Why you need Lora, LoraWAN, Mesh and blockchain
At Pagarba(Pagarba.io) we love LoRa Mesh And LoraWAN. Some of our projects utilizing these protocols and technologies has helped build better systems for hospitals. A hospital needs to track medical devices , beds, equipment, medicines and toiletries. If you dive into how commercial real estate buildings and plazas need to be smarter and safe and energy efficient , Lora Mesh, LoraWAN, blockchain and AI lead the way.
Think More intelligent building infrastructure without needing to rebuild your building from scratch. Just add Lora.
We’ve worked on projects with Lora, Lora Mesh, LoraWAN, and blockchain smart contracts integrated with LoraWAN and RFID tags to better track hospital medical devices and equipment. Other projects integrated blockchain, artificial intelligence, IOT, Lora Mesh and decentralized Mesh technologies for smart water metering, smart energy metering , better monitoring and analytics for heating and cooling systems.
We improved building and construction management and maintenance by integrating LoraWAn with machine learning. Automation was key for better building safety and security standards and features, better space utilization and asset tracking optimization. Smarter and safer elevators during hurricanes and earthquakes with products like HopeBox.
What is LoRa ?
LORA mesh is different than Zigbee or other short range RF technologies such as Bluetooth and WiFi. Because LoRa does not require property management companies to use building installers to deploy the network or highly-skilled technicians to manage it. It’s also unlocked radio frequency vs Zigbee and SigFox which are not open frequencies or open source.
LoRA offers hospitals or commercial building owners and management the ability to reduce costs while generating new sources of revenue by optimizing energy costs and increasing commercial tenant satisfaction to achieve higher property values and rents.Or create a better personalized well run hospital with optimized inventory management and tracking.
That’s all great, but why LoRa ?
LoRa’s advantages include very low asset deployment costs due to a need for fewer gateways and repeaters when compared with WiFi or Bluetooth. A single gateway can cover an entire loT or IIOT system. Imagine your entire building, even entire campus , such as underground parking garages, multi unit buildings and facilities , large airport like manufacturing plant , even an airport or sports stadium and surrounding facilities and neighborhoods. There is no expensive need for a complex system coverage analysis. There is no need for a complicated power source wiring system because LoRa has a protocol for low power consumption and long battery life characteristics.
With things like AES-128 encryption built in and public and private key blockchain technology integration, it’s far more secure than some open WiFI network.
It is also the only commercially available solution that features free GPS service at no extra power cost and operator free spectrum ISM bands making a low-cost solution for smart buildings and intelligent hospitals.
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Data is the lifeblood of many organizations, but it can be hard to get your hands on when data protection protocols are in place. With synthetic data you needn’t worry about access time-consuming roadblocks because this new technology bypasses them entirely!
Consider one financial institution with rich streams containing strategic information for decision makers–it was so highly protected that gaining internal use rights was practically impossible without outside help from specialists who knew how break through these security measures
Synthetic data is the answer to all of your problems. It can protect you from a myriad security threats and vulnerabilities by eliminating any chance that hackers might have at accessing company or customer information without consent, while also maintaining privacy for everyone involved in its creation!
What is synthetic data ?
AI-based synthetic data has been used to bring statistical properties and patterns of real world events into a different context.
The goal is reproduce the probability distribution or sampling theorem, but this time with new variables that weren’t present in any previous datasets
Now you can train machine learning and computer vision models faster than ever! With synthetic data, companies are able to quickly create large sets of training examples. This is a huge advantage for those who need access and time on their hands – it’s like having your own personal data set ready at any moment.
The power to analyze large datasets with speed and accuracy, without the need for third party data sets that are prohibitively expensive.
Synthetic data gives companies access to mimicked real world synthetic datasets and images made from high-quality analyzed real world sources at a low cost – enabling them not only see how their business would perform but also make informed decisions about where it needs improvement or success!
Why is it so hard ?
While the benefits of synthetic data are compelling, realizing them can be difficult. Generating synthetic files requires an organization to do more than just plug in AI tools that analyze their own datasets; this complex process needs people with specialized skill sets and understanding about machine learning and deep learning algorithms who have advanced knowledge on how these technologies work together as well specific frameworks tailored for each task at hand.
What’s next ?
Synthetic data is a complex and often tricky area to work in. Organizations should be sure that the value will outweigh any drawbacks before getting involved with it, which can include pitfalls from doing so wrong or having incorrect assumptions when creating synthetic datasets for use within your company’s operations.
Data-driven leaders who want their organization to be successful should make it a priority and never abandon the idea of using data and analytics as an integral part in decision making.
Engineers at the University of Waterloo combined two existing deep-learning AI techniques to identify players by their sweater numbers with 90-per-cent accuracy.
“That is significant because the only major cue you have to identify a particular player in a hockey video is jersey number,” said Kanav Vats, a Ph.D. student in systems design engineering who led the project. “Players on a team otherwise appear very similar because of their helmets and uniforms.”
Player identification is one aspect of a complicated challenge as members of the Vision and Image Processing (VIP) Lab at Waterloo work with industry partner Stathletes Inc. on AI software to analyze player performance and produce other data-driven insights.
The researchers built a data set of more than 54,000 images from National Hockey League games—the largest data set of its kind—and used it to train AI algorithms to recognize sweater numbers in new images.
Accuracy was boosted by representing the number 12, for instance, as both a two-digit number and two single digits, 1 and 2, put together, an approach known in the field of AI as multi-task learning.
“Using different representations to teach the same thing can improve performance,” Vats said. “We combined a wholistic representation and a digit-wise representation with great results.”
The research team is also developing AI to track players in video, locate them on the ice and recognize what they are doing, such as taking a shot or checking an opposing player, for integration in a single system.
Check out some cool artificial intelligence companies in San Diego, including Pagarba Solutions.