Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Watts-level ultraviolet-CLED integrated light sources for efficient surface and air sterilization

With the epidemic of the coronavirus disease(COVID-19) infection, AlGaN-based ultraviolet-C light emitting diodes(UVC-LEDs) have attracted widespread attention for their sterilization application. However, the sterilization characters of high power integrated light sources(ILSs) haven’t been widely investigated before utilizing in public sanitary security. In this work,by integrating up to 195 UVC-LED chips, high power UVC-LED ILSs with a light output power(LOP) of 1.88 W were demonstrated. The UVC-LED ILSs were verified to have efficient and rapid sterilization capability, which have achieved more than99.9% inactivation rate of several common pathogenic microorganisms within 1 s. In addition, the corresponding air sterilization module based on them was also demonstrated to kill more than 97% of Staphylococcus albus in the air of 20 m3 confined room within 30 min. This work demonstrates excellent sterilization ability of UVC-LED ILSs with high LOP, revealing great potential of UVC-LEDs in sterilization applications in the future.

Chip-scale broadband spectroscopic chemical sensing using an integrated supercontinuum source in a chalcogenide glass waveguide

On-chip spectroscopic sensors have attracted increasing attention for portable and field-deployable chemical detection applications. So far, these sensors largely rely on benchtop tunable lasers for spectroscopic interrogation. Large footprint and mechanical fragility of the sources, however, preclude compact sensing system integration. In this paper, we address the challenge through demonstrating, for the first time to our knowledge, a supercontinuum source integrated on-chip spectroscopic sensor, where we leverage nonlinear Ge_(22)Sb_(18)Se_(60) chalcogenide glass waveguides as a unified platform for both broadband supercontinuum generation and chemical detection. A home-built, palm-sized femtosecond laser centering at 1560 nm wavelength was used as the pumping source. Sensing capability of the system was validated through quantifying the optical absorption of chloroform solutions at 1695 nm. This work represents an important step towards realizing a miniaturized spectroscopic sensing system based on photonic chips.

Photonic microwave true time delays for phased array antennas using a 49 GHz FSR integrated optical micro-comb source[Invited]

We demonstrate significantly improved performance of a microwave true time delay line based on an integrated optical frequency comb source. The broadband micro-comb(over 100 nm wide) features a record low free spectral range(FSR) of 49 GHz, resulting in an unprecedented record high channel number(81 over the C band)—the highest number of channels for an integrated comb source used for microwave signal processing. We theoretically analyze the performance of a phased array antenna and show that this large channel count results in a high angular resolution and wide beam-steering tunable range. This demonstrates the feasibility of our approach as a competitive solution toward implementing integrated photonic true time delays in radar and communications systems.

Application of resist-profile-aware source optimization in 28 nm full chip optical proximity correction

As technology node shrinks, aggressive design rules for contact and other back end of line（BEOL）layers continue to drive the need for more effective full chip patterning optimization. Resist top loss is one of the major challenges for 28 nm and below technology nodes, which can lead to post-etch hotspots that are difficult to predict and eventually degrade the process window significantly. To tackle this problem, we used an advanced programmable illuminator（FlexRay） and Tachyon SMO（Source Mask Optimization） platform to make resistaware source optimization possible, and it is proved to greatly improve the imaging contrast, enhance focus and exposure latitude, and minimize resist top loss thus improving the yield.

Discussion on the Mechanism of Source Integration between Solar Energy and Biomass Energy with Similar Grades

The clean transformation of the China’s energy structure is related to the establishment of China as an energy power in the new world pattern.The multi-energy systems based on renewable energy,especially solar energy and biomass energy,have become a popular energy supply mode.However,there is no clear theoretical guidance for the quantitative source integration of these two types of clean energy.Concretely,whether the source grades of solar energy and biomass energy are similar in different solar energy modes has not been considered yet.Aiming at this problem,based on the theory of space-time unity of the special theory of relativity,a new principle of source integration is proposed,and the corresponding mathematical model is established.The theoretical calculation of the combination of solar energy and different types of biomass energy is carried out.Under the premise of the similarity between the source grades of different biomass energy and solar energy,the specific solar energy concentration ratio is obtained.According to this principle,the matching efficiency of the multi-energy integration for rice and corn plants can be increased by 39.2%and 36.2%,respectively.