploiting the recently proposed approximate discrete Fourier transform (ADFT), which has its own sparse factorization for real-time implementation at a low complexity and power consumption. The ADFT is used to create a wideband multibeam RF digital beamformer and temporal spectrum-based attention unit that monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm with low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high resolution. This two-step process allows the fine-gain FFT to be applied only to directions and bands of interest as determined by the ADFT-based low-complexity 2D spacetime attention unit. The fine-grain FFT provides a spectral signature that can find future use cases in neural network engines for achieving modulation recognition, IoT device identification, and RFI identification. Beamforming and spectral channelization algorithms, a digital computer architecture, and early prototypes using a 32-element fully digital multichannel receiver and field programmable gate array (FPGA)-based high-speed software-defined radio (SDR) are presented.
more »
« less
Detecting fabric density and weft distortion in woven fabrics using the discrete fourier transform
Fabric density and distortion offer important information on fabric attributes and quality during the manufacturing process. However, most current procedures require human effort, which is often inefficient, time-consuming, and imprecise. In this paper, we propose to use an automatic method using the 2D Fast Fourier Transform (2D-FFT) to count the number of yarns and determine the angle rotation of weft yarns in fabric images. First, we explain the mathematical background of Fourier Transform and 2D-FFT. Then, we use a customized and optimized software package to apply a 2D- FFT to extract image magnitude, phase, and power spectrum. We apply the inverse 2D Fast Fourier Transform (2D-iFFT) on selected frequencies corresponding to periodic structures – basic weave patterns – to reconstruct the original image and extract warp and weft yarns separately. Finally, we use a local adaptive threshold process to convert reconstructed images into binary images for the counting and calculating process. For the weft rotation, we apply a mathematical calculation on the frequency domain to collect the angular distribution and then figure out the major rotation of weft yarns. Our experiments show that the proposed method is highly accurate and capable of inspecting different patterns of fabric. We also observe that the processing time of our proposal method is practical and time-efficient.
more »
« less
- Award ID(s):
- 1907838
- PAR ID:
- 10278191
- Date Published:
- Journal Name:
- ACM SE '21: Proceedings of the 2021 ACM Southeast Conference
- Page Range / eLocation ID:
- 108 to 113
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Measuring the organization of the cellular cytoskeleton and the surrounding extracellular matrix (ECM) is currently of wide interest as changes in both local and global alignment can highlight alterations in cellular functions and material properties of the extracellular environment. Different approaches have been developed to quantify these structures, typically based on fiber segmentation or on matrix representation and transformation of the image, each with its own advantages and disadvantages. Here we present AFT − Alignment by Fourier Transform , a workflow to quantify the alignment of fibrillar features in microscopy images exploiting 2D Fast Fourier Transforms (FFT). Using pre-existing datasets of cell and ECM images, we demonstrate our approach and compare and contrast this workflow with two other well-known ImageJ algorithms to quantify image feature alignment. These comparisons reveal that AFT has a number of advantages due to its grid-based FFT approach. 1) Flexibility in defining the window and neighborhood sizes allows for performing a parameter search to determine an optimal length scale to carry out alignment metrics. This approach can thus easily accommodate different image resolutions and biological systems. 2) The length scale of decay in alignment can be extracted by comparing neighborhood sizes, revealing the overall distance that features remain anisotropic. 3) The approach is ambivalent to the signal source, thus making it applicable for a wide range of imaging modalities and is dependent on fewer input parameters than segmentation methods. 4) Finally, compared to segmentation methods, this algorithm is computationally inexpensive, as high-resolution images can be evaluated in less than a second on a standard desktop computer. This makes it feasible to screen numerous experimental perturbations or examine large images over long length scales. Implementation is made available in both MATLAB and Python for wider accessibility, with example datasets for single images and batch processing. Additionally, we include an approach to automatically search parameters for optimum window and neighborhood sizes, as well as to measure the decay in alignment over progressively increasing length scales.more » « less
-
Camps-Valls, Gustau; Ruiz, Francisco J.; Valera, Isabel (Ed.)Bayesian Networks are useful for analyzing the properties of systems with large populations of interacting agents (e.g., in social modeling applications and distributed service applications). These networks typically have large functions (CPTs), making exact inference intractable. However, often these models have additive symmetry. In this paper we show how summation-based CPTs, especially in the presence of symmetry, can be computed efficiently through the usage of the Fast Fourier Transform (FFT). In particular, we propose an efficient method using the FFT for reducing the size of Conditional Probability Tables (CPTs) in Bayesian Networks with summation-based causal independence (CI). We show how to apply it directly towards the acceleration of Bucket Elimination, and we subsequently provide experimental results demonstrating the computational speedup provided by our method.more » « less
-
Abstract MotivationNeural networks have been widely used to analyze high-throughput microscopy images. However, the performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Highly relevant to the goal of automated cell phenotyping from microscopy image data is rotation invariance. Here we consider the application of two schemes for encoding rotation equivariance and invariance in a convolutional neural network, namely, the group-equivariant CNN (G-CNN), and a new architecture with simple, efficient conic convolution, for classifying microscopy images. We additionally integrate the 2D-discrete-Fourier transform (2D-DFT) as an effective means for encoding global rotational invariance. We call our new method the Conic Convolution and DFT Network (CFNet). ResultsWe evaluated the efficacy of CFNet and G-CNN as compared to a standard CNN for several different image classification tasks, including simulated and real microscopy images of subcellular protein localization, and demonstrated improved performance. We believe CFNet has the potential to improve many high-throughput microscopy image analysis applications. Availability and implementationSource code of CFNet is available at: https://github.com/bchidest/CFNet. Supplementary informationSupplementary data are available at Bioinformatics online.more » « less
-
Electronic textile (E-textile) research requires an understanding of the mechanical properties of fabric substrates used to build and support electronics. Because fibers are often non-uniform and fabrics are easily deformed, locating fiber junctions on the irregular surface is challenging, yet is essential for packaging electronics on textiles at the resolution of single fibers that deliver power and signals. In this paper, we demonstrate the need to identify fiber junctions in a task where microelectromechanical structures (MEMS) are integrated on fabrics. We discuss the benefits of fiber-aligned placement compared with random placement. Thereafter we compare three image processing algorithms to extract fiber junction locations from sample fabric images. The Hough line transform algorithm implemented in MATLAB derives line segments from the image to model the fibers, identifying crossings by the intersections of the line segments. The binary image analysis algorithm implemented in MATLAB searches the image for unique patterns of 1s and 0s that represent the fiber intersection. The pattern matching algorithm implemented in Vision Assistant - LabVIEW, uses a pyramid value correlation function to match a reference template to the remainder of the fabric image to identify the crossings. Of the three algorithms, the binary image analysis method had the highest accuracy, while the pattern matching algorithm was fastest.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3409334.3452049
