“AI workloads need to run wherever customer data lives across the hybrid cloud; this makes flexible, standardized and open platforms and tools a necessity, as they enable organizations to select the environments, resources and architectures that best align with their unique operational and data needs”. Matt Hicks, President and CEO, Red Hat.

While the promise of gen AI dominates much of today’s technology landscape, the large language models (LLMs) underpinning these systems continue to increase in size. As a result, building cost-efficient and reliable LLM services requires significant computing power, energy resources and specialized operational skills. These challenges effectively put the benefits of customized, deployment-ready and more security-conscious AI out of reach for most organizations.

Red Hat intends to address these challenges by making gen AI more accessible to more organizations through the open innovation of vLLM. Developed by UC Berkeley, vLLM is a community-driven open source project for open model serving (how gen AI models infer and solve problems), with support for all key model families, advanced inference acceleration research and diverse hardware backends including AMD GPUs, AWS Neuron, Google TPUs, Intel Gaudi, NVIDIA GPUs and x86 CPUs. Neural Magic’s leadership in the vLLM project combined with Red Hat’s strong portfolio of hybrid cloud AI technologies will offer organizations an open pathway to building AI strategies that meet their unique needs, wherever their data lives.

Also Read: A Beginner’s Guide to Mastering Deep Reinforcement Learning in 2024 

Red Hat + Neural Magic: Enabling a future of hybrid cloud-ready gen AI

Neural Magic spun out of MIT in 2018 with the goal of building performant inference software for deep learning. With Neural Magic’s technology and performance engineering expertise, Red Hat aims to accelerate its vision for AI’s future, powered by the Red Hat AI technology portfolio. Built to break through the challenges of wide-scale enterprise AI, Red Hat uses open source innovation to further democratize access to AI’s transformative power via:

• Open source-licensed models, from the 1B to 405B parameter scale, that can run anywhere and everywhere needed across the hybrid cloud – in corporate data centers, on multiple clouds and at the edge.
• Fine-tuning capabilities that enable organizations to more easily customize LLMs to their private data and uses cases with a stronger security footprint;
• Inference performance engineering expertise, resulting in greater operational and infrastructure efficiencies; and
• A partner and open source ecosystem and support structures that enable broader customer choice, from LLMs and tooling to certified server hardware and underlying chip architectures.

vLLM leadership to enhance Red Hat AI

Neural Magic uses its expertise and knowledge in vLLM to build an enterprise-grade inference stack which enables customers to optimize, deploy and scale LLM workloads across hybrid cloud environments with full control over infrastructure choice, security policies and model lifecycle. Neural Magic also develops model optimization research, builds LLM Compressor (a unified library for optimizing LLMs with state-of-the-art sparsity and quantization algorithms) and maintains a repository of pre-optimized models ready to deploy with vLLM.

Red Hat AI aims to help customers lower AI’s costs and skill barriers with powerful technologies, including:

• Red Hat Enterprise Linux AI (RHEL AI), a foundation model platform to more seamlessly develop, test and run the IBM Granite family of open source LLMs for enterprise applications on Linux server deployments;
• Red Hat OpenShift AI, an AI platform that provides tools to rapidly develop, train, serve and monitor machine learning models across distributed Kubernetes environments on-site, in the public cloud or at the edge; and
• InstructLab, an approachable open source AI community project created by Red Hat and IBM that enables anyone to shape the future of gen AI via the collaborative improvement of open source-licensed Granite LLMs using InstructLab’s fine-tuning technology.

Neural Magic’s technology leadership in vLLM will enhance Red Hat AI’s ability to support LLM deployments anywhere and everywhere across the hybrid cloud with a ready-made, highly-optimized and open inference stack.

Source: Red Hat

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What is Deep Reinforcement Learning?

Deep reinforcement learning is a specialized area within machine learning that blends reinforcement learning (RL) with deep learning techniques. RL centers on computational agents refining decision-making through trial and error. In contrast, deep RL integrates deep learning methods, empowering agents to make decisions based on unstructured input data without the need for manual engineering of the state space. This approach equips deep RL algorithms to efficiently handle substantial inputs and refine objectives, like maximizing game scores or addressing intricate tasks.

Imagine this: Deep RL algorithms can handle huge amounts of data and tackle complex challenges. They’re like the superheroes of the tech world, optimizing objectives, whether it’s acing game scores or cracking intricate tasks. And the cool part? They’ve spread their wings across a bunch of fields – robotics, video games, language processing, computer vision, education, transportation, finance, and even healthcare. These algorithms let machines learn from their own experiences, getting better and smarter over time. Deep reinforcement learning is not just tech jargon; it’s the secret sauce making machines savvy learners in a whole bunch of real-world arenas.

Decoding the Different Types of Deep Reinforcement Learning Methods

Reinforcement Learning (RL) sets itself apart from other learning methods like supervised learning and unsupervised machine learning by not relying on labeled datasets or pre-defined rules. Instead, RL employs trial and error to learn from experience and enhance its policy over time. Some prominent RL methods include:

1. Value-Based Methods: Estimate the value function, representing the expected cumulative reward for actions in a given state. Q-Learning and SARSA are common examples.
2. Policy-Based Methods: Directly learn the policy, a mapping between states and actions maximizing expected cumulative reward. REINFORCE and Policy Gradient Methods fall into this category.
3. Actor-Critic Methods: Combine value-based and policy-based methods using two networks – the Actor selects actions, and the Critic evaluates the action’s goodness. The Actor-Critic algorithm updates the policy based on TD (temporal difference) error.
4. Model-Based Methods: Learn environment dynamics by constructing a model, including state transition and reward functions. This model enables the agent to simulate the environment and explore actions before executing them.
5. Model-Free Methods: These methods do not require the agent to build an environment model; instead, they learn directly through trial and error. Examples include TD-Learning, SARSA, and Q-Learning.
6. Monte Carlo Methods: Agents learn about states and rewards by interacting with the environment. Monte Carlo methods apply to both value-based and policy-based approaches.

Each method in RL presents a unique approach to learning and decision-making, catering to different scenarios and applications. Let’s take a look at some of the major end-use industries for this technology.

Also Read: Deep Learning Frameworks Demystified: Which One Fits Your Vision? 

Industrial Manufacturing

Deep reinforcement learning finds widespread application in robotics within industrial manufacturing. With inherently sequential actions, robots learn to navigate dynamic environments, leading to applications in industrial automation and manufacturing. This technology has proven effective in reducing labor expenses, product faults, and unexpected downtime. It brings about significant improvements in transition times and production speed.

Self-Driving Cars

Machine learning technologies, particularly deep reinforcement learning, power self-driving cars. Autonomous vehicles leverage large volumes of visual data, utilizing image processing capabilities within Neural Network architecture. Algorithms learn to recognize pedestrians, roads, and traffic, and detect street signs, excelling in complex scenarios and decision-making skills, such as determining the best route while minimizing human risk.

AI toolkits

AI toolkits like OpenAI Gym, DeepMind Lab, and Psychlab play a pivotal role in providing the essential training environments for large-scale innovation in deep reinforcement learning (DRL). These open-source tools are instrumental in training DRL agents, contributing significantly to the advancement of this field. As organizations increasingly integrate DRL into their unique business use cases, we can anticipate continued substantial growth in practical applications.

Trading and Finance

While supervised learning and time-series analysis aid in predicting stock market performance, reinforcement learning plays a unique role in decision-making. RL agents can decide whether to hold, buy, or sell shares, with their performance benchmarked against market standards. This application ensures optimal decision-making in dynamic financial environments.

Natural Language Processing

Reinforcement learning extends its reach to Natural Language Processing (NLP). Tasks like question-answering, summarization, and chatbot implementation benefit from RL agents. Virtual bots are trained to simulate conversations, with policy gradient approaches rewarding sequences exhibiting crucial conversation properties, including coherence, informativity, and simplicity of response.

Healthcare

Reinforcement learning is a growing area of research in healthcare. Bots equipped with biological information undergo extensive training for precision surgeries and diagnostic tasks. RL bots contribute to better disease diagnosis and prediction, especially in scenarios where treatment delays may impact outcomes. This application showcases the potential of RL in improving healthcare practices.

Winding Up

Deep reinforcement learning represents a powerful combination of reinforcement learning and deep learning techniques. By enabling machines to learn through trial and error and process unstructured input data, deep RL has the potential to revolutionize various fields. From robotics to finance, healthcare to transportation, deep RL has shown its ability to tackle complex tasks and optimize objectives. As this field continues to advance, we can expect even greater breakthroughs and transformative applications. Deep reinforcement learning holds the promise of empowering machines to learn and excel, paving the way for a future where intelligent agents can adapt and thrive in dynamic environments.

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What Are Convolutional Neural Networks?

Convolutional neural networks (CNNs) are deep learning algorithms used for image recognition, classification, and computer vision tasks. CNNs automate feature extraction, learning directly from data, making them effective for various computer vision applications. The fundamental building blocks of CNNs are pooling layers, convolutional layers, and fully connected layers. Convolutional layers apply a set of learnable filters to the input image, which convolves the input and produces feature maps. These feature maps capture varied aspects of the input image viz., textures, edges, and shapes. Pooling layers further downsample the feature maps, reducing the dimensionality and retaining the most important information.

Fully connected layers are responsible for performing the final classification based on the extracted features. These layers take the flattened feature maps as input and predict the probabilities of different classes via the use of activation functions and weight matrices.

Understanding Convolutional Filters and Feature Maps

Convolutional filters are small matrices that are applied to the input image. They scan the image by sliding across it as well as perform mathematical operations between the filter values and corresponding pixel values. These operations result in a feature map, which represents the presence or absence of specific features in the image.

Each filter is designed to detect a specific feature, such as edges, corners, or textures. By applying multiple filters, CNNs can capture various features at different scales and orientations. The size of the filters determines the level of detail that can be extracted. Smaller filters capture finer details, while larger filters capture more general features. The number of filters used in a layer also affects the complexity of the model and its ability to detect diverse features.

Also Read : Why is Human-Computer Interaction Crucial in Today’s Technological Era?

Feature maps are the output of the convolutional layers. Each feature map represents the activation of a specific filter at different spatial locations. By combining multiple feature maps, CNNs create a rich representation of the input image, capturing its unique characteristics.

Understanding convolutional filters and feature maps is crucial for building effective CNN models. In the next section, we will discuss strategies for choosing filter sizes and the impact of different filter architectures on model performance.

Pooling and Stride Techniques in AI Convolutional Neural Networks

Pooling and stride are important components of convolutional neural networks that contribute to their efficiency and effectiveness.

Pooling is a downsampling operation that reduces the dimensions of feature maps by extracting relevant features and disregarding unnecessary details. Common pooling techniques include Max Pooling, Average Pooling, and Sum Pooling. Pooling helps control parameters and prevent overfitting.

Stride determines how the convolutional filters move across the input image or feature maps. It is the step size used in the sliding-window approach. The choice of stride affects the resolution and spatial information captured by CNN.

Both pooling and stride techniques play a crucial role in reducing the dimensionality of the input and extracting relevant features efficiently.

Real-World Applications and Success Stories of Convolutional Neural Networks

Convolutional neural networks have revolutionized various fields, including medical imaging, autonomous vehicles, agriculture, and the creative industry. In medical imaging, CNNs accurately diagnose diseases like cancer, Alzheimer’s, and cardiovascular diseases, leading to improved patient outcomes. CNNs also play a vital role in autonomous vehicles, enabling object detection, traffic sign recognition, and navigation. In agriculture, these networks analyze satellite images to provide insights into crop health and disease detection, helping optimize production and reduce waste. Lastly, CNNs have applications in generating realistic images, enhancing photo quality, and enabling virtual reality experiences in the creative industry. Overall, CNNs have proven to be a versatile and powerful tool in various domains.

Final Thoughts

Convolutional neural networks have revolutionized multiple industries and demonstrated their immense potential in various real-world applications. From aiding in medical imaging diagnosis to enabling autonomous vehicle navigation, CNNs have significantly improved efficiency, safety, and decision-making capabilities. They are also transforming agriculture by optimizing crop production through the analysis of satellite images and aerial data. CNNs are even making their mark in the creative industry by generating realistic images and enhancing photo quality. With advanced techniques, CNNs can further improve performance and robustness. Embracing the power of CNNs in the AI era can help businesses push the boundaries of innovation.

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Machine Learning networks are only as good as the data they’re fed with. Achieving sufficient data coverage is time consuming and costly, often leading developers to compromise their training data, resulting in biased Machine Learning applications that fail when used in the real world.

“As Machine Learning applications are entering more areas in our everyday life, reliable performance and bias reduction are more important than ever,” says Richard Bremer, Chief Executive Officer and Co-Founder. “Devant was established to give Machine Learning teams the power to define their training and validation data at a granular level, enabling a new generation of human-machine applications that reduce risks and enhance our daily lives.”

Also Read: Rimini Street presents Rimini Consult to help organizations optimize, develop and transform their corporate software

With a relentless focus on creating the most lifelike digital humans possible, Devant’s technology captures and translates the subtle movements, behaviors, and imperfections that have been impossible to simulate until now. With this unique approach to data creation, Machine Learning teams can specify the most granular levels of detail in their datasets. This means they can cover a broader range of human diversity, without compromising on delivery times or quality.

Mattias Arrelid, Chief Product Officer, says: “At Devant, our key focus is on pushing the boundaries of synthetic data. Even the slightest improvements have a substantial impact on our ability to help our customers to increase their Machine Learning network performance.”

Customisation is a key facet of Devant’s offering. Customers can specify their own parameters, defining their data to fit complex scenarios and edge cases. This powerful combination of digital human diversity and parameter control enables metadata that’s tailored to each customer’s individual requirements, describing the resulting images right down to pixel level. Once the datasets have been generated, new criteria can be added, returning new training and validation images in a matter of days.

“What we have achieved is an unmatched combination of configurability and speed,” says Oliver Hotz, Chief Technology Officer. ”Machine Learning teams can create exactly the data they need, and get hundreds of thousands of images and animations in a matter of days – delivered together with metadata on a completely new level.”

SOURCE: PRWeb

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Us2.ai’s technology is a patented, automated clinical workflow solution that recognizes and analyzes 2D and Doppler echo images for comprehensive cardiac measurements needed for the diagnosis, prediction and prognosis of heart disease and pulmonary hypertension. Us2.ai produces complete echo reports in less than 2 minutes.[2] Us2.ai’s technology decreases the time from patient assessment to treatment initiation by expediting access to echocardiogram reports and time-critical diagnostics in the acute and post-acute care phases. The partnership furthers TeraRecon’s long-standing commitment to, leadership in. cardiovascular imaging interpretation and augmented decision support.

“Echo is an essential tool in the delivery of cardiovascular care. The average time to complete an echo takes up to 60[1] minutes, plus additional reporting time. Using AI can dramatically reduce this time burden, as well as ensure a uniform standard of excellence in reporting. We believe that no patient should have to wait for care when time is critical,” said Dan McSweeney, President of TeraRecon. “We are proud to partner with Us2.ai to enhance and complement our comprehensive AI platform, tailoring it to cardiovascular needs, so that cardiologists and care teams can provide optimal care to more patients most efficiently.”

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“With a global installed base of ~1,900 health sites, TeraRecon represents a significant and immediate distribution opportunity for the unique capabilities of Us2.ai. Coupled with a national shortage of sonographers, the combination of our leading-edge applications will support hospitals and providers as well as patients,” said Seth Koeppel, head of business development at Us2.ai. “We look forward to bringing the power of our AI-enabled cardiovascular solution to more cardiac care teams and patients across the U.S.”

Eureka Clinical AI is the leading AI SaaS imaging interpretation and Clinical Decision Augmentation solution from ConcertAI’s TeraRecon. As the industry’s most broadly deployed platform it is unique in being open to 3rd party AI algorithms allowing consolidated management of all AI interpretation solutions with seamless PACS integrations. Multi-specialty care teams can see results and receive mobile alerts to confirm AI findings, ensuring optimal and timely patient interventions, management and coordinated care delivery.

SOURCE: PR Newswire

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The ITC has suspended enforcement of its orders pending resolution of AliveCor’s appeal of the U.S. Patent and Trademark Office, Patent Trial and Appeal Board’s (“PTAB”) decision finding the asserted patents unpatentable. Today’s ruling marks a victory for AliveCor and affirms the Initial Determination issued in June by Administrative Law Judge (ALJ) Cameron Elliot of the ITC. The Final Determination will now undergo a 60-day review by President Biden.

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In addition to fighting Apple on patent infringement in the ITC, AliveCor continues to seek relief in the Northern District of California from Apple’s anticompetitive behavior. That case is expected to go to trial in early 2024.

“Today’s ITC ruling is a win for innovation and consumer choice,” said Priya Abani, CEO of AliveCor. “The ruling underscores the importance of upholding intellectual property rights for companies like AliveCor and scores of others whose innovations are at risk of being suppressed by a Goliath like Apple. We look forward to continuing to build and innovate on our cardiac solutions to improve people’s lives.”

AliveCor has introduced a series of game-changing innovations in AI-powered cardiac care, including the first-ever FDA-cleared wireless, patchless, six-lead ECG sensor, the first and only personal ECGs to detect six of the most common arrhythmias, and the first FDA-cleared credit-card-sized personal ECG. With more than 170 patents issued and pending worldwide, AliveCor continues to protect its strong portfolio of innovations in accordance with established patent law.

SOURCE: PR Newswire 

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DA Monitor is a feature (of the DA Desktop Suite software application) that is coming soon to Mac (already available for Windows). This feature will allow users to monitor the health of their (non-Mac) drives on their Mac personal computer. In other words, users will be able to see their non-Mac drives’ health summarized on their Mac desktop computer via notifications that are pushed to their Mac desktop. DA Monitor is capable of showing multiple DA Drive Analyzer accounts in one window, which can be helpful for monitoring the health of anywhere from a single NAS to many NAS systems across the globe. This feature is designed so that the user does not need to constantly check DA Portal (DA Drive Analyzer’s in-depth dashboard). Instead, users can simply check the notifications that are pushed to their Mac desktop.

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Users can start using this feature by installing DA Drive Analyzer onto each computing device whose drive health needs to be monitored. After that, they can download DA Desktop Suite  to their Mac desktop computer and add the DA Drive Analyzer user accounts for the drives they want to monitor to the DA Monitor tab. DA Monitor will start displaying AI-generated and other health alerts for each drive.

SOURCE: PR Newswire

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Also Read: Digital private bank becomes reality – Alpian launches as Switzerland’s first FINMA-licensed

Motion capture (mocap) technology is the key to making virtual humans alive. Mocap is the process of recording the movement of objects or people. However, the majority of other current competitor solutions require special hardware, which results in costly products, says RH Shih, the CEO of Red Pill Lab.

RedPillGo, the latest product designed by RedPill Lab, is a lifesaver for creators who have to animate characters. No need for a specialized studio with cameras and sensors, RedPill Go itself captures movements of the actors and converts voice into 3D facial expressions in real time.

“Our goal is to make motion capture technology more accessible and affordable.” Compared to traditional animation production, which can take up to eight hours to create a single image, RedPillGo not only saves time and reduces costs, but also increases productivity, RH Shih says.

Before RedPillGo, RedPill Lab is the first company in the world to use only 6 sensors to capture the body movements and facial expressions of a live actor, making the interaction between the virtual character and the fans more realistic and vivid. Since the outbreak of Covid-19, they found that it was no longer easy to travel to the customer’s location to install motion capture equipment. Turning a crisis into an opportunity, they began working on new products which could break down the limitations of space.

In addition to RedPillGo, its multilingual al-powered engine Red Pill Voice Engine enables users to generate expressive facial animation easily from just an audio source. Currently, Red Pill Voice Engine plays a key role in all of RedPill Lab’s products and achieves more natural results for digital avatars. Equipped with RedPillGo and Red Pill Voice Engine, people can create personalized avatars in 3D format effortlessly.

Although based in Taiwan, the target market of RedPill Lab is worldwide. They have cooperated with Unreal Engine, a 3D computer graphics game engine developed by Epic Games. In Asia, their clients come from Japan, South Korea, China, Singapore and many other countries.

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Medical imaging AI specialist Avicenna.AI announced a signed distribution agreement with Sectra, an international medical imaging IT and cybersecurity company. The agreement will see Avicenna’s AI solutions for neurovascular pathologies offered through the Sectra Amplifier Marketplace, a platform for contracting, purchasing, and servicing of AI applications validated and verified for use at point of care.

Avicenna’s CINA solutions use deep learning to identify, detect and quantify life-threatening pathologies from CT medical images. The FDA-cleared and CE-Marked tools are seamlessly integrated within the clinical workflow, automatically triggering and reporting algorithm results through the systems already used by radiologists.

Stroke is a leading cause of long-term disability and the second leading cause of death, with one death every 6 seconds worldwide. There are two main types of strokes: ischemic stroke, caused by a blood clot (LVO), and hemorrhagic stroke, caused by an intracranial hemorrhage (ICH).

The AI tools to be included in Sectra’s Amplifier Marketplace include CINA-ICH, CINA-LVO, and CINA-ASPECTS.

• CINA-ICH uses deep learning to identify suspected intracranial hemorrhage and prioritizes those cases in the worklist, dramatically reducing turnaround time for head trauma and stroke patients. (US/EU)
• CINA-LVO is a triage tool for rapid automatic LVO detection and real-time triage and prioritization, accelerating clinical workflow and helping stroke teams in their diagnosis. (US/EU)
• CINA-ASPECTS is an AI-based automatic quantification tool that enables faster, more consistent, and more precise interpretation for the assessment of acute ischemic stroke. (available for European Union only)

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Using a combination of deep learning and machine learning technologies, the CINA solutions automatically detect and prioritize acute ICH and LVO cases within seconds, and assess them for severity, seamlessly alerting radiologists within their existing systems and workflow.

“To help healthcare providers get on the AI adoption journey, we have created the Sectra Amplifier Marketplace. We aim to facilitate easier access and usage of AI applications in medical imaging. This distribution agreement is an example of that. With Avicenna.AI tools deeply embedded in the Sectra diagnostic workspace, we provide our radiologists with enhanced diagnostic confidence for stroke cases,” says Nynke Breimer, Global Product Manager AI Radiology, Sectra.”

“We provide best-in-class AI triage tools that enable fast detection of the leading causes of stroke, leading to more efficient patient management,” said Cyril Di Grandi, co-founder, and CEO of Avicenna.AI. “We are glad to collaborate with a global partner such as Sectra. Through this agreement, we can help more clinicians to facilitate stroke decision making, ensuring a prompt therapeutic response and ultimately improving patient outcomes.”

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Seoul Robotics, the 3D perception solution company using deep learning AI to power the future of mobility, announced that it will exhibit at the 2022 Consumer Electronics Show (CES) from January 5-7, 2022 at the Las Vegas Convention Center at booth #6559.

On the heels of a year with significant growth in 2021, Seoul Robotics will spotlight its industry-leading technology fueled by SENSR, the company’s sensor-agnostic 3D perception platform which is currently being implemented across multiple industries and verticals.

“Last year, Seoul Robotics was able to shape the way the world sees and uses 3D perception, disrupting the industry and experiencing unprecedented growth. At CES, we will showcase our revolutionary technology and the pioneering partners who are leveraging it to make our world safer, smarter, and more efficient,” says HanBin Lee, CEO and Co-Founder of Seoul Robotics.

Live demonstrations of SENSR will be held within the CES booth to provide visitors insight into advancements and application capabilities of the agnostic software, utilizing sensors from Velodyne, Hesai, Ouster and AEye. This will be expanded upon through showcases of key projects and partnerships which utilize the technology to deliver highly accurate, dependable solutions across autonomy through infrastructure, mobility, and smart infrastructure.

Read More: IBM Digital Health Pass Integrating with Healthcare IT Leaders Healthy Returns Practice to Provide COVID-19 Digital Credentials

At the show, Seoul Robotics will unveil its most advanced autonomy through infrastructure solution, a revolutionary level 5 autonomy system currently deployed by a leading OEM. Seoul Robotics has also developed solutions deployed in the smart spaces, security, retail, smart cities and ITS spaces through smart infrastructure partnerships with Skyfii, Evitado, SAIMOS, Milestone, 6SS, The University of Tennessee Chattanooga’s Center for Urban Informatics and Progress (UTC – CUIP) and MH Corbin. Seoul Robotics will showcase the growing partnership with UTC – CUIP including the award-winning MLK Smart Corridor, a first-of-its-kind testbed utilizing 3D technology to understand pedestrian safety in high-traffic areas.

Security technology is another expanding field for 3D perception technology that the company will preview at CES. Seoul Robotics maintains a partnership with global software company Skyfii for a security and occupancy venue system which has seen an integration of technology to improve crowd control. Additionally, two companies – ecosystem partner 6SS and SAIMOS®, Situational Awareness, Infrastructure Management & Operations Security – will demonstrate their plug-in integration with the Milestone Systems XProtect® VMS platform. 6SS enables a deeper understanding of activity within highly customized “zones” to track and detect objects, occupancy, movement, and speed while minimizing the number of false positives. SAIMOS helps customers combine the advantages of complex technologies (LiDAR, AI Video Analytics, VMS & GIS) available to users in the simplest possible way in order to be effective to enable situational awareness. Smart Infrastructure applications enabling safer roads, highways and airports will be highlighted, including a joint aviation system with Evitado to decrease aircraft ground collisions, along with intelligent transportation system partner MH Corbin providing roadway information in real time with traffic flow and vehicle counting advanced metrics.

Furthermore, the CES booth will provide insight into additional industry-leading partnerships within transportation mobility including Herzog and HL Klemove. Announced earlier this year, the partnership with HL Klemove combines SENSR with the OE automotive parts manufacturer’s smart sensors to deliver an all-in-one, hardware-software 3D sensor solution for mobility applications spanning autonomous vehicles, smart cities, smart factories and unmanned robots. Also included in the exhibit is Seoul Robotics’ advanced rail solution with Herzog. Utilizing long-range perception to detect items on tracks, the system can perceive an obstacle and communicate with a train allowing it to slow down in time to reduce impact and increase survivability.

Read More: Access Advance Welcomes Microsoft as a Licensor and Licensee of the HEVC Advance Patent Pool

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