Hierarchical Temporal Memory using Memristor Networks: A Survey

This paper presents a survey of the currently available hardware designs for implementation of the human cortex inspired algorithm, Hierarchical Temporal Memory (HTM). In this review, we focus on the state of the art advances of memristive HTM implementation and related HTM applications. With the advent of edge computing, HTM can be a potential algorithm to implement on-chip near sensor data processing. The comparison of analog memristive circuit implementations with the digital and mixed-signal solutions are provided. The advantages of memristive HTM over digital implementations against performance metrics such as processing speed, reduced on-chip area and power dissipation are discussed. The limitations and open problems concerning the memristive HTM, such as the design scalability, sneak currents, leakage, parasitic effects, lack of the analog learning circuits implementations and unreliability of the memristive devices integrated with CMOS circuits are also discussed

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Analysis and extension of hierarchical temporal memory for multivariable time series

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, junio de 201

Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

Golf courses can be considered as precision agriculture, as being a playing surface, their appearance is of vital importance. Areas with good weather tend to have low rainfall. Therefore, the water management of golf courses in these climates is a crucial issue due to the high water demand of turfgrass. Golf courses are rapidly transitioning to reuse water, e.g., the municipalities in the USA are providing price incentives or mandate the use of reuse water for irrigation purposes; in Europe this is mandatory. So, knowing the turfgrass surfaces of a large area can help plan the treated sewage effluent needs. Recycled water is usually of poor quality, thus it is crucial to check the real turfgrass surface in order to be able to plan the global irrigation needs using this type of water. In this way, the irrigation of golf courses does not detract from the natural water resources of the area. The aim of this paper is to propose a new methodology for analysing geometric patterns of video data acquired from UAVs (Unmanned Aerial Vehicle) using a new Hierarchical Temporal Memory (HTM) algorithm. A case study concerning maintained turfgrass, especially for golf courses, has been developed. It shows very good results, better than 98% in the confusion matrix. The results obtained in this study represent a first step toward video imagery classification. In summary, technical progress in computing power and software has shown that video imagery is one of the most promising environmental data acquisition techniques available today. This rapid classification of turfgrass can play an important role for planning water management

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.This work has received funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE (IT1294-19), EMAITEK and ELK ARTEK programs. D. Camacho also acknowledges support from the Spanish Ministry of Science and Education under PID2020-117263GB-100 grant (FightDIS), the Comunidad Autonoma de Madrid under S2018/TCS-4566 grant (CYNAMON), and the CHIST ERA 2017 BDSI PACMEL Project (PCI2019-103623, Spain)

Unfamiliar face recognition : Security, surveillance and smartphones

A person’s ability to recognize familiar faces across a wide range of viewing conditions is one of the most impressive facets of human cognition. As shown in Figure 1, it is easy to conclude, for a known individual, that each image in the set shows the same person (British Prime Minister David Cameron), despite a wide range of variation in viewing angle, physical appearance, camera and lighting. In fact, familiar face recognition performance is often at or near ceiling level, even when the images are of poor quality [1] or artificially distorted. [2] At first glance, the aptitude for familiar face recognition may suggest a similar level of expertise for the recognition of unfamiliar faces, thus the reliance on face-to-photo ID for identity verification. [3] This is not the case, as recent research shows people are surprisingly poor at recognizing new instances of an unfamiliar person. The poor recognition of unfamiliar faces is a concern for the United States. Many preliminary screenings involve facial recognition by security agents. In order for this method to be effective, more robust training for security agents needs to be established. The Department of Defense utilizes facial and iris recognition technologies in order to eliminate human error in identifying persons of interest during surveillance operations. [4] DoD guidelines should be implemented by security agent guidance programs to ensure best practices in identification of potential threats

Memristive System Based Image Processing Technology: A Review and Perspective

Copyright: © 2021 by the authors. As the acquisition, transmission, storage and conversion of images become more efficient, image data are increasing explosively. At the same time, the limitations of conventional computational processing systems based on the Von Neumann architecture continue to emerge, and thus, improving the efficiency of image processing has become a key issue that has bothered scholars working on images for a long time. Memristors with non-volatile, synapse-like, as well as integrated storage-and-computation properties can be used to build intelligent processing systems that are closer to the structure and function of biological brains. They are also of great significance when constructing new intelligent image processing systems with non-Von Neumann architecture and for achieving the integrated storage and computation of image data. Based on this, this paper analyses the mathematical models of memristors and discusses their applications in conventional image processing based on memristive systems as well as image processing based on memristive neural networks, to investigate the potential of memristive systems in image processing. In addition, recent advances and implications of memristive system-based image processing are presented comprehensively, and its development opportunities and challenges in different major areas are explored as well. By establishing a complete spectrum of image processing technologies based on memristive systems, this review attempts to provide a reference for future studies in the field, and it is hoped that scholars can promote its development through interdisciplinary academic exchanges and cooperationNational Natural Science Foundation of China (Grant U1909201, Grant 62001149); Natural Science Foundation of Zhejiang Province (Grant LQ21F010009)

Surveying trends in analogy-inspired product innovation

Analogies play a well-noted role in innovative design. Analogical reasoning is central to the practices of design-by-analogy and bio-inspired design. In both, analogies are used to derive abstracted principles from prior examples to generate new design solutions. While numerous laboratory and classroom studies of analogy usage have been published, relatively few studies have systematically examined real-world design-by-analogy to describe its characteristics and impacts. To better teach design-by-analogy and develop support tools for engineers, specific insights are needed regarding, for example, what types of product advantages are gained through design-by-analogy and how different design process characteristics influence its outcomes. This research comprises two empirical product studies which investigate analogical inspiration in real-world design to inform the development of new analogy methods and tools. The first, an exploratory pilot study of 57 analogy-inspired products, introduces the product study method and applies several categorical variables to classify product examples. These variables measure aspects such as the composition of the design team, the driving approach to analogical reasoning, and the achieved benefits of using the analogy-inspired concept. The full scale study of 70 analogy-inspired products uses formal collection and screening methods and a refined set of classification variables to analyze examples. It adopts a cross-sectional approach, using statistical tests of association to detect relationships among variables. Combined, these surveys of real-world analogy-inspired innovation inform the development of analogy tools and provide a general account of distant analogy usage across engineering disciplines. The cross-sectional product study method demonstrated in this work introduces a valuable tool for investigating factors and impacts of real-world analogy usage in design.M.S