The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Silver nanoparticles (AgNPs) can be synthesized from a variety of techniques including physical, chemical and biological routes. They have been widely used as nanomaterials for manufacturing cosmetic and healthcare products, antimicrobial textiles, wound dressings, antitumor drug carriers, etc. due to their excellent antimicrobial properties. Accordingly, AgNPs have gained access into our daily life, and the inevitable human exposure to these nanoparticles has raised concerns about their potential hazards to the environment, health, and safety in recent years. From in vitro cell cultivation tests, AgNPs have been reported to be toxic to several human cell lines including human bronchial epithelial cells, human umbilical vein endothelial cells, red blood cells, human peripheral blood mononuclear cells, immortal human keratinocytes, liver cells, etc. AgNPs induce a dose-, size- and time-dependent cytotoxicity, particularly for those with sizes ≤10 nm. Furthermore, AgNPs can cross the brain blood barrier of mice through the circulation system on the basis of in vivo animal tests. AgNPs tend to accumulate in mice organs such as liver, spleen, kidney and brain following intravenous, intraperitoneal, and intratracheal routes of administration. In this respect, AgNPs are considered a double-edged sword that can eliminate microorganisms but induce cytotoxicity in mammalian cells. This article provides a state-of-the-art review on the synthesis of AgNPs, and their applications in antimicrobial textile fabrics, food packaging films, and wound dressings. Particular attention is paid to the bactericidal activity and cytotoxic effect in mammalian cells.

Bactericidal and Cytotoxic Properties of Silver Nanoparticles.

Solid composite electrolytes (SCEs) that combine the advantages of solid polymer electrolytes (SPEs) and inorganic ceramic electrolytes (ICEs) present acceptable ionic conductivity, high mechanical strength, and favorable interfacial contact with electrodes, which greatly improve the electrochemical performance of all-solid-state batteries compared to single SPEs and ICEs. However, there are many challenges to overcome before the practical application of SCEs, including the low ionic conductivity less than 10-3 S cm-1 at ambient temperature, poor interfacial stability, and high interfacial resistance, which greatly restrict the room temperature performance. Herein, the advances of SCEs applied in all-solid-state lithium batteries are presented, including the Li ion migration mechanism of SCEs, the strategies to enhance the ionic conductivity of SCEs by various morphologies of ICEs, and construction methods of the low resistance and stable interfaces of SCEs with both cathode and anode. Finally, some typical applications of SCEs in lithium batteries are summarized and future development directions are prospected. This work presents how it is quite significant to further enhance the ionic conductivity of SCEs by developing the novel SPEs with the special morphology of ICEs for advanced all-solid-state lithium batteries.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/advs.201903088

Progress and Perspective of Ceramic/Polymer Composite Solid Electrolytes for Lithium Batteries

Safety is the most important requirement for autonomous vehicles; hence, the ultimate challenge of designing an edge computing ecosystem for autonomous vehicles is to deliver enough computing power, redundancy, and security so as to guarantee the safety of autonomous vehicles. Specifically, autonomous driving systems are extremely complex; they tightly integrate many technologies, including sensing, localization, perception, decision making, as well as the smooth interactions with cloud platforms for high-definition (HD) map generation and data storage. These complexities impose numerous challenges for the design of autonomous driving edge computing systems. First, edge computing systems for autonomous driving need to process an enormous amount of data in real time, and often the incoming data from different sensors are highly heterogeneous. Since autonomous driving edge computing systems are mobile, they often have very strict energy consumption restrictions. Thus, it is imperative to deliver sufficient computing power with reasonable energy consumption, to guarantee the safety of autonomous vehicles, even at high speed. Second, in addition to the edge system design, vehicle-to-everything (V2X) provides redundancy for autonomous driving workloads and alleviates stringent performance and energy constraints on the edge side. With V2X, more research is required to define how vehicles cooperate with each other and the infrastructure. Last, safety cannot be guaranteed when security is compromised. Thus, protecting autonomous driving edge computing systems against attacks at different layers of the sensing and computing stack is of paramount concern. In this paper, we review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges.

Edge Computing for Autonomous Driving: Opportunities and Challenges

Hydrogen atom makes graphene magnetic Graphene has many extraordinary mechanical and electronic properties, but it's not magnetic. To make it so, the simplest strategy is to modify its electronic structure to create unpaired electrons. Researchers can do that by, for example, removing individual carbon atoms or adsorbing hydrogen onto graphene. This has to be done in a very controlled way because of a peculiarity of the graphene's crystal lattice, which consists of two sublattices. Gonzales-Herrero et al. deposited a single hydrogen atom on top of graphene and used scanning tunneling microscopy to detect magnetism on the sublattice lacking the deposited atom (see the Perspective by Hollen and Gupta). Science, this issue p. 437; see also p. 415 Scanning tunneling microscopy shows that a hydrogen atom deposited on graphene makes the complementary sublattice magnetic. [Also see Perspective by Hollen and Gupta] Isolated hydrogen atoms absorbed on graphene are predicted to induce magnetic moments. Here we demonstrate that the adsorption of a single hydrogen atom on graphene induces a magnetic moment characterized by a ~20–millielectron volt spin-split state at the Fermi energy. Our scanning tunneling microscopy (STM) experiments, complemented by first-principles calculations, show that such a spin-polarized state is essentially localized on the carbon sublattice opposite to the one where the hydrogen atom is chemisorbed. This atomically modulated spin texture, which extends several nanometers away from the hydrogen atom, drives the direct coupling between the magnetic moments at unusually long distances. By using the STM tip to manipulate hydrogen atoms with atomic precision, it is possible to tailor the magnetism of selected graphene regions.

/pdf/atomic-scale-control-of-graphene-magnetism-by-using-hydrogen-53d9rq6gri.pdf

Atomic-scale control of graphene magnetism by using hydrogen atoms

Data Encryption/Decryption has become an essential part of pervasive computing systems. However, executing these cryptographic algorithms often introduces a high overhead. In this paper, we select nine widely used cryptographic algorithms to improve their performance by providing hardware-assisted solutions. For each algorithm, we identify the software performance bottleneck, i.e., those “hotspot functions” or “hot-blocks” which consume a substantial portion of the overall execution time. Then, based on the percentage of execution time of a specific function and its relationship with the overall algorithm, we select candidates for our hardware acceleration. We design our hardware accelerators of the chosen candidates. The results show that our implementations achieve speedups as high as 60 folds for specific functions and 5.4 for the overall algorithm compared with the performance of the software-only implementation. Through the associated hardware cost analysis, we point to an opportunity to perform these functions in an SIMD fashion.

/pdf/hardware-acceleration-for-cryptography-algorithms-by-hotspot-1siq03b4i6.pdf

Hardware Acceleration for Cryptography Algorithms by Hotspot Detection

An integrate and ultra-flexible solid-state lithium battery enabled by in situ polymerized solid electrolyte

A three-phase approach to differentially private crucial patterns mining over data streams

Anodic self-assembly method for synthesizing hierarchical FeS/FeOx hollow nanospheres

Autonomous driving is not one single technology but rather a complex system integrating many technologies, which means that teaching autonomous driving is a challenging task. Indeed, most existing autonomous driving classes focus on one of the technologies involved. This not only fails to provide a comprehensive coverage, but also sets a high entry barrier for students with different technology backgrounds. In this paper, we present a modular, integrated approach to teaching autonomous driving. Specifically, we organize the technologies used in autonomous driving into modules. This is described in the textbook we have developed as well as a series of multimedia online lectures designed to provide technical overview for each module. Then, once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other. To verify this teaching approach, we present three case studies: an introductory class on autonomous driving for students with only a basic technology background; a new session in an existing embedded systems class to demonstrate how embedded system technologies can be applied to autonomous driving; and an industry professional training session to quickly bring up experienced engineers to work in autonomous driving. The results show that students can maintain a high interest level and make great progress by starting with familiar concepts before moving onto other modules.

Chen Liu

Papers

Hardware Acceleration for Cryptography Algorithms by Hotspot Detection

An integrate and ultra-flexible solid-state lithium battery enabled by in situ polymerized solid electrolyte

A three-phase approach to differentially private crucial patterns mining over data streams

Anodic self-assembly method for synthesizing hierarchical FeS/FeOx hollow nanospheres

Teaching Autonomous Driving Using a Modular and Integrated Approach