#ao * algorithm in artificial intelligence

[nuadox]

AI accelerates retinal imaging by a factor of 100 compared to manual approach

- By Nuadox Crew -

Researchers at the National Institutes of Health (NIH) have enhanced a cell imaging technique used to study retinal diseases like age-related macular degeneration (AMD) by applying artificial intelligence.

Video: "AI makes retinal imaging (almost) a snap" by National Eye Institute, YouTube.

Researchers at the National Institutes of Health (NIH) have enhanced a cell imaging technique used to study retinal diseases like age-related macular degeneration (AMD) by applying artificial intelligence. 

They combined adaptive optics (AO) with optical coherence tomography (OCT) to achieve high-resolution images of retinal pigment epithelium (RPE) cells.

However, processing these images was time-consuming due to speckle interference. To address this, they developed a deep learning algorithm called parallel discriminator generative adversarial network (P-GAN), which significantly improved image processing speed and contrast (roughly by a factor of 100 compared to manual methods).

This advancement not only aids in understanding retinal diseases but also enhances routine clinical imaging using AO-OCT.

--

Source: National Institutes of Health

Full study: Vineeta Das, Furu Zhang, Andrew Bower, et al. Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography, Communications Medicine (2024). DOI: 10.1038/s43856-024-00483-1

Read Also

New AI could predict whether or not those at high risk will develop glaucoma

[lanshengic]

Microchip launches MPLAB® Machine Learning Development Kit to help developers easily integrate machine learning into MCUs and MPUs

【Lansheng Technology News】Microchip Technology Inc. recently launched a new MPLAB® machine learning development toolkit, providing a complete integrated workflow to simplify machine learning model development. Available across Microchip's broad portfolio of microcontrollers (MCUs) and microprocessors (MPUs), this software toolkit enables developers to quickly and efficiently add machine learning inference.

Rodger Richey, vice president of Microchip's Development Systems Business Unit, said: "Machine learning is the new normal for embedded controllers. Leveraging machine learning at the edge can make products more efficient, more secure, and less power-intensive than systems that rely on cloud communications for processing. Lower. Designed specifically for embedded engineers, Microchip’s unique integrated solutions are the first to support not only 32-bit MCUs and MPUs, but also 8-bit and 16-bit devices, enabling efficient product development.”

Machine learning works by using a set of algorithms to analyze and generate patterns from large data sets to support decision-making. Machine learning is generally faster, easier to update, and more accurate than human processing. Microchip customers can leverage this new set of tools to enable predictive maintenance solutions to accurately predict potential problems with equipment used in a variety of industrial, manufacturing, consumer and automotive applications.

MPLAB Machine Learning Development Kit helps engineers build efficient, small-footprint machine learning models. Powered by AutoML, the toolkit eliminates many repetitive, tedious, and time-consuming model building tasks, including extraction, training, validation, and testing. It also provides model optimization capabilities to meet the memory constraints of MCUs and MPUs.

When used in conjunction with the MPLAB X integrated development environment (IDE), the new toolkit provides a complete solution. It can be easily implemented by people with almost no knowledge of machine learning programming, saving the cost of hiring data scientists. It also has advanced features that meet the needs of experienced machine learning designers.

Microchip also offers the option to extract models from TensorFlow Lite and use them in any MPLAB Harmony v3 project. MPLAB Harmony v3 is a fully integrated embedded software development framework that provides flexible, interoperable software modules to simplify the development of value-added functions and shorten product time to market. Additionally, the VectorBlox™ Accelerator Software Development Kit (SDK) provides the most energy-efficient artificial intelligence/machine learning (AI/ML) inference capabilities based on convolutional neural networks (CNN) using PolarFire® FPGAs.

The MPLAB Machine Learning Development Kit provides the necessary tools to design and optimize edge products that run machine learning inference. Visit the Microchip Machine Learning Solutions page to learn more about streamlining your development process, reducing costs and accelerating time to market with Microchip’s intuitive machine learning tools.

Lansheng Technology Limited, which is a spot stock distributor of many well-known brands, we have price advantage of the first-hand spot channel, and have technical supports. 

Our main brands: STMicroelectronics, Toshiba, Microchip, Vishay, Marvell, ON Semiconductor, AOS, DIODES, Murata, Samsung, Hyundai/Hynix, Xilinx, Micron, Infinone, Texas Instruments, ADI, Maxim Integrated, NXP, etc

To learn more about our products, services, and capabilities, please visit our website at http://www.lanshengic.com

[project-management-vision]

TRENDS IN INSTRUMENTATION AND CONTROL IN THE OIL, GAS AND MINING INDUSTRY 

Overall trends in instrumentation and control in the oil, gas and mining industry, suggest that instrumentation and control will become increasingly connected, data-driven, and automated in the future, enabling more efficient and optimised production while ensuring safety and sustainability. 

 Digitalisation will enable predictive maintenance, remote monitoring, and automation, leading to increased efficiency and cost savings. 

 Asset Integrity Management (AIM) maintains the integrity of critical assets such as pipelines, tanks, and processing equipment.  AIM can be achieved through real-time monitoring, inspection, and predictive maintenance, reducing the risk of equipment failure and downtime. 

 Autonomous operations (AO) using artificial intelligence (AI) and machine learning (ML), have gained popularity where remote and hazardous locations require minimal human intervention.  

 Cybersecurity is becoming a crucial concern. Instrumentation and control systems must be secured against cyber threats to prevent potential damage or disruption to critical operations. 

 The drive towards sustainability and energy efficiency has led to development low energy two wire sensors, control algorithms, and automation systems are to reduce energy consumption. 

To keep up with above technological advancements, PMV recommends you upgrade your skills as an instrument technician to:   

 Operate and maintain modern instrumentation equipment effectively and enhance job performance and your chances of promotion or advancement within the company.  

 Understand and implement safety protocols and procedures, reducing the risk of accidents and injuries. 

 Improve your troubleshooting and problem-solving abilities, thus enabling you to fix any issues, reducing downtime and increasing productivity. 

 Develop a strong skillset for you can pursue higher-level positions or subject matter expert (SME) instrumentation and control, such as process control or automation technician or engineer. 

A great entry level course in Instrumentation is UEE40420 Certificate IV in Electrical Instrumentation.

This course attracts funding in WA and SA for eligible participants. Contact the office for more information.

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Know more:

Upskill with PMV to Start a Career in Instrumentation and Control

Leading provider of EEHA Courses in Western Australia and South Australia

Steps to Manage Regulatory Compliance in Hazardous Areas

Explosion Protection (Ex) Certification Requirements for Australia

Recent Updates to AS/NZS Standards for Explosion Protection

Contact us:

Perth

10/22 Shields Crescent, Booragoon, WA, 6154

Contact no.: (08) 9317 2146

Adelaide

1042 Port Road, Albert Park, SA, 5014 Contact no.: (08) 9317 2147

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3/16 Hedland Place, Karratha, WA, 6714 Contact no.: (08) 9317 2147

Also, follow us:

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[alwaysunabashedsalad-blog]

AO* Search Algorithm In Artificial Intelligence

AO* Search Algorithm In Artificial Intelligence

In this article we will discuss about AO* Search Algorithm In Artificial Intelligence I. Also we will see properties of AO* Search Algorithm in Artificial Intelligence, Algorithm of AO* Search Algorithm , example of AO* Search Algorithm & Advantages – Disadvantages of AO* Search Algorithm in Artificial Intelligence. AO* Search Algorithm In Artificial Intelligence comes under the informed search…

[sciencespies]

Researchers use deep learning method to delve into predicting RNA structures in world first

https://sciencespies.com/biology/researchers-use-deep-learning-method-to-delve-into-predicting-rna-structures-in-world-first/

Researchers use deep learning method to delve into predicting RNA structures in world first

A hairpin loop from a pre-mRNA. Highlighted are the nucleobases (green) and the ribose-phosphate backbone (blue). Note that this is a single strand of RNA that folds back upon itself. Credit: Vossman/ Wikipedia

In a world-first, a team of Griffith University researchers has used an artificial intelligence method to better predict RNA secondary structures, with the hope it can be developed into a tool to better understand how RNAs are implicated in various diseases such as cancer.

Professor Yaoqi Zhou, Professor Kuldip Paliwal, Ph.D. student Jaswinder Singh and Dr. Jack Hanson from Griffith’s Institute for Glycomics and Signal Processing Laboratory led the research, which has been published in Nature Communications.

In all forms of life, ribonucleic acid (RNA) is essential for the coding, decoding, regulation and expression of genes. RNA and DNA are among the four major macromolecules in lifeforms.

The team employed the use of deep learning—a subset of artificial intelligence used to create complex, numerical functions to approximate specific tasks automatically without explicit human instructions—to build a more accurate model of the relationship between RNA sequence and structure.

This advancement comes after more than a decade of stagnation in the performance of previous methods to predict RNA structures.

Professor Zhou hoped this new method would be useful for designing new RNA molecules with therapeutic potentials.

“Imagine if protein and RNA were two people, with protein standing in front of RNA—our focus is naturally on the protein,” Professor Zhou said.

“Consequently, despite the fact that the number of proteins are outnumbered by the number of RNA by more than a factor of 10, we are clueless about what these RNAs are for in our human body.

“That’s why we developed this tool: to provide some structural clues. Getting clues is very important because more and more RNAs are implicated in more diseases including various cancers.

“The most exciting aspect is that we can now better link the sequencing information with the structure. Our sequence is encoded in our genomes, but how they are related to their function through the structure is an unknown.

“Using this deep learning technique we can better link the sequence to the structure and have better clues as to what their function might be. Once we understand how the sequence encodes the structure and therefore function, we can design the RNA to do it for a particular purpose, such as new drugs or molecular sensors.”

In order to develop the method, the team had to expand on existing data sets for known RNA structures by sourcing approximated computational data, then refine the automated training method with the exact data.

Professor Paliwal said only having access to less than 250 non-redundant known RNA structures among about 30 million unknown was a challenge that only the use of their deep learning method could address.

“Deep learning was used in this research to model the fundamental relationship between an RNA’s nucleotide sequence and the pairing of these nucleotide bases in its functional structure,” Professor Paliwal said.

“This is a very complex function as, theoretically, a nucleotide can be paired with any other base within the RNA, so it is the job of the deep learning neural network to find out which nucleotides are paired together.

“Making matters even more complex is that these algorithms have to be general and work for the millions of unique RNA sequences.

“Before our work, most of the previous studies had relied on comparative schemes based on RNA biological families or handcrafted scoring algorithms based on statistics. These methods can somewhat model the incredibly intricate function linking an RNA’s nucleotide sequence to its paired structure, but had reached a stagnant performance ceiling of about 80% accuracy for basepair predictions.

“Using deep learning, we were able to overcome all of these shortcomings to provide a blanket solution for all RNA structures while simultaneously breaking the performance ceiling which had existed for more than a decade, attaining a basepair accuracy of 93%.”

The team said the use of deep learning for the prediction of RNA basepairs was a feasible tool and a world first, and achieved superior performance in almost every facet compared to previous attempts.

Founder and Director of the Institute for Glycomics Professor Mark von Itzstein AO said the finding “opened up avenues for future research into this problem from other computational research groups, while providing a more accurate tool for experimental laboratories working in the fields such as biomedicine, drug discovery, and molecular biology.”

The research “RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning’ has been published in Nature Communications.

Explore further

AI reveals nature of RNA-protein interactions

More information: Jaswinder Singh et al. RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning, Nature Communications (2019). DOI: 10.1038/s41467-019-13395-9

Provided by Griffith University

Citation: Researchers use deep learning method to delve into predicting RNA structures in world first (2019, November 27) retrieved 27 November 2019 from https://phys.org/news/2019-11-deep-method-delve-rna-world.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

#Biology

[thereporterasiastuff-blog]

Aruba Introduces New Secure, AI-Powered

Aruba, a Hewlett Packard Enterprise company, today announced a new family of 802.11ax (Wi-Fi 6) IoT-ready wireless access points and complementary access switches, along with innovations in security, intelligent power management, and Artificial Intelligence (AI)-powered automation and service assurance, to deliver the performance, simplicity and reliability that organizations need to give users exceptional digital experiences. The new wireless access points support the latest Wi-Fi standard and are the first to be Wi-Fi Alliance (WFA) certified for the new WPA3 and Enhanced Open security standards to provide stronger encryption and simpler IoT security configuration. Aruba is also the first Wi-Fi vendor to integrate Bluetooth 5 into its APs combined with an integrated Zigbee® capabilities. These new capabilities enable IoT use cases, such as smart door locks and electronic shelf labels, while Bluetooth 5 also delivers user-aware indoor location allowing IT to create personalized experiences. In addition, Aruba is delivering industry-first power management innovations allowing customers to preserve their existing PoE switch investments while significantly reducing access point power consumption during off hours. These unique features include Intelligent Power Monitoring, a capability delivered by Aruba Operating System (AOS) 8, and NetInsight Green AP, part of Aruba’s AI-powered analytics and assurance solution. Enabling the Experience Edge Organizations are expecting IT professionals to deliver IoT-enabled innovation that will allow them to create extraordinary digital experiences for their employees, customers and guests. New types of experiences such as location-aware mobile engagement, digitally-assisted patient care, and user-aware meeting rooms can give organizations a competitive advantage. According to Gartner, “Enterprises preparing for the future of work must offer engaging, consumer-like experiences and deliver technologies that enable, rather than hinder, streamlined work execution1.” The network at the edge is what connects people and IoT to this digital world. It is the platform for building these digital experiences and it must be secure, intelligent and always on. However, a solid network foundation is not enough. To enable these new experiences, IT must be able to deliver improved and consistent service levels to address growing business demands and heightened user expectations. This requires not only a state-of-the-art network, but also the ability for IT to proactively anticipate issues in an ever-changing environment before they impact users and the business. New products and innovations: The Aruba 510 Series APs , a new series of 802.11ax, IoT-ready APs, with advanced security, AI-powered RF optimization, intelligent power monitoring, and integrated Zigbee and Bluetooth 5 capabilities. The Aruba 2930M access switches , with support for the 802.3bt standard to provide higher power PoE (up to 60 watts per port), a requirement for some high-end 802.11ax access points. Support for Wi-Fi Alliance WPA3 and Enhanced Open Security Standards to deliver state-of-the-art device security. Aruba is the first vendor in the industry to receive WFA certification for these new standards. Green AP, a unique, new feature of NetInsight , Aruba’s AI-powered analytics and assurance solution, that dynamically powers down APs when user devices are not present, offsetting the increased power requirements associated with select 11ax APs. Leveraging Aruba’s Rich Heritage to Bring Intelligence to the Network An effective AI solution requires domain expertise, a historical pool of clean data to feed the algorithms that deliver precise and trusted network automation and assurance, and real-world experiences to validate the solution. Aruba has a unique advantage over many competitors, including 16 years of Wi-Fi expertise, with learnings and best practices built into the AI algorithms from the largest edge networks in the world and from millions of installed APs to deliver secure, autonomous network operations. The new 510 Series APs work in concert with Aruba NetInsight to proactively monitor and troubleshoot the network, generating actionable insights and recommendations based on peer comparisons and benchmarks, and applying these recommendations to the network autonomously. This allows businesses to deliver the kind of improved performance and efficiency needed for today’s highly mobile and IoT-centric environments, while continually adapting to changing requirements and improving experiences for their users and customers. Built for IoT with Zigbee and Bluetooth 5 Integration The Aruba 510 series is the industry’s first set of 802.11ax APs with integrated support for Zigbee and Bluetooth 5, enabling Aruba customers to support 74% of IoT devices. Having all three wireless technologies available in a single access point gives customers powerful, extensive connectivity. In addition, customers can significantly reduce both their capital and operational expenditures since the Aruba infrastructure with Zigbee integration eliminates the need to deploy and operate a separate Zigbee network. Smart Energy Management with Green AP and Intelligent Power Monitoring As higher performance 802.11ax APs will handle a greater number of devices and traffic, they will also consume more power. In addition, network architects generally design AP configurations for the highest capacity scenarios, and these combined factors mean that many organizations are confronted with rising power costs. Green AP, a new feature of NetInsight and a new innovation for the networking industry, allows IT to intelligently manage APs to reduce power consumption by up to 72% dramatically lowering costs, while supporting social responsibility. Using Green AP, APs can be automatically turned on or off based on utilization, resulting in significant energy costs savings and an environmentally-friendly network. Additionally, Aruba’s Intelligent Power Monitoring (IPM), a feature in AOS 8, actively measures the power utilization of an AP and dynamically adapts to the available power resources. IT organizations can define and prioritize which capabilities to disable when an AP is operating over its power budget. IPM will begin taking power reduction steps autonomously as defined by the priority definition until the AP is operating within the power budget. State-of-the-Art Security with WPA3 and Enhanced Open Aruba is the first networking vendor to release products that have received WPA3 and Enhanced Open certifications from the Wi-Fi Alliance. With support for WPA3 and Enhanced Open, Aruba’s new suite of 11ax APs can deliver the security enterprises need as more users, devices and things join their networks. WPA3 adds new features to simplify Wi-Fi security, enable more robust authentication, and deliver increased cryptographic strength for highly sensitive data markets, such as government or finance. Wi-Fi Enhanced Open complements the security protection WPA3 delivers by improving data privacy while maintaining ease-of-use in open public networks where user authentication is not used, such as local coffee shops, airports and stadiums. Also, The Aruba 510 Series APs are available now, beginning at a list price of $1,095. The Aruba 2930M access switch is available now, starting at $10,799 list. The new version of NetInsight with Green AP will be available in the first quarter of 2019, with one-year subscriptions beginning at $50 per year per AP. Related Link Aruba Read the full article

[antoniomcx]

Learning Log 10 - AOS all the way!

With a teary eye (haha), I have to say that today is our last day at our Advance Operating System class. As a part of the requirement, I need to make this final log as an overall review of the subject matter and of course the class itself.

Initially I really thought that at the Advance Operating System will only take the key differences of the common Operating Systems such as Linux, MacOS and Windows. Boy o Boy! I was wrong. This subject is technical as it could get. There is nothing depth as the Advance Operating System subject could get.

As we go back in the past, the operating system we have today is a far improvement of what we have in the past. From operating systems who can only handle one process at a time to today's multi tasking processes. From computers which took a day to run a single mathematical computation to computers today that has Artificial Intelligence. Advance Operating Systems class enables me to understand how computer theories and algorithms connects with the hardware of a computer. How multi-core processors , RAM and storage devices works with the Operating System. I can now explain thoroughly how the number cores in a processor can correlates with faster computing and not just telling that it only means it can process faster.

Overall we can say that the Advance Operating System course is about Optimization. How the processes are run through the system and how modern advancements in computer science was able to run processes faster and simultaneously like today’s computers.

[lanshengic]

Microchip launches MPLAB machine learning development toolkit to help developers easily integrate machine learning into MCUs and MPUs

【Lansheng Technology News】September 8, 2023 - Machine learning is becoming a standard requirement for embedded designers to develop or improve various products. To meet this need, Microchip Technology Inc. recently launched the new MPLAB® machine learning development toolkit, which provides a complete integrated workflow to simplify machine learning model development. Available across Microchip’s broad portfolio of microcontrollers and microprocessors, this software toolkit helps developers quickly and efficiently add machine learning inference.

Rodger Richey, vice president of Microchip's Development Systems Business Unit, said: "Machine learning is the new normal for embedded controllers. Leveraging machine learning at the edge can make products more efficient, safer, and more power-efficient than systems that rely on cloud communications for processing. Lower. Microchip’s unique integrated solution is designed for embedded engineers and is the first to support not only 32-bit MCUs and MPUs, but also 8-bit and 16-bit devices, enabling efficient product development.”

Machine learning works by using a set of algorithms to analyze and generate patterns from large data sets to support decision-making. Machine learning is generally faster, easier to update, and more accurate than human processing. Microchip customers can leverage this new set of tools to enable predictive maintenance solutions to accurately predict potential problems with equipment used in a variety of industrial, manufacturing, consumer and automotive applications.

MPLAB Machine Learning Development Kit helps engineers build efficient, small-footprint machine learning models. Powered by AutoML, the toolkit eliminates many repetitive, tedious and time-consuming model building tasks, including extraction, training, validation and testing. It also provides model optimization capabilities to meet the memory constraints of MCUs and MPUs.

When used in conjunction with the MPLAB It also has advanced features that meet the needs of experienced machine learning designers.

Microchip also offers the option to extract models from TensorFlow Lite and use them in any MPLAB Harmony v3 project. MPLAB Harmony v3 is a fully integrated embedded software development framework that provides flexible, interoperable software modules to simplify the development of value-added functions and shorten product time to market. Additionally, the VectorBlox™ Accelerator Software Development Kit (SDK) provides the most energy-efficient artificial intelligence/machine learning (AI/ML) inference capabilities based on convolutional neural networks (CNN) using PolarFire® FPGAs.

The MPLAB Machine Learning Development Kit provides the necessary tools to design and optimize edge products that run machine learning inference. Visit the Microchip Machine Learning Solutions page to learn more about simplifying your development process, reducing costs and accelerating time to market with Microchip’s intuitive machine learning tools.

Lansheng Technology Limited, which is a spot stock distributor of many well-known brands, we have price advantage of the first-hand spot channel, and have technical supports. 

Our main brands: STMicroelectronics, Toshiba, Microchip, Vishay, Marvell, ON Semiconductor, AOS, DIODES, Murata, Samsung, Hyundai/Hynix, Xilinx, Micron, Infinone, Texas Instruments, ADI, Maxim Integrated, NXP, etc

To learn more about our products, services, and capabilities, please visit our website at http://www.lanshengic.com

[lanshengic]

Texas Instruments Introduces New Family of Vision Processors to Enable Scalable Edge AI Performance in Smart Camera Applications

【Lansheng Technology Information】To drive the development of edge intelligence based on innovation, Texas Instruments launched a new series of six Arm® Cortex®-based vision processors, enabling designers to add more vision and artificial intelligence (AI) processing functions at a lower cost and with higher power efficiency in applications such as video doorbells, machine vision and autonomous mobile robots.

The new family includes the AM62A, AM68A and AM69A processors, supported by open-source evaluation and model development tools, and general-purpose software programmable through industry-common application programming interfaces (APIs), frameworks and models. Using this platform of vision processors, software and tools, designers can easily develop and scale edge AI designs across multiple systems while reducing time-to-market.

"Real-time responsiveness in the world of electronic devices that matter most to the world requires decision-making locally and improved power efficiency," said Sameer Wasson, vice president of Texas Instruments' Processor Division. "This new processor family's affordable, highly integrated SoCs enable the use of more cameras and added vision processing in edge applications, enabling the future of embedded AI."

Scalable AI camera performance at the edge with vision processors

When implementing vision processing and deep learning capabilities in low-power edge AI applications, Texas Instruments' new vision processors bring intelligence from the cloud to the real world by removing barriers of cost and design complexity.

These processors feature a system-on-chip (SoC) architecture that supports extensive integration. The integrated components include an Arm Cortex-A53 or Cortex-A72 central processing unit, a third-generation Texas Instruments image signal processor, internal memory, interfaces, and hardware accelerators to provide deep learning algorithms with 1 trillion to 32 trillion operations per second (TOPS) of AI processing.

Lansheng Technology Limited, which is a spot stock distributor of many well-known brands, we have price advantage of the first-hand spot channel, and have technical supports. 

Our main brands: STMicroelectronics, Toshiba, Microchip, Vishay, Marvell, ON Semiconductor, AOS, DIODES, Murata, Samsung, Hyundai/Hynix, Xilinx, Micron, Infinone, Texas Instruments, ADI, Maxim Integrated, NXP, etc

To learn more about our products, services, and capabilities, please visit our website at http://www.lanshengic.com