The world is adapting itself to the digital age changes. We are getting more familiar with the terms of the disruptive technologies that are making it happen. Internet of things (IoT) is one of them. The term was coined by in 1999 by Kevin Ashton, a British technologist. It refers to the connected ecosystem of devices and gadgets, which is benefiting businesses and industries of all types. These devices can be RFID chips, smart devices, or mobile sensors.
Bottom Line: Real-time analysis of remote video feeds is rapidly improving thanks to AI, increasing the accuracy of remote equipment and facility monitoring. Agriculture, construction, oil & gas, utilities, and critical infrastructure all need to merge cybersecurity and physical security to adapt to an increasingly complex threatscape. What needs to be the top priority is improving the accuracy, insight, and speed of response to remote threats that AI-based video recognition systems provide. Machine learning techniques as part of a broader AI strategy are proving effective in identifying anomalies and threats in real-time using video, often correlating them back to cyber threats, which are often part of an orchestrated attack on remote facilities. The future of remote security monitoring is being defined by the rapid advances in supervised, unsupervised, and reinforcement machine learning algorithms and their contributions to AI-based visual recognition systems.
Manufacturers need to reduce the time and effort required to have experts on-site for the maintenance of their equipment. This means finding the right balance for maintenance tasks, which frequently adds unnecessary costs, and increase the risk of failures. Introducing predictive maintenance, based on AI and cellular connectivity can address these issues. Predictive maintenance is all about identifying problems before they occur, and the first step toward implementing it involves introducing sensors on equipment to measure anything measurable. Almost all new state-of-the-art industrial equipment already has embedded sensors.
Pandemics and nationwide systemic tensions only emphasize an underlying fundamental problem in our country. We cannot have only 500,000 law enforcement and security professional at any one given point in time trying to secure 300 million people across 50 states. The brave women and men in uniform require new advanced tools for them to be able to do their jobs effectively – and now, with the looming threat of coronavirus negatively impacting our nation's law enforcement professionals – even more so than ever before. The obvious point here is that security robots are immune to COVID-19 and provide security and law enforcement an ability to help manage during a time of difficult circumstances. Similar to COVID-19, crime is a virus set to spiral out of control.
The traditional threat landscape comprised of conventional IT assets is difficult enough to protect, detect and respond to, but the landscape seems to be quickly expanding beyond traditional IT. Those new domains are operational technology (OT), the internet of things (IoT) and the internet of medical things (IoMT). Devices from non-traditional IT environments are finding their way onto corporate intranets, which can create a shadow IT environment. These devices are unmanaged and some managers don't have a full understanding of the risks associated with these devices. More visibility into these devices could help a chief information security officer (CISO) to understand whether they are acting appropriately.
Z Advanced Computing, Inc. (ZAC), the pioneer startup on Explainable-AI (Artificial Intelligence) (XAI), is developing its Smart Home product line through a paid-pilot for Smart Appliances for BSH Home Appliances (a subsidiary of the Bosch Group, originally a joint venture between Bosch and Siemens), the largest manufacturer of home appliances in Europe and one of the largest in the world. ZAC just successfully finished its Phase 1 of the pilot program. "Our cognitive-based algorithm is more robust, resilient, consistent, and reproducible, with a higher accuracy, than Convolutional Neural Nets or GANs, which others are using now. It also requires much smaller number of training samples, compared to CNNs, which is a huge advantage," said Dr. Saied Tadayon, CTO of ZAC. "We did the entire work on a regular laptop, for both training and recognition, without any dedicated GPU. So, our computing requirement is much smaller than a typical Neural Net, which requires a dedicated GPU," continued Dr. Bijan Tadayon, CEO of ZAC.
Air Quality Multi-sensors Systems (AQMS) are IoT devices based on low cost chemical microsensors array that recently have showed capable to provide relatively accurate air pollutant quantitative estimations. Their availability permits to deploy pervasive Air Quality Monitoring (AQM) networks that will solve the geographical sparseness issue that affect the current network of AQ Regulatory Monitoring Systems (AQRMS). Unfortunately their accuracy have shown limited in long term field deployments due to negative influence of several technological issues including sensors poisoning or ageing, non target gas interference, lack of fabrication repeatability, etc. Seasonal changes in probability distribution of priors, observables and hidden context variables (i.e. non observable interferents) challenge field data driven calibration models which short to mid term performances recently rose to the attention of Urban authorithies and monitoring agencies. In this work, we address this non stationary framework with adaptive learning strategies in order to prolong the validity of multisensors calibration models enabling continuous learning. Relevant parameters influence in different network and note-to-node recalibration scenario is analyzed. Results are hence useful for pervasive deployment aimed to permanent high resolution AQ mapping in urban scenarios as well as for the use of AQMS as AQRMS backup systems providing data when AQRMS data are unavailable due to faults or scheduled mainteinance.
The coronavirus pandemic is having a peculiarly American side effect: Gun sales are surging. The stocks of publicly traded guns and ammo companies American Outdoor Brands Corp., Vista Corp., and Sturm Ruger & Co. are up. Sales leaped by more than 19% in January and 17% in February, compared with the same months in 2019, according to Small Arms Analytics & Forecasting. Gun buyers in the United States bought an estimated 1.24 million guns in, January up from 1.04 million the year before, and 1.36 million in February, up from 1.26 million the year before, according to Small Arms Analytics, which bases estimates on background check data. Those millions of new guns are in addition to the approximately 400 million guns American already own.
Measurement of spatial fields is of interest in environment monitoring. Recently mobile sensing has been proposed for spatial field reconstruction, which requires a smaller number of sensors when compared to the traditional paradigm of sensing with static sensors. A challenge in mobile sensing is to overcome the location uncertainty of its sensors. While GPS or other localization methods can reduce this uncertainty, we address a more fundamental question: can a location-unaware mobile sensor, recording samples on a directed non-uniform random walk, learn the statistical distribution (as a function of space) of an underlying random process (spatial field)? The answer is in the affirmative for Lipschitz continuous fields, where the accuracy of our distribution-learning method increases with the number of observed field samples (sampling rate).