#automated counting machines (ACMs)

[carlocarrasco]

24,000 automated counting machines of Miru Systems now in the Philippines

The initial batch of 24,000 automated counting machines (ACMs) from the contracted provider Miru Systems arrived here in the Philippines for use in the 2025 National and Local Elections, according to a news article by the Philippine News Agency (PNA). Be reminded that Miru Systems previously declared that it would deliver 110,000 ACMs for the said polls. To put things in perspective, posted…

[acmeelectronics]

Choosing the Right Coil Winding Machine: A Comprehensive Buyer’s Guide

Investing in a coil winding machine isn't just about buying a piece of equipment—it's about choosing a long-term solution that directly impacts your production quality, efficiency, and bottom line. With so many models and features on the market, how do you make the right decision? In this guide, we’ll break down everything you need to know to confidently select the ideal coil winding machine for your operation.

Know What You Need Before You Buy

Before diving into technical specs, take a step back and evaluate your application:

Wire Gauge: Are you working with ultra-fine wire or heavy-gauge conductors?

Coil Dimensions: Consider coil length, diameter, and the number of turns required.

Production Volume: Is this for prototyping, small batches, or mass production?

Material Types: Some machines are better suited for certain insulation types or core shapes.

Understanding these variables will help narrow down the right machine category and features.

Types of Coil Winding Machines

Different machines serve different purposes. Here’s a quick overview:

Benchtop/Laboratory Winders: Great for R&D, prototyping, or small-scale custom work.

Automatic Winders: Ideal for high-volume production with repeatable accuracy.

Toroidal Winders: Specifically designed for toroidal coil production, often used in transformers and chokes.

Layer Winders: Best for evenly layered coils, commonly found in motors and precision components.

Specialized Winders: Custom solutions for winding stators, transformers, or other complex geometries.

Each machine type has strengths, so matching it to your product is critical.

Features That Matter

Once you’ve chosen the type, it’s time to look under the hood. Here are key features to prioritize:

Spindle Speed & Control: Adjustable speed helps tailor the winding process to different wire types and coil configurations.

Tensioning Systems: Consistent wire tension is vital to avoid loose windings or insulation damage.

Wire Guiding Mechanisms: Precision wire guides ensure uniformity and reduce the chance of tangling or misalignment.

Control Systems (PLC/CNC): More advanced control systems allow for programmable settings, storing of recipes, and multi-step automation.

Tooling & Accessories: Quick-change tooling, safety guards, and expansion options can add value and future-proof your investment.

Software Integration: The Digital Edge

Modern coil winding machines come with advanced software capabilities, such as:

Data Logging: Track performance, errors, and output over time.

Custom Programming: Easily switch between different winding patterns.

Remote Monitoring: Gain real-time insights and troubleshoot from anywhere.

These features are especially beneficial for scaling operations or quality-sensitive applications.

Budget vs. ROI: Make It Count

Don’t just look at the upfront cost. Consider the return on investment—a more advanced machine may pay for itself in reduced labor, fewer defects, and faster cycle times. Think long-term, especially if you're planning to scale.

Vendor Support: It Matters More Than You Think

Choose a machine not just for its specs but also for the company behind it. Strong after-sales service, training, spare parts availability, and technical support can make or break your experience. One trusted name in the field is ACME Electronics, a seasoned Coil Winding Machine Manufacturer Supplier known for its dependable machines and industry-leading support.

Final Thoughts

Choosing the right coil winding machine requires a blend of technical insight, budget awareness, and strategic foresight. With the right choice, you're not just buying a machine—you're empowering your production. So, what features are at the top of your list when selecting your next coil winding solution?

[acmemechatronics1]

Transformer Winding Machine Manufacturer Supplier

A transformer winding machine is essential for the precise production of transformer coils used in electrical power systems. These machines ensure accurate and uniform winding of copper or aluminum wire onto the transformer core, which is critical for the device’s performance and efficiency. The CNC winding machine takes this process a step further by using computer numerical control (CNC) technology. This allows for higher precision, faster production speeds, and the ability to handle complex winding patterns without manual intervention. By automating adjustments to tension, speed, and layer count, CNC winding machines reduce human error and improve consistency. ACME Electronics offers cutting-edge transformer winding machines that integrate CNC technology, ensuring optimal performance and reliability for high-demand applications. Investing in advanced winding solutions can significantly enhance your production capabilities and product quality.

[kindlecomparedinfo]

FluSense system tracks sickness trends by autonomously monitoring public spaces

One of the obstacles to accurately estimating the prevalence of sickness in the general population is that most of our data comes from hospitals, not the 99.9 percent of the world that isn’t hospitals. FluSense is an autonomous, privacy-respecting system that counts the people and coughs in public spaces to keep health authorities informed.

Every year has a flu and cold season, of course, though this year’s is of course far more dire. But it’s like an ordinary flu season in that the main way anyone estimates how many people are sick is by analyzing stats from hospitals and clinics. Patients reporting “influenza-like illness” or certain symptoms get aggregated and tracked centrally. But what about the many folks who just stay home, or go to work sick?

We don’t know what we don’t know here, and that makes estimates of sickness trends — which inform things like vaccine production and hospital staffing — less reliable than they could be. Not only that, but it likely produces biases: Who is less likely to go to a hospital, and more likely to have to work sick? Folks with low incomes and no healthcare.

Researchers at the University of Massachusetts Amherst are attempting to alleviate this data problem with an automated system they call FluSense, which monitors public spaces, counting the people in them and listening for coughing. A few of these strategically placed in a city could give a great deal of valuable data and insight into flu-like illness in the general population.

Tauhidur Rahman and Forsad Al Hossain describe the system in a recent paper published in an ACM journal. FluSense basically consists of a thermal camera, a microphone, and a compact computing system loaded with a machine learning model trained to detect people and the sounds of coughing.

To be clear at the outset, this isn’t recording or recognizing individual faces; Like a camera doing face detection in order to set focus, this system only sees that a face and body exists and uses that to create a count of people in view. The number of coughs detected is compared to the number of people, and a few other metrics like sneezes and amount of speech, to produce a sort of sickness index — think of it as coughs per person per minute.

A sample setup, above, the FluSense prototype hardware, center, and sample output from the thermal camera with individuals being counted and outlined.

Sure, it’s a relatively simple measurement, but there’s nothing like this out there, even in places like clinic waiting rooms where sick people congregate; Admissions staff aren’t keeping a running tally of coughs for daily reporting. One can imagine not only characterizing the types of coughs, but visual markers like how closely packed people are, and location information like sickness indicators in one part of a city versus another.

“We believe that FluSense has the potential to expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS,” Rahman told TechCrunch. “By understanding the ebb and flow of the symptoms dynamics across different locations, we can have a better understanding of the severity of a novel infectious disease and that way we can enforce targeted public health intervention such as social distancing or vaccination.”

Obviously privacy is an important consideration with something like this, and Rahman explained that was partly why they decided to build their own hardware, since as some may have realized already, this is a system that’s possible (though not trivial) to integrate into existing camera systems.

“The researchers canvassed opinions from clinical care staff and the university ethical review committee to ensure the sensor platform was acceptable and well-aligned with patient protection considerations,” he said. “All persons discussed major hesitations about collection any high-resolution visual imagery in patient areas.”

Similarly, the speech classifier was built specifically to not retain any speech data beyond that someone spoke — can’t leak sensitive data if you never collect any.

The plan for now is to deploy FluSense “in several large public spaces,” one presumes on the UMass campus in order to diversify their data. “We are also looking for funding to run a large-scale multi-city trial,” Rahman said.

In time this could be integrated with other first- and second-hand metrics used in forecasting flu cases. It may not be in time to help much with controlling COVID-19, but it could very well help health authorities plan better for the next flu season, something that could potentially save lives.

from RSSMix.com Mix ID 8176395 https://techcrunch.com/2020/03/20/flusense-system-tracks-sickness-trends-by-autonomously-monitoring-public-spaces/ via http://www.kindlecompared.com/kindle-comparison/

[un-enfant-immature]

FluSense system tracks sickness trends by autonomously monitoring public spaces

One of the obstacles to accurately estimating the prevalence of sickness in the general population is that most of our data comes from hospitals, not the 99.9 percent of the world that isn’t hospitals. FluSense is an autonomous, privacy-respecting system that counts the people and coughs in public spaces to keep health authorities informed.

Every year has a flu and cold season, of course, though this year’s is of course far more dire. But it’s like an ordinary flu season in that the main way anyone estimates how many people are sick is by analyzing stats from hospitals and clinics. Patients reporting “influenza-like illness” or certain symptoms get aggregated and tracked centrally. But what about the many folks who just stay home, or go to work sick?

We don’t know what we don’t know here, and that makes estimates of sickness trends — which inform things like vaccine production and hospital staffing — less reliable than they could be. Not only that, but it likely produces biases: Who is less likely to go to a hospital, and more likely to have to work sick? Folks with low incomes and no healthcare.

Researchers at the University of Massachusetts Amherst are attempting to alleviate this data problem with an automated system they call FluSense, which monitors public spaces, counting the people in them and listening for coughing. A few of these strategically placed in a city could give a great deal of valuable data and insight into flu-like illness in the general population.

Tauhidur Rahman and Forsad Al Hossain describe the system in a recent paper published in an ACM journal. FluSense basically consists of a thermal camera, a microphone, and a compact computing system loaded with a machine learning model trained to detect people and the sounds of coughing.

To be clear at the outset, this isn’t recording or recognizing individual faces; Like a camera doing face detection in order to set focus, this system only sees that a face and body exists and uses that to create a count of people in view. The number of coughs detected is compared to the number of people, and a few other metrics like sneezes and amount of speech, to produce a sort of sickness index — think of it as coughs per person per minute.

A sample setup, above, the FluSense prototype hardware, center, and sample output from the thermal camera with individuals being counted and outlined.

Sure, it’s a relatively simple measurement, but there’s nothing like this out there, even in places like clinic waiting rooms where sick people congregate; Admissions staff aren’t keeping a running tally of coughs for daily reporting. One can imagine not only characterizing the types of coughs, but visual markers like how closely packed people are, and location information like sickness indicators in one part of a city versus another.

“We believe that FluSense has the potential to expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS,” Rahman told TechCrunch. “By understanding the ebb and flow of the symptoms dynamics across different locations, we can have a better understanding of the severity of a novel infectious disease and that way we can enforce targeted public health intervention such as social distancing or vaccination.”

Obviously privacy is an important consideration with something like this, and Rahman explained that was partly why they decided to build their own hardware, since as some may have realized already, this is a system that’s possible (though not trivial) to integrate into existing camera systems.

“The researchers canvassed opinions from clinical care staff and the university ethical review committee to ensure the sensor platform was acceptable and well-aligned with patient protection considerations,” he said. “All persons discussed major hesitations about collection any high-resolution visual imagery in patient areas.”

Similarly, the speech classifier was built specifically to not retain any speech data beyond that someone spoke — can’t leak sensitive data if you never collect any.

The plan for now is to deploy FluSense “in several large public spaces,” one presumes on the UMass campus in order to diversify their data. “We are also looking for funding to run a large-scale multi-city trial,” Rahman said.

In time this could be integrated with other first- and second-hand metrics used in forecasting flu cases. It may not be in time to help much with controlling COVID-19, but it could very well help health authorities plan better for the next flu season, something that could potentially save lives.

[ladystylestores]

How AI can empower communities and strengthen democracy

Each Fourth of July for the past five years I’ve written about AI with the potential to positively impact democratic societies. I return to this question in hopes of shining a light on technology that can strengthen communities, protect privacy and freedoms, and otherwise support the public good.

This series is grounded in the principle that artificial intelligence is capable of not just value extraction, but individual and societal empowerment. While AI solutions often propagate bias, they can also be used to detect that bias. As Dr. Safiya Noble has pointed out, artificial intelligence is one of the critical human rights issues of our lifetimes. AI literacy is also, as Microsoft CTO Kevin Scott asserted, a critical part of being an informed citizen in the 21st century.

This year, I posed the question on Twitter to gather a broader range of insights. Thank you to everyone who contributed.

I’m writing a story and wondering: What’s some of your favorite AI that can strengthen or defend democracy?

— Khari Johnson (@kharijohnson) July 2, 2020

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

This selection is not meant to be comprehensive, and some ideas included here may be in the early stages, but they all represent ways AI might enable the development of more free and just societies.

Machine learning for open source intelligence 

Open source intelligence, or OSINT, is the collection and analysis of freely available public material. This can power solutions for cryptology and security, but it can also be used to hold governments accountable.

Crowdsourced efforts by groups like Bellingcat were once looked upon as interesting side projects. But findings based on open source evidence from combat zones — like an MH-17 being shot down over Ukraine and a 2013 sarin gas attack in Syria — have proved valuable to investigative authorities.

Groups like the International Consortium of Investigative Journalists (ICIJ) are using machine learning in their collaborative work. Last year, the ICIJ’s Marina Walker Guevara detailed lessons drawn from the Machine Learning for Investigations reporting process, conducted in partnership with Stanford AI Lab.

In May, researchers from Universidade Nove de Julho in Sao Paulo, Brazil published a systematic review of AI for open source intelligence that found nearly 250 examples of OSINT using AI in works published between 1990 and 2019. Topics range from AI for crawling web text and documents to applications for social media, business, and — increasingly — cybersecurity.

Along similar lines, an open source initiative out of Swansea University is currently using machine learning to investigate alleged war crimes happening in Yemen.

AI for emancipation 

Last month, shortly after some of the largest protests in U.S. history engulfed American cities and spread around the world, I wrote about an analysis of AI bias in language models. Although I did not raise the point in that piece, the study stood out as the first time I’d come across the word “emancipation” in AI research. The term came up in relation to researchers’ best practice recommendations for NLP bias analysts in the field of sociolinguistics.

I asked lead author Su Lin Blodgett to speak more about this idea, which would treat marginalized people as coequal researchers or producers of knowledge. Blodgett said she’s not aware of any AI system today that can be defined as emancipatory in its design, but she is excited by the work of groups like the Indigenous Protocol and Artificial Intelligence Working Group.

Blodgett said AI that touches on emancipation includes NLP projects to help revitalize or reclaim languages and projects for creating natural language processing for low-resource languages. She also cited AI directed at helping people resist censorship and hold government officials accountable.

Chelsea Barabas explored similar themes in an ACM FAccT conference presentation earlier this year. Barabas drew on the work of anthropologist Laura Nader, who finds that anthropologists tend to study disadvantaged groups in ways that perpetuate stereotypes. Instead, Nader called for anthropologists to expand their fields of inquiry to include “study of the colonizers rather than the colonized, the culture of power rather than the culture of the powerless, the culture of affluence rather than the culture of poverty.”

In her presentation, Barabas likewise urged data scientists to redirect their critical gaze in the interests of fairness. As an example, both Barabas and Blodgett endorsed research that scrutinizes “white collar” crimes with the level of attention typically reserved for other offenses.

In Race After Technology, Princeton University professor Ruha Benjamin also champions the notion of abolitionist tools in tech. Catherine D’Ignazio and Lauren F. Klein’s Data Feminism and Sasha Costanza-Chock’s Design Justice: Community-Led Practices to Build the Worlds We Need offer further examples of data sets that can be used to challenge power.

Racial bias detection for police officers

Taking advantage of NLP’s ability to process data at scale, Stanford University researchers examined recordings of conversations between police officers and people stopped for traffic violations. Using computational linguistics, the researchers were able to demonstrate that officers paid less respect to Black citizens during traffic stops.

The work published in the Proceedings of the National Academy of Science in 2017 highlighted ways police body camera footage can be used to build trust between communities and law enforcement agencies. The analysis was based on recordings collected over the course of years and drew conclusions from a batch of data instead of parsing instances one by one.

An algorithmic bill of rights

The idea of an algorithmic bill of rights recently came up in a conversation with Black roboticists about building better AI. The notion was introduced in the 2019 book A Human’s Guide to Machine Intelligence and further fleshed out by Vox staff writer Sigal Samuel.

A core tenet of the idea is transparency, meaning each person has the right to know when an algorithm is making a decision that affects them, along with any factors being considered. An algorithmic bill of rights would include freedom from bias, data portability, freedom to grant or refuse consent, and a right to dispute algorithmic results with human review.

As Samuel points out in her reporting, some of these notions, such as freedom from bias, have appeared in laws proposed in Congress, such as the 2019 Algorithmic Accountability Act.

Fact-checking and fighting misinformation

Beyond bots that provide civic services or promote public accountability, AI can be used to fight deepfakes and misinformation. Examples include Full Fact’s work with Africa Check, Chequeado, and the Open Data Institute to automate fact-checking as part of the Google AI Impact Challenge.

Deepfakes are a major concern heading into the U.S. election this November. In a fall 2019 report about upcoming elections, the New York University Stern Center for Business and Human Rights warned of domestic forms of disinformation, as well as potential external interference from China, Iran, or Russia. The Deepfake Detection Challenge aims to help counter such deceptive videos, and Facebook has also introduced a data set of videos for training and benchmarking deepfake detection systems.

Pol.is

Recommendation algorithms from companies like Facebook and YouTube — with documented histories of stoking division to boost user engagement — have been identified as another threat to democratic societies.

Pol.is uses machine learning to achieve opposite aims, gamifying consensus and grouping citizens on a vector map. To reach consensus, participants need to revise their answers until they reach agreement. Pol.is has been used to help draft legislation in Taiwan and Spain.

Algorithmic bias and housing

In Los Angeles County, individuals who are homeless and White exit homelessness at a rate 1.4 times greater than people of color, a fact that could be related to housing policy or discrimination. Citing structural racism, a homeless population count for Los Angeles released last month found that Black people make up only 8% of the county population but nearly 34% of its homeless population.

To redress this injustice, the University of Southern California Center for AI in Society will explore ways artificial intelligence can help ensure housing is fairly distributed. Last month, USC announced $1.5 million in funding to advance this effort in partnership with the Los Angeles Homeless Services Authority.

USC’s School for Social Work and the Center for AI in Society have been investigating ways to reduce bias in the allocation of housing resources since 2017. Homelessness is a major problem in California and could worsen in the months ahead as more people face evictions due to pandemic-related job losses. 

Putting AI ethics principles into practice

Implementing principles for ethical AI is not just an urgent matter for tech companies, which have virtually all released vague statements about their ethical intentions in recent years. As a study from the UC Berkeley Center for Long-Term Cybersecurity found earlier this year, it’s also essential that governments establish ethical guidelines for their own use of the technology.

Through the Organization for Economic Co-operation and Development (OECD) and G20, many of the world’s democratic governments have committed to AI ethics principles. But deciding what constitutes ethical use of AI is meaningless without implementation. Accordingly, in February the OECD established the Public Observatory to help nations put these principles into practice.

At the same time, governments around the world are outlining their own ethical parameters. Trump administration officials introduced ethical guidelines for federal agencies in January that, among other things, encourage public participation in establishing AI regulation. However, the guidelines also reject regulation the White House considers overly burdensome, such as bans on facial recognition technology.

One analysis recently found the need for more AI expertise in government. A joint Stanford-NYU study released in February examines the idea of “algorithmic governance,” or AI playing an increasing role in government. Analysis of AI used by the U.S. federal government today found that more than 40% of agencies have experimented with AI but only 15% of those solutions can be considered highly sophisticated. The researchers implore the federal government to hire more in-house AI talent for vetting AI systems and warn that algorithmic governance could widen the public-private technology gap and, if poorly implemented, erode public trust or give major corporations an unfair advantage over small businesses.

Another crucial part of the equation is how governments choose to award contracts to AI startups and tech giants. In what was believed to be a first, last fall the World Economic Forum, U.K. government, and businesses like Salesforce worked together to produce a set of rules and guidelines for government employees in charge of procuring services or awarding contracts.

Such government contracts must be closely monitored as businesses with ties to far-right or white supremacist groups — like Clearview AI and Banjo — continue selling surveillance software to governments and law enforcement agencies. Peter Thiel’s Palantir has also collected a number of lucrative government contracts in recent months. Earlier this week, Palmer Luckey’s Anduril, also backed by Thiel, raised $200 million and was awarded a contract to build a digital border wall using surveillance hardware and AI.

AI ethics documents like those mentioned above invariably espouse the importance of “trustworthy AI.” If you’re inclined to roll your eyes at the phrase, I certainly don’t blame you. It’s a favorite of governments and businesses peddling principles to push through their agendas. The White House uses it, the European Commission uses it, and tech giants and groups advising the U.S. military on ethics use it, but efforts to put ethics principles into action could someday give the term some meaning and weight.

Protection against ransomware attacks

Before local governments began scrambling to respond to the coronavirus and structural racism, ransomware attacks had established themselves as another growing threat to stability and city finances.

In 2019, ransomware attacks on public-facing institutions like hospitals, schools, and governments were rising at unprecedented rates, siphoning off public funds to pay ransoms, recover files, or replace hardware.

Security companies working with U.S. cities told VentureBeat earlier this year that machine learning is being used to combat these attacks through approaches like anomaly detection and quickly isolating infected devices.

Robot fish in city pipes

Beyond averting ransomware attacks, AI can help municipal governments avoid catastrophic financial burdens by monitoring infrastructure, catching leaks or vulnerable city pipes before they burst.

Engineers at the University of Southern California built a robot for pipe inspections to address these costly issues. Named Pipefish, it can swim into city pipe systems through fire hydrants and collect imagery and other data.

Facial recognition protection with AI

When it comes to shielding people from facial recognition systems, efforts range from shirts to face paint to full-on face projections.

EqualAIs was developed at MIT’s Media Lab in 2018 to make it harder for facial recognition tech to identify subjects in photographs, project manager Daniel Pedraza told VentureBeat. The tool uses adversarial machine learning to modify images in order to evade facial recognition detection and preserve privacy. EqualAIs was developed as a prototype to show the technical feasibility of attacking facial recognition algorithms, creating a layer of protection around images uploaded in public forums like Facebook or Twitter. Open source code and other resources from the project are available online.

Other apps and AI can recognize and remove people from photos or blur faces to protect an individual’s identity. University of North Carolina at Charlotte assistant professor Liyue Fan published work that applies differential privacy to images for added protection when using pixelization to hide a face. Should tech like EqualAIs be widely adopted, it may offer a glimmer of hope to privacy advocates who call Clearview AI the end of privacy.

Legislators in Congress are currently considering a bill that would prohibit facial recognition use by federal officials and withhold some funding from state or local governments that choose to use the technology.

Whether you favor the idea of a permanent ban, a temporary moratorium, or minimal regulation, facial recognition legislation is an imperative issue for democratic societies. Racial bias and false identification of crime suspects are major reasons people across the political landscape are beginning to agree that facial recognition tech is unfit for public use today.

ACM, one of the largest groups for computer scientists in the world, this week urged governments and businesses to stop using the technology. Members of Congress have also voiced concern about the use of facial recognition at protests or political rallies. Experts testifying before Congress have warned that the technology has the potential to dampen people’s constitutional right to free speech.

Protestors and others might have used face masks to evade detection in the past, but in the COVID-19 era, facial recognition systems are getting better at recognizing people wearing masks.

Final thoughts

This story is written with a clear understanding that techno-solutionism is no panacea and AI can be used for both positive and negative purposes. But the series is published on an annual basis because we all deserve to keep dreaming about ways AI can empower people and help build stronger communities and a more just society.

We hope you enjoyed this year’s selection. If you have additional ideas, please feel free to comment on the tweet or email khari@venturebeat.com to share suggestions for stories on this or related topics.

Source link

قالب وردپرس

from World Wide News https://ift.tt/3dYnKOT

[automaticpostinfluencer]

FluSense system tracks sickness trends by autonomously monitoring public spaces – TechCrunch

One of the obstacles to accurately estimating the prevalence of sickness in the general population is that most of our data comes from hospitals, not the 99.9 percent of the world that isn’t hospitals. FluSense is an autonomous, privacy-respecting system that counts the people and coughs in public spaces to keep health authorities informed.

Every year has a flu and cold season, of course, though this year’s is of course far more dire. But it’s like an ordinary flu season in that the main way anyone estimates how many people are sick is by analyzing stats from hospitals and clinics. Patients reporting “influenza-like illness” or certain symptoms get aggregated and tracked centrally. But what about the many folks who just stay home, or go to work sick?

We don’t know what we don’t know here, and that makes estimates of sickness trends — which inform things like vaccine production and hospital staffing — less reliable than they could be. Not only that, but it likely produces biases: Who is less likely to go to a hospital, and more likely to have to work sick? Folks with low incomes and no healthcare.

Researchers at the University of Massachusetts Amherst are attempting to alleviate this data problem with an automated system they call FluSense, which monitors public spaces, counting the people in them and listening for coughing. A few of these strategically placed in a city could give a great deal of valuable data and insight into flu-like illness in the general population.

Tauhidur Rahman and Forsad Al Hossain describe the system in a recent paper published in an ACM journal. FluSense basically consists of a thermal camera, a microphone, and a compact computing system loaded with a machine learning model trained to detect people and the sounds of coughing.

To be clear at the outset, this isn’t recording or recognizing individual faces; Like a camera doing face detection in order to set focus, this system only sees that a face and body exists and uses that to create a count of people in view. The number of coughs detected is compared to the number of people, and a few other metrics like sneezes and amount of speech, to produce a sort of sickness index — think of it as coughs per person per minute.

A sample setup, above, the FluSense prototype hardware, center, and sample output from the thermal camera with individuals being counted and outlined.

Sure, it’s a relatively simple measurement, but there’s nothing like this out there, even in places like clinic waiting rooms where sick people congregate; Admissions staff aren’t keeping a running tally of coughs for daily reporting. One can imagine not only characterizing the types of coughs, but visual markers like how closely packed people are, and location information like sickness indicators in one part of a city versus another.

“We believe that FluSense has the potential to expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS,” Rahman told TechCrunch. “By understanding the ebb and flow of the symptoms dynamics across different locations, we can have a better understanding of the severity of a novel infectious disease and that way we can enforce targeted public health intervention such as social distancing or vaccination.”

Obviously privacy is an important consideration with something like this, and Rahman explained that was partly why they decided to build their own hardware, since as some may have realized already, this is a system that’s possible (though not trivial) to integrate into existing camera systems.

“The researchers canvassed opinions from clinical care staff and the university ethical review committee to ensure the sensor platform was acceptable and well-aligned with patient protection considerations,” he said. “All persons discussed major hesitations about collection any high-resolution visual imagery in patient areas.”

Similarly, the speech classifier was built specifically to not retain any speech data beyond that someone spoke — can’t leak sensitive data if you never collect any.

The plan for now is to deploy FluSense “in several large public spaces,” one presumes on the UMass campus in order to diversify their data. “We are also looking for funding to run a large-scale multi-city trial,” Rahman said.

In time this could be integrated with other first- and second-hand metrics used in forecasting flu cases. It may not be in time to help much with controlling COVID-19, but it could very well help health authorities plan better for the next flu season, something that could potentially save lives.

Source link

from WordPress http://justtoosilly.com/2020/03/21/flusense-system-tracks-sickness-trends-by-autonomously-monitoring-public-spaces-techcrunch/

[magzoso-tech]

FluSense system tracks sickness trends by autonomously monitoring public spaces

New Post has been published on https://magzoso.com/tech/flusense-system-tracks-sickness-trends-by-autonomously-monitoring-public-spaces/

FluSense system tracks sickness trends by autonomously monitoring public spaces

One of the obstacles to accurately estimating the prevalence of sickness in the general population is that most of our data comes from hospitals, not the 99.9 percent of the world that isn’t hospitals. FluSense is an autonomous, privacy-respecting system that counts the people and coughs in public spaces to keep health authorities informed.

Every year has a flu and cold season, of course, though this year’s is of course far more dire. But it’s like an ordinary flu season in that the main way anyone estimates how many people are sick is by analyzing stats from hospitals and clinics. Patients reporting “influenza-like illness” or certain symptoms get aggregated and tracked centrally. But what about the many folks who just stay home, or go to work sick?

We don’t know what we don’t know here, and that makes estimates of sickness trends — which inform things like vaccine production and hospital staffing — less reliable than they could be. Not only that, but it likely produces biases: Who is less likely to go to a hospital, and more likely to have to work sick? Folks with low incomes and no healthcare.

Researchers at the University of Massachusetts Amherst are attempting to alleviate this data problem with an automated system they call FluSense, which monitors public spaces, counting the people in them and listening for coughing. A few of these strategically placed in a city could give a great deal of valuable data and insight into flu-like illness in the general population.

Tauhidur Rahman and Forsad Al Hossain describe the system in a recent paper published in an ACM journal. FluSense basically consists of a thermal camera, a microphone, and a compact computing system loaded with a machine learning model trained to detect people and the sounds of coughing.

To be clear at the outset, this isn’t recording or recognizing individual faces; Like a camera doing face detection in order to set focus, this system only sees that a face and body exists and uses that to create a count of people in view. The number of coughs detected is compared to the number of people, and a few other metrics like sneezes and amount of speech, to produce a sort of sickness index — think of it as coughs per person per minute.

A sample setup, above, the FluSense prototype hardware, center, and sample output from the thermal camera with individuals being counted and outlined.

Sure, it’s a relatively simple measurement, but there’s nothing like this out there, even in places like clinic waiting rooms where sick people congregate; Admissions staff aren’t keeping a running tally of coughs for daily reporting. One can imagine not only characterizing the types of coughs, but visual markers like how closely packed people are, and location information like sickness indicators in one part of a city versus another.

“We believe that FluSense has the potential to expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS,” Rahman told TechCrunch. “By understanding the ebb and flow of the symptoms dynamics across different locations, we can have a better understanding of the severity of a novel infectious disease and that way we can enforce targeted public health intervention such as social distancing or vaccination.”

Obviously privacy is an important consideration with something like this, and Rahman explained that was partly why they decided to build their own hardware, since as some may have realized already, this is a system that’s possible (though not trivial) to integrate into existing camera systems.

“The researchers canvassed opinions from clinical care staff and the university ethical review committee to ensure the sensor platform was acceptable and well-aligned with patient protection considerations,” he said. “All persons discussed major hesitations about collection any high-resolution visual imagery in patient areas.”

Similarly, the speech classifier was built specifically to not retain any speech data beyond that someone spoke — can’t leak sensitive data if you never collect any.

The plan for now is to deploy FluSense “in several large public spaces,” one presumes on the UMass campus in order to diversify their data. “We are also looking for funding to run a large-scale multi-city trial,” Rahman said.

In time this could be integrated with other first- and second-hand metrics used in forecasting flu cases. It may not be in time to help much with controlling COVID-19, but it could very well help health authorities plan better for the next flu season, something that could potentially save lives.

[futuremarketinsightsreports]

Leadscrew Market to Witness Robust Expansion throughout the Forecast 2017 – 2027

Leadscrew is also known as power screw which used as a linkage in a machine to convert turning motion into linear motion. Leadscrew is mainly used to carry high power, and sometimes with a split nut which enables nut to disengage from the threads and move axially. Additionally, leadscrew is also utilized in a data storage systems that help in reducing tolerance stack up, part count and minimize overall product cost. To reduce friction between the nut and screw it is used as sliding instead of rolling, and have a large load carrying capabilities. Leadscrew is compact, simple to design, smooth, easy to maintain, easily modified nut designs, minimal parts, and have self-locking features. The growing popularity of smaller packaged applications mainly involves lower load capacity coupled with high precisions. Moreover, miniaturization of technology in various markets has created a diverse opportunity for leadscrew to power applications namely photonics, insulin pumps, auto-focusing optics, automotive equipment, and more.

Request For Report Sample @ https://www.futuremarketinsights.com/reports/sample/rep-gb-3851

Leadscrew Market:Drivers and Restraints

Expanding trend of miniaturization, innovative product development with nanotechnology, developing the need for automated systems, rising demand for customized design, advanced taper-lock technology, product precision, and an alternative to driving belts owing to its low production cost are the primary factor driving the growth of global leadscrew market. Moreover, Advanced features such as high efficiency, adaptability, precision, increase torque density, reduce power consumption, and enhance functioning battery life are some of the prominent factors fueling the growth of leadscrew market over the forecast period. However, leadscrew has a high friction on the threads, and cannot be used in continuous electricity transmission applications coupled with substitute products such as ball screws, fluid power, and piezoelectric actuation, and growing higher standards for medical device reliability may limiting the growth of the leadscrew market during the forecast period.

Leadscrew Market:Segmentation

The leadscrewmarket has been classified by product type, material type, application and end use.

Based on product type, the leadscrewmarket is segmented into the following:

Acme thread

Square thread

Buttress thread

Based on the material type, the leadscrew market is segmented into the following:

Stainless Steel

Aluminum

Polymer

Plastic

Others

Based on application, the leadscrewmarket is segmented into the following:

Linear actuators

Machine Tools

Presses

Jacks

Vises

Others

Based on end use, the leadscrew market is segmented into the following:

Engraving Equipment

Semi-conductor Manufacturing Equipment

Medical Equipment

Laboratory Equipment

Others

Based on end user industry, the leadscrewmarket is segmented into the following:

Medical & Diagnostics Industry

Automotive Industry

Manufacturing Industry

Aerospace & Defense Industry

Others

Leadscrew Market:Overview

Leadscrew market revenue is expected to grow at a rapid growth rate, over the forecast period. The market is anticipated to perform well soon owing to a perfect substitute for hydraulic, and pneumatic cylinders, advancements in linear to rotary conversion, and aids in reducing noise. Additionally, leadscrew has a unique design that enables low-friction acceleration along with free positioning, operates without any secondary forces, and innovative technology which decouples lead screw from stepper motor are the factors that can propel the market revenue growth of leadscrew in the near future. Based on end user industry, medical and diagnostics industry segment is projected to lead the global leadscrew market over the forecast period attributed to rising demand for miniature precision leadscrews for lightweight medical devices that deliver faster speed along with close tolerance positioning.

Leadscrew Market: Region-wise Outlook

Depending on the geographic region, leadscrew market is classified into seven key regions: North America, Eastern Europe, Latin America, Western Europe, Japan, Asia-Pacific, and the Middle East & Africa. North America is expected to be the leading markets in the global leadscrew market followed by Europe, and Japan is owing to the high demand of leadscrew in the complex military, medical, and semiconductor applications which provide precise positional accuracy. The market in Asia-Pacific is projected to have the fastest growth due to the emergence of novel leadscrew technology, rising demand for precision linear motion products, and expanding the need for advanced medical devices. Also, the surge in demand for miniaturization of mechatronic systems is some of the factors which are anticipated to rise the growth of leadscrew market throughout the forecast period.

Request For Report Table of Content (TOC):https://www.futuremarketinsights.com/toc/rep-gb-3851

Leadscrew Market:Key Players

Some of the prominent players in the leadscrewmarket are Nook Industries Inc., Roton Products, Inc., Moore International Ltd, Thomson Industries, Inc., Helix Co., Ltd., Haydon Kerk Motion Solutions, Inc., THK Co., Ltd., Barnes Industries, Inc., MISUMI Group Inc., Stock Drive Products/Sterling Instrument Company, Dynatect Manufacturing, Inc., Beaver Aerospace & Defense, Inc., Thread-Craft Inc., Joyce/Dayton Corporation, and others.

[carlocarrasco]

COMELEC claims that Miru Systems is ready to deliver 110,000 automated counting machines for 2025 national & local elections

With the 2025 national and local elections happening less than a year from now, the Commission on Elections (COMELEC) announced that the contracted provider Miru Systems is ready to deliver more than one hundred thousand automated counting machines (ACM) for the said polls, according to a Philippine News Agency (PNA) news article. To put things in perspective, posted below is an excerpt from PNA…

[carlocarrasco]

COMELEC to launch massive voter education campaign for 2025 elections this December

Starting December 2, the Commission on Elections (COMELEC) will launch a massive voter education campaign related to the 2025 National and Local Elections and the use of automated counting machines (ACMs), according to a GMA Network news report. To put things in perspective, posted below is an excerpt from the GMA news report. Some parts in boldface… The Commission on Elections (Comelec) is set…

[acmemechatronics1]

Automatic Coil Winding Machine Manufacturer Supplier

A coil winder is a crucial tool in industries that require precise coil winding for motors, transformers, and other electrical components. The coil winding machine automates this process, ensuring consistent and accurate windings. This is particularly important for manufacturers aiming to maintain high quality while increasing production efficiency. For those looking for advanced solutions, an automatic winding machine can take the process further by automatically adjusting parameters like tension, speed, and layer count, significantly reducing human intervention. ACME Electronics provides state-of-the-art winding machines that offer both flexibility and precision for various applications. Whether you're winding small coils for delicate electronics or large ones for industrial machinery, investing in the right coil winding equipment is essential for optimizing performance and reducing operational costs.

[acmemechatronics1]

The Future of Coil Production: Automatic and Programmable Winding Machines

In today’s fast-paced manufacturing landscape, efficiency and precision are crucial for staying competitive. Automatic and programmable winding machines have emerged as game-changers in coil production, offering advanced technology that significantly enhances productivity and quality. This article will explore the features, benefits, and applications of these machines, while highlighting ACME Electronics as a leading manufacturer and supplier in the field.

What is an Automatic Winding Machine?

An automatic winding machine is designed to wrap wire around a core with minimal manual intervention. These machines utilize advanced technology to automate the winding process, allowing for high-speed production while maintaining precision. They are ideal for creating coils used in a variety of applications, from transformers to electric motors, ensuring consistent quality and efficiency.

Key Features of Automatic Winding Machines

High-Speed Operation: Automatic winding machines can operate at much higher speeds compared to manual machines, allowing manufacturers to increase output and meet demand more effectively.

Precision Control: Equipped with sophisticated control systems, these machines ensure that the winding process is accurate, reducing the risk of defects and enhancing overall product quality.

User-Friendly Interfaces: Most automatic winding machines feature intuitive interfaces that simplify operation. Operators can easily program parameters such as tension, layer count, and winding speed, making adjustments quick and efficient.

What is a Programmable Winding Machine?

A programmable winding machine is a specialized type of automatic winding machine that allows for the input of specific winding programs. This capability enables the machine to execute complex winding patterns and configurations, tailored to meet precise requirements. Programmable winding machines are especially valuable for industries that demand customization and flexibility in their coil production processes.

Benefits of Programmable Winding Machines

Customization: Programmable winding machines allow manufacturers to create coils with unique specifications, accommodating various applications and customer needs. This level of customization can be a significant competitive advantage.

Enhanced Efficiency: By reducing setup time and enabling rapid program changes, these machines improve overall production efficiency. This flexibility allows manufacturers to switch between different products with minimal downtime.

Reduced Waste: The precision and control offered by programmable machines minimize material waste, leading to cost savings and a more sustainable manufacturing process.

ACME Electronics: Your Trusted Partner in Winding Solutions

ACME Electronics is a renowned manufacturer and supplier of automatic winding machines. With a strong commitment to quality and innovation, ACME provides advanced solutions that cater to the diverse needs of the industry. Their automatic and programmable winding machines are designed to enhance productivity while ensuring that customers receive reliable and high-quality equipment for their winding processes.

Conclusion

In conclusion, automatic and programmable winding machines represent the future of coil production, offering unparalleled efficiency, precision, and customization. As industries continue to evolve, these machines will play a crucial role in meeting the demands of modern manufacturing. With ACME Electronics leading the way as a trusted manufacturer and supplier, businesses can confidently invest in the technology that will enhance their operations. Are you ready to transform your coil production with an automatic winding machine?

[acmemechatronics1]

Understanding Linear Winding Machines: The Key to Efficient Coil Production

In the world of coil winding, precision and efficiency are paramount. Linear winding machines have emerged as essential tools for manufacturers seeking to optimize their production processes. With their ability to create coils with consistent quality and minimal waste, these machines are transforming the landscape of industries ranging from electronics to automotive. In this article, we will explore the features and benefits of linear winding machines, spotlighting ACME Electronics as a leading manufacturer and supplier in this domain.

What is a Linear Winding Machine?

A linear winding machine is designed to produce coils by winding wire around a core in a straight, linear manner. Unlike traditional winding machines that may use circular or complex movements, linear winding machines employ a straightforward approach that maximizes efficiency. This type of machine is particularly suited for applications where precision is critical, such as in the production of transformers, inductors, and various electronic components.

Key Features of Linear Winding Machines

Precision Control: Linear winding machines come equipped with advanced control systems that ensure accurate winding. Users can set specific parameters for tension, speed, and layer count, which leads to improved product consistency.

Automation Capabilities: Many modern linear winding machines feature automation technology that reduces the need for manual intervention. This not only speeds up the production process but also minimizes the likelihood of human error.

Versatility: These machines can accommodate various wire sizes and types, making them suitable for a wide range of applications. Whether it’s small, intricate coils or larger industrial applications, linear winding machines can adapt to meet specific needs.

Space Efficiency: The linear design of these machines often requires less floor space compared to traditional winding setups. This makes them an ideal choice for manufacturers looking to optimize their workspace.

Benefits of Using Linear Winding Machines

Enhanced Production Speed: The automation and precision of linear winding machines significantly increase production speeds, allowing manufacturers to meet higher demand without compromising quality.

Cost Efficiency: By minimizing material waste and reducing labor costs through automation, businesses can achieve greater cost efficiency. This is especially important in competitive markets where margins are tight.

Improved Product Quality: Consistency in coil production translates to better quality products. Linear winding machines reduce the variability often associated with manual winding processes, leading to higher customer satisfaction.

ACME Electronics: A Leader in Linear Winding Solutions

As a renowned manufacturer and supplier of linear winding machines, ACME Electronics stands out for its commitment to innovation and quality. With a focus on providing cutting-edge technology, ACME’s winding machines are designed to meet the evolving needs of the industry. Their extensive range of products caters to various applications, ensuring that customers receive tailored solutions for their winding requirements.

Conclusion

In summary, linear winding machines are revolutionizing coil production with their precision, efficiency, and versatility. As industries continue to demand higher quality and faster production times, these machines offer a viable solution. With ACME Electronics leading the charge as a trusted manufacturer and supplier, businesses can confidently invest in technology that will enhance their production capabilities. Are you ready to elevate your coil production with the latest in linear winding technology?