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Earlier sources: Hardware
Hardware is the physical foundation of computing: chips, sensors, devices, networks, and the processes that manufacture them. Follow semiconductor research, compute architectures, and engineering developments alongside their software and energy connections.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗533 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- NASA · Source date · First archived
Under the Microscope: NASA-Made Material for Moon Manufacturing ↗
Credit: NASA Credit: NASA Photos taken using a microscope reveal the colorful, kaleidoscope-like crystal structure of a new NASA-made material that could be used for manufacturing during future space missions. Developed at NASA’s Glenn Research Center in Cleveland, the material could be created directly on the Moon or Mars, allowing NASA to pack fewer supplies — saving weight and reducing launch costs. To make the material, research chemical engineer Allison Christy and her team of summer interns at NASA Glenn — Tyler Klinchuch, Ethan Bilodeau, and Emma Levenson — mixed a special plastic with…
- GitHub · Repository created · First archived
jdmarshall95/atenboro-nav ↗
DIY мотонавигатор / motorcycle bike OLED HUD: 2ГИС turn-by-turn → ESP8266 SSD1306. Phone in pocket, open-source Android bridge. MIT.
- OpenAlex · Published · First archived
Regression Model for a Sensor‐Integrating Jaw Coupling—Compensation for the Viscoelastic Material Behavior ↗
ABSTRACT In recent years, there has been an increasing demand to digitize production processes. In order to obtain the necessary data for these processes, numerous sensors must be installed on or in the machines. This can be difficult, especially when done retroactively, as the existing installation space must be able to accommodate the sensors. To address this issue, sensor‐integrating machine elements are currently being developed. In these machine elements, the sensors are integrated directly into the respective element, and thus enabling a space‐neutral installation. In the current work a…
- OpenAlex · Published · First archived
A comprehensive review of materials and process parameters in Wire Arc Additive Manufacturing (WAAM): Current insights and future directions ↗
Wire Arc Additive Manufacturing (WAAM) has emerged as a transformative metal additive manufacturing technology, capable of fabricating large-scale, geometrically complex metallic components at high deposition rates and relatively low cost. This article presents a systematic and critical review of recent WAAM research, covering five principal material systems, alloy steels, stainless steels, aluminum alloys, titanium alloys, and nickel-based superalloys, alongside a comparative analysis of key process variants including Gas Metal Arc Welding (GMAW), Gas Tungsten Arc Welding (GTAW), Plasma Arc W…
- OpenAlex · Published · First archived
Geometry-aware deep reinforcement learning for real-time integrated scheduling and nesting in additive manufacturing ↗
Additive Manufacturing (AM) is a promising technology for small-volume and customized production. However, the diversity of parts, coupled with batch scheduling and geometric constraints on heterogeneous machines, poses significant challenges for production planning. Unlike most existing studies on AM scheduling that underestimate geometric features, this paper investigates a real-time integrated scheduling and nesting problem with dynamic batch processing and geometric feasibility to minimize the overall makespan across heterogeneous AM machines. We propose GeoPPO, a geometry-aware deep reinf…
- OpenAlex · Published · First archived
An Efficient Binary-Field ECC Hardware Design for an SM2-Style Digital Signature Flow ↗
We present a binary-field ECC hardware benchmark for an SM2-style signing flow, rather than a standard-compliant implementation of the 256-bit prime-field SM2 parameters. The design uses the NIST K-233 curve over GF(2233), a Karatsuba multiplier, and a Montgomery ladder in López–Dahab projective coordinates. Parallel scheduling reduces the point-addition and point-doubling critical paths from 4M+S+2A and 2M+3S+A to 2M+S+2A and M+2S+A. Implemented on a Virtex-7 VC707 at 300 MHz, the standalone point-multiplication core uses 32,720 LUTs and 46,830 flip-flops and completes its start-to-done opera…
- OpenAlex · Published · First archived
Post-measurement readout and class-resolved hardware sensitivity of a variational quantum classifier on two IBM Heron r2 backends ↗
Variational quantum classifiers (VQCs) are increasingly evaluated on real quantum processors, yet their deployment behaviour can depend on post-measurement readout and variation across independently trained models. We study a four-qubit VQC for three-class traffic-state classification on two IBM Heron r2 backends. In a single-seed comparison, a four-channel linear-projection readout achieves higher clean balanced accuracy than a three-channel direct-argmax readout (94.4% versus 78.9%), whereas their hardware recoveries on ibm_kingston are 53.0% and 94.4%, respectively. A matched synthetic-nois…
- OpenAlex · Published · First archived
Contrastive Gradient-Flow Interpretation (CGFI): A Composable Operator for Class-Discriminative Explanation of Convolutional Neural Networks ↗
Growing deployment of Convolutional Neural Networks in safety-critical domains has intensified the need for transparent, discriminative explanations, yet gradient-based methods such as Grad-CAM compute explanations for a single class score, producing saliency maps that activate regions shared with competing alternatives. This limitation is particularly consequential in multi-class and fine-grained recognition settings, where non-discriminative explanations reduce the practical utility of the method and impede reliable model auditing. This research study contributes to the body of knowledge by…
- OpenAlex · Published · First archived
Potentials and limitations of RoboSkin: a MEMS pressure sensor based tactile sensing solution ↗
Abstract Tactile sensing has become a key enabling technology for robotic manipulation by providing robots with information about physical interactions, including contact, force, slip, and object properties. Among the various tactile sensing approaches, RoboSkin employs commercially available MEMS pressure sensors embedded within compliant elastomeric structures to realize compact, robust, and scalable tactile sensing systems. By combining low-cost hardware with customizable mechanical transduction layers, RoboSkin offers a versatile platform for applications ranging from dexterous manipulatio…
- OpenAlex · Published · First archived
UAV-Induced Measurement Bias in Air Pollution Sensing: A Physics-Informed Framework for Characterization, Sensor Placement, and Correction ↗
Unmanned aerial vehicles (UAVs) offer flexible, three-dimensional access for air pollution monitoring, but rotor-induced aerodynamic disturbance can bias onboard sensor readings, an effect not yet well characterized for multi-rotor platforms. This paper presents a physics-informed framework unifying rotor wake characterization, turbulence zone classification, pollutant sensitivity analysis, sensor placement strategy, and bias correction into one design tool. Grounded in actuator disk momentum theory and literature-constrained relationships, it predicts or bounds measurement bias across nine po…
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How to assess evidence about Hardware
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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