Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

LeapMind (Tokyo, Japan) announced its entry into the processor IP business with Efficiera, an ultra-low-power AI inference accelerator IP product. Efficiera is optimized for models that have been heavily quantized using LeapMind's'extremely low-bit quantization' software techniques. It is designed for convolutional neural networks (CNNs), the type of network typically used for image processing and analysis tasks today. "This is the company's first hardware IP product. But we are working on the development of a core technology called extreme quantization technology that operates at both software and hardware-IP levels with a network optimized for practical applications and a dedicated compiler," a LeapMind spokesperson told EE Times.

How to implement custom object detection with template matching. Today, state-of-the-art object detection algorithms (algorithms aiming to detect objects in pictures) are using neural networks such as Yolov4. Template matching is a technique in digital image processing for finding small parts of an image that matches a template image. It is a much simpler solution than a neural network to conduct object detection. In my experience, combining a neural network like Yolov4 and object detection with template matching here is a good way to considerably improve your neural network performance! When you use OpenCV template matching, your template slides pixel by pixel on your image.

Image enhancement is an important research topic in image processing and computer vision. It is mainly used as image pre-processing or post-processing to make the processed image clearer for subsequent image analysis and understanding. There are many sources of noise in images, and these noises come from various aspects such as image acquisition, transmission, and compression. The types of noise are also different, such as salt and pepper noise, Gaussian noise, etc. There are different processing algorithms for different noises.

On New Year's Day, 1925, Henry Russell, director of the Princeton University Observatory, presented to the joint meeting of the American Association for the Advancement of Science and the American Astronomical Society a research paper that would change humanity's understanding of the Universe and our place in it. Busy with ongoing observations at the Mount Wilson Observatory near Los Angeles, the paper's author, Edwin Hubble, mailed his work to the meeting rather than traveling to Washington, D.C., to present it himself. Using Mount Wilson's 100-inch telescope—the world's largest from 1917 to 1949—Hubble had taken images of the Andromeda Nebula, determining that it is a galaxy of its own, composed of individual stars. The observation proved that our Milky Way does not take up nearly the entire Universe, as many astronomers of the time believed, but instead is one galaxy of many. “This paper is the product of a young man of conspicuous and recognized ability in a field which he has made peculiarly his own,” Russell and Joel Stebbins, secretary of the American Astronomical Society, wrote in a letter to the AAAS Committee on Awards. “It has already expanded one hundred-fold the known volume of the material universe.” Of approximately 1,700 scientists who shared research at the meeting, the committee chose two—Hubble and a zoologist named L. R. Cleveland—to share the 1924 Thousand Dollar Prize, an award honoring the most noteworthy contributions to science presented at the AAAS Annual Meeting each year. Now called the Newcomb Cleveland Prize, the yearly award goes to the author or authors of a particularly impactful paper published in Science . “The Association Prize will never be awarded to a more appreciative student than your present choice,” Hubble wrote to AAAS secretary Burton Livingston in February 1925. “The occasion of the reward, however, must be regarded as a triumph of modern instruments rather than a personal achievement.… This was accomplished by the use of the largest telescopes in existence.” Decades later, the National Aeronautics and Space Administration launched into Earth orbit an instrument exponentially larger and more precise than anything available to Hubble, and it bore his name. In April 1990, the Hubble Space Telescope became the first major optical telescope to be placed in space. Free from the atmospheric distortion and light pollution faced by land-based telescopes, Hubble has provided data leading to more than 17,000 peer-reviewed publications on topics including star formation, galaxy mergers, and dark matter, the largely unseen mass that occupies most of the Universe. NASA is celebrating a series of the Hubble mission's 30th-anniversary milestones this year, from the launch in April to its “first light” image in May and its observations of Supernova 1987A in August. Throughout the mission's first three decades, AAAS has honored its scientists with awards and fellowships and used their expertise to guide AAAS programs. AAAS and Hubble also overlap in their vision for the science, technology, engineering, and mathematics (STEM) enterprise, as both work to enhance public engagement, educate and diversify the STEM workforce, and foster international collaboration. “Hubble, slam dunk, aligns with almost all of them,” astronomer Kathryn Flanagan said of AAAS's goals. Prior to retiring in March, Flanagan oversaw the Hubble mission as deputy director and interim director of the Baltimore-based Space Telescope Science Institute (STScI), a nonprofit science center operated for NASA and responsible for Hubble's science operations and public outreach. She also chaired AAAS's astronomy section in 2016 and was inducted as an elected AAAS fellow this year, the latest of dozens of Hubble-affiliated scientists to receive the honor. “Hubble was, when I assessed it a few years back, contributing as a recommended or required component in half the state departments of education in the U.S.,” Flanagan said, referencing the prevalence of the telescope's findings in public school curricula. “It reached half the public middle school students in the country.” Flanagan also highlighted the Hubble mission's dedication to international collaboration through its partnership with the European Space Agency in development and operations, publicly available archives, research time allotted to scientists from around the world, and a double-anonymous peer-review process, which ensures that unconscious bias does not play a role in determining which research proposals are selected. Many AAAS programs, including Science in the Classroom, the Center for Science Diplomacy, and SEA Change, have a similar focus on science education, international collaboration, and achieving equity in STEM disciplines. Carol Christian, Hubble Space Telescope outreach project scientist at STScI, works to share the mission's discoveries with the world. Christian is part of the Hubble news team, highlighting science results from hundreds of refereed papers each year. Additionally, she leads a program that brings Hubble data to individuals with blindness and visual impairments using 3D printing. Christian also has served as a screener for the AAAS Kavli Science Journalism Awards since 2010. Each year, approximately 70 scientists volunteer to assess the scientific accuracy of the award program's print, digital, and video reporting candidates. Christian said that the review panels consist of researchers from diverse disciplines, including geology and the life sciences. “You learn a lot from the people that are your fellow reviewers,” she said. Prior to working with the Kavli Awards, Christian brought her expertise in satellite imagery to the U.S. Department of State, where she worked from 2003 to 2007 through the AAAS Science and Technology Policy Fellowships program, which places more than 250 scientists each year in policy roles across all three branches of the federal government. She enjoyed putting her scientific knowledge to practical use during her time as a fellow. “Suddenly, to be doing something that actually has import—policy may depend on it, economics may depend on it—that was pretty interesting,” Christian said. Margaret Burbidge, an influential astrophysicist who died in April at the age of 100, was perhaps the first scientist to contribute to both the Hubble mission and AAAS. During her term as president of AAAS in 1983, she was on the team that developed the Faint Object Spectrograph, one of the five science instruments on Hubble when it launched. Burbidge and her colleagues designed the instrument to detect the physical and chemical properties of faint objects in galaxies beyond our own, and it provided the first strong observational evidence for the existence of a supermassive black hole in the core of another galaxy. “She was one of the names that we all knew of as pioneers in the field of modern astronomy,” astrophysicist Jennifer Wiseman said following Burbidge's death. “An inspirational person as a brilliant scientist and in particular as a courageous woman scientist who opened up the field for many of us coming later on.” Like Burbidge did for a time, Wiseman works with the Hubble Space Telescope and is also active in AAAS activities. As Hubble's senior project scientist, Wiseman works with a team of scientists and engineers at NASA's Goddard Space Flight Center to keep the telescope as scientifically productive as possible. Prior to her NASA career, Wiseman served as an American Physical Society congressional science fellow, a role under the umbrella of the AAAS Science and Technology Policy Fellowships. She brought her subsequent connection with Hubble to a broad AAAS audience by co-organizing a symposium on the Hubble Space Telescope mission—its scientific discoveries, image processing feats, and future promise—at the AAAS 2015 Annual Meeting, and she has given Hubble overview talks at subsequent AAAS gatherings. Since 2010, Wiseman has also directed the AAAS Dialogue on Science, Ethics, and Religion (DoSER) program, which works to foster communication between scientific and religious communities. “Being an active scientist while attentive to public science engagement helps me to recognize how scientists and the other communities that DoSER works with—including ethicists and religious communities—can better understand each other and our common interests in science, technology, and their positive contributions to society,” Wiseman said. “Astronomy in general gives everyone a sense of humility, awe, wonder, and curiosity,” she added. “I enjoy sharing with many types of public audiences the amazing things we're discovering with telescopes and other scientific facilities. I'm very humbled and encouraged by how such discoveries can inspire deeper discussions of meaning and purpose and of using science to help one another, as we all ask questions together of who we are as human beings in an awesome Universe and how we can use our knowledge to uplift the human spirit.”

New carbon emission tracking technology will quantify emissions of greenhouse gas, holding the energy industry accountable for its CO2 output. Backed by Google, this cutting-edge initiative will be known as Climate TRACE (Tracking Real-Time Atmospheric Carbon Emissions). Advanced AI and machine learning now make it possible to trace greenhouse gas (GHG) emissions from factories, power plants and more. By using image processing algorithms to detect carbon emissions from power plants, AI technology makes use of the growing global satellite network to develop a more comprehensive global database of power plant activity. Because most countries self-report emissions and manually compile results, scientists often rely on data that is several years out of date.

Pune, 30 July 2020: A letter of intent was signed between the Centre for Innovation, Incubation and Enterprise(CIIE) at Savitribai Phule Pune University (SPPU) with healthcare startup DeepTek to provide market linkage and work on innovative research proposals in healthcare and Artificial Intelligence (AI) domain jointly. "SPPU is keen to support startups working in COVID diagnostics and provide them access to the ecosystem . " Centre for innovation helps startups for validating and scaling up and Deeptek a startup that provides innovative solution using AI in image processing through X Ray and CT scan is a novel initiative that can be a great breather when testing is most crucial", said Dr Apoorva Palkar, Director IIL-SPPU. CIIE will support the healthcare startup working in the space of radiology artificial intelligence to validate the innovative "Radiology Optimization Platform" called Augmento. Augmento has innovated the space of diagnosis and radiology reporting by embedding AI artificial intelligence (AI) in medical image analysis and workflow thereby augmenting imaging experts, radiology administrators, floor managers and hospital administrators. Augmento is an ancillary tool for diagnosis of Covid19 pneumonia-like pattern from Digital Radiographs and CT. Augmento allows to do an instant triage and prescreen from an Xray and/or CT imaging into normal or suspicious for Covid19 within few seconds and it can be supplemented with a clinically validated structured quantified radiology report within 60 minutes of completing the study. Augmento has a powerful analytics tool which has been used for disease detection, notification, generating instant alerts and strengthening patient follow up and last-mile screening, a tool especially valuable for nodal officers monitoring infectious diseases and will be very useful for Covid19 screening. This tool also has empowered imaging experts, thereby allowing them to significantly reduce radiology report turnaround time in hospitals and/or mobile van-based x-ray screening. CIIE is working very closely with startups and focusing on healthcare and diagnostics. Currently, it has more than 40 startups working in the centre. "Over the last 12 months, more than one lakh patients have been screened using the AI-enabled smart platform for TB screening.

I made this app, as my pilot task for Tessellate coding. The task included finding a suitable model, making the inference algorithm, wrapping it in a REST API, and finally dockerizing the application. For the task, I used Keras with a Tensorflow backend and Flask. This blog is about the same challenges I faced in the task, and how to overcome them when you are making your project. For the model, I researched a bit on the topic of the super-resolution of the image, and found the SRCNN model.

Over the past few decades, medical imaging techniques, such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), mammography, ultrasound, and X-ray, have been used for the early detection, diagnosis, and treatment of diseases. In the clinic, medical image interpretation has been performed mostly by human experts such as radiologists and physicians. However, given wide variations in pathology and the potential fatigue of human experts, researchers and doctors have begun to benefit from the machine learning methods. The process of applying machine learning methods in medical image analysis is called medical image computation. We will introduce our work in medical image synthesis, classification, and segmentation. Complementary imaging modalities are always acquired simultaneously to indicate the disease areas, present the various tissue properties, and help to make an accurate and early diagnosis.

For a given audio dataset, can we do audio classification using Spectrogram? We'll be converting our audio files into their respective spectrograms and use spectrogram as images for our classification problem. A Spectrogram is a visual representation of the spectrum of frequencies of a signal as it varies with time. For this experiment, I'm going to use the following audio dataset from Kaggle For my experiment, I have rented a Linux virtual machine on Google Could Platform (GCP) and I'll be performing all the steps from there. Now that we have our audio data in place, let's create spectrograms for each audio file.