As I cover tech news daily for Daily Tech News Show, I get a front row seat as trends arrive, accelerate and disappear. Here's my take on the top five tech trends to look for in 2022. You have seen coverage of this about limited experiments in China or small countries implementing them. I mean actual government digital currencies like the Bahamas not adopting bitcoin like El Salavador. This year, more major governments will get in the game of creating a government-issued digital currency in an attempt to stave off perceived threats of the cryptocurrency wave.

The sustainability of urban environments is an increasingly relevant problem. Air pollution plays a key role in the degradation of the environment as well as the health of the citizens exposed to it. In this chapter we provide a review of the methods available to model air pollution, focusing on the application of machine-learning methods. In fact, machine-learning methods have proved to importantly increase the accuracy of traditional air-pollution approaches while limiting the development cost of the models. Machine-learning tools have opened new approaches to study air pollution, such as flow-dynamics modelling or remote-sensing methodologies.

Predict the quantity of fuel consumed during drives. The mileage of a vehicle is defined as the average distance traveled on a specified amount of fuel. But distance is not the only factor that affects fuel consumption. Here, we take into account multiple factors like speed, temperatures inside and outside, AC, and other weather conditions like rain or sun besides distance to predict the consumption of different types of fuels during drives. Predicting the fuel consumption given distance and other factors vice versa (predicting distance given fuel) can prove useful in planning trips as well as performing real-time predictions during driving.

Electrical utilities depend on short-term demand forecasting to proactively adjust production and distribution in anticipation of major variations. This systematic review analyzes 240 works published in scholarly journals between 2000 and 2019 that focus on applying Artificial Intelligence (AI), statistical, and hybrid models to short-term load forecasting (STLF). This work represents the most comprehensive review of works on this subject to date. A complete analysis of the literature is conducted to identify the most popular and accurate techniques as well as existing gaps. The findings show that although Artificial Neural Networks (ANN) continue to be the most commonly used standalone technique, researchers have been exceedingly opting for hybrid combinations of different techniques to leverage the combined advantages of individual methods. The review demonstrates that it is commonly possible with these hybrid combinations to achieve prediction accuracy exceeding 99%. The most successful duration for short-term forecasting has been identified as prediction for a duration of one day at an hourly interval. The review has identified a deficiency in access to datasets needed for training of the models. A significant gap has been identified in researching regions other than Asia, Europe, North America, and Australia.

Hamiltonian learning is an important procedure in quantum system identification, calibration, and successful operation of quantum computers. Through queries to the quantum system, this procedure seeks to obtain the parameters of a given Hamiltonian model and description of noise sources. Standard techniques for Hamiltonian learning require careful design of queries and $O(\epsilon^{-2})$ queries in achieving learning error $\epsilon$ due to the standard quantum limit. With the goal of efficiently and accurately estimating the Hamiltonian parameters within learning error $\epsilon$ through minimal queries, we introduce an active learner that is given an initial set of training examples and the ability to interactively query the quantum system to generate new training data. We formally specify and experimentally assess the performance of this Hamiltonian active learning (HAL) algorithm for learning the six parameters of a two-qubit cross-resonance Hamiltonian on four different superconducting IBM Quantum devices. Compared with standard techniques for the same problem and a specified learning error, HAL achieves up to a $99.8\%$ reduction in queries required, and a $99.1\%$ reduction over the comparable non-adaptive learning algorithm. Moreover, with access to prior information on a subset of Hamiltonian parameters and given the ability to select queries with linearly (or exponentially) longer system interaction times during learning, HAL can exceed the standard quantum limit and achieve Heisenberg (or super-Heisenberg) limited convergence rates during learning.

A successful deployment of drones provides an ideal solution for surveillance systems. Using drones for surveillance can provide access to areas that may be difficult or impossible to reach by humans or in-land vehicles gathering images or video recordings of a specific target in their coverage. Therefore, we introduces a data delivery drone to transfer collected surveillance data in harsh communication conditions. This paper proposes a Myerson auction-based asynchronous data delivery in an aerial distributed data platform in surveillance systems taking battery limitation and long flight constraints into account. In this paper, multiple delivery drones compete to offer data transfer to a single fixed-location surveillance drone. Our proposed Myerson auction-based algorithm, which uses the truthful second-price auction (SPA) as a baseline, is to maximize the seller's revenue while meeting several desirable properties, i.e., individual rationality and incentive compatibility while pursuing truthful operations. On top of these SPA-based operations, a deep learning-based framework is additionally designed for delivery performance improvements.

Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart. Due to the one-to-many nature of the SSR problem, a single RGB image can be reprojected to many HSIs. The key to tackle this illposed problem is to plug into multi-source prior information such as the natural RGB spatial context-prior, deep feature-prior or inherent HSI statistical-prior, etc., so as to improve the confidence and fidelity of reconstructed spectra. However, most current approaches only consider the general and limited priors in their designing the customized convolutional neural networks (CNNs), which leads to the inability to effectively alleviate the degree of ill-posedness. To address the problematic issues, we propose a novel holistic prior-embedded relation network (HPRN) for SSR. Basically, the core framework is delicately assembled by several multi-residual relation blocks (MRBs) that fully facilitate the transmission and utilization of the low-frequency content prior of RGB signals. Innovatively, the semantic prior of RGB input is introduced to identify category attributes and a semantic-driven spatial relation module (SSRM) is put forward to perform the feature aggregation among the clustered similar characteristics using a semantic-embedded relation matrix. Additionally, we develop a transformer-based channel relation module (TCRM), which breaks the habit of employing scalars as the descriptors of channel-wise relations in the previous deep feature-prior and replaces them with certain vectors, together with Transformerstyle feature interactions, supporting the representations to be more discriminative. In order to maintain the mathematical correlation and spectral consistency between hyperspectral bands, the second-order prior constraints (SOPC) are incorporated into the loss function to guide the HSI reconstruction process.

It was the kind of message Connor McCluskey loves to find in his inbox. As a member of the product innovation team at FirstEnergy Corp. -- an electric utility serving 6 million customers from central Ohio to the New Jersey coast -- his job is to find technologies that open new revenue streams or cut costs. In the email, Chris Ricciuti, the founder of Noteworthy AI, explained his ideas for using edge computing to radically improve how utilities track their assets. For FirstEnergy, those assets include tens of millions of devices mounted on millions of poles across more than 269,000 miles of distribution lines. Ricciuti said his startup aimed to turn every truck in a utility's fleet into a smart camera that takes pictures of every pole it passes.

IdentiFlight is an AI-powered bird detection system that is used in conjunction with wind turbine farms. The device scans the area for large birds, and can turn off individual turbines if it believes a bird is at risk of colliding with a blade. "The IdentiFlight bird detection system blends artificial intelligence with the high-precision optical technology to detect eagles and other protected avian species. In an operating wind farm, IdentiFlight contributes to eagle conservation by helping protect them from collisions with rotating wind turbine blades. In wind project development, IdentiFlight helps in permitting sites by accurately quantifying avian activity at prospective sites. Automatic detection and species determination occur within seconds for birds flying within a one kilometer hemisphere around an IdentiFlight tower. The IdentiFlight system has completed real-world testing and validation in pilot programs at wind farms with elevated eagle activity and is now commercially deployed at projects around the world. The IdentiFlight uses 47 million images to identify protected species, and it appears the tech works. A recent study found that there was an 82% reduction in bird fatalities at a site using the IdentiFlight compared with a control site. "This technology therefore has the potential to lessen the conflict between wind energy and raptor conservation.

Designing manufacturing processes with high yield and strong reliability relies on effective methods for rare event estimation. Genealogical importance splitting reduces the variance of rare event probability estimators by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is difficult when applied to deterministic systems where the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. However, this random perturbation strategy may be effective for some systems while failing for others, preventing variance reduction in the probability estimate. This work seeks to address this limitation using a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The proposed GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).