Providing Peace of Mind for Caregivers and Safety for Loved Ones

LAS VEGAS, Jan. 2, 2025 /PRNewswire/ — Nomo Smart Care (Nomo), an in-home, no camera safety technology, is reshaping caregiving by solving a key challenge: how to keep loved ones safe in their home without sacrificing their privacy or independence. Unlike traditional systems that rely on invasive cameras, Nomo’s intelligent sensors monitor activity, detect unusual patterns and send alerts directly to a caregiver’s phone via the intuitive and user-friendly Nomo app. From fall detection to monitoring daily routines, Nomo provides real-time alerts and information so families can act quickly when it matters most. Check out this revolutionary technology powered by artificial intelligence at Pepcom’s Digital Experience @ CES, January 6 in Las Vegas.

“Caregivers are often stretched between their responsibilities at home and work, all while worrying about the safety of their loved ones,” said David Baer, president, Nomo Smart Care. “Nomo Smart Care is here to ease that burden by providing the tools families need to feel confident that their loved ones are safe, cared for, and living with independence. It’s not just about safety—it’s about preserving dignity and creating peace of mind for everyone involved.”

How Nomo Smart Care Works
The Nomo Smart Care system includes a hub, satellites, and tags that work together seamlessly to monitor daily activity in the home. It tracks routines like ensuring the medicine cabinet or refrigerator were opened, detects falls, and flags irregularities in daily routines. This information is delivered directly to caregivers in real time via the Nomo app, empowering them to stay connected no matter where they are.

Key Features & Benefits:

Fall Detection: Detects potential falls and sends immediate alerts so caregivers can quickly respond.Routine Monitoring: Tracks essential daily activities, such as whether the medicine cabinet or refrigerator door were opened on time and notifies caregivers if something is amiss.Privacy-First Technology: Uses advanced motion sensing, not cameras, to protect loved ones’ privacy while keeping them safe.Proactive Care: Tracks changes in routines or behaviors, allowing caregivers to address potential issues before they become serious.Real-Time Alerts: Instant notifications via the Nomo app keep caregivers informed and prepared to act when needed.Emergency Services. Connects to local emergency services to dispatch help when needed

New at CES, Nomo Smart Care features elevated health monitoring solutions and motion-sensing enhancements that work with the Nomo system such as connected blood pressure cuffs, connected thermometers, a GPS tracking device, and a connected medical alert bracelet. In addition, through the new Nomo Partner Portal, medical experts can receive certain health related data collected by the Nomo system.

About Nomo Smart Care
Nomo Smart Care is the first fully digital, privacy-focused caregiving solution designed to help families protect their loved ones while maintaining their independence. By combining intelligent monitoring with a user-friendly app, Nomo empowers caregivers to support their loved ones from anywhere.

To learn more, visit www.NomoSmartCare.com.

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Ansys is collaborating with Cognata and Microsoft on a web-based platform enabling users to test and validate ADAS/AV automotive sensors in a virtual environment that mimics real-world conditions

/ Key Highlights

Ansys AVxcelerate Sensors™ simulation software will bolster the Automated Driving Perception Hub (ADPH) platform, managed by Cognata and running on Microsoft Azure, with high-fidelity radar and electromagnetic (EM) wave propagation simulation capabilitiesThe platform is powered by AMD EPYC™ central processing units (CPUs) and Radeon™ PRO graphics processing units (GPUs) for machine learning inference and visualization workloadADPH hosts a library of manufacturer-certified virtual sensor models, including thermal camera, radar, and LiDAR systems

PITTSBURGH, Jan. 2, 2025 /PRNewswire/ — Ansys (NASDAQ: ANSS) today announced AVxcelerate Sensors is accessible through Cognata’s Automated Driving Perception Hub. The ADPH platform runs on Microsoft Azure and 4th Generation AMD EPYC™ processors and Radeon™ PRO GPUs. ADPH gives original equipment manufacturers (OEMs) easy access to certified, web-based sensor models from manufacturers, enabling collaborative testing and validation of advanced driver assistance systems (ADAS) and autonomous vehicle (AV) functions using a high-fidelity simulation platform with virtual twin technology.

The ADPH allows OEMs and sensor manufacturers to test and validate certified sensors against diverse industry standards, including those put forth by the National Highway Traffic Safety Administration (NHTSA) and the New Car Assessment Program (NCAP). The platform currently includes Cognata sensor models for thermal cameras, LiDAR, RGB cameras with varying lens distortions, and leverages Deep Neural Network (DNN) technology that enables photorealistic images and simulations.

With the addition of Ansys AVxcelerate Sensors, users have access to physics-based radar models that reproduce EM wave propagation — accounting for material properties within high frequencies — to enhance signal strength and accuracy. The radar simulation provides raw data that can be used to test and improve the algorithms that process radar signal interference, like small changes in frequency caused by moving objects (doppler effect). When connected to a virtual model from a radar supplier, AVxcelerate Sensors produces a virtual twin of the sensor, enabling OEMs to evaluate its performance with enhanced predictive accuracy.

“We are excited to integrate Ansys’ radar simulation technology into the ADPH platform, bringing OEMs and tier-one suppliers an unmatched level of accuracy in sensor validation,” said Danny Atsmon, founder and CEO at Cognata. “Ansys’ ability to simulate complex EM wave interactions enhances our platform’s ability to deliver precise, real-world insights for radar-based ADAS and AV systems. This collaboration significantly advances the industry’s ability to test and refine sensor performance under diverse conditions.”

Cognata’s generative AI transfer technology, enabled by AMD Radeon PRO V710 GPUs, enhances the RGB camera simulation platform by delivering high-fidelity virtual sensors. It accurately captures and replicates the real-world behavior of sensors within the simulation.

“Ansys’ AVxcelerate Sensors platform includes real-time radar capabilities for accurate modeling of radar interactions in complex environments,” said Shane Emswiler, vice president of products at Ansys. “By offering the solution on Cognata’s ADPH platform, we are enabling customers to design for real-world operations to meet strict regulatory standards. As the industry works toward fully autonomous driving, safety validation is paramount, and the joint effort between Ansys and Cognata streamlines this typically long and complicated process.”

“We’re pleased to collaborate with Ansys and Cognata to enhance automated driving validation and simulation on Microsoft Azure,” said Nidhi Chappell, Vice President, Azure AI Infrastructure at Microsoft. “By integrating Ansys’ advanced radar simulation technology, we’re empowering OEMs and tier-one suppliers with high levels of accuracy in sensor validation. This collaboration underscores our commitment to providing leading-edge cloud infrastructure that supports the development and validation of ADAS and autonomous vehicle technologies.”

/ About Ansys

Our Mission: Powering Innovation that Drives Human Advancement™

When visionary companies need to know how their world-changing ideas will perform, they close the gap between design and reality with Ansys simulation. For more than 50 years, Ansys software has enabled innovators across industries to push boundaries by using the predictive power of simulation. From sustainable transportation to advanced semiconductors, from satellite systems to life-saving medical devices, the next great leaps in human advancement will be powered by Ansys.

Ansys and any and all ANSYS, Inc. brand, product, service and feature names, logos and slogans are registered trademarks or trademarks of ANSYS, Inc. or its subsidiaries in the United States or other countries. All other brand, product, service and feature names or trademarks are the property of their respective owners.

/ About Cognata
Cognata provides cutting-edge autonomous driving technologies with its end-to-end solutions for autonomous platforms. Other than an advanced engine creating a photorealistic simulation platform, Cognata offers the know-how of the market offerings, product integration, and a comprehensive V&V walkthrough, end-to-end. Working with some of the largest autonomous vehicle makers tier 1’s in the world, Cognata accelerates the autonomous and ADAS engineering capabilities, and brings the unique power and expertise of artificial intelligence and computer vision, taking off years of the development process.

ANSS–T

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SOURCE Ansys

SHENZHEN, China, Jan. 2, 2025 /PRNewswire/ — MicroAlgo Inc. (the “Company” or “MicroAlgo”) (NASDAQ: MLGO), today announced the development of an innovative hybrid algorithm that combines the advantages of classical and quantum computing to optimize Multi-Query Optimization (MQO) problems.

Quantum computing is a technology that uses the principles of quantum mechanics to process information. Compared to traditional classical computers, quantum computers exhibit the potential to outperform classical computers in handling certain types of problems, such as search, optimization, and simulating quantum systems. However, the realization of quantum computers faces technical challenges, particularly in constructing quantum computers with a sufficient number of qubits and low error rates.

The Multi-Query Optimization (MQO) problem is a class of data-intensive problems that are NP-hard, and it has applications in many fields such as database query optimization, machine learning algorithms, and network routing. The core of the MQO problem lies in how to effectively handle multiple query requests to minimize the overall computational cost or time.

Although quantum computers theoretically have tremendous potential, current quantum computers are far from being fully practical. The limited number of qubits and high error rates restrict their ability to solve large-scale problems. To address these issues, MicroAlgo has proposed a hybrid algorithm that combines the stability of classical computers with the efficiency of quantum computers.

MicroAlgo’s hybrid algorithm design is based on the following key points:

Efficient Use of Qubits: Through carefully designed quantum circuits, the algorithm ensures efficient utilization of qubits, achieving a qubit efficiency close to 99%.

Reduction of Error Rates: By integrating error correction mechanisms from classical algorithms, the error rate during the quantum computation process is significantly reduced.

Scalability of the Algorithm: The algorithm design by MicroAlgo takes scalability into account, enabling it to adapt to problems of varying sizes.

Compatibility with Existing Technologies: MicroAlgo’s algorithm is compatible with existing gate-based quantum computers, meaning it can run on current hardware.

MicroAlgo’s hybrid algorithm first transforms the MQO problem into a form that can be handled by quantum computing. Quantum circuits are designed to perform the necessary quantum operations, including quantum state preparation, application of quantum gates, and quantum measurement. Then, during the quantum computation process, classical computers are used to assist the quantum computation, such as in qubit error correction and post-processing of the results. Through experiments and simulations, the algorithm’s performance is continuously optimized to ensure optimal performance with limited qubit resources.

MicroAlgo has conducted detailed experimental evaluations of the algorithm, including testing its performance on problems of various scales. The experimental results show that, despite the current limitations in qubit numbers, our algorithm is still able to handle smaller-scale problems and demonstrate a qubit efficiency close to 99%. Compared to quantum computers based on quantum annealing, MicroAlgo’s algorithm shows a significant improvement in efficiency.

In exploring the vast field of quantum computing, MicroAlgo’s hybrid algorithm represents an innovative solution that combines the stability of classical computing with the efficiency of quantum computing to address the challenges of Multi-Query Optimization (MQO). Through carefully designed quantum circuits and algorithmic optimizations, the algorithm not only improves qubit utilization efficiency but also significantly reduces error rates, enabling it to run on existing quantum hardware while maintaining scalability for large-scale problems. This achievement marks a significant step forward in the practical realization of quantum computing.

With the ongoing advancements in quantum technology, there is every reason to believe that MicroAlgo’s hybrid algorithm will play an even more important role in the future. As quantum computer hardware improves and the number of qubits increases, the algorithm will be able to tackle larger-scale problems, unlocking greater potential in fields such as chemistry, physics, and machine learning.

MicroAlgo’s hybrid algorithm is not only a major breakthrough in existing technology but also a powerful outlook on the future applications of quantum computing. We firmly believe that, through continuous research and innovation, quantum computing will gradually transition from theory to practice, becoming a powerful driver of technological progress and societal development. We look forward to a future where quantum computing will bring even more surprises and possibilities, opening a new era of computing.

About MicroAlgo Inc.

MicroAlgo Inc. (the “MicroAlgo”), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo’s services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo’s ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo’s long-term development.

Forward-Looking Statements

This press release contains statements that may constitute “forward-looking statements.” Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo’s periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC’s website, www.sec.gov. Words such as “expect,” “estimate,” “project,” “budget,” “forecast,” “anticipate,” “intend,” “plan,” “may,” “will,” “could,” “should,” “believes,” “predicts,” “potential,” “continue,” and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo’s expectations with respect to future performance and anticipated financial impacts of the business transaction.

MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law.

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