Mobile and embedded system software designer are often torn between choosing security and functionality. In particular, the security of out-of-band execution environment is sensitive to rich functionality. ARM TrustZone has been used to develop a Trusted Execution Environment (TEE), which runs in parallel with rich functionality commodity OS and provides an isolated and tamper-resistant execution context for trusted applications. ARM TrustZone splits access of the processor, memory and peripherals into two different worlds, namely normal world and secure world. The secure world is more privileged and the recommended context to implement TEE. However, despite the security of TrustZone TEE, the functionality is very limited.
Teachers are the main force in implementing curriculum. Only after the comprehension and absorption d the essence of the curriculum can the effective teaching behavior be put into effect. In the reform in PE curriculum, teachers are necessarily required to partake the practice d education and teaching as a self - reflective PE teacher, who is able to reflect his practice. In order to meet the need of implementing the new curriculum, the paper discusses the ways of becoming self - reflective PE teacher from the perspective of the connotation and strategy of self -reflective teacher, by using the method of documentary and logical analysis.
This paper considers and validates the applicability of leveraging pervasively-available performance counters for detecting and reasoning about security breaches. Our key observation is that many security breaches, which typically cause abnormal control flow, usually incur precisely identifiable deviation in performance samples captured by processors. Based on this observation, we implement a prototype system called Eunomia, which is the first non-intrusive system that can detect emerging attacks based on return-oriented programming without any changes to applications (either source or binary code) or special-purpose hardware. Our security evaluation shows that Eunomia can detect some realistic attacks including code-injection attacks, return-to-libc attacks and return-oriented programming attacks on unmodified binaries with relatively low overhead.
Current control flow integrity (CFI) enforcement approaches either require instrumenting application executables and even shared libraries, or are unable to defend against sophisticated attacks due to relaxed security policies, or both; many of them also incur high runtime overhead. This paper observes that the main obstacle of providing transparent and strong defense against sophisticated adversaries is the lack of sufficient runtime control flow information. To this end, this paper describes FlowGuard, a lightweight, transparent CFI enforcement approach by a novel reuse of Intel Processor Trace (IPT), a recent hardware feature that efficiently captures the entire runtime control flow. The main challenge is that IPT is designed for offline performance analysis and software debugging such that decoding collected control flow traces is prohibitively slow on the fly. FlowGuard addresses this challenge by reconstructing applications' conservative control flow graphs (CFG) to be compatible with the compressed encoding format of IPT, and labeling the CFG edges with credits in the help of fuzzing-like dynamic training. At runtime, FlowGuard separates fast and slow paths such that the fast path compares the labeled CFGs with the IPT traces for fast filtering, while the slow path decodes necessary IPT traces for strong security. We have implemented and evaluated FlowGuard on a commodity Intel Skylake machine with IPT support. Evaluation results show that FlowGuard is effective in enforcing CFI for several applications, while introducing only small performance overhead. We also show that, with minor hardware extensions, the performance overhead can be further reduced.
We present XIndex, a concurrent ordered index designed for fast queries. Similar to a recent proposal of the learned index, XIndex uses learned models to optimize index efficiency. Comparing with the learned index, XIndex is able to effectively handle concurrent writes without affecting the query performance by leveraging fine-grained synchronization and a new compaction scheme, Two-Phase Compaction. Furthermore, XIndex adapts its structure according to run-time workload characteristics to support dynamic workload. We demonstrate the advantages of XIndex with both YCSB and TPC-C (KV), a TPC-C variant for key-value stores. XIndex achieves up to 3.2X and 4.4X performance improvement comparing with Masstree and Wormhole, respectively, on a 24-core machine, and it is open-sourced1.
The recent commercial availability of Intel SGX (Software Guard eXtensions) provides a hardware-enabled building block for secure execution of software modules in an untrusted cloud. As an untrusted hypervisor/OS has no access to an enclave's running states, a VM (virtual machine) with enclaves running inside loses the capability of live migration, a key feature of VMs in the cloud. This paper presents the first study on the support for live migration of SGX-capable VMs. We identify the security properties that a secure enclave migration process should meet and propose a software-based solution. We leverage several techniques such as two-phase checkpointing and self-destroy to implement our design on a real SGX machine. Security analysis confirms the security of our proposed design and performance evaluation shows that it incurs negligible performance overhead. Besides, we give suggestions on the future hardware design for supporting transparent enclave migration.
The broad landscape of new technologies currently being explored makes the current times very exciting for computer systems research. The community is actively researching an extensive set of topics, ranging from the small (e.g., energy-independent embedded devices) to the large (e.g., brain-scale deep learning), simultaneously addressing technology discontinuities (End of Moore's Law and EnergyWall), new challenges in security and privacy, and the rise of artificial intelligence (AI).
While industry is applying some of these technologies, its efforts are necessarily focused on only a few areas, and on relatively short-term horizons. This offers academic researchers the opportunity to attack the problems with a broader and longer-term view. Further, in recent times, the computer systems community has started to pay increasing attention to non-performance measures, such as security, complexity, and power. To make progress in this multi-objective world, the composition of research teams needs to change. Teams have to become inter-disciplinary, enabling the flow of ideas across computing fields.
While many research directions are interesting, this report outlines a few high-priority areas where inter-disciplinary research is likely to have a high payoff:
a) Developing the components for a usable planet-scale Internet of Things (IoT), with provably energy-efficient devices. This report envisions a highly-available, geographically distributed, heterogeneous large-scale IoT system with the same efficiency, maintainability, and usability as today's data centers. This planet-scale IoT will be populated by many computationally-sophisticated IoT devices that are ultra-low power and operate energy-independently.
b) Rethinking the hardware-software security contract in the age of AI. In light of the recent security vulnerabilities, this report argues for building hardware abstractions that communicate security guarantees, and for allowing software to communicate its security and privacy requirements to the hardware. Further, security and privacy mechanisms should be integrated into the disruptive emerging technologies that support AI.
c) Making AI a truly dependable technology that is usable by all the citizens in all settings. As AI frameworks automate an increasing number of critical operations, this report argues for end-to-end dependable AI, where both the hardware and the software are understood and verified. Further, AI needs to turn from a centralized tool into a capability easily usable by all the citizens in all settings to meet an ever expanding range of needs.
d) Developing solutions to tackle extreme complexity, possibly based on formal methods. This report argues for the need to tame the explosion of system complexity and heterogeneity by creating new abstractions and complexity-management solutions. Such solutions need to be accessible to domain experts. An important step towards this goal is to scale out and extend formal methods for the real world.
This report also describes other, related research challenges.
With changing climate and farmland ecological conditions, pest outbreaks in agricultural landscapes are becoming more frequent, increasing the need for improved crop production tools and methods. UAV-based agricultural spraying is anticipated to be an important new technology for providing efficient and effective applications of crop protection products. This paper reviews and summarizes the status of the current research and progress on UAV application technologies for plant protection, and it discusses the characteristics of atomization by unmanned aircraft application systems with a focus on spray applications of agrichemicals. Additionally, the factors influencing the spraying performance including downwash airflow field and operating parameters are analyzed, and a number of key technologies for reducing drift and enhancing the application efficiency such as remote sensing, variable-rate technologies, and spray drift models are considered. Based on the reviewed literature, future developments and the impacts of these UAV technologies are projected. This review may inspire the innovation of the combined use of big data analytics and UAV technology, precision agricultural spraying technology, drift reduction technology, swarm UAV cooperative technology, and other supporting technologies for UAV-based aerial spraying for scientific research in the world.
Keywords: UAV, plant protection, spraying technology, drift reduction, pesticide efficacy, spraying model, big data analytics
DOI: 10.25165/j.ijabe.20211401.5714
Citation: Chen H B, Lan Y B, Fritz B K, Hoffmann W C, Liu S B. Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV). Int J Agric & Biol Eng, 2021; 14(1): 38–49.
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