Results 1 to 14 of 14

Thread: Sensors

  1. #1


    Leaked FDA Study: Toxic “Forever Chemicals” Contaminate Many Foods

    The FDA has not made its findings public yet, but agency researchers discussed the results at a conference held by the Society of Environmental Toxicology and Chemistry last week in Finland.

    Per- and polyfluoroalkyl substances (PFAS) are a group of man-made chemicals that includes PFOA, PFOS, GenX, and many other chemicals. PFAS have been manufactured and used in a wide variety of industries around the world, including in the United States since the 1940s.

    Studies show that chemicals, including PFAS, can migrate into food from food packaging and food contact substances. PFAS are commonly used in food packaging.

    PFAS can also migrate into fruits, vegetables and grains that are irrigated with PFAS-contaminated water or grown in soils that are contaminted with PFAS.

    The use of PFAS-contaminated sewage sludge, concentrated waste from residential and industrial sources, as a fertilizer on farm fields is another way PFAS chemicals find their way into crops grown for food and animal feed. Almost half of the seven million tons of sewage sludge generated in the U.S. every year are applied to land, including on farm fields.

    There is evidence that exposure to PFAS can lead to serious adverse human health effects, including cancer, reproductive harm, developmental harm, high cholesterol, damage to the immune system, hormone disruption, weight gain in children and dieting adults, and liver and kidney damage. The presence of PFOA in the blood has been associated with increased cholesterol and uric acid levels, which can lead to kidney stones and gout.

  2. #2
    Environmental Sensor that reduces food waste

    "RipeTime has developed a form of amplification technology which is being utilised as an Environmental Sensor," said Mitch Denton of RipeTime. "This allows for users to accurately measure and forecast optimal delivery times, maximise outgoings and minimise food wastage.

    RipeTime’s amplification technology allows for atmospheric reads in the form of parts per billion (PPB), this measurement is often used to describe concentrations of contaminants found within an atmosphere in its most precise and finite form (currently an unmatched industry standard)."

    He added, "Ethylene is a naturally occurring chemical, which is released throughout the life-cycle of fresh produce. One of the biggest contributors to mass amounts of waste within the fresh produce sector is directly linked to cases of overexposure to ethylene during the ripening and storage process."

    Denton said that RipeTime’s Environmental Sensor can work across both Climacteric and Non-Climacteric fresh produce and is available worldwide. "There simply needs to be a specific calibration process upfront that is geared towards the particular produce in circulation," he said.

    "RipeTime headquarters are located in New Zealand, but any orders of the Environmental Sensor can be shipped globally within two to three business days."

  3. #3
    Researchers develop a graphene-based biosensor that detects bacterial presence

    Researchers from Myongji University, Sungkyunkwan University, Gachon University and Korea Institute of Science and Technology in South Korea, along with U.S-based Villanova University, have developed a new device concept for bacterial sensing by Raman spectroscopy and voltage-gated monolayer graphene.

    The monolayer-graphene based biosensor reportedly demonstrated its potential for rapid bacterial detection and differentiation between Gram-positive and Gram-negative bacteria.

    The experimental data supports the notion that identification, differentiation, detection, and classification of bacteria can be achieved by voltage gated monolayer graphene via direct Raman spectroscopy measurements.

    Graphene: the evolving application landscape

  4. #4
    How to build a business that lasts more than 200 years – lessons from Japan’s shinise companies

    Japan is home to a number of the world’s oldest companies. There is even a specific Japanese term for companies that have survived for more than a century, retained ownership within the same family and continued plying the same trade for the duration. They are called “shinise” firms.

    Kyoto, Japan’s ancient capital, holds the highest proportion of these century-old firms. They operate in traditional sectors such as sake brewing, sweet making and arts and crafts. The Gekkeikan sake company, for example, is nearly 400 years old and has been run by 14 consecutive generations of the Okura family. Sasaya Iori, meanwhile, is now in its 303rd year of making and selling sweets.

    Colleagues and I interviewed the people who run these shinise firms and many others to understand their relationship with the local community. We found that a key part of their success was maintaining high social standing in the city amid a changing business environment characterised by loss of traditional values and practices, changing consumer tastes due to Japan’s Westernisation, and increasing competition from larger and internationally operating firms.

  5. #5
    Building a roadmap towards Activity Based Working

    Activity-based working (ABW) is about providing employees with a range of options for how they carry out their activities in the workspace. Instead of every employee having a fixed desk, which they are expected to use for every task, ABW provides spaces for informal or formal meetings, collaborative and open areas for project work as well as quieter spaces for tasks that require more focus. The flexible workspace is one of 2019's key trends.

    It's not just about giving employees a more comfortable way to get their work done, though there's certainly no harm in that. According to the Harvard Business Review, the most highly engaged employees score highly for 'being able to choose where to work according to the task at hand'.

    A Leesman Index report found that employees who agree that 'I [...] rarely based myself at a single location within the office' are more effective in their jobs than their more static colleagues’.

    The way that we work is changing as millennials make up a significant proportion of the workforce, bringing with them expectations of more autonomy and flexibility, so the trend towards ABW will only continue. But it isn't just employees who benefit. Employers, facilities managers and commercial real estate providers will see benefits too.

    We can't easily add space and in high demand cities office space is extraordinarily expensive, so we need to make more effective use of the space that we have and ABW allows that. In an environment where you will never have all of your workers at their desks at the same time, there's no reason to have a desk for everyone. Of course, you can't just take desks away without a plan.

    Most facility and corporate real estate teams are moving away from manual surveys to determine how their building spaces are being used. Manual surveys are generally time consuming as somebody has to observe the office to see how space is being utilised, then process that data and produce reports. On top of this they only provide a single snapshot of space utilisation, which could be incomplete and not representative.

    Instead, more and more facility and corporate real estate teams are using passive infrared sensors (PIR) to capture and measure utilisation rates. PIR sensors detect heat and motion; and can measure the utilisation of:

    • Desks
    • Meeting rooms
    • Phone booths
    • Shared spaces
    • Lifts or stairways
    • Different building locations

    These sensors are unobtrusive, always on and produce accurate data that can be analysed in a variety of different ways, such as:

    • Utilisation comparison between buildings, floors, departments or teams
    • Average utilisation rates of desk, meeting rooms or shared spaces
    • Identify peak vs. off peak utilisation rates
    • Help determine and increase person to desk ratio if needed
    • Inform workspace redesign projects, such as right sizing meeting rooms

    There are other types of workspace occupancy sensors depending on the business need but in our experience this is the most popular.

    This method of measuring utilisation brings the added advantage of not having to rely on what people think is happening, such as complaints that the office lacks sufficient meeting room space. However, with concrete data in hand you often find that there is plenty of space, only it isn't used efficiently. People book big meeting rooms for small meetings or block book two hours for a 45 minute meeting.

  6. #6
    Food freshness sensors could replace ‘use-by’ dates to cut food waste

    These new laboratory prototype sensors, developed at Imperial College London, cost two US cents each to make. Known as ‘paper-based electrical gas sensors’ (PEGS), they detect spoilage gases like ammonia and trimethylamine in meat and fish products.

    The sensor data can be read by smartphones, so that people can hold their phone up to the packaging to see whether the food is safe to eat.

    The materials are biodegradable and nontoxic, so they don’t harm the environment and are safe to use in food packaging. The sensors are combined with ‘near field communication (NFC)’ tags – a series of microchips that can be read by nearby mobile devices.

    The researchers say the sensors could also eventually replace the ‘use-by’ date – a less reliable indicator of freshness and edibility. Lower costs for retailers may also eventually lower the cost of food for consumers.

    “Use-by dates estimate when a perishable product might no longer be edible – but they don’t always reflect its actual freshness.

    “Although the food industry – and consumers – are understandably cautious about shelf life, it’s time to embrace technology that could more accurately detect food edibility and reduce food waste and plastic pollution.”

    The authors hope that PEGS could have applications beyond food processing, like sensing chemicals in agriculture, air quality, and detecting disease markers in breath like those involved in kidney disease. However, before they can be applied beyond their current use, the researchers will address how sensitive PEGS are to lower humidity.

    They are also developing an array of PEGS in which each sensor detects a different chemical. Using this technique, the array will give unique signals for different gases and/or changing humidity, which would make the technology applicable to a wider variety of food types.

    PEGS are made of carbon electrodes printed onto readily available cellulose paper.

  7. #7
    How to measure Workplace Occupancy

    More organisations are using a combination of occupancy and environmental data to drive conversations around what type of workplaces are best suited to them.

    Using sensors to gather data is a cost effective and convenient way to get a lot of information quickly. It gives facility management teams an objective and realistic view of how buildings and workspaces are utilised.

  8. #8
    Mango Fruit Load Estimation Using a Video Based MangoYOLO

    Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree (“dual-view”) underestimates the fruit yield as fruit can be hidden from view. A method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting from 10 frame-per-second videos captured of trees from a platform moving along the inter row at 5 km/h. The deep learning based mango fruit detection algorithm, MangoYOLO, was used to detect fruit in each frame.

  9. #9
    Agri-tech companies are teaching robots to be growers

    How do you teach a computer what a good strawberry looks like?

    According to AgShift founder and CEO Miku Jha, whose company is innovating food inspections with deep learning, you do it the same way you train a three year old. “You give them a ping pong ball and an egg, and you keep telling them, ‘this is a ball, this is an egg, this is a ball, this is an egg.’ Both are white, but eventually you figure it out. That’s how our minds are wired.”

    Training a strawberry-inspecting AI program follows that same logic: “We take hundreds of images of bruises in a strawberry, and we keep training the model that this is a bruise. ‘Good berry, bad berry, good berry, bad berry.’ That’s it,” she said. Jha was a part of a panel on automation moderated by Forbes associate editor Alex Knapp on Thursday at the 2019 Forbes AgTech Summit in Salinas, California.

    The growing intersection between Silicon Valley’s technology and the Salinas Valley’s agriculture hasn’t extended to Silicon Valley’s VC money. Agtech struggles to find investment because it takes longer to grow companies, and also because most investors are “sheep,” Kellerman said. Traditional venture capitalists are opportunistic, following the crowd and chasing the “dumb money.” He argues that agtech companies need VC firms that understand “patient capital,” making a long term investment instead of expecting a quick profit.

    A major reason agtech can’t iterate as quickly as software is because companies can’t test their innovations year-round. They’re limited by harvesting periods that last only a few months per year. California, especially the Salinas Valley, is a prime location to counter this problem because the area has low seasonality, said Palomares, who made the 2019 Forbes 30 Under 30 Manufacturing and Industry List for Farmwise. The company uses robots with computer vision and deep learning capabilities to remove weeds with herbicides.

    Today’s automated robots likely still aren’t perfect—which can make it a hard sell to growers. “You create a broccoli harvester or a strawberry harvester, it might be 60% or 80% of what a human can do,” Kellerman said. “So, you may have to rethink your business model. If you’re just looking to swap [people] out, it’s not going to happen that way. Technology evolves at an iterative pace.”

  10. #10
    IBM’s e-tongue can taste and identify liquids

    IBM Research is developing Hypertaste, an AI-assisted portable electronic tongue that can taste and identify liquids quickly and at lower cost than conventional lab tests.

    The chemical sensor could be used for a water quality check of a lake or stream in the wilderness, or by a food maker wanting to identify a counterfeit wine, according to a blog.

    Healthcare providers could also use Hypertaste to fingerprint liquids such as a person’s urine, which constantly changes depending on lifestyle and nutrition, making a series of snapshots with the sensor valuable. “Such a tool could allow sub-grouping of patients in chemical trials for new drugs by matching the individual responses of patients to a treatment with information on their personal metabolomes,” wrote Patrick Ruch, research staff member, in the blog. “The spectrum of possible applications is vast.”

    An IBM video depicts Hypertaste as a small circular device that looks like a slice of lemon that can be hooked on the edge of a beaker to identify a liquid. Inside of Hypertaste are off-the-shelf electronics and swappable polymer-coated electrochemical sensors. The electronics measure voltages across electrodes in an array. A liquid sample can be identified in less than a minute with results transmitted wirelessly to a smartphone or other portable device.

    IBM gets that speed by using cross-sensitive sensors with intelligent software that can be outsourced in the cloud. Using the cloud means that the sensors can be reconfigured from anywhere without altering the hardware. Sensors will also be able to learn from one another by exchanging information about new liquids they encounter in an AI and machine learning framework, IBM said.

    It will be hard to fool Hypertaste because it doesn’t rely on identifying any single substance in a liquid. Instead, it uses a “combinatorial” sensing model, which resembles human natural senses of taste and smell where people don’t have a receptor for each molecule in food or drink. Instead, "combinatorial sensing relies on individual sensors to respond simultaneously to different chemicals,” the blog said. “By building an array of such cross-sensitive sensors, one can obtain a holistic signal, or fingerprint, of the liquid in question.”

    The electrochemical sensors in Hypertaste are comprised of pairs of electrodes and they respond to the presence of a combination of molecules with a voltage signal. The combined voltage signals of all the pairs of electrodes represent the liquid’s fingerprint, which is compared to known chemicals in a database in the cloud for identification.

  11. #11
    New sensor detects spoiled milk

    Scientists at Washington State University developed a sensor that can “smell” if milk has gone bad—without opening the package.

    The sensor changes color when its chemically coated nanoparticles react to gas and bacterial growth that indicate spoilage, says Shyam Sablani, a professor in the Department of Biological Systems Engineering. The sensor doesn’t touch the milk directly.

    Now the team wants to expand the technology to show how long a product has before it spoils. Sablani envisions the sensor getting integrated in a milk bottle’s plastic cap. It could be more accurate than an expiration date.

  12. #12
    Ultrasonic sensor provides multiple object scanning over wide viewing

    Using ultrasound scanning, the Toposens TS3 sensor is an embedded sensor system that achieves a wide field of view of up to 160° and provide simultaneous 3D measurements for multiple objects within the scanning area. The sensor is suited for various applications in the autonomous systems market that require a strong need for reliable object detection and situational awareness.

    In addition to cars, use cases include home cleaning robots and delivery/service robots. The TS3 sensor enables them to reliably map an environment with minimal processing power and to localize themselves in predefined maps to execute complex path planning algorithms. The sensors are unaffected by ambient light and can detect mirrored and transparent surfaces.

    The sensor sends out ultrasound waves in a frequency range inaudible by humans. An array of microphones subsequently records the echoes from all objects in the sensor’s vicinity and computes their location in a 3D space. The sensor provides a detection range of up to 5 meters and a scan rate of approximately 28 Hz. The TS3 returns up to 200 points per second with each 3D point corresponding to the Cartesian coordinates.

    Due to its wide viewing angle and multiple object detection capability, the ultrasonic sensor mimics the echolocation techniques used by bats and dolphins for navigation and orientation in the wild.

    “Because our new “Bat Vision” TS3 sensor is compact, affordable and integration-ready,” said Tobias Bahnemann, Managing Director of Toposens, in a statement. “Engineers can easily add it to their sensor stacks to replace or complement their existing optical sensing systems, providing both redundancy and an improved level of accuracy compared to standard ultrasonic sensors in various autonomous navigation applications.”

    The sensor’s core technology is based on the Toposens’ SoundVision1 chip, making the sensor system easily adaptable to various product designs. This makes the TS3 a suitable technology platform to develop next-level mass market vehicles in robotic and even automotive use cases like automated parking and next-level ADAS functionality.

    Along with the TS3 sensor, Toposens offers a feature-rich toolkit for the Robotics Operating System (ROS) middleware, making the sensor easy to integrate into preexisting systems and enabling fast prototyping for high-level navigation capabilities. The team is currently working on adding advanced decision-making algorithms to their software and complimentary tutorial stack.

  13. #13
    This autonomous bicycle shows China’s rising expertise in AI chips

    It might not look like much, but this wobbly self-driving bicycle is a symbol of Chinese growing expertise in advanced chip design.

    Look, no hands: The bike not only balances itself but steers itself around obstacles and even responds to simple voice commands. But it’s the brains behind the bike that matter. It uses a new kind of computer chip, called Tianjic, that was developed by Luping Shi and colleagues at Tsinghua University, a top academic institution in Beijing.

    Two in one: The Tianjic chip features a hybrid design that seeks to bring together two different architectural approaches to computing: a conventional, von Neumann design and a neurologically inspired one. The two architectures are used in cooperation to run artificial neural networks for obstacle detection, motor and balance control, and voice recognition, as well as conventional software.

    AI’s future? In a paper outlining the chip and the bicycle, published in the journal Nature today, the researchers suggest that such a hybrid architecture could be crucial for the future of artificial intelligence, perhaps even providing a route toward more general forms of AI. That’s a bit bold, given how far we are from AGI, but Tianjic does show the growing value of new chip designs optimized for running AI algorithms.

    Made in China: The chip also hints at the progress China is making in developing its own chip design capabilities. As outlined in this feature article, China has long struggled to build its own chip industry, a major weakness in its technological capabilities that have been exploited in the ongoing trade war with the US. But while manufacturing the most advanced computer chips remains out of reach, Chinese researchers are showing they can make specialized AI chips as well as anyone.

  14. #14
    Fruit Detection and Segmentation for Apple Harvesting Using Visual Sensor in Orchards

    Autonomous harvesting shows a promising prospect in the future development of the agriculture industry, while the vision system is one of the most challenging components in the autonomous harvesting technologies.

    This work proposes a multi-function network to perform the real-time detection and semantic segmentation of apples and branches in orchard environments by using the visual sensor.

    The developed detection and segmentation network utilises the atrous spatial pyramid pooling and the gate feature pyramid network to enhance feature extraction ability of the network.

    To improve the real-time computation performance of the network model, a lightweight backbone network based on the residual network architecture is developed.

    The network model with lightweight backbone showed the best computation efficiency in the results. It achieved an F1 score of 0.827 on the detection of apples and 86.5% and 75.7% on the segmentation of apples and branches, respectively.

    The weights size and computation time of the network model with lightweight backbone were 12.8 M and 32 ms, respectively.

    The experimental results show that the detection and segmentation network can effectively perform the real-time detection and segmentation of apples and branches in orchards

Tags for this Thread

Posting Permissions

  • You may not post new threads
  • You may not post replies
  • You may not post attachments
  • You may not edit your posts