Information Processing Technology Research Articles

Strong photon–magnon coupling at microwave and millimeter-wave frequencies in planar hybrid circuits

Photon–magnon hybrid systems have potential applications in modern information processing technologies. Although planar hybrid circuits based on split ring resonators have shown strong coherent photon–magnon coupling, none of those operates at millimeter-wave frequencies. With specially designed electric-field-coupled resonators, strong coupling between resonator modes and ferromagnetic resonance modes (either in-plane or out-of-plane) was experimentally observed in two circuits working at 4.1 and 30 GHz. Their dynamics were well described by quantum models. The miniature, integrable, and physically robust circuits pave a way for planar photon–magnon hybrid systems at even higher frequencies, demonstrating the possibility to integrate magnon-based systems with millimeter-wave devices.

Research on the Application of Multi-Source Information Fusion in Multiple Gait Pattern Transition Recognition.

Multi-source information fusion technology is a kind of information processing technology which comprehensively processes and utilizes multi-source uncertain information. It is an effective scheme to solve complex pattern recognition and improve classification performance. This study aims to improve the accuracy and robustness of exoskeleton gait pattern transition recognition in complex environments. Based on the theory of multi-source information fusion, this paper explored a multi-source information fusion model for exoskeleton gait pattern transition recognition in terms of two aspects of multi-source information fusion strategy and multi-classifier fusion. For eight common gait pattern transitions (between level and stair walking and between level and ramp walking), we proposed a hybrid fusion strategy of multi-source information at the feature level and decision level. We first selected an optimal feature subset through correlation feature extraction and feature selection algorithm, followed by the feature fusion through the classifier. We then studied the construction of a multi-classifier fusion model with a focus on the selection of base classifier and multi-classifier fusion algorithm. By analyzing the classification performance and robustness of the multi-classifier fusion model integrating multiple classifier combinations with a number of multi-classifier fusion algorithms, we finally constructed a multi-classifier fusion model based on D-S evidence theory and the combination of three SVM classifiers with different kernel functions (linear, RBF, polynomial). Such multi-source information fusion model improved the anti-interference and fault tolerance of the model through the hybrid fusion strategy of feature level and decision level and had higher accuracy and robustness in the gait pattern transition recognition, whose average recognition accuracy for eight gait pattern transitions reached 99.70%, which increased by 0.15% compared with the highest average recognition accuracy of the single classifier. Moreover, the average recognition accuracy in the absence of different feature data reached 97.47% with good robustness.

Michael Sedbon: Explorations in Coupling Artificial and Natural Systems.

As a timely artist investigating the potential of coupling natural systems with silicon-based systems, Michael Sedbon's work suggests a unique perspective on the relationship of manmade information processing technologies to computing, intelligence, and consciousness found in nature. His hope for a better world through building systems based on biomimicry exploration piqued our interest as we interviewed him for this article.

Medical Image Fusion using ECNN- and OMBO-based Adaptive Weighted Fusion Rule

Medical imaging and information processing technologies are constantly evolving, resulting in a wide range of multimodality therapeutic pictures for clinical illness investigation. Physicians often require medical images produced using various modalities such as computed tomography (CT), magnetic resonance (MR), and positron emission computed tomography (PET) for clinical diagnosis. Many deep learning-based fusion methods have recently been proposed. In Convolutional Neural Network (CNN)-based fusion methods, only the last layer results are used as the image features, which result in the loss of useful information at middle layers. The fusion rule, based on the weighted averaging, causes noises in the source images and suppresses salient features of the image. In order to solve these issues, this paper proposes medical image fusion using Enhanced CNN (ECNN)- and Opposition-based Monarch Butterfly Optimization (OMBO)-based adaptive weighted fusion rule (AWFR). The ECNN contains feature extraction and reconstruction components. Both these components are trained in order to minimize the pixel loss and structural similarity loss. A pair of multimodal medical image is passed as input to the ECNN model to extract the low level and high level features. For the extracted features from ECNN, weighted fusion rule is applied in which OMBO algorithm is applied to adaptively optimize the weights of the fusion rule.

Simple, Reliable, and Noise-Resilient Continuous-Variable Quantum State Tomography with Convex Optimization

Precise reconstruction of unknown quantum states from measurement data, a process commonly called quantum state tomography, is a crucial component in the development of quantum information processing technologies. Many different tomography methods have been proposed over the years. Maximum likelihood estimation is a prominent example, being the most popular method for a long period of time. Recently, more advanced neural network methods have started to emerge. Here, we go back to basics and present a method for continuous variable state reconstruction that is both conceptually and practically simple, based on convex optimization. Convex optimization has been used for process tomography and qubit state tomography, but seems to have been overlooked for continuous variable quantum state tomography. We demonstrate high-fidelity reconstruction of an underlying state from data corrupted by thermal noise and imperfect detection, for both homodyne and heterodyne measurements. A major advantage over other methods is that convex optimization algorithms are guaranteed to converge to the optimal solution.

Operational interpretation of bipartite quantum correlation

Operational interpretations of quantum resource measures lie at the heart of quantum information processing technologies. In this paper, we show that the bipartite quantum correlation in terms of Wigner-Yanase skew information, is the minimal failure probability of a particular state ensemble discrimination task via the associated least square measurement. We then derive a general relation between quantum coherence with respect to quantum operation and discriminating probability specific to least square measurement. Furthermore, we provide a wave-particle duality relation between bipartite quantum correlation and which-path information.

Low‐Power Logic‐in‐Memory Complementary Inverter Based on p‐WSe2 and n‐WS2

AbstractTransition metal dichalcogenides have been considered as candidate materials to construct logic‐in‐memory devices for realizing non‐von‐Neumann architecture. Thus, reducing the power consumption is extremely critical for their applications in big data and artificial intelligence. Here, a low‐power logic‐in‐memory device is demonstrated by constructing complementary inverter with p‐WSe2 and n‐WS2 transistors. By engineering the interface states between WSe2 (WS2) and substrate artificially, non‐volatile memory with resistance ratio of 104 and 103 after 500 s are achieved in individual WSe2 and WS2 transistors, respectively. Furthermore, a complementary inverter with a retention time longer than 500 s is realized by connecting p‐WSe2 and n‐WS2 transistors. More importantly, the static operating source‐drain current Ids of this inverter is around 0.5/0.1 nA at low/high resistance states with source‐drain voltage Vds = 5 V, and the hysteresis window is located around 0 V, both of which can reduce the energy consumption dramatically and leads to the low operation power. This work provides a convenient strategy to build a non‐von‐Neumann device toward post‐Moore information processing technology.

Office of Naval Research Undersea Signal Processing, Contributions of Dr. Lisa Zurk

The Undersea Signal Processing team at the Office of Naval Research (ONR 321US) is tasked with developing advanced active/passive sonar signal and information processing technologies that improve the anti-submarine warfare (ASW) performance of US Navy sonar systems. Pursuant to this goal ONR supports academics whose research, being on the cutting edge, lead to high-risk technologies with potential for the greatest advancement of fleet capability. Dr. Lisa Zurk was such an academic, first supported by ONR 321US in 2005. Dr. Zurk’s research focused on developing signal and information processing algorithms which exploited the physics of the underwater operating environment. She believed the path to significant gains in sonar system performance would be obtained through exploitation of environmental knowledge. This belief is now a realization within the Naval Research Enterprise (NRE) and was a primary motivation for initiation of the ONR Task Force Ocean (TFO) Program in 2017. From establishment of the NEAR-Lab at Portland State University in 2005 until her transition to a Program Manager at the Defense Advanced Research Projects Agency in 2016, Dr. Zurk made significant contributions to the NRE. This presentation looks to highlight these past contributions and to consider their present impact.

Application of Image Processing Variation Model Based on Network Control Robot Image Transmission and Processing System in Multimedia Enhancement Technology

The rapid development of the information age brings convenience to human life, but it also brings great challenges to information processing technology. Multimedia enhancement technology is an organic combination of multimedia technology and information processing technology, and it is also an important way of modern multimedia image information processing. However, its usefulness and effectiveness are increasingly negatively affected by the open information environment. The processing effect is also unable to meet the development requirements of the visual field. In order to improve this problem, this paper studied the image transmission and processing system of network-controlled robot on the basis of analyzing the characteristics of the problems existing in the current stage of multimedia technology. On this basis, a new image processing variational model was established and applied to multimedia enhancement technology, which improved the efficiency and effect of image information processing. Finally, the feasibility of its application function and performance was tested by experiments. The test results showed that in the difficult mode of the image processing task, the refresh time of the model in this paper in the multimedia enhancement technology was 1.13 s in total, which was not much different from the test results in the easy mode. Also, in the load stress test, the comprehensive test values under full-function operation and partial-function operation were 42.04% and 20.92%, respectively. Compared with the traditional model, the model in this paper has stronger carrying capacity in multimedia enhancement technology and has better processing ability and stability.

Nanopore Detection Assisted DNA Information Processing.

The deoxyribonucleotide (DNA) molecule is a stable carrier for large amounts of genetic information and provides an ideal storage medium for next-generation information processing technologies. Technologies that process DNA information, representing a cross-disciplinary integration of biology and computer techniques, have become attractive substitutes for technologies that process electronic information alone. The detailed applications of DNA technologies can be divided into three components: storage, computing, and self-assembly. The quality of DNA information processing relies on the accuracy of DNA reading. Nanopore detection allows researchers to accurately sequence nucleotides and is thus widely used to read DNA. In this paper, we introduce the principles and development history of nanopore detection and conduct a systematic review of recent developments and specific applications in DNA information processing involving nanopore detection and nanopore-based storage. We also discuss the potential of artificial intelligence in nanopore detection and DNA information processing. This work not only provides new avenues for future nanopore detection development, but also offers a foundation for the construction of more advanced DNA information processing technologies.

Computer Network Security Management of Data Encryption Technology

With the continuous promotion of high and new technologies such as computers and big data, humans enter a new period-information age. Mass data and information can be transmitted and exchanged online through the network. In recent years, research on image encryption based on optical information processing technology is becoming more and more favored by researchers. In this regard, this study will focus on the research of encryption algorithms in optical images and realize encryption through fractional Fourier transform and Arnold transform. This article first introduces the research background of the subject and analyzes the current situation of the subject at home and abroad and then proposes the theoretical guidance basis for the subject research, which covers the fractional Fourier transform, Arnold transform, computational hologram, and picture evaluation index system. Finally, MATLAB simulation and performance analysis are carried out for fractional Fourier transform, Arnold transform, and holographic encryption technology. The results shows that the improved algorithm in this study performs better in the encryption and decryption process.

Optical encryption in spatial frequencies of light fields with metasurfaces

Optical encryption has attracted attention recently as information security becomes important in modern society. For most encryption methods based on metasurfaces, however, information is encrypted into the metasurfaces of physical forms, limiting information sharing and compatibility with digital information processing technology. Here, we demonstrate an alternative scheme in which information is encrypted on optical images other than metasurfaces, releasing the constraints mentioned above. This is achieved by modulating the spatial frequencies of optical images with metasurfaces. Specifically, information encryption and decryption based on the intensity or phase of spatial frequency have been demonstrated with the developed microscopic Fourier optics technique. The abundance of spatial frequency also promises a flexible multiplexing platform for increasing information capacity, which has been verified by a 4-bit encryption experiment. These findings could enable high-security and high-capacity optical encryption platforms.

Design of an Automatic Evaluation System for English Translation Based on Artificial Intelligence Fusion Control Algorithm

In the process of globalization, language plays an increasingly important role in international communication. As a global language, English involves many fields, so the translation of English is very important. Not all translations can be used, and the quality of the translation must be accurate. Therefore, it is necessary to evaluate the quality of the translation of the translation system. Only when it reaches a certain level can it meet the application standard. The automatic evaluation system is a specific application of intelligent information processing technology. The system can automatically complete the compilation and feed back the system space-time resource information consumed by the user to the user in time, so that the user can check the pros and cons of the algorithm. The purpose of this article is to study the design and research of the English translation automatic evaluation system based on the artificial intelligence fusion control algorithm, and it was expected to combine the artificial intelligence fusion algorithm with the automatic evaluation of the English translation system. In this way, the traditional evaluation efficiency of translation could be further improved and the consumption of human resources can be reduced. Data fusion refers to an information processing technology that uses a computer to automatically analyze and synthesize several observational information obtained in time series under certain criteria to complete the required decision-making and evaluation tasks. This article proposed an automatic terminology translation method based on central word constraints aimed at the different linguistic characteristics of English and Chinese terms; the materials were sequenced before translation. The macroevaluation index was also introduced into the machine translation fusion method to achieve a balance between the robustness and effectiveness of the translation fusion technology. The experimental results of this article showed that under the traditional evaluation system, the response time of the system was 5.5 s and 6.3 s when the number of translations to be judged was 9 sentences and 11 sentences, respectively, whereas under the fusion control method, it was 4.1 s and 4.6 s, respectively. Therefore, the system evaluation efficiency under the fusion control algorithm was higher.

Landscape Ecological Construction and Spatial Pattern Optimization Design Based on Genetic Algorithm Optimization Neural Network

With the development of computer visual information processing technology, designers use visual image processing methods to analyze and extract the features of landscape ecological space environment, establish visual reconstruction models of landscape ecological construction and spatial pattern optimization design images, and improve their ability to identify and reconstruct the artistic features of landscape ecological space environment. In this paper, a landscape ecological construction and spatial pattern optimization design scheme based on NN and GA is put forward. In the process of image visual reconstruction of landscape ecological construction and spatial pattern optimization design, urban ecological landscape information fusion perception and block area template matching are carried out to realize urban ecological landscape design. Finally, simulation test and analysis are carried out. The results show that the accuracy of this algorithm is high, which is 9.34% higher than that of traditional GA. This method can effectively reduce the iteration times and running time of the algorithm and improve the efficiency of landscape space optimization design. Carrying out the research on ecological planning and design with landscape ecological protection and restoration as the core has important practical guiding significance for guiding all localities to construct landscape ecological security pattern and promote green urbanization.

Audit IT Process Po-01 to Academic Information Systemin Higher Education Polytechnic using the Cobit Framework

The role of information technology in higher education is to realize academic effectiveness. Academic Information System (SIAK) which is loaded with the use of Information Technology (IT). The PO1 IT Process Governance audit based on the COBIT standard, namely “Define a Strategic Information Technology Plan” then became a strategic issue for the success of higher education management, especially the SIAK Polytechnic campus which uses information technology-based tools in its teaching and learning process. This study aims to measure the level of IT Process PO-01 governance in relation to Academic Information Systems in Vocational Model High Schools (Polytechnics). The sample of this research is nine Vocational High Schools (Polytechnics) in Bandung. From the results of the study, it is known that the IT Process Governance PO-01 at SIAk Vocational Colleges is still at Level 2, namely "Repeatable but Intuitive". The effectiveness of governance is still determined by the competence of the individual or part of the personnel who carry out the IT Function (technical) and the assignment is ad-hoc. IT management at SIAk has not been internalized and refers to Best Practice. But on the other hand, it has begun to be seen that the Polytechnic management has a fairly good awareness of the importance of IT Governance and wants to make it an enabler for the Academic Information System (SIAk). However, there is still a fairly high gap between what is and what should be desired as in COBIT Best Practice.

Audit IT Process Po-01 to Academic Information Systemin Higher Education Polytechnic using the Cobit Framework

The role of information technology in higher education is to realize academic effectiveness. Academic Information System (SIAK) which is loaded with the use of Information Technology (IT). The PO1 IT Process Governance audit based on the COBIT standard, namely “Define a Strategic Information Technology Plan” then became a strategic issue for the success of higher education management, especially the SIAK Polytechnic campus which uses information technology-based tools in its teaching and learning process. This study aims to measure the level of IT Process PO-01 governance in relation to Academic Information Systems in Vocational Model High Schools (Polytechnics). The sample of this research is nine Vocational High Schools (Polytechnics) in Bandung. From the results of the study, it is known that the IT Process Governance PO-01 at SIAk Vocational Colleges is still at Level 2, namely "Repeatable but Intuitive". The effectiveness of governance is still determined by the competence of the individual or part of the personnel who carry out the IT Function (technical) and the assignment is ad-hoc. IT management at SIAk has not been internalized and refers to Best Practice. But on the other hand, it has begun to be seen that the Polytechnic management has a fairly good awareness of the importance of IT Governance and wants to make it an enabler for the Academic Information System (SIAk). However, there is still a fairly high gap between what is and what should be desired as in COBIT Best Practice.

Information Technology Governance Design in DevOps-Based E-Marketplace Companies Using COBIT 2019 Framework

The E-Marketplace system is a digital place where sellers and buyers meet virtually, and transactions can be processed safely. E-Marketplace companies are oriented toward consumer-centric products and services and operate and have 24/7 non-stop transaction activities. E-Marketplace system development has become the digital transformation in Information Technology (IT) governance that meets the requirements of a system-based automated control with the application of DevOps principles. This study uses a mixed method approach through questionnaires, interviews, expert judgment, and relevant literature reviews related to the e-Marketplace system and IT governance concepts using the COBIT 2019 framework, which has 11 design factors with 40 IT processes to obtain the most appropriate governance model for e-Marketplace companies. The results were obtained in the form of 6 (six) methods in the IT governance model using the DevOps approach, namely the APO03 – Managed Enterprise Architecture, APO04 – Managed Innovation, BAI04 – Managed Availability and Capacity, BAI06 – Managed IT Changes, BAI11 – Managed Projects, and DSS03 – Managed Problems with a score ≥ 75 and the capability level target is at level 4.

Statistical and Comparative Analysis of Multi-Channel Infrared Anomalies before Earthquakes in China and the Surrounding Area

Abundant infrared remote sensing images and advanced information processing technologies are used to predict earthquakes. However, most studies only use single long-wave infrared data or its products, and the accuracy of prediction is not high enough. To solve this problem, this paper proposes a statistical method based on connected domain recognition to analyze multi-channel anomalies. We extract pre-seismic anomalies from multi-channel infrared remote sensing images using the relative power spectrum, then calculate positive predictive values, true positive rates and probability gains in different channels. The results show that the probability gain of the single-channel prediction method is extremely low. The positive predictive value of four-channel anomalies is 41.94%, which is higher than that of single-channel anomalies with the same distance threshold of 200 km. The probability gain of the multi-channel method is 2.38, while that of the single-channel method using the data of any channel is no more than 1.26. This study shows the advantages of the multi-channel method to predict earthquakes and indicates that it is feasible to use multi-channel infrared remote sensing images to improve the accuracy of earthquake prediction.

Collision Avoidance and Direction Planning for Autonomous Vehicles

The rapid development of information and signal processing technology has contributed significantly to the development of autonomous driving (AD) techniques, which improve driving safety while minimizing human efforts. According to analyst predictions, automatic cars will soon surpass manual cars in number as automatic driving becomes more sophisticated. Ensuring safety and reducing the road accidents is the primary concerns in automated vehicles. Recent research has shown that computer vision, deep learning, and other fields have advanced beyond the imagination. The paper covers the deep learning algorithms and techniques necessary to make a reliable and real-time collision avoidance system using concepts such as Convolutional Neural Networks (CNN), YOLOv4 (You Look Only Once), and other deep learning architectures while also reviewing the current state of the art strategies for Advanced Driver Assistance System (ADAS). By using CNN algorithms, it is possible to detect lane markings and signboards in real time as well as estimate distances between them.

IT and Strategic Human Capital: Impact on Knowledge-Based Processes, Networks, and Outcomes

Advancements in information technology (IT) can have profound impacts on the development and management of human capital. Extant research has examined how human capital in markets and organizations are shaped by increased digitization and the deployment of strategies relying on collection and analysis of big data, cloud computing, social media, Internet of Things (IoT), and the like. In knowledge-intensive settings, however, IT-enabled mechanisms have heterogenous impacts on issues related to human capital. Although IT potentially expands capacity for collection, storage, and utilization of knowledge, some firms and individuals successfully leverage technology and information systems while others do not--or are even possibly left worse off. This symposium contributes to examining why such discrepancies may exist by shedding light on the market and organizational processes that underlie the intersection of IT and knowledge-based processes, networks, and outcomes as they relate to issues in strategic human capital. Can Social Media Alleviate Inequality? Evidence from Venture Capital Financing Presenter: Gavin Wang; Wharton OPIM Presenter: Lynn Wu; The Wharton School, U. of Pennsylvania Presenter: Lorin Hitt; U. of Pennsylvania Are Knowledge Sharing and Learning Tradeoffs? Linking Performance Incentives with KMS Usage Presenter: Sae-Seul Park; Carnegie Mellon U. - Tepper School of Business Cloud Adoption and Strategic Human Capital: Evidence from Norway Presenter: Derrick Choe; - Presenter: Amir Sasson; BI Norwegian Business School Presenter: Robert Channing Seamans; NYU Stern Hunting For Talent: Firm-Driven Labor Market Search in the United States Presenter: Ines Black; - Presenter: Sharique Hasan; Fuqua School of Business, Duke U.