Lifeboat Foundation

Don’t worry, Betelguese is still there.

In a new report published on Scientific Reports, Milan M. Milošević and an international research team at the Zepler Institute for Photonics and Nanoelectronics, Etaphase Incorporated and the Departments of Chemistry, Physics and Astronomy, in the U.S. and the U.K. Introduced a hyperuniform-disordered platform to realize near-infrared (NIR) photonic devices to create, detect and manipulate light. They built the device on a silicon-on-insulator (SOI) platform to demonstrate the functionality of the structures in a flexible, silicon-integrated circuit unconstrained by crystalline symmetries. The scientists reported results for passive device elements, including waveguides and resonators seamlessly integrated with conventional silicon-on-insulator strip waveguides and vertical couplers. The hyperuniform-disordered platform improved compactness and enhanced energy efficiency as well as temperature stability, compared to silicon photonic devices fabricated on rib and strip waveguides.

Academic and commercial efforts worldwide in the field of silicon photonics have led to engineer optical data communications at the Terabit-scale at increasingly lower costs to meet the rapidly growing demand in data centers. Explosive growth in cloud computing and entertainment-on-demand pose increasingly challenging costs and energy requirements for data transmission, processing and storage. Optical interconnects can replace traditional copper-based solutions to offer steadily increasing potential to minimize latency and power consumption, while maximizing the bandwidth and reliability of the devices. Silicon photonics also leverage large-scale, complementary metal-oxide semiconductor (CMOS) manufacturing processes to produce high-performance optical transceivers with high yield at low-cost. The properties allow applications of optical transceivers (fiber optical technology to send and receive data) to be increasingly compelling across shorter distances.

More than three decades ago, physicist Richard Soref identified silicon as a promising material for photonic integration. Leading to the present-day steady development and rapid production of increasingly complex photonic integrated circuits (PICs). Researchers can integrate large numbers of massively-parallel compact energy-efficient optical components on a single chip for cloud computing applications from deep learning to artificial intelligence and the internet of things. Compared to the limited scope of commercial silicon photonic systems, photonic crystal (PhC) architectures promise smaller device sizes, although they are withheld by layout constraints imposed by waveguide requirements along the photonic crystal’s axis. Until recently, photonic band gap (PBG) structures that efficiently guide light were limited to photonic crystal platforms. Now, newer classes of PBG structures include photonic quasicrystals, hyperuniform disordered solids (HUDs) and local self-uniform structures.

The mystery of why quantum matter jumps from a wave-like state to a well-defined particle when it is observed has puzzled scientists for nearly a 100 years.

Known as ‘the measurement problem’ it is widely seen as the major complication in quantum theory and has led even well-respected scientists to suggest that the human mind may be having some kind of telepathic influence on the fabric of the universe — our thoughts actually shaping reality around us.

But physicist Jonathan Kerr, who has studied quantum mechanics for 35 years from his cottage in Surrey, believes he has solved the riddle, and the answer is more prosaic than some might have hoped.

Analog machine learning hardware offers a promising alternative to digital counterparts as a more energy efficient and faster platform. Wave physics based on acoustics and optics is a natural candidate to build analog processors for time-varying signals. In a new report on Science Advances Tyler W. Hughes and a research team in the departments of Applied Physics and Electrical Engineering at Stanford University, California, identified mapping between the dynamics of wave physics and computation in recurrent neural networks.

The map indicated the possibility of training physical wave systems to learn complex features in temporal data using standard training techniques used for neural networks. As proof of principle, they demonstrated an inverse-designed, inhomogeneous medium to perform English vowel classification based on raw audio signals as their waveforms scattered and propagated through it. The scientists achieved performance comparable to a standard digital implementation of a recurrent neural network. The findings will pave the way for a new class of analog machine learning platforms for fast and efficient information processing within its native domain.

The recurrent neural network (RNN) is an important machine learning model widely used to perform tasks including natural language processing and time series prediction. The team trained wave-based physical systems to function as an RNN and passively process signals and information in their native domain without analog-to-digital conversion. The work resulted in a substantial gain in speed and reduced power consumption. In the present framework, instead of implementing circuits to deliberately route signals back to the input, the recurrence relationship occurred naturally in the time dynamics of the physics itself. The device provided the memory capacity for information processing based on the waves as they propagated through space.

Physicists Neil Turok and Sabine Hossenfelder are among those who worry that physics is in a funk, in part because of the love of “beautiful” mathematics.

In 1900, so the story goes, prominent physicist Lord Kelvin addressed the British Association for the Advancement of Science with these words: “There is nothing new to be discovered in physics now.”

How wrong he was. The following century completely turned physics on its head. A huge number of theoretical and experimental discoveries have transformed our understanding of the universe, and our place within it.

Don’t expect the next century to be any different. The universe has many mysteries that still remain to be uncovered – and new technologies will help us to solve them over the next 50 years.

Scientists at the Faculty of Physics and Engineering, working with the Tomsk company Scientific and Production Center Chemical Technologies, have created and tested an improved model of a hybrid rocket engine. The team synthesized new fuel components that increased its calorie content, and therefore its efficiency.

The development emerged from a project to improve the design of a solid–fuel hybrid rocket engine and the fuel used in such engines. The scientists mathematically modeled an optimized engine and made fuel compositions based on aluminum diboride and dodecaboride. This is one of the most promising areas increasing fuel efficiency.

Rocket fuel with the addition of the components proposed by TSU specialists is distinguished by the highest calorific value, which characterizes fuel efficiency. Alexander Zhukov, professor at the Department of Mathematical Physics says that boron is the highest-energy solid component known today, but directly introducing it into the fuel is inefficient because a dense oxide film forms, leading to a high degree of burning out. But in combination with aluminum, boron burns well and increases energy.

Physics has thrived over the last 10 years through some remarkable breakthroughs — and more excitement lies in store as a new decade dawns.

It’s no secret that the average smart phone today packs an abundance of gadgets fitting in your pocket, which could have easily filled a car trunk a few decades ago. We like to think about video cameras, music playing equipment, and maybe even telephones here, but let’s not ignore the amount of measurement equipment we also carry around in form of tiny sensors nowadays. How to use those sensors for educational purposes to teach physics is presented in [Sebastian Staacks]’ talk at 36C3 about the phyphox mobile lab app.

While accessing a mobile device’s sensor data is usually quite straightforwardly done through some API calls, the phyphox app is not only a shortcut to nicely graph all the available sensor data on the screen, it also exports the data for additional visualization and processing later on. An accompanying experiment editor allows to define custom experiments from data capture to analysis that are stored in an XML-based file format and possible to share through QR codes.

Continue reading “36C3: Phyphox – Using Smartphone Sensors For Physics Experiments” »