Applications of deep learning to the radio frequency (RF) domain have largely concentrated on the task of narrowband signal classification after the signals of interest have already been detected and extracted from a wideband capture. To encourage broader research with wideband operations, we introduce the WidebandSig53 (WBSig53) dataset which consists of 550 thousand synthetically-generated samples from 53 different signal classes containing approximately 2 million unique signals. We extend the TorchSig signal processing machine learning toolkit for open-source and customizable generation, augmentation, and processing of the WBSig53 dataset. We conduct experiments using state of the art (SoTA) convolutional neural networks and transformers with the WBSig53 dataset. We investigate the performance of signal detection tasks, i.e. detect the presence, time, and frequency of all signals present in the input data, as well as the performance of signal recognition tasks, where networks detect the presence, time, frequency, and modulation family of all signals present in the input data. Two main approaches to these tasks are evaluated with segmentation networks and object detection networks operating on complex input spectrograms. Finally, we conduct comparative analysis of the various approaches in terms of the networks' mean average precision, mean average recall, and the speed of inference.

The nation's chief scientist has urged software developers on the cusp of artificial intelligence breakthroughs not to lose their "moral compass" amid fears humans will be treated as data points by our largest companies. Alan Finkel, speaking at an artificial intelligence summit at Monash University in Melbourne on Thursday, said there was a "golden opportunity" for Australia to be a world leader in scientific discovery while also holding to the ideals of a virtuous society. He said there was enormous potential for artificial intelligence to deliver substantial benefits to Australians in areas as varied as manufacturing and financial services. Chief Scientist Alan Finkel says the advent of artificial intelligence will need both technological as well as ethical considerations.Credit:Alex Ellinghausen But Dr Finkel pointed to how Google this month obtained a patent to use sensors and cameras to monitor home activity. It claims to be able to work out the title of a book someone is reading in their bed.

Motivated by the Bagging Partial Least Squares (PLS) and Principal Component Analysis (PCA) algorithms, we propose a Principal Model Analysis (PMA) method in this paper. In the proposed PMA algorithm, the PCA and the PLS are combined. In the method, multiple PLS models are trained on sub-training sets, derived from the original training set based on the random sampling with replacement method. The regression coefficients of all the sub-PLS models are fused in a joint regression coefficient matrix. The final projection direction is then estimated by performing the PCA on the joint regression coefficient matrix. The proposed PMA method is compared with other traditional dimension reduction methods, such as PLS, Bagging PLS, Linear discriminant analysis (LDA) and PLS-LDA. Experimental results on six public datasets show that our proposed method can achieve better classification performance and is usually more stable.

The goal of this document is to provide a pedagogical introduction to the main concepts underpinning the training of deep neural networks using gradient descent; a process known as backpropagation. Although we focus on a very influential class of architectures called "convolutional neural networks" (CNNs) the approach is generic and useful to the machine learning community as a whole. Motivated by the observation that derivations of backpropagation are often obscured by clumsy index-heavy narratives that appear somewhat mathemagical, we aim to offer a conceptually clear, vectorized description that articulates well the higher level logic. Following the principle of "writing is nature's way of letting you know how sloppy your thinking is", we try to make the calculations meticulous, self-contained and yet as intuitive as possible. Taking nothing for granted, ample illustrations serve as visual guides and an extensive bibliography is provided for further explorations. (For the sake of clarity, long mathematical derivations and visualizations have been broken up into short "summarized views" and longer "detailed views" encoded into the PDF as optional content groups. Some figures contain animations designed to illustrate important concepts in a more engaging style. For these reasons, we advise to download the document locally and open it using Adobe Acrobat Reader. Other viewers were not tested and may not render the detailed views, animations correctly.)

Artificial Intelligence Laborato y, Wright-Patterson AFl?, Ohio 45433 Abstract The Air Force Institute of Technology [AFIT] provides master's degree education to Air Force and Army Officers in various engineering fields. It is in a unique position to educate and perform research in the area of applications of artificial intelligence to military problems. Its two AI faculty members are the only military officers with Ph D's in Artificial Intelligence. In the past two years, the artificial intelligence Laboratory of the AFIT has become a major focal point for AI research and applications within the government In this article, we describe our ongoing applications research in the areas of automated cockpit systems, natural language understanding, maintenance expert systems, expert systems for planning, and knowledge based software design. In response to the need for rapid training of engineers in artificial intelligence, AFIT has developed a Master's degree curriculum for AI.

"The Tesla's automation did not detect, nor was it required [to], nor was it designed to detect the crossing vehicle," Robert L. Sumwalt, chairman of the National Transportation Safety Board, said at the start of a hearing reviewing the Florida crash. Tests by the National Highway Traffic Safety Administration determined that Tesla and other vehicles with semiautonomous driving technology had great difficulty sensing cross traffic. The NTSB staff also said that Tesla's reliance on sensing a driver's hands on the wheel was not an effective way of monitoring whether the driver was paying attention. The NTSB staff recommended the use of a more effective technology to determine whether a driver is paying attention, such as a camera tracking the driver's eyes.