#Optical Encoders Research Report

[materialsscienceandengineering]

Team develops new method for communicating around arbitrary opaque walls

Information transfer in free space using ultraviolet, visible, or infrared waves has been gaining interest because of the availability of large bandwidth for high-data-rate communication. However, the presence of opaque occlusions or walls along the path between the transmitter and the receiver often impedes information transfer by blocking the direct line of sight. In a new article published in Nature Communications, a team of researchers at UCLA Samueli School of Engineering and the California NanoSystems Institute, led by Dr. Aydogan Ozcan, the Chancellor's Professor of Electrical & Computer Engineering, and Dr. Mona Jarrahi, the Northrop Grumman Endowed Chair at UCLA, has reported a fundamentally new method for delivering optical information around arbitrarily shaped opaque occlusions or walls. This method permits the transmission of optical information, for example, images, around large and dynamically changing opaque occlusions. It is based on digital encoding at the transmitter and diffractive all-optical decoding at the receiver for transferring information around arbitrary opaque occlusions that completely block the direct line of sight between the transmitter and the receiver apertures.

Read more.

[semiconductorlogs]

Global Encoder Chips Market: Driving Precision in Next-Gen Electronics 2025–2032

Encoder Chips Market Analysis:

The global Encoder Chips Market size was valued at US$ 1.6 billion in 2024 and is projected to reach US$ 2.7 billion by 2032, at a CAGR of 7.9% during the forecast period 2025-2032

Encoder Chips Market Overview

This report provides a deep insight into the global Encoder Chips market covering all its essential aspects. This ranges from a macro overview of the market to micro details of the market size, competitive landscape, development trend, niche market, key market drivers and challenges, SWOT analysis, value chain analysis, etc. The analysis helps the reader to shape the competition within the industries and strategies for the competitive environment to enhance the potential profit. Furthermore, it provides a simple framework for evaluating and accessing the position of the business organization. The report structure also focuses on the competitive landscape of the Global Encoder Chips Market, this report introduces in detail the market share, market performance, product situation, operation situation, etc. of the main players, which helps the readers in the industry to identify the main competitors and deeply understand the competition pattern of the market. In a word, this report is a must-read for industry players, investors, researchers, consultants, business strategists, and all those who have any kind of stake or are planning to foray into the Encoder Chips market in any manner.

Encoder Chips Key Market Trends  :

Rise in Industrial Automation

Increased use of encoder chips in automation systems is driving demand, especially across manufacturing plants and robotics applications.

Growing Adoption in Consumer Electronics

The integration of encoder chips in consumer gadgets like printers, cameras, and smart home devices is growing rapidly.

Miniaturization and Efficiency

Technological advancements are leading to smaller, more power-efficient encoder chips suitable for compact devices.

Increased Demand from Medical Equipment

Encoder chips are being widely used in imaging and diagnostic tools, enhancing precision and accuracy in healthcare applications.

Advancements in Optical and Magnetic Encoders

Continuous innovation in optical and magnetic encoder technologies is improving performance across industrial and electronics sectors.

Encoder Chips Market Regional Analysis :

North America:Strong demand driven by EVs, 5G infrastructure, and renewable energy, with the U.S. leading the market.

Europe:Growth fueled by automotive electrification, renewable energy, and strong regulatory support, with Germany as a key player.

Asia-Pacific:Dominates the market due to large-scale manufacturing in China and Japan, with growing demand from EVs, 5G, and semiconductors.

South America:Emerging market, driven by renewable energy and EV adoption, with Brazil leading growth.

Middle East & Africa:Gradual growth, mainly due to investments in renewable energy and EV infrastructure, with Saudi Arabia and UAE as key contributors.

Encoder Chips Market Segmentation :

The research report includes specific segments by region (country), manufacturers, Type, and Application. Market segmentation creates subsets of a market based on product type, end-user or application, Geographic, and other factors. By understanding the market segments, the decision-maker can leverage this targeting in the product, sales, and marketing strategies. Market segments can power your product development cycles by informing how you create product offerings for different segments. Key Company

Broadcom

AMS

New Japan Radio

TE Connectivity

IC-Haus

SEIKO NPC

RLS

PREMA Semiconductor

Hamamatsu

Market Segmentation (by Type)

Magnetic Encoder Chips

Optical Encoder Chips

Market Segmentation (by Application)

Industrial Automation

Motors

Medical

Office Automation

Consumer Electronics

Semiconductor Equipment

Measurement Equipment

Others

Market Drivers

Growth in Industrial Automation The increasing shift towards smart factories and Industry 4.0 is driving demand for encoder chips in motion and position control systems.

Surging Demand from Consumer Electronics Encoders are essential in applications like printers, gaming controllers, and smartphones, spurring market expansion.

Technological Innovation in Optical and Magnetic Encoders R&D investments are leading to high-precision, compact, and energy-efficient encoder chips for advanced applications.

Market Restraints

High Cost of Advanced Encoder Technologies Premium pricing of high-performance optical and magnetic encoders may limit adoption, especially among cost-sensitive sectors.

Complex Integration Process Designing encoder chips into systems like motors or automation equipment can be technically challenging and time-consuming.

Market Fragmentation and Intense Competition The presence of multiple small and large players increases pricing pressure and creates challenges for market differentiation.

Market Opportunities

Adoption in Emerging Medical Devices The growing healthcare industry, especially precision diagnostic and surgical equipment, presents new avenues for encoder chip usage.

Expansion in Asia-Pacific Region Countries like China, Japan, and India are witnessing rapid growth in manufacturing and electronics, offering major growth potential.

Growing Demand in Semiconductor Equipment Encoders play a critical role in wafer inspection and fabrication systems, benefiting from the booming semiconductor industry.

Market Challenges

Technological Obsolescence Rapid innovation can make existing encoder models outdated, pressuring companies to invest constantly in upgrades.

Dependence on Semiconductor Supply Chains Disruptions in chip manufacturing or raw material availability can significantly affect production and market supply.

Data Security and Signal Interference With increasing digital integration, maintaining secure and interference-free data transmission is becoming more difficult.

Key Benefits of This Market Research:

Industry drivers, restraints, and opportunities covered in the study

Neutral perspective on the market performance

Recent industry trends and developments

Competitive landscape & strategies of key players

Potential & niche segments and regions exhibiting promising growth covered

Historical, current, and projected market size, in terms of value

In-depth analysis of the Encoder Chips Market

Overview of the regional outlook of the Encoder Chips Market:

Key Reasons to Buy this Report:

Access to date statistics compiled by our researchers. These provide you with historical and forecast data, which is analyzed to tell you why your market is set to change

This enables you to anticipate market changes to remain ahead of your competitors

You will be able to copy data from the Excel spreadsheet straight into your marketing plans, business presentations, or other strategic documents

The concise analysis, clear graph, and table format will enable you to pinpoint the information you require quickly

Provision of market value (USD Billion) data for each segment and sub-segment

Indicates the region and segment that is expected to witness the fastest growth as well as to dominate the market

Analysis by geography highlighting the consumption of the product/service in the region as well as indicating the factors that are affecting the market within each region

Provides insight into the market through Value Chain

Market dynamics scenario, along with growth opportunities of the market in the years to come

6-month post-sales analyst support

[nursingwriter]

Sensory Systems Systems involved in visual processing are constantly being bombarded with various stimuli. In order to function effectively, these systems must selectively attend to information that is pertinent to the task at hand. Several biological and cognitive functions are employed or implemented for the execution of any visual task. Moreover, the visual system does not passively absorb all external stimuli, but instead introduces systematic bias in order to attach meaning to stimuli, or process raw visual data into usable information (Rosenzweig et al., (2004). Several factors influence the process of visual information, including visual masking (Macknik, 2006), spatial location and color of stimuli (Grabbe & Pratt, 2004), the manner in which stimuli are grouped (Min-Shik & Cave, 1999), as well as expectation of the presentation of visual stimuli (Anderson & Carpenter, 2006). Visual masking happens when visual targets become invisible through the modification of the context in which they occur, without modification of the targets themselves (Macknik, 2006). Macknik (2006) explained how there is a minimum number of conditions that are necessary for the maintenance of awareness of the visibility of stimuli that is not being attended to. First of all, in order for targets to be visible, spatiotemporal edges must be present, and they must be encoded by fleeting bursts of spikes observable in the early visual system. Visibility fails if these bursts are inhibited. Another factor required for the achievement of visibility is a rise in activity within the visual hierarchy and further processing within the occipital lobe. This researcher also explains the important role that lateral inhibition plays in visibility, because it results in interactions between spatially positioned stimuli and forms certain responses to stimuli temporally. Lateral inhibition essentially acts as a filter that sorts through and discards or enhances visual information before it is sent through the optic nerve to the brain. Also, lateral inhibition has been shown to increase in strength for both monoptic and dichoptic stimuli throughout the visual hierarchy (Macknik, 2006). Both color information and position information are involved in the process of visual stimuli. Grabbe & Pratt (2004) examined whether these factors were equivalent in their influence on visual processing. In this study, participants observed a briefly flashed array of letters and were then asked to report a letter of a certain color from a specific region of the presentation. They were also asked to report any other letters they could remember from the presented stimuli. The results indicated that in the reports of additional letters, more letters were reported of similar location than letters of the same color or neutral color. This suggested that location information has priority over color information when participants had to perform letter selection based on these two factors. Furthermore, according to Grabbe & Pratt (2004), "position information had a unique role in top-down - guided visual selection, and that it predominates over color when selection is required on both dimensions." This spatial dimension priority is demonstrated when the instructions for the task do not explicitly indicate that there is only one selection dimension, and location seems to be the default dimension under these circumstances (Grabbe & Pratt, 2004). The actual pathways on which spatial information and feature information, such as color, travel from the eye to the brain may be separate but parallel (Grabbe & Pratt, 2004). This would mean that there is a fundamental anatomical and functional difference in the way these types of information are processes. Furthermore, "selection happens by differential activation (excitation, inhibition, or both), of certain representations (not necessarily location representations) (Grabbe & Pratt, 2004)." What are the specific anatomical areas responsible for spatial and nonspatial information? Spatial information is routed into posterior parietal areas, while nonspatial information is directed into inferior temporal cortical areas. Attentional focus may also play a role in the fact that location information took priority over color information in the study by Grabbe & Pratt (2004). If visual targets are within attentional focus, reaction times for detection are decreased and accuracy of discrimination in responses increases. The researchers suggested that spatial attention was focused first to locate the appropriate section of the visual target, which was followed consecutively by color selection in top-down guided selection tasks (Grabbe & Pratt, 2004). The mechanisms involved in feature-specific attention to color were investigated by Muller et al. (2006) through the examination of selective stimulus processing using an electrophysiological measure called the steady-state visual evoked potential (SSVEP). In this study, participants observed a display of red and blue dots that were intermingled and randomly and continually shifted their positions. The frequencies at which these red and blue dots flickered differed, and this resulted in the elicitation of distinct SSVEP signals in the visual cortex. Selective attention to either the red or blue dots resulted in an enhanced amplitude of its specific SSVEP. These signals were anatomically localized to early levels of the visual cortex through the use of source modeling (Muller et al. (2006). This observed amplification of signals associated with attended color items provides empirical evidence for the rapid identification of feature information during visual search tasks (Muller et al., 2006). Perceptual grouping functions may also play an important role in determining location in visual selection tasks (Min-Shik & Cave, 1999). There are generally two hierarchical, functionally independent mechanisms contained in visual information processing theory (Min-Shik & Cave, 1999). These are an early, preattentive, parallel mechanism and a later, attentive, serial one as well. The first mechanism occurs when participants recognize and detect a target with no increase in reaction time as the number of present distractors increases in tasks where the taret is defined by a certain feature, such as color. Based on this observation Tresiman has proposed the feature integration theory of attention (FIT). This theory maintains that there is a preattentive stage of the visual system that processes all the information pertaining to primitive visual features of stimuli, such as orientation, color, brightness, depth, etc. (Min-Shik & Cave, 1999). This information is processed spatially in an automatic manner, and this is done so in parallel across the entire visual field. However, when object recognition must take place according to a conjunction of certain features, spatial attention is required, and this preattentive stage can not execute the required selection. Min-Shik & Cave (1999) used this theory as a basis for their research into the role that perceptual groupings play in visual processing. These researchers contrasted two different types of visual search models, which either emphasized the role that perceptual grouping plays in visual search or did not. The former type of visual model propose that visual search begins with a preattentive stage in which the visual field is segmented into distinct objects according to gestalt properties like contiguity, similarity and proximity. Attention can then step in and continue on the process using these perceptual units that are already preattentive organized. In this class of visual search model, location of the target is considered to be equivalent to all other properties, including color, movement, shape, etc. (Min-Shik & Cave, 1999). The other type of visual search model propse that location plays a special role in visual selection, and that spatial information organizes representations necessary for the search task (Min-Shik & Cave, 1999). The study by Min-Shik & Cave (1999) demonstrated grouping processes based on selection of certain locations. Results of their study indicated no significant evidence for task irrelevant color grouping effect in a simple feature search. However, findings did indicate a grouping effect that was location-based in a conjunction target that is defined by nonspatial features. Furthermore, "arranging elements into groups affected conjunction search but not feature search (Min-Shik & Cave, 1999)." Overall, these results support the idea that spatial attention functions in visual search tasks by inhibiting nonselected locations based on grouping principles rather than on an individual basis (Min-Shik & Cave, 1999). Expectation also plays a crucial role in the processing of visual information, and this expectation depends almost exclusively on previous experience (Anderson & Carpenter, 2006). This is empirically demonstrated through the fact that expectation influences response time to a visual stimulus. Anderson & Carpenter (2006) used this observation as a basis for their investigation into the effects of experience on visual processing. In this study, the probability of a visual target changed suddenly during the experiment, and this resulted in the response time for eye movement adapted and continuously changed. This change in eye movement was observed to eventually stabilize in a way that reflected the new probability that was presented. The researcher attempted to model this change based on the assumption that the brain discards old, irrelevant information about the probability of an event by a certain factor that is relative to new probability information (Anderson & Carpenter, 2006). This factor represents a compromise in processes between accurately and rapidly responding to actual changes in the environment and not hastily discounting any information that may still be valuable (Anderson & Carpenter, 2006). Moreover, Anderson & Carpenter (2006) demonstrated that the visual environment of the real world is continuously changing and the probability of the appearance of visual targets is dynamic. Fluctuations in expectation may be described by a model that actually calculates expectation using a weighted combination of new and old information. According to this model, when the probability of a target's appearance changes abruptly, a smooth change occurs that encodes prior probability. This model even predicts small changes in expectation even when there is a constant probability of appearance of the target (Anderson & Carpenter, 2006). The experience-based techniques employed that are used to predict probability of the appearance of a stimulus requires that certain pieces of information are held in storage over several trials, which requires an additional number of neurons for the process. Anderson & Carpenter (2006) explain how "the main virtue of (their) model is its simplicity and ease with which its exponential decay in the effect of stimulus history can be implemented by biologically plausible means." Reference Anderson, A., Carpenter, R. (2006). Changes in expectation consequent on experience, modeled by a simple, forgetful neural circuit. Journal of Vision, 6(8), 822-35. https://www.paperdue.com/customer/paper/sensory-systems-involved-in-visual-41424#:~:text=Logout-,SensorySystemsinvolvedinvisual,-Length6pages Grabbe, Y., Pratt, J. (2004). Competing top-down processes in visual selection: evidence that selection by location is stronger than selection by color. Journal of General Psychology, 131, 137-49. Macknik, S. (2006). Chapter 11 - Visual masking approaches to visual awareness. Progress in Brain Research, 155, 177-215. Min-Shik, K., Cave, K. (1999). Grouping effects on spatial attention in visual search. Journal of General Psychology, 126, 326-52. Muller, M., Anderson, N., Trujillo, N., Valdes-Sosa, P., Malinowski, P., Hillyard, S. (2006). Feature-selective attention enhances color signals in early visual areas of the human brain. Proceedings of the National Academy of Sciences of the United States of America, 103(38), 14250-4. Rosenzweig, M., Breedlove, M., Watson, N. (2004). Biological Psychology: An Introduction to Behavioral and Cognitive Neuroscience (Fourth Edition). Read the full article

[bigraagsbigblog]

Final Project / Specifics about final project

3/16/25

Deep research has been extremely helpful in helping me design my final project. Since this is a Models project, I plan on creating a gauntlet of tests for AI models and my fellow cwru students. I've decided that my main testing areas should be narrowed down into specifically vision of humans versus AI models, as this is a very well researched current issue in Computer Vision, and I believe there is enough current psychological literature for me to build a good test. Some of this will be similar to CAPTCHA tests, and some will be similar to the tests we've seen presented in the Cogsci Colloquium. Relatedly, but not centrally, i'm still interested in human vs machine time perception, memory encoding, and processing information and how it affects learning. The focus of my research project is how we see and analyze our surrounding, and how it's different than current computer vision models. More than just showing they're different (well researched and shown in self driving cars for example), I want to conclude in how those differences affect the ways in which we interact with the world. My hypothesis is that the ways in which humans go through the world is fundamentally different than the current machine learning computer vision models, and that our ability to pull meaning out of image is some that cannot be replicated by AI.

I will be designing tests and gathering data myself from fellow students and different ML models. I'll be graphing responses to hopefully showcase the differences in response and highlighting the differences in ability. My goal is to also design the tests in such a way that highlights the scenarios in which humans blow machines out of the water (analyzing sentiment behind a photograph, maybe picking out the elements of a scene, analyzing an artist's style) vs where computational power comes in handy and is better than us (counting things, paying attention to all elements in a scene, not being thrown by optical illusions). It'll be formatted as a report.

If you're reading this and have any thoughts about the types of tests I should build, specific images that would be good for analysis, what models might be worth testing, or anything else, let me know!

other notes after talking with mark: - vision -> empirical bottom up like computers

focus on cognition impacts of vision

predictive brain

we're driven to notice what we need based on predictions

computer vision - it doesn’t decide where to look, we tell it where to look

phovia is small (focus)

we’re directing our attention around

where you look / what you focus on / what catches your eye

what makes it into consciousness 

predictions drive vision?

this is top down

cognitive bc you’re making predictions / solving things / trying to do things

samier zebkey - neuroscientist  “a vision of the brain” 

start with things about how human vision works 

where this guides us to where science should go next 

what guidance does this give us for cognition 

we’re evolved to do something right - we’re doing it 

change blindness / counting

vs

optical illusions / semantics

[govindtbrc]

Comprehensive Analysis and Forecast of the DNA Encoded Semiconductor Libraries Market up to 2033

Market Definition

The DNA encoded semiconductor libraries market involves the development and use of semiconductor libraries that are encoded with DNA sequences for applications in fields like drug discovery, biotechnology, and materials science. These semiconductor libraries integrate DNA-based encoding techniques with semiconductor technology, enabling the creation of vast libraries of molecules or compounds that can be screened for specific properties or interactions. The use of DNA as an encoding medium allows for the rapid generation and analysis of diverse molecular structures, which is crucial for innovations in personalized medicine, targeted therapies, and advanced material design.

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DNA Encoded Semiconductor Libraries Market is anticipated to expand from 4.2 billion in 2024 to 9.8 billion by 2034, growing at a CAGR of approximately 8.8%.

Market Outlook

The DNA encoded semiconductor libraries market is poised for significant growth, driven by advancements in biotechnology, semiconductor technology, and the increasing need for faster, more efficient drug discovery and material development processes. DNA encoded libraries offer a unique combination of high-throughput screening, versatility, and precision, making them invaluable tools for researchers looking to identify novel bioactive compounds, potential drug candidates, and new materials.

In the pharmaceutical and biotechnology industries, DNA encoded libraries are revolutionizing drug discovery by enabling the rapid identification of lead compounds that can be further developed into therapeutic agents. By encoding large numbers of chemical compounds on DNA strands, researchers can quickly screen vast libraries of molecules for specific biological activities, dramatically accelerating the process of drug development.

The market is also benefiting from the increasing interest in personalized medicine, as DNA encoded libraries facilitate the development of drugs that are tailored to an individual’s genetic makeup, improving the efficacy and safety of treatments. Additionally, the ability to design and synthesize new materials with specific electronic, optical, or mechanical properties through DNA encoded libraries opens up new possibilities in semiconductor and nanotechnology fields, further driving market growth.

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[skyjohn009]

Encoder Market Report: An Overview of Current Trends and Key Players

Encoder Market Report: An Overview of Current Trends and Key Players

The global encoder market, valued at approximately $2.4 billion in 2021, is anticipated to grow significantly, reaching $4.5 billion by 2030, with a compound annual growth rate (CAGR) of 7.83% during the forecast period from 2022 to 2030. This growth reflects the increasing demand for automation across various industries and the integration of advanced technologies.

The global encoder market is a vital segment of the automation and control systems industry, encompassing devices that convert motion or position into a coded signal. Encoders play a crucial role in various applications, including robotics, industrial automation, automotive systems, and consumer electronics. These devices ensure precise measurements of rotational position, speed, and direction, which are essential for the efficient operation of machinery and equipment.

Market Definition and Latest Trends

Encoders are essential devices that convert motion or position into an electrical signal, which can then be read by a controller or a computer. They play a critical role in various applications, including robotics, industrial automation, and automotive systems. The encoder market is witnessing several notable trends:

Increased Adoption of Industry 4.0: The shift towards smart manufacturing and Industry 4.0 is driving the demand for encoders as industries seek to enhance operational efficiency and precision.

Integration with IoT and AI: The growing integration of encoders with Internet of Things (IoT) devices and artificial intelligence (AI) systems is enabling more sophisticated data analysis and machine learning applications.

Rising Demand in Automotive Sector: The automotive industry is increasingly utilizing encoders for applications such as electric vehicles and advanced driver-assistance systems (ADAS), contributing to market growth.

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Key Opportunities

The encoder market presents significant opportunities for growth due to the following factors:

Technological Advancements: Continuous innovations in encoder technology, such as the development of more reliable and efficient models, are expected to create new market opportunities.

Emerging Markets: Rapid industrialization in regions like Asia-Pacific offers lucrative prospects for encoder manufacturers as demand for automation solutions increases.

Market Segmentation

The encoder market can be segmented based on type, technology, and end-user:

By Type

Rotary Encoder

Linear Encoder

By Technology

Optical

Magnetic

Photoelectric

Others

By End-User

Automotive

Electronics

Textile

Printing Machinery

Industrial

Medical

Others

Key Players in the Encoder Market

Several prominent companies are leading the encoder market, including:

Omron Corporation

Honeywell International

Schneider Electric

Rockwell Automation Inc.

Panasonic Corporation

Baumer Group

Renishaw PLC

Dynapar Corporation (Fortive Corporation)

FAULHABER Drive Systems

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These companies are focusing on strategic partnerships, mergers, and acquisitions to expand their market presence and enhance product offerings.

Why Straits Research?

Straits Research is a trusted market research provider known for its in-depth analysis and strategic insights. With a team of experts, Straits Research offers comprehensive market reports that help businesses and stakeholders make informed decisions.

[globalinsightblog]

DNA Data Storage Market Forecast to Expand from $1.5B in 2023 to $14.0B by 2033, Reflecting a 25.0% CAGR

DNA Data Storage Solutions Market : DNA data storage is emerging as a groundbreaking solution to address the global demand for compact, high-capacity, and durable storage systems. Unlike traditional silicon-based technologies, DNA can store vast amounts of data in an ultra-compact format, with a single gram capable of holding 215 petabytes. This revolutionary technology uses synthetic DNA strands to encode digital information, offering unmatched longevity and stability compared to hard drives or optical discs. With exponential data generation from IoT, AI, and cloud services, DNA storage promises a sustainable and scalable alternative, making it a pivotal innovation in the era of big data.

To Request Sample Report : https://www.globalinsightservices.com/request-sample/?id=GIS32614 &utm_source=SnehaPatil&utm_medium=Article

The environmental benefits of DNA data storage are equally impressive. Its energy-efficient architecture drastically reduces the carbon footprint associated with traditional data centers. Additionally, advancements in sequencing and synthesis technologies are driving down costs, bringing DNA storage closer to commercial viability. Researchers and tech giants are collaborating to integrate this technology into existing systems, ensuring seamless transitions in data management. As the world moves towards data-driven economies, DNA data storage stands poised to redefine how humanity preserves its digital legacy.

#DNADataStorage #DataRevolution #FutureOfStorage #SustainableTech #BigDataSolutions #NextGenTech #DataPreservation #SyntheticDNA #TechInnovation #GreenDataCenters #DataArchiving #IoTData #AIStorageNeeds #DigitalTransformation #TechForFuture

[pranalip]

Optical Encoder Market – In Depth Insight Analysis to 2033 | Global Insight Services

An optical encoder is a type of sensor that uses light to measure position or speed. Optical encoders are used in a variety of applications, including industrial machinery, automotive systems, and medical devices.

Optical encoders work by shining a light on a photosensitive surface, such as a photodiode, phototransistor, or photoelectric cell. As the light hits the surface, it is reflected back in a pattern that can be read by the encoder. The encoder then converts the light pattern into electrical signals that can be interpreted by a computer or other type of controller.

Optical encoders are often used in applications where precise measurements are required, such as in CNC machines and 3D printers. They are also used in applications where high speeds are involved, such as in automotive systems.

Key Trends

Some of the key trends in optical encoder technology include miniaturization, higher resolutions, and improved durability.

Miniaturization is important for applications where space is limited, such as in handheld devices.

Higher resolutions allow for more precise positioning and control, while improved durability ensures that the encoder can withstand harsh environments.

Key Drivers

Some of the key drivers of the optical encoder market are:

Increasing demand for precision and high-speed applications: Optical encoders are increasingly being used in a variety of applications where precision and high speeds are required.

Miniaturization trend: The trend of miniaturization is also driving the demand for optical encoders as they can be very easily integrated into smaller devices and systems.

Improved performance and reliability: Optical encoders have also gained popularity due to the fact that they offer improved performance and reliability as compared to other types of encoders.

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Research Objectives

Estimates and forecast the overall market size for the total market, across product, service type, type, end-user, and region

Detailed information and key takeaways on qualitative and quantitative trends, dynamics, business framework, competitive landscape, and company profiling

Identify factors influencing market growth and challenges, opportunities, drivers and restraints

Identify factors that could limit company participation in identified international markets to help properly calibrate market share expectations and growth rates

Trace and evaluate key development strategies like acquisitions, product launches, mergers, collaborations, business expansions, agreements, partnerships, and R&D activities

Thoroughly analyze smaller market segments strategically, focusing on their potential, individual patterns of growth, and impact on the overall market

To thoroughly outline the competitive landscape within the market, including an assessment of business and corporate strategies, aimed at monitoring and dissecting competitive advancements.

Identify the primary market participants, based on their business objectives, regional footprint, product offerings, and strategic initiatives

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Market Segments

The optical encoder market is segmented by configuration, application, and region. By configuration, the market is classified into shafted, absolute, and others. Based on application, it is bifurcated into healthcare equipment, consumer electronics, and others. Region-wise, the market is segmented into North America, Europe, Asia Pacific, and the Rest of the World.

Key Players

The global optical encoder market includes players such as Honeywell International, Rockwell International, Renishaw PLC, Allied Motion Technologies, Sensata Technologies, US Digital, Bourns Inc., Dynapar, GrayHill, CodeChamp, and others.

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Research Scope

Scope – Highlights, Trends, Insights. Attractiveness, Forecast

Market Sizing – Product Type, End User, Offering Type, Technology, Region, Country, Others

Market Dynamics – Market Segmentation, Demand and Supply, Bargaining Power of Buyers and Sellers, Drivers, Restraints, Opportunities, Threat Analysis, Impact Analysis, Porters 5 Forces, Ansoff Analysis, Supply Chain

Business Framework – Case Studies, Regulatory Landscape, Pricing, Policies and Regulations, New Product Launches. M&As, Recent Developments

Competitive Landscape – Market Share Analysis, Market Leaders, Emerging Players, Vendor Benchmarking, Developmental Strategy Benchmarking, PESTLE Analysis, Value Chain Analysis

Company Profiles – Overview, Business Segments, Business Performance, Product Offering, Key Developmental Strategies, SWOT Analysis.

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In-depth segmentation which can be customized as per your requirements

Free consultation with lead analyst of the report

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[credenceresearchdotblog]

The global demand for optogenetics market was valued at USD 115.8 Million in 2023 and is expected to reach USD 198.99 Million in 2032, growing at a CAGR of 6.2% between 2024 and 2032.Optogenetics, a revolutionary technique combining genetic and optical methods to control and monitor activities of individual neurons in living tissue, has transformed neuroscience research. This innovative approach has opened new avenues for understanding the brain’s intricate workings and holds potential for developing novel treatments for neurological disorders. The optogenetics market, therefore, is poised for significant growth, driven by advancements in technology, increasing research activities, and rising prevalence of neurological diseases.

Browse the full report at https://www.credenceresearch.com/report/optogenetics-market

Market Overview

The global optogenetics market is experiencing robust growth due to several factors. Technological advancements in genetic engineering and the increasing application of optogenetics in various fields, such as neuroscience, cardiology, and behavioral science, are key drivers. Additionally, growing investments in research and development (R&D) by pharmaceutical companies, academic institutions, and government bodies are further propelling market expansion.

Key Drivers

1. Technological Advancements: Innovations in light-sensitive proteins, gene delivery systems, and imaging technologies have significantly enhanced the precision and efficacy of optogenetic techniques. These advancements enable researchers to manipulate neuronal activity with unprecedented accuracy, fostering greater adoption of optogenetics in research and clinical settings.

2. Rising Prevalence of Neurological Disorders: The increasing incidence of neurological conditions such as Parkinson’s disease, epilepsy, and depression underscores the need for advanced research tools like optogenetics. By allowing precise control over neuronal circuits, optogenetics offers promising potential for developing targeted therapies for these disorders.

3. Growing Research Activities: The surge in neuroscience research, driven by initiatives such as the BRAIN Initiative in the United States and the Human Brain Project in Europe, has accelerated the adoption of optogenetics. These large-scale projects aim to map the brain’s neural circuits, and optogenetics is a critical tool in achieving this goal.

4. Investment and Funding: Significant investments from both public and private sectors are bolstering the development and application of optogenetics. Government grants, venture capital funding, and collaborations between academic institutions and biotech companies are fueling R&D activities, thereby driving market growth.

Market Segmentation

The optogenetics market can be segmented based on product type, application, and end-user.

1. By Product Type: - Light Instruments: LED light sources and lasers are crucial for optogenetic experiments, providing the necessary light wavelengths to activate or inhibit specific neurons. - Actuators: These include light-sensitive proteins such as channelrhodopsins, halorhodopsins, and archaerhodopsins, which are genetically encoded into neurons. - Sensors: Genetically encoded calcium indicators (GECIs) and voltage-sensitive dyes are used to monitor neuronal activity in real-time.

2. By Application: - Neuroscience: Optogenetics is predominantly used in neuroscience for studying neural circuits and brain functions. - Cardiology: Emerging applications in cardiology involve using optogenetics to study heart rhythms and develop treatments for cardiac arrhythmias. - Behavioral Science: Researchers use optogenetics to understand the neural basis of behavior, including learning, memory, and emotion.

3. By End-User: - Academic and Research Institutes: These institutions are the primary users of optogenetic tools for fundamental research and discovery. - Pharmaceutical and Biotechnology Companies**: These companies leverage optogenetics for drug discovery and the development of novel therapies. - Hospitals and Clinics: Although currently limited, the clinical application of optogenetics is expected to grow, particularly in the development of precision therapies for neurological disorders.

Regional Analysis

The optogenetics market is geographically segmented into North America, Europe, Asia-Pacific, and the rest of the world. North America holds the largest market share, attributed to the region’s advanced healthcare infrastructure, significant R&D investments, and the presence of leading research institutions. Europe follows closely, driven by substantial government funding and collaborative research projects. The Asia-Pacific region is anticipated to witness the fastest growth, fueled by increasing research activities, rising healthcare expenditure, and growing awareness of advanced scientific techniques.

Challenges and Future Outlook

Despite its promising potential, the optogenetics market faces challenges such as ethical concerns regarding genetic manipulation, technical complexities, and high costs associated with optogenetic tools. However, ongoing advancements in gene-editing technologies, such as CRISPR, and the development of more cost-effective solutions are expected to mitigate these challenges.

Looking ahead, the optogenetics market is set to expand significantly, driven by continuous technological innovations, increasing research activities, and the growing need for advanced treatments for neurological disorders. As optogenetics continues to illuminate the complexities of the brain and other biological systems, its impact on science and medicine will undoubtedly be profound.

Key Players

Coherent Inc.

Thorlabs Inc.

Profacgen

Bruker Corporation

Mightex

GenSight Biologics

Shanghai Laser & Optics Century Co. Ltd

Prizmatix

Laserglow Technologies

Elliot Scientific Ltd

Others

Segmentation

By Light Equipment

Lasers

Light-Emitting Diodes (LEDs)

Fiber Optic Systems

By Actuators

Channelrhodopsin

Halorhodopsin

Archaerhodopsin

Others

By Application

Neuroscience Research

Behavioral Tracking

Retinal Diseases Treatment

Cardiovascular Research

Others

By End-User

Research Institutes

Pharmaceutical and Biotechnology Companies

Academic Institutes

Contract Research Organizations (CROs)

By Sensors

Calcium Sensors

Voltage Sensors

Neurotransmitter Release Sensors

Others

By Technique

Channelrhodopsin-Assisted Circuit Mapping (CRACM)

Halorhodopsin-Assisted Silencing

Optical Coherence Tomography (OCT)

Two-Photon Excitation Microscopy

Others

By Delivery Method

Viral Vectors

Fiber Photometry

Intracranial Implants

Others

By Region

North America

The U.S.

Canada

Mexico

Europe

Germany

France

The U.K.

Italy

Spain

Rest of Europe

Asia Pacific

China

Japan

India

South Korea

South-east Asia

Rest of Asia Pacific

Latin America

Brazil

Argentina

Rest of Latin America

Middle East & Africa

GCC Countries

South Africa

Rest of the Middle East and Africa

Browse the full report at https://www.credenceresearch.com/report/optogenetics-market

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[blsm-m]

"What we have here is incredibly simple." Wu Tianwei"We can change the laser patterns on the fly and reprogram it," says the study's lead author. Using this system, the researchers designed a neural network that successfully identifies vowel sounds. Most photonic systems need to be trained before they are built, because training necessarily involves reconfiguring connections. But this system is easily reconfigurable, so the researchers trained the model after installing it on the semiconductor. They now plan to increase the size of the chip and encode more information in different colors of light, which should increase the amount of data it can process.Even Psaltis, who developed facial recognition systems in the '90s, is impressed by the progress: "Our grandiose dreams from 40 years ago were very modest compared to what has actually happened."The first rayOptical computing has advanced rapidly in the past few years, but it's still a long way from replacing electronic chips that run neural networks outside of the lab. Papers have shown optical systems outperforming electronic systems, but they're typically running small models using older network designs and small workloads. And many of the reported numbers on optical superiority don't tell the whole story, said Bhavin Shastri of Queen's University in Ontario. "It's very hard to fully equate with electronics," he said. "For example, when you use lasers, it doesn't say much about the energy that's powering the laser."The lab's systems need to scale before they can provide a competitive advantage. "How big do you need to be to win?" McMahon asked. The answer is: extraordinary. That's why no one can match the Nvidia chips that power many of the most advanced AI systems today. Along the way, there will be a ton of hard engineering problems to solve. These are problems that the electronics side has been solving for decades. "Electronics is starting out with a huge advantage," McMahon said.Some researchers believe ONN-based AI systems will first find success in specialized applications where they offer unique advantages. Shastri said one promising use is canceling out interference between different radio transmissions, such as those in 5G cell towers or radar altimeters that help airplanes navigate. Earlier this year, Shastri and several colleagues Created ONN It can sift through the various transmitted signals and select the one of interest in real time with a processing delay of less than 15 picoseconds (15 trillionths of a second) – more than 1000 times faster than electronic systems, and 70 times less power consumption.But McMahon said the grand vision of optical neural networks going beyond general-purpose electronic systems is worth pursuing. Last year, his group The simulation was run He suggests that within a decade, with a large enough optical system, some AI models could be 1,000 times more efficient than future electronic systems. "Right now, a lot of companies are trying really hard to get 1.5 times the return. If we could get 1,000 times the return, that would be great," he says. "If we're successful, it's probably going to be a 10-year project."original work Reprinted with permission. Quanta Magazine, Editorially independent publication Simons Foundation Its mission is to enhance public understanding of science through reporting on research developments and trends in mathematics, physical sciences, and life sciences.

[jcmarchi]

MIT ARCLab announces winners of inaugural Prize for AI Innovation in Space

New Post has been published on https://thedigitalinsider.com/mit-arclab-announces-winners-of-inaugural-prize-for-ai-innovation-in-space/

MIT ARCLab announces winners of inaugural Prize for AI Innovation in Space

Satellite density in Earth’s orbit has increased exponentially in recent years, with lower costs of small satellites allowing governments, researchers, and private companies to launch and operate some 2,877 satellites into orbit in 2023 alone. This includes increased geostationary Earth orbit (GEO) satellite activity, which brings technologies with global-scale impact, from broadband internet to climate surveillance. Along with the manifold benefits of these satellite-enabled technologies, however, come increased safety and security risks, as well as environmental concerns. More accurate and efficient methods of monitoring and modeling satellite behavior are urgently needed to prevent collisions and other disasters.

To address this challenge, the MIT Astrodynamics, Space Robotic, and Controls Laboratory (ARCLab) launched the MIT ARCLab Prize for AI Innovation in Space: a first-of-its-kind competition asking contestants to harness AI to characterize satellites’ patterns of life (PoLs) — the long-term behavioral narrative of a satellite in orbit — using purely passively collected information. Following the call for participants last fall, 126 teams used machine learning to create algorithms to label and time-stamp the behavioral modes of GEO satellites over a six-month period, competing for accuracy and efficiency.

With support from the U.S. Department of the Air Force-MIT AI Accelerator, the challenge offers a total of $25,000. A team of judges from ARCLab and MIT Lincoln Laboratory evaluated the submissions based on clarity, novelty, technical depth, and reproducibility, assigning each entry a score out of 100 points. Now the judges have announced the winners and runners-up:

First prize: David Baldsiefen — Team Hawaii2024

With a winning score of 96, Baldsiefen will be awarded $10,000 and is invited to join the ARCLab team in presenting at a poster session at the Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference in Hawaii this fall. One evaluator noted, “Clear and concise report, with very good ideas such as the label encoding of the localizer. Decisions on the architectures and the feature engineering are well reasoned. The code provided is also well documented and structured, allowing an easy reproducibility of the experimentation.”

Second prize: Binh Tran, Christopher Yeung, Kurtis Johnson, Nathan Metzger — Team Millennial-IUP

With a score of 94.2, Y, Millennial-IUP will be awarded $5,000 and will also join the ARCLab team at the AMOS conference. One evaluator said, “The models chosen were sensible and justified, they made impressive efforts in efficiency gains… They used physics to inform their models and this appeared to be reproducible. Overall it was an easy to follow, concise report without much jargon.”

Third Prize: Isaac Haik and Francois Porcher — Team QR_Is

With a score of 94, Haik and Porcher will share the third prize of $3,000 and will also be invited to the AMOS conference with the ARCLab team. One evaluator noted, “This informative and interesting report describes the combination of ML and signal processing techniques in a compelling way, assisted by informative plots, tables, and sequence diagrams. The author identifies and describes a modular approach to class detection and their assessment of feature utility, which they correctly identify is not evenly useful across classes… Any lack of mission expertise is made up for by a clear and detailed discussion of the benefits and pitfalls of the methods they used and discussion of what they learned.”

The fourth- through seventh-place scoring teams will each receive $1,000 and a certificate of excellence.

“The goal of this competition was to foster an interdisciplinary approach to problem-solving in the space domain by inviting AI development experts to apply their skills in this new context of orbital capacity. And all of our winning teams really delivered — they brought technical skill, novel approaches, and expertise to a very impressive round of submissions.” says Professor Richard Linares, who heads ARCLab.

Active modeling with passive data

Throughout a GEO satellite’s time in orbit, operators issue commands to place them in various behavioral modes—station-keeping, longitudinal shifts, end-of-life behaviors, and so on. Satellite Patterns of Life (PoLs) describe on-orbit behavior composed of sequences of both natural and non-natural behavior modes.

ARCLab has developed a groundbreaking benchmarking tool for geosynchronous satellite pattern-of-life characterization and created the Satellite Pattern-of-Life Identification Dataset (SPLID), comprising real and synthetic space object data. The challenge participants used this tool to create algorithms that use AI to map out the on-orbit behaviors of a satellite.

The goal of the MIT ARCLab Prize for AI Innovation in Space is to encourage technologists and enthusiasts to bring innovation and new skills sets to well-established challenges in aerospace. The team aims to hold the competition in 2025 and 2026 to explore other topics and invite experts in AI to apply their skills to new challenges. 

[spookysaladchaos]

Global Top 7 Companies Accounted for 73% of total Quantum Key Distribution (QKD) market (QYResearch, 2021)

Quantum Key Distribution (QKD) uses physics instead of mathematics to encode messages, which provides greater security.

The genesis of QKD (Quantum Key Distribution) traces back to the late 1960s, when Stephen Wiesner first proposed the idea of encoding information on photons to securely transfer messages. In 1984, the physicist Charles Bennett and cryptographer Gilles Brassard worked together to mature this idea by introducing the first QKD protocol, known as “BB84”. Five years later, they built the first QKD prototype system which was said to be “secure against any eavesdropper who happened to be deaf” as it made audible noises while encoding crypto key onto single photons.

From its relatively humble beginnings, QKD has gained global interest as a unique cybersecurity solution with active research groups across North America, Europe, Australia, and Asia.

According to the new market research report “Global Quantum Key Distribution (QKD) Market Report 2023-2029”, published by QYResearch, the global Quantum Key Distribution (QKD) market size is projected to reach USD 6.82 billion by 2029, at a CAGR of 35.7% during the forecast period.

Figure.   Global Quantum Key Distribution (QKD) Market Size (US$ Million), 2018-2029

Figure.   Global Quantum Key Distribution (QKD) Top 7 Players Ranking and Market Share (Ranking is based on the revenue of 2022, continually updated)

The global key manufacturers of Quantum Key Distribution (QKD) include MagiQ Technologies, ID Quantique, Quintessence Labs, QuantumCTek, Qasky, etc. In 2021, the global top four players had a share approximately 64.0% in terms of revenue.

About QYResearch

QYResearch founded in California, USA in 2007.It is a leading global market research and consulting company. With over 16 years’ experience and professional research team in various cities over the world QY Research focuses on management consulting, database and seminar services, IPO consulting, industry chain research and customized research to help our clients in providing non-linear revenue model and make them successful. We are globally recognized for our expansive portfolio of services, good corporate citizenship, and our strong commitment to sustainability. Up to now, we have cooperated with more than 60,000 clients across five continents. Let’s work closely with you and build a bold and better future.

QYResearch is a world-renowned large-scale consulting company. The industry covers various high-tech industry chain market segments, spanning the semiconductor industry chain (semiconductor equipment and parts, semiconductor materials, ICs, Foundry, packaging and testing, discrete devices, sensors, optoelectronic devices), photovoltaic industry chain (equipment, cells, modules, auxiliary material brackets, inverters, power station terminals), new energy automobile industry chain (batteries and materials, auto parts, batteries, motors, electronic control, automotive semiconductors, etc.), communication industry chain (communication system equipment, terminal equipment, electronic components, RF front-end, optical modules, 4G/5G/6G, broadband, IoT, digital economy, AI), advanced materials industry Chain (metal materials, polymer materials, ceramic materials, nano materials, etc.), machinery manufacturing industry chain (CNC machine tools, construction machinery, electrical machinery, 3C automation, industrial robots, lasers, industrial control, drones), food, beverages and pharmaceuticals, medical equipment, agriculture, etc.

[nursingwriter]

Biometric Optical Technology Retina Biometric Technology Applications This work reviews the "retina biometric technological application' in relation to the practical use of this application as well as the drawbacks, failures and successes that have been experienced with this technological security application. Biometric technology has been called the most precise biometric technology available today. This technology is under deployment at many airports across the globe to tighten security screening of airline employees and passengers. The product was patented in 18987 by Leonard Flom and Aran Safir for recognizing that the human irises have features that are unique to the point that the positive and accurate identification of individuals is possible and viable. In 1994 another patent was awarded to Dr. John Daugman who developed mathematical algorithms enabling image of the iris to be encoded and sorted digitally and then made the image compared to an iris image in real-time. This work explores the specifics of this application in the mapping of the iris for biometric recognition. Retina Biometric Technology Applications Objective The objective of this work is to research and examine an application that uses retina biometric technology in security scanning and to detail the specific functions in this application. I. Introduction In the high-tech world of today security systems are all prevalent in both the public eye but as well in the view of the individual who seeks to protect their property and their family. The equipment utilized is stated to be "electronic. Optical and acoustic security, detection, monitoring and surveillance systems" (Market Research Report, Business Communications Co. 2002) Further all of this technological advances in security are used for the purpose of protecting" persons, organizations and companies, commercial and social operations, civil and military installations both at national and international levels.... "the personalized security systems of a home to large-scale systems for the protection of crucial national installations." Inclusive in these systems are closed-circuit television cameras which comprise the omnipresent all-seeing eyes throughout the cities and in railway and subway stations. The constant recording and transmission of these hidden cameras informs law enforcement and security personnel of any impending threats. Biometric Identification Technology Systems Retina recognition and retinal pattern recognition has been used for many years. However the high costs of this type of Biometric identification resulted in the product being removed from the market. Recently the emergence of biometric technology systems has witnessed a climb in use rates for the purpose of identification all across the globe. Iris or Retinal scanning is done through high-resolution imaging using infrared and a specialized camera. The VRD or 'Virtual Retinal Display" through use of both horizontal and vertical scanning technology in the function of inputting small slices of picture superimposed on the retina in the scanning identification process. Biometric technology has been called the most precise biometric technology available today. This technology is under deployment at many airports across the globe to tighten security screening of airline employees and passengers. The product was patented in 1987 by Leonard Flom and Aran Safir for recognizing that the human irises have features that are unique to the point that the positive and accurate identification of individuals is possible and viable. In 1994 another patent was awarded to Dr. John Daugman who developed mathematical algorithms enabling image of the iris to be encoded and sorted digitally and then made the image compared to an iris image in real-time. II. Biometric Technology - Superior to Other Applications In the work entitled "Physical Security in Mission Critical Facilities" the author, Suzanne Niles, relates the fact that in methods of making identification of people fall into three primary categories "of increasing reliability - and increasing equipment costs: 1) What you have 2) What you know; and 3) Who you are. Niles states that the most reliable method of identification is "Who you are" which "refers to identification by recognition of unique physical characteristics." III. Two Existing Failures in Biometric Recognition There are two failures that exist in the application of Biometric Recognition. Those two failures are: 1) False rejection in which the system fails to recognize the individual who is legitimately a user creating great frustration for users denied access because of system failure in recognition of them. 2) False acceptance which is through confusion of one user and another or the acceptance of an invalid individual as being a user that is legitimate. Although the rate of failure is easily adjusted through modifying the threshold through decreasing the rate of failure on the end of rejection or acceptance the increase of failure on the other end of the spectrum increases In the act of choosing equipment with biometric functions the considerations are stated to be: 1) Equipment cost and 2) Failure Rates. Drawbacks to consumer embrasure of the retinal scanners are due to the requirement of the eye to be one to two inches away from the scanner while an LED is focused directly into the individual's eye. IV. Specifics of the Biometric Technology Application This type of technology is an application that through locating the iris with a monochrome camera approximately three feet from the eye. The narrowing of the algorithm moves from the right and left of the iris and then makes location of the outside edge while at the same time locating the pupil and the eye's inner edge. Visible and infrared light are used by the camera and the algorithm upon locating the iris utilized 20-D Gabor wavelets in a filtering and mapping function of the iris breaking it down into many separate phasors/vectors. Values are assigned by the wavelets relating to the spatial frequency and orientation of those areas selected. The portion of the iris used is technologically converted into values of 512-byte IrisCode template. The iris has identification in 266 unique spots which serves as a template for live-scan images of iris comparisons. One example of a product using this type of biometric capability is that of the Optical Biometric Reader with a template capacity of 720. Identification is through a one-to-many matching operation in which the captured image is compared to all images stored in the system until a match is located. False acceptance rates based on security settings of medium are sated to be 0.001% while false rejection rates are stated at 0.1%. There are nine security levels ranging from low to very high with acceptance rates depending on the level of security in use. Concerns of Optical Biometric Technology Both infrared and visible light is used in retinal scanning. The visible light is not overly bright and everyone does not experience the spots in the eyes after the scanning that some report. The application has been deemed through testing to be safe in its' use. Summary and Conclusion While there are those that feel retina scanning is too intrusive there are others that consider the small inconvenience well worth the security provided by the retinal scanning biometric technology applications in the realm of security assurance. There are also those who feel that this type of application could result in company abuse of the information relating to employees due to the fact that "while Iridians algorithms are only used for identification and verification, modifications could theoretically be made that would analyze the iris for disease, alcoholism, drug abuse and even a predisposition toward certain kinds of diseases." This fact alone leads to the conclusion that much legislation is certain in the future for the regulation of the optical biometric scanning application's use. References Niles, Suzanne (2004) "Physical Security in Mission Critical Facilities White Paper No. 82 - Revision 1 American Power Conversion APC 2004 . Electronic, Acoustic and Optical Surveillance and Monitoring Systems Market Research Report- Business Communications Co. September 2002 R2-612 Online available at http://www.mindbranch.com/listing/product/R2-612.html. Guevin, Laura (2002) Is The Iris the Gateway to Our True Identities Biometritech Online available at http://productfinder.gcn.com/search/keyword/gcn/EyeSca n Tools/EyeScanTools Optical Biometric Reader Control Module Inc. (2005) Product Specification Online available at http://www.controlmod.com/tier2/pdfs/bioscan/PS2105_125.pdf Retina Biometric Technology Applications Read the full article

[jayanthitbrc]

Navigating Growth Trajectories: In-Depth Optical Coherence Tomography Market Size Analysis, Share and Future Trends

  Overview and Scope Optical coherence tomography (OCT) is an optical biopsy and effective imaging technology for medical diagnostics that provides images of tissue in situ and in real-time. Optical coherence tomography is used for high-resolution cross-sectional imaging. Sizing and Forecast The optical coherence tomography market size has grown rapidly in recent years. It will grow from $1.72 billion in 2023 to $1.94 billion in 2024 at a compound annual growth rate (CAGR) of 12.9%.  The optical coherence tomography market size is expected to see rapid growth in the next few years. It will grow to $3.17 billion in 2028 at a compound annual growth rate (CAGR) of 13.1%.  To access more details regarding this report, visit the link: https://www.thebusinessresearchcompany.com/report/optical-coherence-tomography-global-market-report Segmentation & Regional Insights The optical coherence tomography market covered in this report is segmented – 1) By Type: Catheter-Based OCT Devices, Doppler OCT Devices, Handheld OCT Devices, Tabletop OCT Devices   2) By Technology: Time Domain OCT (TDOCT), Frequency Domain OCT (FD-OCT), Spatial Encoded Frequency Domain OCT 3) By Application: Ophthalmology, Cardiovascular, Oncology, Dermatology, Other Applications North America was the largest region in the optical coherence tomography market in 2023. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the optical coherence tomography market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. Intrigued to explore the contents? Secure your hands-on sample copy of the report: https://www.thebusinessresearchcompany.com/sample.aspx?id=8231&type=smp Major Driver Impacting Market Growth The rising prevalence of eye disorders is expected to support the growth of the optical coherence tomography market. Globally, eye conditions, including cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration (AMD) are leading causes of vision loss and blindness. The demand for advanced disease diagnostic tools like optical coherence tomography systems is growing due to the rise in eye ailments. Key Industry Players Major companies operating in the optical coherence tomography market report are Agfa-Gevaert N.V., Carl Zeiss Meditec AG, Heidelberg Engineering GmbH, Imalux Corporation, Michelson Diagnostics Limited, Novacam Technologies Inc., OPTOPOL Technology Sp. z o.o. The optical coherence tomography market report table of contents includes: 1. Executive Summary 2. Market Characteristics 3. Market Trends And Strategies 4. Impact Of COVID-19 5. Market Size And Growth 6. Segmentation 7. Regional And Country Analysis . . . 27. Competitive Landscape And Company Profiles 28. Key Mergers And Acquisitions 29. Future Outlook and Potential Analysis Contact Us: The Business Research Company Europe: +44 207 1930 708 Asia: +91 88972 63534 Americas: +1 315 623 0293 Email: info@tbrc.info Follow Us On: LinkedIn: https://in.linkedin.com/company/the-business-research-company Twitter: https://twitter.com/tbrc_info Facebook: https://www.facebook.com/TheBusinessResearchCompany YouTube: https://www.youtube.com/channel/UC24_fI0rV8cR5DxlCpgmyFQ Blog: https://blog.tbrc.info/ Healthcare Blog: https://healthcareresearchreports.com/ Global Market Model: https://www.thebusinessresearchcompany.com/global-market-model

[govindhtech]

Incredible Speed: ACCEL Chip Beats A100 & A800 GPUs

ACCEL Chip Applications

According to a Chinese research study, the internal analog AI processing chip “ACCEL” may operate three thousand times quicker than the A100 and A800 GPUs from NVIDIA.

According to reports, the Chinese ACCEL Analog AI Chip outperforms NVIDIA’s A100 and A800 by “3000 times” in performance.

China looks to be quickly enhancing its “homegrown” solutions in an effort to sustain the current rate of industrial expansion, even in the face of international sanctions. According to a paper released by Tsinghua University in China, the institution has created a novel method for artificial intelligence computing performance and created a chip called ACCEL (All-Analog Chip Combining Electronic and Light Computing), which essentially uses analog technology and photonics to produce extraordinary performance. The results of this effort are pretty astounding.

The simulated ACCEL processor achieves 4,600 tera-operations per second (TOPS) in visual tasks, according to the article published in Nature. With sparsity, this translates into a 3.7X performance advantage over Nvidia’s A100 (Ampere), which is advertised as having a high of 1,248 TOPS in INT8 workloads. The research study claims that ACCEL can achieve a 74.8 peta-operations per second per watt systemic energy efficiency. Even Hopper, with its 80 billion transistor H100 super-chip, appears lackluster in comparison to these achievements, surpassing Nvidia’s A100.

Benefits of ACCEL’s 3000x Speedup

Calling a chip the “industry’s fastest” without any kind of real-time benchmark is a fair description, but to test the chip’s “deep-learning” capabilities, ACCEL was experimentally compared to the Fashion-MNIST, 3-class ImageNet classification, and time-lapse video recognition scenarios. Delivering accuracy of 85.5%, 82.0%, and 92.6%, respectively, shows that the chip is not confined to a certain market sector but has broad industry applications. We’re excited to see what the chip has in store for the future because this heightens the excitement surrounding ACCEL.

With scalability, nonlinearity, and flexibility in a single chip, Tsinghua’s architecture in ACCEL functions by diffractive optical analog computing (OAC) supported by electronic analog computing (EAC). However, the optical system handles 99% of the architecture’s operations. The research claims that this aids in overcoming limitations seen in various vision architectures, including diffractive Deep Neural Networks (DNNs) and Mach Zehnder interferometers.

On the other hand, a photonic optical system makes use of non-electric methods for information encoding, manipulation, and transport. This can be accomplished by operating on that light (changing it) virtually in transit and extracting and communicating features of visual data (an image) via laser pulses at particular wavelengths (we discussed this in our recent article on China’s Quantum Key Distribution [QKD] satellite system, which is also photonic-based).

This optical processing technology reduces the amount of energy needed and electrons lost to heat dissipation. One of the main ways that photonics unlocks performance benefits is by eliminating the high energy and delay cost of analog-to-digital converters, or ADCs. This is also the reason why quantum computing and HPC (High-Performance Computing) installations employ photonics equipment.

By eschewing the systematic yet chaotic flow of electrons through semiconductors, we may achieve faster operating speeds that are only constrained by light. According to the study article, internal testing of the device demonstrated a low computational latency of 72 ns for each frame, generating a throughput of over 13,000 frames per second more than enough to cause any Doom player to lose all sense of reality. Additionally, it appears that there would be sufficient frames for any computing-vision operation to allow a co-processor to examine a subset of those pictures. It doesn’t seem likely that the ACCEL deep learning processing of these photos will be the bottleneck.

Given that it is photonic and analog, the new ACCEL device may remind people of the analog AI-acceleration processor that IBM just announced, called Hermes. Despite all of the restrictions imposed on China, it’s amazing to see how the nation’s R&D is enabling it to catch up with and in some cases, even surpass whatever it was that they were being prevented from doing. China is definitely thinking about sanctions in terms of being able to get around restrictions.

What changes in the industry can we expect from ACCEL and comparable analog AI chip developments? It’s difficult to answer this issue at this time because analog-based AI accelerators are still mostly for the future. The performance figures and statistics are encouraging, but “deployment” of them in the business is harder than it seems, requiring more time, money, and inquiry. Nobody can argue that computers has a bright future, and it will soon be mass-marketed.

Read more on Govindhtech.com