Though neuromorphic computers have typically targeted applications in machine learning and neuroscience ('cognitive' applications), they have many computational characteristics that are attractive for a wide variety of computational problems. In this work, we review the current state-of-the-art for non-cognitive applications on neuromorphic computers, including simple computational kernels for composition, graph algorithms, constrained optimization, and signal processing. We discuss the advantages of using neuromorphic computers for these different applications, as well as the challenges that still remain. The ultimate goal of this work is to bring awareness to this class of problems for neuromorphic systems to the broader community, particularly to encourage further work in this area and to make sure that these applications are considered in the design of future neuromorphic systems.

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.

Magnetic field sensors are an integral part of many industrial and biomedical applications, and their utilization continues to grow at a high rate. The development is driven both by new use cases and demand like internet of things as well as by new technologies and capabilities like flexible and stretchable devices. Magnetic field sensors exploit different physical principles for their operation, resulting in different specifications with respect to sensitivity, linearity, field range, power consumption, costs etc. In this review, we will focus on solid state magnetic field sensors that enable miniaturization and are suitable for integrated approaches to satisfy the needs of growing application areas like biosensors, ubiquitous sensor networks, wearables, smart things etc. Such applications require a high sensitivity, low power consumption, flexible substrates and miniaturization. Hence, the sensor types covered in this review are Hall Effect, Giant Magnetoresistance, Tunnel Magnetoresistance, Anisotropic Magnetoresistance and Giant Magnetoimpedance.

The objective of this research paper is to design, simulate and compare components of a race car braking system. The Racecar is an FSAE car that is designed around the rules and regulations of the FSAE rulebook, the main aim of this project is to make components lightweight and improve their performance as compared to their OEM counterparts. The braking system involves the mathematical calculation of pedal ratio, brake torque, heat generated in brake discs, and required clamping force using MATLAB to achieve peak deceleration after which these values would be used to design and simulate the components. The paper presents some innovative new ideas applied in an FSAE car and also involves designing techniques like topology optimization which was done using Altair inspire. Finally, all the components were designed in Solidworks, and simulations like Factor of safety, von mises stress, and strain were performed using ANSYS 18.1. The paper also compares the work of other authors as well and explains the differentiating factors between our and their design.

Additive manufacturing (AM) is an emerging customized three-dimensional (3D) functional product fabrication technology. It provides a higher degree of design freedom, reduces manufacturing steps, cost and production cycles. However, existing metallic component 3D printing techniques are mainly for the manufacture of single material components. With the increasing commercial applications of AM technologies, the need for 3D printing of more than one type of dissimilar materials in a single component increases. Therefore, investigations on multi-material AM (MMAM) emerge over the past decade. Lasers are currently widely used for the AM of metallic components where high temperatures are involved. Here we report the progress and trend in laser-based macro- and micro-scale AM of multiple metallic components. The methods covered in this paper include laser powder bed fusion, laser powder directed energy deposition, and laser-induced forward transfer for MMAM applications. The principles and process/material characteristics are described. Potential applications and challenges are discussed. Finally, future research directions and prospects are proposed.

The growing demand for electronic devices, smart devices, and the Internet of Things constitutes the primary driving force for marching down the path of decreased critical dimension and increased circuit intricacy of integrated circuits. However, as sub-10 nm high-volume manufacturing is becoming the mainstream, there is greater awareness that defects introduced by original equipment manufacturer components impact yield and manufacturing costs. The identification, positioning, and classification of these defects, including random particles and systematic defects, are becoming more and more challenging at the 10 nm node and beyond. Very recently, the combination of conventional optical defect inspection with emerging techniques such as nanophotonics, optical vortices, computational imaging, quantitative phase imaging, and deep learning is giving the field a new possibility. Hence, it is extremely necessary to make a thorough review for disclosing new perspectives and exciting trends, on the foundation of former great reviews in the field of defect inspection methods. In this article, we give a comprehensive review of the emerging topics in the past decade with a focus on three specific areas: (a) the defect detectability evaluation, (b) the diverse optical inspection systems, and (c) the post-processing algorithms. We hope, this work can be of importance to both new entrants in the field and people who are seeking to use it in interdisciplinary work.

Surface texturing has gained significant attention over the last 30 years to tailor friction and wear under various tribological conditions in fundamental and applied tribological systems. Under dry conditions, surface textures help to improve friction or wear by reducing adhesion and the real area of contact as well as trapping wear particles. However, especially under high load conditions, surface textures rapidly wear away, thus losing their friction- and wear-reducing capability. A potential strategy to improve their durability under more severe conditions is the combination with protective solid lubricant coatings. In this regard, MXene nano-sheets are the most recent success story related to 2D materials as solid lubricant coatings. They appear particularly interesting due to their ability to generate low-friction and wear-resistant tribo-films thus providing an excellent durability and wear resistance. This aspect makes the combination of MXene solid lubricant coatings and surface textures highly prospective. Therefore, this perspective aims at summarizing and analyzing the existing state-of-the art related to the combined use of surface textures and MXene coatings.

Energy harvesting technologies have been explored by researchers for more than two decades as an alternative to conventional power sources (e.g. batteries) for small-sized and low-power electronic devices. The limited life-time and necessity for periodic recharging or replacement of batteries has been a consistent issue in portable, remote, and implantable devices. Ambient energy can usually be found in the form of solar energy, thermal energy, and vibration energy. Amongst these energy sources, vibration energy presents a persistent presence in nature and manmade structures. Various materials and transduction mechanisms have the ability to convert vibratory energy to useful electrical energy, such as piezoelectric, electromagnetic, and electrostatic generators. Piezoelectric transducers, with their inherent electromechanical coupling and high power density compared to electromagnetic and electrostatic transducers, have been widely explored to generate power from vibration energy sources. A topical review of piezoelectric energy harvesting methods was carried out and published in this journal by the authors in 2007. Since 2007, countless researchers have introduced novel materials, transduction mechanisms, electrical circuits, and analytical models to improve various aspects of piezoelectric energy harvesting devices. Additionally, many researchers have also reported novel applications of piezoelectric energy harvesting technology in the past decade. While the body of literature in the field of piezoelectric energy harvesting has grown significantly since 2007, this paper presents an update to the authors' previous review paper by summarizing the notable developments in the field of piezoelectric energy harvesting through the past decade.

The performance of seismic exploration instruments directly affects the quality of acquired seismic data as well as the efficiency of seismic survey operation. Consequently, they play a pivotal role in oil/gas and mineral resource exploration. Compared with traditional cabled seismic acquisition systems, nodal seismic acquisition systems have the advantages of light weight, small size, low capital and operational cost, reduced health safety and environment risk and strong adaptability to complex terrain environment. Therefore, they have been widely used in seismic exploration and have shown a trend of accelerated development. The major manufacturers have carried out research and development of nodal instruments, and various types of nodal seismographs have appeared. Based on the investigation of influential nodal seismographs, we summarize the research status of nodal seismographs. Based on different wireless monitoring capabilities and data harvesting modes, we classify the nodal seismographs into a shoot-blind system, semi-blind system, real-time system and enhanced real-time system. We discuss structural principles and key technologies of the four types of nodal seismographs, analyze their characteristics and predict their future development directions. Focusing on node data quality monitoring, we discuss the application of communication technologies, such as Bluetooth, Wi-Fi, ZigBee, Long Term Evolution, and satellites in nodal seismographs in detail. Furthermore, we analyze and evaluate three main networking architectures including planar multi-hop networks, hierarchical cluster networks and hybrid networks, and sum up the research progress of wireless routing algorithms and large-scale seismic data real-time harvesting methods. Finally, the latest applications of nodal seismographs in energy and mineral resource exploration, geological environment monitoring, urban subsurface space survey and novel seismic technologies are covered. As research on the application of micro-electro-mechanical systems technology, cloud computing, 5G, Internet of Things, edge computing, machine learning and robotics in nodal systems deepens, the performance of nodal seismographs will be greatly promoted.