Efficient Information Processing Research Articles

A New Perspective of Flexible Clocking Ideology for Driving and Devising Circuits in Emerging Resource-Constrained Applications

Clock is the most important signal in electronic system. In current practice, the dominant clocking style is the fixed-frequency approach. For a given application, the clock signal is only required to work at a few selected frequencies. Moreover, in the process of performing a task, it must not change its frequency. This style has worked well in the past. However, it is preventing information processing efficiency from being raised to next level. Worse yet, the emergence of resource-constrained applications, such as edge computing and IoT, can render this approach unserviceable. In this paper, a new perspective of flexible clocking ideology is advocated. It promotes the use of a clock generator, TAF-DPS, that possesses features of “arbitrary frequency generation” and “instantaneous frequency switching”. Owing to its superior capability in synthesizing waveform, TAF-DPS can handle many signal processing problems in a more elegant manner and serve some special purposes more efficiently. Further, it can be used for generating data for security purposes. TAF-DPS is especially valuable for environments with large frequency instability, which is a major problem in resource-constrained applications. The contribution of this paper is the advocacy of a new perspective of flexible clocking. This ideology is gradually developed in this paper as the argument is carried out through examples of novel architectures enabled by TAF-DPS. The perspective naturally emerges from the collective effect of those novel architectures. The large number of emerging applications, exemplified by edge computing and IoT, inspire system-level innovations that inevitably present new challenges to circuit-level. Those challenges are so demanding that they require overhaul in our circuit design philosophy. This new perspective is such an overhaul in fundamental level. Its aim is to answer the challenges, for better serving the emerging demands in higher levels.

Being incidentally exposed to a sport context: same consequences on gender schema activation as being in a sport context?

The main goal of the present research was to examine whether the activation of gender schema would be indifferent to the method of induction: placing individuals into contexts or incidentally exposing them to contexts. Based on Clément-Guillotin and Fontayne’s (2011. Situational malleability of gender schema: The case of the competitive sport context. Sex Roles, 64, 426–439) study examining gender-related information processing efficiency while explicitly placing individuals into contexts, we investigated the same aspect while incidentally exposing individuals to contexts. We hypothesized that an exposure to a single, powerfully gendered context (i.e., competitive sport) may be sufficient to render gender and sex categories accessible when compared to a gender-neutral context (i.e., cinema). Three studies showed that participants primed with a competitive sport context were faster to respond to gender attributes and to indicate their own sex than participants primed with a cinema context. This underlines that the activation of sex and gender category systems is not identical when being incidentally exposed to versus actually being immersed in a gendered context. While explicitly placing individuals into a gendered context seems to activate a single gender schema dimension related to the context, being incidentally exposed to a single gendered context seems to activate both gender schema dimensions.

THE UNIVERSITY LECTURER'S INNOVATION CULTURE IN DIGITAL AGE

The world is witnessing dramatic changes in the digital age, marking the advancement and rise of digital technologies that enable more efficient processing, transmission, storage and review of information. As digital technology is increasingly affecting all aspects of social life, innovation is considered the key to making competitiveness and sustainble development of individuals, organizations and the whole society. Innovation is the process of creating new values by applying new solutions to existing problems. And innovation culture is an enviroment that nurtures, promotes and realizes innovation. This shows that the formation of an individual’s innovation culture is essential to be able to build an innovation culture of the organization and even of society. Higher education is also not out of this trend. Therefore, it is necessary to study the innovation culture in the field of higher education, first, the university lecturer’s innovation culture. The article aims to clarify some issues related to innovation culture. On that basis, the content of the concept of university lecturer’s innovation culture will be analyzed, simultaneously, the impacts of the digital age on higher education in general and university lecturer in particular will be mentioned to highlight the role of university lecturer’s innovation culture in the new context of society.

FPGA Design of a High- Resolution FIR Band-Pass Filter by Using LabVIEW Environment

Designers regularly use Finite Impulse Response (FIR) filters to fulfil the need for current electronic design applications such as signal or image processing and digital communications because of the remarkable selectivity computational efficiency. Fast and efficient information processing requires a dedicated microprocessor or a digital signal processor that may not always be available or provide enough performance. In such scenarios, designers can configure FPGAs for processing digitized signals. One of the most popular signal processing applications is filtering. Unlike the Infinite Impulse Response (IIR) filters, FIR filters do not have analog equivalent circuits. For this purpose, continuous time-discrete time conversion is not possible with the help of transforms. Because analog filters cannot have a finite impulse response, the design methods of FIR filters can be made as windowing method, pulse response truncation, and optimal filter design method. Considering this information, it aims to digitally separate two signals with different frequencies (2.4 kHz and 4.2 kHz), which are given to the input as analog, to obtain the desired information signal and suppress other signals. We preferred to use LabVIEW graphical programming language to get the digital FIR filter coefficients. We selected rectangular windowing, set the digital filter's sampling frequency as 18720 Hz, and determined the filter's coefficient with high-frequency resolution as 24. Using filter coefficients in the real-time FPGA-VHDL environment, we showed the performance and resource consumption. LabVIEW is used for simulation as well as obtaining filter coefficients. In addition, we compared both simulation and real-time FPGA-VHDL application output waveforms and examined both platforms' advantages and disadvantages.

Analysis of approaches for identification the ontological model components of the searching system

The object of research is the components of an intelligent system for searching information in electronic repositories of unstructured documents, which based on the ontologies of the subject area. One of the most problematic areas is the processing and analysis of information contained in electronic repositories of unstructured documents. There are considered the some possibilities of increasing the efficiency of information processing. In the course of the study, using the method in which ontologies comprise sets of terms presented in it. In addition, the ontological set also includes information about subject areas, areas of definitions, etc. There are obtained the sequence of defining the conceptual representation of an intelligent search system based on ontological components. There are presented the composition of the ontological system model. There are described the main functional components of the system for intelligent processing of information about electronic documents. The proposed approaches for identifying the component components of the ontological model of the search system have a lot of features. This is due to the fact that the search system model must have a set of properties: integrity, coherence, organization, integrability, mobility. Ontologies which representing the basic concepts of the domain in a format available for automated processing in the form of a hierarchy of classes and relationships between them allow automated processing. The using of ontologies in the role of an intermediary between the user and the search process, between the search process and the search system that can facilitate the solution of a number of complex and non-standard tasks of information retrieval (for example, the automation of the search process). It is possible to solve the problem of knowledge representation for displaying information relevant to user requests, as well as to solve the problems of filtering and classifying information. Compared to similar well-known search systems, this provides such advantages as creating a common terminology for software agents and users, protecting the information store from total overflow and errors, as well as solving the issue of information aging.

Ultralow Light‐Power Consuming Photonic Synapses Based on Ultrasensitive Perovskite/Indium‐Gallium‐Zinc‐Oxide Heterojunction Phototransistors

AbstractThe brain‐inspired neuromorphic parallel computing has become one of the most promising technologies for efficient information processing by overcoming the von Neumann bottleneck of sequential operations. Synapses transmit information among neurons and act as the basic component in neuromorphic computing platforms. Despite the rapid advances in developing artificial synapses, most synaptic devices function electronically and thus numerous merits for photonics such as visual/imaging information processing, less corsstalk, and fast response, have to be compromised. Herein, a light‐stimulated synaptic device (photonic synapses) based on perovskite/In‐Ga‐Zn‐O heterojunction phototransistor is reported. The combination of high‐efficiency light absorber, high‐mobility channel, and heterojunction device architecture leads to efficient photon‐to‐electron conversion and intrinsic high‐gain mechanism for such light‐stimulated synapses. As a result, high performance photonic synapses are obtained with the basic functions of excitatory postsynaptic current (EPSC), paired‐pulse facilitation (PPF), and short‐term memory to long‐term memory conversion (STM‐LTM). Importantly, owing to the ultrasensitive photodetection characteristics, the light power consumption of such photonic artificial synapse can be as low as 2.6 picojoule. This study proposes a simple, efficient, and industry‐compatible device concept providing the photosensitive synapses for photonic neural networks by combining the merits of appropriate materials and device architecture.

Collaborative Production Task Decomposition and Allocation among Multiple Manufacturing Enterprises in a Big Data Environment

To realize the efficient decomposition and allocation of collaborative production tasks and resources among multiple enterprises, a task decomposition and allocation model for collaborative production among multiple manufacturing enterprises is proposed in a big data environment. The model is designed for the efficient and fast processing of production information using big data technology. This study innovatively applies the 5S management method to conduct data preprocessing for a manufacturing service provider and design the operation process of data cleaning and conversion to improve the efficiency of data processing. A collaborative optimization model, based on a hierarchical model with seven levels and considering time, costs, and services, is established for the task of production to achieve a reasonable match between supply and demand. Finally, the correlation coefficients of manufacturing service providers are configured according to weight order, so that the weight order is symmetrical with that of the manufacturer. The model also engages all manufacturing service providers with different production capabilities in collaborative production. The model is proved to be scientific and effective by using a specific example. In cooperative production activities, the production tasks of small and medium-sized enterprises can be effectively allocated. It can also realize efficient cooperative production among multiple manufacturing enterprises.

Toponym-assisted map georeferencing: Evaluating the use of toponyms for the digitization of map collections.

A great deal of information is contained within archival maps—ranging from historic political boundaries, to mineral resources, to the locations of cultural landmarks. There are many ongoing efforts to preserve and digitize historic maps so that the information contained within them can be stored and analyzed efficiently. A major barrier to such map digitizing efforts is that the geographic location of each map is typically unknown and must be determined through an often slow and manual process known as georeferencing. To mitigate the time costs associated with the georeferencing process, this paper introduces a fully automated method based on map toponym (place name) labels. It is the first study to demonstrate these methods across a wide range of both simulated and real-world maps. We find that toponym-based georeferencing is sufficiently accurate to be used for data extraction purposes in nearly half of all cases. We make our implementation available to the wider research community through fully open-source replication code, as well as an online georeferencing tool, and highlight areas of improvement for future research. It is hoped that the practical implications of this research will allow for larger and more efficient processing and digitizing of map information for researchers, institutions, and the general public.

Research of artificial intelligence in computer network technology

At present, China has gradually entered the era of computer network, which is widely used in daily life and production. The previous information operation mode and information database can no longer meet the needs of the current stage of work. Its birth not only improves the defects in the previous computing network technology, At the same time, with its efficient information collection, analysis and processing functions, people’s living standards and work efficiency have been significantly improved. Based on the concept, characteristics and application advantages of artificial intelligence technology, this paper further analyzes the key points of application, hoping to provide powerful help for relevant enterprises or units at the present stage.

Information processing by community health nurses using mobile health (mHealth) tools for early identification of suicide and depression risks in Fiji Islands

ObjectivesHigh rates of depression and suicide and a lack of trained psychiatrists have emerged as significant concerns in the low-income and middle-income countries (LMICs) such as the Pacific Island Countries...

Neuromorphic computing: Devices, hardware, and system application facilitated by two-dimensional materials

Conventional computing based on von Neumann architecture cannot satisfy the demands of artificial intelligence (AI) applications anymore. Neuromorphic computing, emulating structures and principles based on the human brain, provides an alternative and promising approach for efficient and low consumption information processing. Herein, recent progress in neuromorphic computing enabled by emerging two-dimensional (2D) materials is introduced from devices design and hardware implementation to system integration. Especially, the advances of hopeful artificial synapses and neurons utilizing the resistive-switching-based devices, 2D ferroelectric-based memories and transistors, ultrafast flash, and promising transistors with attractive structures are highlighted. The device features, performance merits, bottlenecks, and possible improvement strategies, along with large-scale brain-inspired network fulfillment, are presented. Challenges and prospects of system application for neuromorphic computing are briefly discussed, shedding light on its great potential for AI.

Which Comes First in Sports Vision Training: The Software or the Hardware Update? Utility of Electrophysiological Measures in Monitoring Specialized Visual Training in Youth Athletes.

In the present study we combined popular methods of sports vision training (SVT) with traditional oculomotor protocols of Optometric Vision Therapy (OVT) and electrophysiological indexes of EEG and VEP activity to monitor training progress and changes in performance of youth ice hockey players without the history of concussion. We hypothesized that administration of OVT protocols before SVT training may result in larger performance improvements compared to the reverse order due to the initial strengthening of visual hardware capable of handling greater demands during training of visuomotor integration and information processing skills (visual software). In a cross-over design 53 youth ice hockey players (ages 13–18) were randomly assigned to one of the two training groups. Group one (hardware-software group) completed 5 weeks of oculomotor training first followed by 5 weeks of software training. For group 2 (software-hardware) the order of procedures were reversed. After 10 weeks of training both groups significantly improved their performance on all but one measure of the Nike/Senaptec Sensory station measures. Additionally, the software-hardware training order resulted in significantly lower frontal theta-to-gamma amplitude ratios on the Nike/Senaptec test of Near-Far Quickness as well as in faster P100 latencies. Both training orders also resulted in significant decreases in post-treatment P100 amplitude to transient VEP stimuli as well as decreased theta-gamma ratios for perception span, Go/No-Go and Hand Reaction time. The observed changes in the electrophysiological indexes in the present study are thought to reflect greater efficiency in visual information processing and cognitive resource allocation following 10 weeks of visual training. There is also some evidence of the greater effectiveness of the software-hardware training order possibly due to the improved preparedness of the oculomotor system in the youth athletes for administration of targeted protocols of the Optometric Vision Therapy.

Алгоритм оцінювання частоти комплексного гармонічного сигналу з сегментацією даних спостережень

The article deals with the technology of estimating the frequency of harmonic components in the presence of additive normal interferences for solving applied problems of spectral analysis. Objective: to develop a methodology for the synthesis of algorithms for determining the frequency of a complex harmonic signal in discrete sections of the process, this is observed when using data segmentation. Objective: to develop the optimal technology for determining the frequency of the hormonal component of the process, provided by a finite number of discrete compartments, according to model representations and requirements that meet the problems of the current state of spectral analysis practice. These results were obtained. The problem of estimating the harmonic frequency from segmented data in the presence of additive Gaussian interference in observations based on the method of maximum likelihood is solved. The processing algorithm and the consequences of digital modeling of the synthesized evaluation technology for a given number of discrete process samples are given. The analysis of both the practical capacity of the technology for determining the assessment and certain qualitative indicators of assessment is performed. Conclusions. The scientific novelty of the obtained results is as follows: further development as a method for solving problems of estimating the frequency of the harmonic signal from a few sample values of the process under conditions of additive normal interference and methods for optimizing the structure of digital processing of observations in data segmentation. The synthesized technology uses one sample of observations to determine the estimates, which ensure the efficiency of information processing in a simple software implementation. The use of segmentation in the technological process of digital processing of observations allows obtaining estimates, the quality of which corresponds to the indicators of maximum likelihood. For unambiguous assessment, there is a need to eliminate ambiguity. Under these conditions, the technology with a given number of samples can significantly solve the range of signal-to-noise ratios at which can be obtained unbiased estimates.

Electric field control of magnon spin currents in an antiferromagnetic insulator.

Pure spin currents can be generated via thermal excitations of magnons. These magnon spin currents serve as carriers of information in insulating materials, and controlling them using electrical means may enable energy efficient information processing. Here, we demonstrate electric field control of magnon spin currents in the antiferromagnetic insulator Cr2O3. We show that the thermally driven magnon spin currents reveal a spin-flop transition in thin-film Cr2O3. Crucially, this spin-flop can be turned on or off by applying an electric field across the thickness of the film. Using this tunability, we demonstrate electric field–induced switching of the polarization of magnon spin currents by varying only a gate voltage while at a fixed magnetic field. We propose a model considering an electric field–dependent spin-flop transition, arising from a change in sublattice magnetizations via a magnetoelectric coupling. These results provide a different approach toward controlling magnon spin current in antiferromagnets.

Application of the Backpropagation Neural Network Image Segmentation Method with Genetic Algorithm Optimization in Micropores of Intersalt Shale Reservoirs.

The pore types of intersalt shale reservoirs are diverse, and the pore structures are relatively complex. The size of the pores ranges from a few nanometers to a few microns, showing obvious heterogeneity and multiscale. Image segmentation is an important link in the study of micropore structures of intersalt shale using digital core technology. It can identify characteristics such as pore category, shape, size, spatial distribution, and connectivity characteristics. Therefore, how to improve the accuracy of image segmentation becomes very important. In this study, the research object is the 10 rhythmic layers of the Qian 34 oil group in the Wangping 1 well area of oil field A. First, focused ion beam scanning electron microscopy was used to obtain core imaging data. Then, in order to realize efficient processing of two-dimensional image information and compensate for the shortcomings of conventional segmentation methods such as long iteration period, slow convergence speed, and low accuracy, the backpropagation (BP) neural network segmentation method with a genetic algorithm (GA) was adopted. Also, the segmentation results before and after the improvement were compared. The results show the following: (1) Among the selected intersalt shale core samples, 90% of the pore radius is less than 150 nm and more than 90% of the throats are less than 100 nm. (2) Compared with the conventional BP neural network, the number of convergence steps is reduced to 10, the speed is 10 times faster, and the porosity prediction accuracy is increased by 4.03% on average, which is closer to the gas-measured porosity value. It shows that the BP neural network image segmentation method with a GA has the advantages of small calibration error, fast convergence speed, high efficiency, and high precision.

Methylammonium halide-doped perovskite artificial synapse for light-assisted environmental perception and learning

Optoelectronic synapses that integrate and process light and electric signals simultaneously received giant attention for the development of artificial visual system. However, to mimic robust perception and processing ability of biological vision demands exceptional photoresponse and tunable plasticity of the optoelectronic synaptic units. Herein, we demonstrated a Methylammonium (MA) halide-doped perovskite artificial synapse. The introduction of MA halide additives into formamidinium lead iodide (FAPbI3) can effectively improve the film morphology and crystallinity, leading to a wider regulatory range of synaptic plasticity. The effect of light irradiation on shaping synaptic plasticity was revealed to realize important synaptic functions. With the assistance of light stimuli, high-order learning behaviors have been achieved, including dynamic logic operation and conditioned reflex. A higher pattern recognition accuracy (88.2%) of handwritten digits was achieved in simulation. Moreover, the correlation between light irradiation and the hybrid perovskite-based artificial synapse can be utilized to perceive environmental change and to tune information processing efficiency. These results provide a new strategy for constructing future artificial intelligent system.

Information modeling and system realization for supporting engineering education accreditation process based on polychromatic graph

PurposeThe engineering education accreditation (EEA) is a principal quality assurance mechanism. However, at many education institutions, the most labor-intensive work of EEA process is accomplished manually. Without the support of computer and information technology, the EEA process leads to high labor intensity, low work efficiency and poor management level. The purpose of this paper is to build a complex network model and realize an information management system of talent training program for supporting the EEA process.Design/methodology/approachBased on polychromatic graph (PG), this paper builds a network model of talent training program for engineering specialty. The related information and data are organized and processed in this network model. From the bidirections of top-down and bottom-up, the user requirements are retrieved automatically in logic layer. Together with the specialty of mechanical engineering, the proposed PG-based network modeling method is applied and the corresponding information management system is realized.FindingsThe study results show that the PG-based network modeling method takes full advantages of the strong simulation ability of PG to model the complex network system and has some unique merits in formal expression of problem, efficient processing of information and lightweight realization of system. Further, the information management system of talent training program can reduce the tedious human labor and improve the management level of EEA process dramatically.Originality/valueThis paper proposes a PG-based network modeling method, in which the nodes and the edges can be painted by some unified colors to describe the different kinds of activities and the various types of interactions. Theoretically, this modeling method does not distinguish the activities, the interactions and their properties in graphic symbol and the problem size is diminished about a half. Furthermore, this paper provides an effective experience and idea to the education institutions for implementing the engineering education accreditation, increasing the education management efficiency and promoting the talent training quality.

Context and attention control determine whether attending to competing information helps or hinders learning in school-aged children.

Attention control regulates efficient processing of goal-relevant information by suppressing interference from irrelevant competing inputs while also flexibly allocating attention across relevant inputs according to task demands. Research has established that developing attention control skills promote effective learning by minimizing distractions from task-irrelevant competing information. Additional research also suggests that competing contextual information can provide meaningful input for learning and should not always be ignored. Instead, attending to competing information that is relevant to task goals can facilitate and broaden the scope of children's learning. We review this past research examining effects of attending to task-relevant and task-irrelevant competing information on learning outcomes, focusing on relations between visual attention and learning in childhood. We then present a synthesis argument that complex interactions across learning goals, the contexts of learning environments and tasks, and developing attention control mechanisms will determine whether attending to competing information helps or hinders learning. This article is categorized under: Psychology > Attention Psychology > Learning Psychology > Development and Aging.

The Relationship Between Habitual Physical Activity, Sitting Time, and Cognitive Function in Young Adult Women.

There is evidence that physical activity (PA), sitting time, and obesity may impact cognition, but few studies have examined this in young women. Healthy women (18-35y), without conditions that impair cognition, were recruited for this cross-sectional study. Participants completed anthropometric and validated computerized cognitive assessments (IntegNeuro™). Performance on 5 cognitive domains (impulsivity, attention, information processing, memory, and executive function) was reported as z scores. Sitting hours and weekly PA calculated from time in low-, moderate-, and high-intensity activity were obtained via the International Physical Activity Questionnaire. Analysis of variance/analysis of covariance, chi-square, and linear regression were used. 299 (25.9 [5.1]y) women (low PA = 19%; moderate PA = 40%; high PA = 41%) participated. High PA women had lower body mass index (high PA = 26.1 [6.5]; moderate PA = 30.0 [8.7]; low PA = 31.0 [11.1]kg/m2; P < .001) and less sitting time (high PA = 6.6 [3.1]; moderate PA = 7.7 [2.8]; low PA = 9.3 [3.6]hr/weekday; P < .0001). Cognitive function was within normal ranges and did not differ between any PA groups (P = .42). Adjusting for body mass index, C-reactive protein, or sitting hours did not alter results. Weak correlations were found between time in high-intensity activity and impulsivity (b = 0.12, r2 = .015; P = .04), and between sitting hours and information processing efficiency (b = -0.18, r2 = .03; P = .002). Valuesare presented as mean (SD). Cognitive function was within the normal range, regardless of PA or sitting time.

The landscape of Blockchain research: impacts and opportunities

Blockchain technology has emerged as an important research domain in recent years. It not only supports the secure and efficient storage and processing of information but may also transform the business principles and processes embedded in traditional centralized organizations and societies. This editorial first provides a framework that identifies the emerging areas of blockchain research. The key characteristics of this framework in Blockchain 1.0, 2.0, and 3.0 are defined and introduced. The impacts and opportunities associated with blockchain research are identified and discussed. At last, the six articles in this special issue are characterized using the proposed research framework of blockchain research.