Tech

High-Performance Computing at the Edge

Published

3 years ago

November 9, 2021

This is a low-power, small-footprint edge computing device that may be deployed on-site or on the go. For a variety of commercial, industrial, and security applications, it provides real-time, secure, and automated computer vision artificial intelligence monitoring and intelligent analytics.

Artificial intelligence processor chips allow neural image processing for high-resolution intelligent video analytics when used in conjunction with the Central Processing Unit. Artificial intelligence adds powerful deep learning capabilities to clients’ consumer, industrial, and business cameras, providing for the most cost-effective enhancement of edge artificial intelligence performance.

The Advantages of Edge AI Box

The edge AI box enables better responsiveness and results that are closer to real-time than the traditional centralized Internet of Things architecture. It also guarantees enhanced security by delivering and processing insights promptly, most frequently within the same hardware or devices.

Even with security measures in place, sending data back and forth with Internet-connected devices exposes it to tampering and exposure. Processing at the edge reduces this danger while providing additional benefits. The gadgets come with improved security features.

The operation of an edge-based artificial intelligence box does not necessitate highly skilled labor. The devices don’t require data scientists to maintain because they’re self-contained. Required insights are either given automatically where they are needed or made apparent on the fly via highly graphical interfaces or dashboards.

Customer experiences are enhanced by an edge-based artificial intelligence box. It helps organizations create trust and connection with their customers by enabling responsiveness through location-aware services or rerouting travel plans in the event of delays.

Intelligence will inevitably go to the edge as we move forward into the highly connected digital economy. The potent combination of Artificial Intelligence and the Internet of Things opens up new vistas for companies to truly sense and respond to events and opportunities in their environment.

Applications of the Edge Artificial Intelligence Box

Medical devices, manufacturing systems, and cars are examples of applications that benefit from artificial intelligence-powered edge computing. Medical devices, in particular, have a pressing need for at-the-edge intelligence: Crucial data in the operating room, for example, must be analyzed quickly in order to provide physicians or doctors with the knowledge they need to act.

Artificial intelligence processing is increasingly being done in a cloud-based data center. Deep learning model training, which requires a lot of computational power, overshadows the relevance of artificial intelligence processing.

Artificial intelligence inference, which is performed after training and is hence less compute-intensive, has been largely ignored from the perspective of artificial intelligence processing. Inference, like training, has traditionally been done in a data center.

However, as the diversity of artificial intelligence applications grows on a daily basis, centralized, cloud-based training and inference management is being called into question.

What Is an AI Accelerator?

This is a strong machine learning hardware chip designed to execute artificial intelligence and machine learning applications both smoothly and quickly.

Artificial Intelligence Accelerator Hardware

When it comes to executing compute-intensive processes for machine learning, Central Processing Units were not as powerful or efficient a few decades ago. Hardware designers have labored tirelessly to develop a processing unit capable of executing any artificial intelligence task.

Machine Learning is the practice of applying algorithms and statistical models to let computer systems learn without explicit instructions by analyzing and making inferences from data patterns.

Computational capacity has long been one of the key limits of the ai accelerator, which has been a source of concern for researchers. When it comes to executing huge calculations for machine learning, Central Processing Units were not as powerful and efficient a few decades ago.

Optimized Deep Learning AI Accelerator Hardware

With the rise of deep learning, new accelerator hardware architectures are required to offer improved performance for machine learning activities during both training and inference.

General-purpose processors are limited in their usage for machine learning applications, owing to the irregularity of memory access that comes with extended memory stalls and high bandwidth needs. As a result, power consumption and heat dissipation requirements are significantly increased.

New tensor-based data formats were introduced as a result of software innovations. A tensor is a multidimensional array that is a generalization of vectors and matrices. In terms of performance and power consumption, these advancements offer numerous benefits.

The industry is moving toward a Central Processing Unit design that prioritizes cost, power, and thermal dissipation. As a result, specialized co-processors have arisen with the goal of lowering energy usage while increasing overall computing performance for deep learning workloads.

Impact of the AI box

Neural networks are gaining traction in a number of important industries, including healthcare, transportation, and law. Artificial intelligence algorithms are having an impact on people’s lives in a number of ways, from credit scores to distorted image matching.

For example, the technology assists by automating store orders. It makes use of artificial intelligence to prepare orders quickly and accurately. Because the technology is all over the place, the store’s existence is on a disastrous death spiral.

Advantages

The technique enables neural network operators to spend more time with their systems. As computers become faster, the likelihood of such learning occurring in real-time grows. It also aids in the development of robots that can quickly adapt to new tasks and learn from their failures.

The technology represents a picture of artificial intelligence model training, operation, infusion, and monitoring that has advanced dramatically in recent years.

Applications

Automation

Industries have always attempted to use technology to boost productivity. As a result, they have automated many repetitive operations and processes to reduce the amount of human intervention required, lowering manufacturing costs. Automation allows machines and computers to do repeated activities and adapt to changing conditions. In both blue-collar and white-collar sectors, automation is frequently used.

Machine Learning

Computer learning is a revolutionary concept: feed a machine a significant amount of data, and it will use the data’s experience to improve its algorithm and process data more efficiently in the future. Neural networks are the most important branch of machine learning. Neurons or perceptrons are nodes in a neural network that are interconnected. These are based on how information is processed in the human brain.

Deep Learning

Deep learning is a subset of machine learning that attempts to further mimic human learning. Neural networks are constructed into expansive networks with a huge number of layers in deep learning, and they are trained with massive amounts of data. It differs from the majority of other types of machine learning, which emphasizes training on labeled data. The sprawling artificial neural network is fed unlabelled data and given no instructions in deep learning. While saving the data as experience, it establishes the main qualities and purpose of the data.

Machine Vision

Machine vision aims to give computers the ability to see. Images from a mounted camera are captured and converted from analog to digital by a computer. Machine vision systems frequently attempt to mimic the human eye. Machine vision offers a wide range of applications, including signature recognition and medical image analysis.

Conclusion

Previously, the operation of powerful artificial intelligence programs necessitated the use of massive, expensive servers of the data center level. Edge computing devices, on the other hand, can be located everywhere. Artificial intelligence at the edge opens up a world of possibilities that can greatly benefit society in ways never envisioned before.

Business Solutions

Geneo Glam: Skin Firming Treatment for Radiant, Youthful Skin

Geneo Glam is the ultimate skin firming treatment designed to restore elasticity, enhance radiance, and leave you with a glowing, youthful complexion.

Published

14 hours ago

May 9, 2025

Marks Strand

The Geneo Glam skin firming treatment is a luxurious, non-invasive facial that revitalizes the skin by improving firmness, elasticity, and hydration. Using advanced OxyPod technology, this treatment delivers a unique combination of exfoliation, oxygenation, and infusion of active ingredients to help the skin look smoother, tighter, and more radiant.

Key Benefits

Firms and Hydrates
The treatment boosts collagen and elastin production, helping skin feel firmer and more supple.
Improves Elasticity
Increases the skin’s resilience and reduces the appearance of fine lines and wrinkles.
Prevents Collagen Breakdown
Helps preserve the skin’s youthful structure by protecting existing collagen and supporting healthy cell function.

Powerful Natural Ingredients

24K Gold Particles
Stimulate collagen production, protect skin fibers, and encourage cell renewal for a firmer, lifted appearance.
Silk Amino Acids
Strengthen the skin barrier, lock in moisture, and support collagen synthesis to reduce visible signs of aging.
Carnosine Peptides
Help protect the skin from sugar-related damage (glycation), delay cellular aging, and extend the life of skin cells.
Copper
An antioxidant and anti-inflammatory that supports collagen development, smooths fine lines, and helps with skin regeneration.

How the Treatment Works

Exfoliation and Oxygenation
The Geneo Glam OxyPod is activated with a Primer Gel, gently exfoliating the skin and triggering a natural oxygenation process that increases blood flow and enhances skin vitality.
Infusion of Actives
Active ingredients such as gold particles, peptides, and amino acids are infused deep into the skin to firm and rejuvenate.
Hydration and Nourishment
A final serum containing hyaluronic acid, rosehip oil, and marula oil hydrates and soothes the skin, leaving it soft and glowing.

Who Should Try Geneo Glam?

This treatment is ideal for people who want to:

Reduce fine lines and early signs of aging
Firm and tighten sagging skin
Restore hydration and improve skin tone

Geneo Glam offers a refreshing way to firm, lift, and hydrate your skin—leaving you with a youthful glow and smooth, resilient skin. It’s a perfect solution for anyone seeking visible results without invasive procedures or downtime.

Business Solutions

H.265 miniature UAV encoders: A comprehensive Overview

H.265 miniature UAV encoders revolutionize aerial technology with advanced video compression, ensuring high efficiency and superior performance for modern UAV systems.

Published

2 days ago

May 8, 2025

Adva

As the demand for high-quality, real-time video transmission from unmanned aerial vehicles (UAVs) continues to rise in both military and commercial applications, the need for efficient, compact video encoding solutions has become paramount. H.265 miniature UAV encoders represent a significant advancement in this space, providing robust video compression in a small, lightweight package ideal for drones with stringent size, weight, and power (SWaP) constraints. Leveraging the power of High Efficiency Video Coding (HEVC), also known as H.265, these encoders allow UAVs to deliver high-resolution video over constrained data links, enhancing situational awareness and operational effectiveness without overwhelming available bandwidth.

H.265 is a video compression standard that succeeds H.264/AVC and offers approximately double the data compression ratio at the same video quality level. This efficiency is particularly beneficial for UAV applications, where bandwidth and power availability are limited, especially during beyond-line-of-sight (BLOS) missions or in contested environments. With H.265 encoders, UAVs can stream 1080p or even 4K encoder video in real time while consuming significantly less data than older standards. This is critical for operations such as intelligence, surveillance, and reconnaissance (ISR), where maintaining video clarity over long distances or through relay networks is essential for accurate decision-making.

Miniature H.265 UAV encoders are engineered to operate under harsh environmental conditions while maintaining optimal performance. These devices are typically ruggedized, featuring extended temperature ranges, shock resistance, and electromagnetic shielding to ensure reliable operation in military or field environments. Despite their small size—often no larger than a deck of cards—they include advanced features such as low-latency encoding, dynamic bitrate control, encryption, and support for multiple streaming protocols including RTSP, RTP, and MPEG-TS. This allows them to integrate seamlessly into existing command-and-control infrastructure and support a variety of end-user applications, from real-time ground monitoring to autonomous navigation and object tracking.

H.265 miniature UAV encoders

The integration of H.265 encoders into small UAVs has significantly expanded the capability of tactical drone systems. For example, military units can deploy hand-launched drones equipped with these encoders to provide persistent ISR coverage over a battlefield, transmitting clear, actionable video intelligence back to command centers in near real time. Law enforcement agencies and border security forces also benefit from these technologies, using UAVs to monitor large or remote areas with minimal personnel. In disaster response scenarios, such encoders enable drones to deliver live aerial assessments of affected regions, helping responders prioritize actions and coordinate relief efforts efficiently.

Beyond video transmission, modern H.265 UAV encoders are increasingly integrated with onboard artificial intelligence modules that enable edge processing. This allows UAVs to perform real-time object recognition, motion detection, and scene analysis directly within the encoder, reducing the need to send raw data to centralized systems for processing. Such capabilities are crucial in time-sensitive missions where latency can affect outcomes, such as tracking moving targets or identifying threats in complex terrain.

Despite their many advantages, the deployment of H.265 miniature encoders does come with some technical considerations. The encoding process, while more efficient than previous standards, requires higher computational resources. Manufacturers must therefore strike a careful balance between processing power, thermal management, and energy consumption. Additionally, the compatibility of H.265 streams with legacy systems remains a factor, as not all ground stations or video players natively support HEVC decoding without updates or specialized software.

Manufacturers of H.265 miniature UAV encoders include companies such as IMT Vislink, Soliton Systems, Haivision, and VITEC, all of which provide solutions tailored to UAV and robotics applications. These encoders are often modular, allowing integrators to select configurations based on mission requirements, payload limitations, and transmission needs. As the ecosystem of compact, high-efficiency video systems grows, continued innovation in low-power silicon and AI integration is expected to drive the next wave of capability enhancements in this field.

In the evolving landscape of drone technology, H.265 miniature UAV encoders stand out as a critical enabler of high-performance video transmission. By combining advanced compression with minimal SWaP impact, these systems provide UAV operators with the tools to observe, analyze, and act with unprecedented precision and clarity—no matter how small the platform or how demanding the environment.

Business Solutions

IEEE 802.11p and V2X Communication: Enabling Smarter, Safer Roads

IEEE 802.11p revolutionizes V2X communication, driving smarter, safer roads through advanced vehicle connectivity. This cutting-edge technology enhances transportation systems, enabling intelligent and secure interactions for a safer future.

Published

2 days ago

May 7, 2025

Adva

Modern vehicles are no longer isolated machines; they are becoming intelligent, connected nodes within a larger transportation ecosystem. At the heart of this transformation is Vehicle-to-Everything (V2X) communication, which enables cars to talk to each other and to the infrastructure around them. One of the first and most influential technologies developed to support V2X is the IEEE 802.11p standard—a wireless standard specifically tailored for vehicular environments.

What is IEEE 802.11p?

IEEE 802.11p is an amendment to the IEEE 802.11 standard (commonly known as Wi-Fi), designed to enable wireless access in vehicular environments. It was approved in 2010 and forms the basis for Dedicated Short-Range Communications (DSRC).

Key Characteristics of 802.11p:

Frequency Band: Operates in the 5.9 GHz band reserved for Intelligent Transportation Systems (ITS).
Low Latency: Optimized for fast, real-time communication necessary for safety-critical applications.
Range: Effective communication range of up to 1 kilometer, suitable for high-speed vehicle interaction.
Decentralized Architecture: Enables direct communication (V2V and V2I) without the need for cellular or network infrastructure.
Robustness: Handles high-speed mobility and rapidly changing topologies typical of vehicular environments.

Role of 802.11p in V2X Communication

V2X (Vehicle-to-Everything) is a broader term encompassing various communication paradigms, including:

V2V (Vehicle-to-Vehicle)
V2I (Vehicle-to-Infrastructure)
V2P (Vehicle-to-Pedestrian)
V2N (Vehicle-to-Network)
V2C (Vehicle-to-Cloud)

802.11p primarily supports V2V and V2I communications, forming the backbone of DSRC-based V2X implementations. Its low latency and direct communication capabilities make it ideal for applications such as:

Forward collision warnings
Intersection movement assist
Emergency electronic brake lights
Lane change warnings

Comparison with Cellular V2X (C-V2X)

As V2X technology has evolved, C-V2X (based on LTE and 5G standards) has emerged as a strong alternative to 802.11p. Here’s how they compare:

Feature	IEEE 802.11p (DSRC)	C-V2X (LTE/5G)
Latency	~10 ms	~5–10 ms (LTE), <5 ms (5G)
Coverage	Short-range, direct	Short + long-range via network
Deployment	Mature, field-tested	Growing, especially with 5G
Infrastructure	Minimal (no cellular needed)	Requires cellular networks (for V2N/V2C)
Interoperability	Limited with C-V2X	Newer versions support dual-mode

Adoption and Use Cases

Global Deployment:

United States: Initially favored DSRC based on 802.11p, though recent FCC rulings have shifted focus toward C-V2X.
Europe: ETSI has defined ITS-G5, a protocol stack based on 802.11p.
Japan and South Korea: Active use of DSRC for tolling and traffic safety.

Real-World Applications:

Collision avoidance systems
Smart intersections
Road hazard notifications
Platooning for commercial vehicles
Public transport priority systems

Advantages of 802.11p

Mature and Proven: Used in numerous pilot programs and early deployments.
Fast Time to Communication: No need for handshake protocols; devices can communicate almost instantly.
No Subscription Costs: Operates independently of cellular networks.

Limitations and Challenges

Scalability: In high-density traffic, packet collisions may reduce reliability.
Spectrum Allocation: Regulatory changes in some countries have limited the bandwidth available to DSRC.
Limited Ecosystem Growth: Many automakers and countries are shifting investment to C-V2X and 5G-based platforms.

Future Outlook

While 802.11p has laid the foundation for V2X communication, the industry is gradually pivoting toward more advanced and scalable technologies such as 5G NR-V2X. However, 802.11p remains relevant in regions where DSRC infrastructure is already deployed and continues to serve as a dependable option for immediate, low-latency vehicular communication.

Hybrid Solutions:

Some industry players are exploring dual-mode V2X devices that support both 802.11p and C-V2X, ensuring backward compatibility and smoother transitions.

IEEE 802.11p has played a pivotal role in launching the era of connected vehicles, offering reliable, low-latency communication tailored for high-speed mobility. While newer technologies like C-V2X and 5G are beginning to dominate the roadmap, 802.11p’s contributions remain foundational in the evolution of V2X systems. As the automotive industry moves forward, a mix of technologies, including legacy support for 802.11p, will ensure that safety, efficiency, and connectivity continue to advance on roads around the world.