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Deep learning (DL) has significantly transformed the field of computational imaging, offering powerful solutions to enhance performance and address a variety of challenges. Traditional methods often rely on discrete pixel representations, which limit resolution and fail to capture the continuous and multiscale nature of physical objects. Recent research from Boston University (BU) presents a novel approach to overcome these limitations.
As reported in Advanced Photonics Nexus, researchers from BU’s Computational Imaging Systems Lab have introduced a local conditional neural field (LCNF) network, which they use to address the problem. Their scalable and generalizable LCNF system is known as “neural phase retrieval”—” NeuPh” for short.
NeuPh leverages advanced DL techniques to reconstruct high-resolution phase information from low-resolution measurements. This method employs a convolutional neural network (CNN)-based encoder to compress captured images into a compact latent-space representation.
Microelectromechanical systems (MEMS) are tiny devices that integrate various components, such as miniature sensors, electronics and actuators, onto a single chip. These small devices have proved highly promising for precisely detecting biological signals, acceleration, force and other measurements.
Most of the MEMS developed to date are made of silicon and silicon nitride. While some of these devices have achieved promising results, their material composition and design limit their sensitivity and versatility, for instance limiting their use in wet environments.
In a recent Nature Electronics paper, researchers at Ecole Polytechnique Fédérale de Lausanne (EPFL) introduced an innovative cantilever design for MEMS based on a polymer, a semiconductor and ceramic. Cantilevers are tiny flexible beams that can adapt their shape in response to external forces or molecular interactions, thus potentially serving as sensors or actuators.
Researchers at the Department of Instrumentation and Applied Physics (IAP), Indian Institute of Science (IISc) and collaborators have designed a new supercapacitor that can be charged by light shining on it. Such supercapacitors can be used in various devices, including streetlights and self-powered electronic devices such as sensors.
Capacitors are electrostatic devices that store energy as charges on two metal plates called electrodes. Supercapacitors are upgraded versions of capacitors—they exploit electrochemical phenomena to store more energy, explains Abha Misra, Professor at IAP and corresponding author of the study published in the Journal of Materials Chemistry A.
The electrodes of the new supercapacitor were made of zinc oxide (ZnO) nanorods grown directly on fluorine-doped tin oxide (FTO), which is transparent. It was synthesized by Pankaj Singh Chauhan, first author and CV Raman postdoctoral fellow in Misra’s group at IISc.
A wearable ‘smart’ choker for speech recognition has the potential to redefine the field of silent speech interface (SSI), say researchers—thanks to embedded ultrasensitive textile strain sensor technology.
Where verbal communication is hindered, such as in locations with lots of background noise or where an individual has an existing speech impairment, SSI systems are a cutting-edge solution, enabling verbal communication without vocalization. As such, it is a type of electronic lip-reading using human-computer interaction.
In new research, led by the University of Cambridge, an overlying structured graphene layer is applied to an integrated textile strain sensor for robust speech recognition performance, even in noisy environments.
The 2011 accident at the Fukushima-Daiichi plant in Japan inspired extensive research and analysis that elevated nuclear energy into a standard bearer for safety. It also inspired a number of studies at the U.S. Department of Energy’s (DOE) Argonne National Laboratory. Scientists want to look more closely at nuclear fuel materials to better understand how they will behave at extremely high temperatures.
Researchers have developed a new way to see organs within a body by rendering overlying tissues transparent to visible light. The counterintuitive process—a topical application of food-safe dye—was reversible in tests with animal subjects, and may ultimately apply to a wide range of medical diagnostics, from locating injuries to monitoring digestive disorders to identifying cancers.
In the case of NiPS3, the researchers observed an intermediate symmetry breaking which leads to a vestigial order. Just as the term “vestigial” refers to the retention of certain traits during the process of evolution, the vestigial order here can also be viewed as the retention during the process of symmetry breaking.
This happens when the primary magnetic long-range order state melts or breaks down into a simpler form, in the NiPS3 case, a 2D vestigial order state (known as Z3 Potts-nematicity), as the material is thinned. Unlike conventional symmetry breaking, which involves the breaking of all symmetries, vestigial order only involves the breaking of some symmetries.
While there are numerous examples from a theoretical standpoint, experimental realizations of vestigial order have remained challenging. However, the investigation of this 2D magnetic material has shed the first light on this issue, demonstrating that such a phenomenon can be observed through dimension crossover.
A better understanding of the inner workings of neutron stars will lead to a greater knowledge of the dynamics that underpin the workings of the universe and also could help drive future technology, said the University of Illinois Urbana-Champaign physics professor Nicolas Yunes. A new study led by Yunes details how new insights into how dissipative tidal forces within double—or binary—neutron star systems will inform our understanding of the universe.
Pain is a complex, multifaceted experience shaped by various factors beyond physical sensation, such as a person’s mindset and their expectations of pain. The placebo effect, the tendency for a person’s symptoms to improve in response to inactive treatment, is a well-known example of how expectations can significantly alter a person’s experience. Mindfulness meditation, which has been used for pain management in various cultures for centuries, has long been thought to work by activating the placebo response. However, scientists have now shown that this is not the case.
A new study, published in Biological Psychiatry, has revealed that mindfulness meditation engages distinct brain mechanisms to reduce pain compared to those of the placebo response. The study, conducted by researchers at University of California San Diego School of Medicine, used advanced brain imaging techniques to compare the pain-reducing effects of mindfulness meditation, a placebo cream and a “sham” mindfulness meditation in healthy participants.
The study found that mindfulness meditation produced significant reductions in pain intensity and pain unpleasantness ratings, and also reduced brain activity patterns associated with pain and negative emotions. In contrast, the placebo cream only reduced the brain activity pattern associated with the placebo effect, without affecting the person’s underlying experience of pain.
