Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
389,642
result(s) for
"Computer peripherals"
Sort by:
To Be: Discovering Malicious USB Peripherals through Neural Network-Driven Power Analysis
Nowadays, The Universal Serial Bus (USB) is one of the most adopted communication standards. However, the ubiquity of this technology has attracted the interest of attackers. This situation is alarming, considering that the USB protocol has penetrated even into critical infrastructures. Unfortunately, the majority of the contemporary security detection and prevention mechanisms against USB-specific attacks work at the application layer of the USB protocol stack and, therefore, can only provide partial protection, assuming that the host is not itself compromised. Toward this end, we propose a USB authentication system designed to identify (and possibly block) heterogeneous USB-based attacks directly from the physical layer. Empirical observations demonstrate that any extraneous/malicious activity initiated by malicious/compromised USB peripherals tends to consume additional electrical power. Driven by this observation, our proposed solution is based on the analysis of the USB power consumption patterns. Valuable power readings can easily be obtained directly by the power lines of the USB connector with low-cost, off-the-shelf equipment. Our experiments demonstrate the ability to effectively distinguish benign from malicious USB devices, as well as USB peripherals from each other, relying on the power side channel. At the core of our analysis lies an Autoencoder model that handles the feature extraction process; this process is paired with a long short-term memory (LSTM) and a convolutional neural network (CNN) model for detecting malicious peripherals. We meticulously evaluated the effectiveness of our approach and compared its effectiveness against various other shallow machine learning (ML) methods. The results indicate that the proposed scheme can identify USB devices as benign or malicious/counterfeit with a perfect F1-score.
Journal Article
Today’s computing challenges: opportunities for computer hardware design
Due to the explosive increase of digital data creation, demand on advancement of computing capability is ever increasing. However, the legacy approaches that we have used for continuous improvement of three elements of computer (process, memory, and interconnect) have started facing their limits, and therefore are not as effective as they used to be and are also expected to reach the end in the near future. Evidently, it is a large challenge for computer hardware industry. However, at the same time it also provides great opportunities for the hardware design industry to develop novel technologies and to take leadership away from incumbents. This paper reviews the technical challenges that today’s computing systems are facing and introduces potential directions for continuous advancement of computing capability, and discusses where computer hardware designers find good opportunities to contribute.
Journal Article
Protein machine: This 3D printer makes plant-based beef
Attendees at at a tech conference in Barcelona on March 5 were able to try 3D-printed shredded beef made by the Spain-based company Novameat.
Streaming Video
Heterointerface Engineered Core-Shell Fesub.2Osub.3@TiOsub.2 for High-Performance Lithium-Ion Storage
The rational design of the heterogeneous interfaces enables precise adjustment of the electronic structure and optimization of the kinetics for electron/ion migration in energy storage materials. In this work, the built-in electric field is introduced to the iron-based anode material (Fe[sub.2]O[sub.3]@TiO[sub.2]) through the well-designed heterostructure. This model serves as an ideal platform for comprehending the atomic-level optimization of electron transfer in advanced lithium-ion batteries (LIBs). As a result, the core-shell Fe[sub.2]O[sub.3]@TiO[sub.2] delivers a remarkable discharge capacity of 1342 mAh g[sup.−1] and an extraordinary capacity retention of 82.7% at 0.1 A g[sup.−1] after 300 cycles. Fe[sub.2]O[sub.3]@TiO[sub.2] shows an excellent rate performance from 0.1 A g[sup.−1] to 4.0 A g[sup.−1]. Further, the discharge capacity of Fe[sub.2]O[sub.3]@TiO[sub.2] reached 736 mAh g[sup.−1] at 1.0 A g[sup.−1] after 2000 cycles, and the corresponding capacity retention is 83.62%. The heterostructure forms a conventional p-n junction, successfully constructing the built-in electric field and lithium-ion reservoir. The kinetic analysis demonstrates that Fe[sub.2]O[sub.3]@TiO[sub.2] displays high pseudocapacitance behavior (77.8%) and fast lithium-ion reaction kinetics. The capability of heterointerface engineering to optimize electrochemical reaction kinetics offers novel insights for constructing high-performance iron-based anodes for LIBs.
Journal Article
Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN
The intelligent crack detection method is an important guarantee for the realization of intelligent operation and maintenance, and it is of great significance to traffic safety. In recent years, the recognition of road pavement cracks based on computer vision has attracted increasing attention. With the technological breakthroughs of general deep learning algorithms in recent years, detection algorithms based on deep learning and convolutional neural networks have achieved better results in the field of crack recognition. In this paper, deep learning is investigated to intelligently detect road cracks, and Faster R-CNN and Mask R-CNN are compared and analyzed. The results show that the joint training strategy is very effective, and we are able to ensure that both Faster R-CNN and Mask R-CNN complete the crack detection task when trained with only 130+ images and can outperform YOLOv3. However, the joint training strategy causes a degradation in the effectiveness of the bounding box detected by Mask R-CNN.
Journal Article
Secure, privacy-preserving and federated machine learning in medical imaging
The broad application of artificial intelligence techniques in medicine is currently hindered by limited dataset availability for algorithm training and validation, due to the absence of standardized electronic medical records, and strict legal and ethical requirements to protect patient privacy. In medical imaging, harmonized data exchange formats such as Digital Imaging and Communication in Medicine and electronic data storage are the standard, partially addressing the first issue, but the requirements for privacy preservation are equally strict. To prevent patient privacy compromise while promoting scientific research on large datasets that aims to improve patient care, the implementation of technical solutions to simultaneously address the demands for data protection and utilization is mandatory. Here we present an overview of current and next-generation methods for federated, secure and privacy-preserving artificial intelligence with a focus on medical imaging applications, alongside potential attack vectors and future prospects in medical imaging and beyond.
Medical imaging data is often subject to privacy and intellectual property restrictions. AI techniques can help out by offering tools like federated learning to bridge the gap between personal data protection and data utilisation for research and clinical routine, but these tools need to be secure.
Journal Article
cisTEM, user-friendly software for single-particle image processing
We have developed new open-source software called cisTEM (computational imaging system for transmission electron microscopy) for the processing of data for high-resolution electron cryo-microscopy and single-particle averaging. cisTEM features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full processing pipeline including movie processing, image defocus determination, automatic particle picking, 2D classification, ab-initio 3D map generation from random parameters, 3D classification, and high-resolution refinement and reconstruction. Some of these steps implement newly-developed algorithms; others were adapted from previously published algorithms. The software is optimized to enable processing of typical datasets (2000 micrographs, 200 k – 300 k particles) on a high-end, CPU-based workstation in half a day or less, comparable to GPU-accelerated processing. Jobs can also be scheduled on large computer clusters using flexible run profiles that can be adapted for most computing environments. cisTEM is available for download from cistem.org.
Journal Article
Additive Manufacturing: A Comprehensive Review
Additive manufacturing has revolutionized manufacturing across a spectrum of industries by enabling the production of complex geometries with unparalleled customization and reduced waste. Beginning as a rapid prototyping tool, additive manufacturing has matured into a comprehensive manufacturing solution, embracing a wide range of materials, such as polymers, metals, ceramics, and composites. This paper delves into the workflow of additive manufacturing, encompassing design, modeling, slicing, printing, and post-processing. Various additive manufacturing technologies are explored, including material extrusion, VAT polymerization, material jetting, binder jetting, selective laser sintering, selective laser melting, direct metal laser sintering, electron beam melting, multi-jet fusion, direct energy deposition, carbon fiber reinforced, laminated object manufacturing, and more, discussing their principles, advantages, disadvantages, material compatibilities, applications, and developing trends. Additionally, the future of additive manufacturing is projected, highlighting potential advancements in 3D bioprinting, 3D food printing, large-scale 3D printing, 4D printing, and AI-based additive manufacturing. This comprehensive survey aims to underscore the transformative impact of additive manufacturing on global manufacturing, emphasizing ongoing challenges and the promising horizon of innovations that could further elevate its role in the manufacturing revolution.
Journal Article