Special Issue on Intelligent Integrated Systems for Human-Oriented Information Society

2000 ◽  
Vol 12 (5) ◽  
pp. 501-501
Author(s):  
Michitaka Kameyama ◽  
Keyword(s):  
Real World ◽  
Intelligent Transportation System ◽  
Data Transfer ◽  
Image Sensor ◽  
Sensor Data ◽  
Service Robot ◽  
Human Beings ◽  
Special Issue ◽  
Real World Data

Recent advance in the information technology makes our society very convenient from the viewpoint of human-to-human information communication. However, our new living style will require not only human-tohuman communication but also autonomous intelligent applications that support human beings such as an intelligent robot system, an intelligent transportation system, and a security/safe system as shown in Figure. These applications will contribute to human-oriented information society.Intelligent vehicle Home service robot Security The use of special-purpose VLSI processors capable of processing a large amount of real-world data is essential to make such applications realistic. In recent industrial trend, the special-purpose processors are called ""System LSIs"". One of the most important environmental informations in real-world applications is a vision information. The factor common to the applications is to catch an environment information moment by moment and to respond quickly with it. Therefore, it is important to make the response time from inputs to outputs very small. In this case, sensor data transfer bottleneck is not allowed as well as memory-to-PE (Processing Element) data transfer bottleneck. An image sensor signal processing VLSI together with image sensor devices is a key issue in such applications. From the above point of views, this special issue was planned to demonstrate the recent results of this area. Finally, I would like to express my appreciation to the authors for their efforts and contributions to this special issue and also the members of the Editorial Board for their cooperation.

scholarly journals Special Issue on VLSI Computing for Real-World Intelligent Systems

2005 ◽  
Vol 17 (4) ◽  
pp. 371-371
Author(s):  
Masanori Hariyama ◽  
Keyword(s):  
Real World ◽  
Visual Information ◽  
Intelligent Systems ◽  
High Speed ◽  
Data Transfer ◽  
Service Robots ◽  
Low Latency ◽  
Special Issue ◽  
Information Image

Recently, intelligent systems are desired to support human in real world such as advanced safe vehicles, home service robots, wearable computing devices, and intelligent home security systems. Such intelligent systems require extremely high computational power that exceeds that of state-of-the-art microprocessors. They also require <ul><li>Low power consumption</li><li>Low latency from input to output</li><li>Compactness</li></ul> Special-purpose processors called ""system LSIs"" play an essential role in meeting these requirements. This special issue focuses on the latest advances in system LSIs for real-world intelligent systems. One of their most important tasks is sensing environmental information such as visual information. Image and angle sensors, for example, are implemented in system LSIs. Image processing is the most time-consuming in real-world intelligent systems due to the extremely large amount of data. To overcome this problem, novel parallel architectures are presented. Electrical wires between processing modules must be minimized to make intelligent systems compact. High-speed serial data transfer is one most effective way to minimize the electrical wires. An architecture that handles processing order based on task priorities is a key to low latency. Processing of human interfaces such as face detection and speech recognition are also important factors in making intelligent systems user-friendly. I thank the authors of the articles in this issue for their effort and contributions, and the members of the Editorial Board for their cooperation.

scholarly journals DPUK Cohort Explorer: An Interactive Visualisation Tool

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Laura North
Keyword(s):  
Real World ◽  
Data Transfer ◽  
Real World Data ◽  
World Data ◽  
Visualisation Tool ◽  
Field Level ◽  
Level Information ◽  
Data Portal ◽  
Portal Website ◽  
Interactive Visualisation

IntroductionThe Dementias Platform UK (DPUK) Cohort Explorer is an interactive, online visualisation tool that allows users to explore data for a number of DPUK cohorts. Over 30 variables across cohorts have been harmonised, including information on demographics, lifestyle, cognition, health, and genetic biomarkers. Objectives and ApproachThe tool has been developed to complement existing DPUK cohort metadata to provide a visual representation of participant numbers and field-level information for a selection of cohorts. This enables users to determine a cohort’s eligibility before applying for access to a cohort’s data, and aid in shaping potential hypotheses. Developed using Microsoft PowerBI, the Explorer hosts a subset of the cohort’s baseline, harmonised data, allowing a user to interrogate the visualisations of the uploaded data in a secure manner on the DPUK Data Portal website. Visualisations are linked so that participant numbers and distributions can be explored interactively. ResultsThis approach allows the user to explore the harmonised data across a number of cohorts simultaneously whilst setting and adjusting filters that are of interest to the user’s search criteria. This provides a better understanding of the real-world data and enables the user to determine the feasibility of each cohort for potential studies, whilst facilitating meaningful comparisons across cohorts. The tool currently visualises five DPUK cohorts with a total of 82,391 participants, however it is being incrementally developed with more cohorts being added continually. Conclusion / ImplicationsBy combing an easy-to-use, interactive dashboard with harmonised sets of real-world data, the tool allows the user to explore, interrogate and better understand field-level information in a secure manner with zero data transfer. This provides more insight for the user when applying for access to a cohort dataset using the DPUK Data Portal and may help the user to make more informed decisions and/or hypotheses.

Special Issue on Mobiligence: Emergence of Adaptive Motor Function Through Interaction Among the Body, Brain and Environment

2007 ◽  
Vol 19 (4) ◽  
pp. 363-363
Author(s):  
Hajime Asama ◽  
◽  
Jun Ota ◽  
Keyword(s):  
Motor Function ◽  
Adaptive Behavior ◽  
Neurological Disorders ◽  
The Body ◽  
Human Beings ◽  
Special Issue ◽  
Adaptive Function ◽  
Motor Functions ◽  
Sensory Motor

Animals behave adaptively in diverse environments. Adaptive behavior, which is one of intelligent sensory-motor functions, is disturbed in patients with neurological disorders. Mechanisms for the generation of intelligent adaptive behaviors are not well understood. Such an adaptive function is considered to emerge from the interaction of the body, brain, and environment, which requires that a subject acts or moves. Intelligence for generating adaptive motor functions is thus called mobiligence. This special issue features papers dealing with mobiligence. The 18 papers were selected after a thorough peer review. The scope of these papers extends from analytical studies close to biology to synthetic studies close to engineering. Subjects are diverse – insects, monkeys, human beings, robots, networks. All papers play a part in mobiligence studies. We thank the Editorial Board of Journal of Robotics and Mechatronics for giving us the opportunity for publishing this special issue. We also thank the authors for their perseverance and expertise, and deeply appreciate the timely and helpful comments of the reviewers.

scholarly journals Developing a machine learning environmental allergy prediction model from real world data through a novel decentralized mobile study platform.

2020 ◽  
Author(s):  
Chethan Sarabu ◽  
Sandra Steyaert ◽  
Nirav Shah
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Predictive Models ◽  
Clinical Care ◽  
Sensor Data ◽  
Real World Data ◽  
World Data ◽  
Demographic Groups ◽  
Prediction And Prevention ◽  
Wide Range

Environmental allergies cause significant morbidity across a wide range of demographic groups. This morbidity could be mitigated through individualized predictive models capable of guiding personalized preventive measures. We developed a predictive model by integrating smartphone sensor data with symptom diaries maintained by patients. The machine learning model was found to be highly predictive, with an accuracy of 0.801. Such models based on real-world data can guide clinical care for patients and providers, reduce the economic burden of uncontrolled allergies, and set the stage for subsequent research pursuing allergy prediction and prevention. Moreover, this study offers proof-of-principle regarding the feasibility of building clinically useful predictive models from 'messy,' participant derived real-world data.

Special Issue on Human Modeling in Robotics

2005 ◽  
Vol 17 (6) ◽  
pp. 607-607
Author(s):  
Taketoshi Mori ◽  
Keyword(s):  
Personal Characteristics ◽  
Humanoid Robots ◽  
Research Field ◽  
Human Beings ◽  
Special Issue ◽  
Intelligent Support ◽  
Human Modeling ◽  
Psychological Models ◽  
Interface Devices

Human modeling is becoming an essential key technology for robotics and mechatronics systems that aid and expand human activities. Human modeling is indispensable in designing systems that conduct tasks difficult or even impossible for human beings to accomplish. Such systems include humanoid robots, power assistance suits, communication robots, intelligent support rooms, and user interface devices. This special issue focuses on the latest state-of-the-art human modeling research, especially in robotics, presenting a wide variety of human modeling areas. To support human beings in real-world environments, human behavior model is considerably important. Adaptation to personal characteristics may be the core function of next-generation system mechanisms, and human social modeling is the principal focus of interfacing for interaction systems. Cognitive and psychological models of human beings have always been an important domain in human-machine systems. Probabilistic and static methods have attracted attention in this research field. Not only mechanical but physiological human modeling may soon become 'vital' for all kind of robotic systems. This special issue is the kernel node for cultivating these rapidly advancing areas. I thank the authors of the articles in this issue for their invaluable effort and contributions. I also thank the members of the Editorial board, without whose work this special issue would not have been possible.

scholarly journals Overcoming Limitations of LoRa Physical Layer in Image Transmission

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3257 ◽  
Author(s):  
Akram Jebril ◽  
Aduwati Sali ◽  
Alyani Ismail ◽  
Mohd Rasid
Keyword(s):  
Data Transfer ◽  
Low Cost ◽  
Image Transmission ◽  
Image Sensor ◽  
Physical Layer ◽  
Forest Monitoring ◽  
Sensor Data ◽  
Area Network ◽  
Wide Area Network ◽  
The Future

As a possible implementation of a low-power wide-area network (LPWAN), Long Range (LoRa) technology is considered to be the future wireless communication standard for the Internet of Things (IoT) as it offers competitive features, such as a long communication range, low cost, and reduced power consumption, which make it an optimum alternative to the current wireless sensor networks and conventional cellular technologies. However, the limited bandwidth available for physical layer modulation in LoRa makes it unsuitable for high bit rate data transfer from devices like image sensors. In this paper, we propose a new method for mangrove forest monitoring in Malaysia, wherein we transfer image sensor data over the LoRa physical layer (PHY) in a node-to-node network model. In implementing this method, we produce a novel scheme for overcoming the bandwidth limitation of LoRa. With this scheme the images, which requires high data rate to transfer, collected by the sensor are encrypted as hexadecimal data and then split into packets for transfer via the LoRa physical layer (PHY). To assess the quality of images transferred using this scheme, we measured the packet loss rate, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) index of each image. These measurements verify the proposed scheme for image transmission, and support the industrial and academic trend which promotes LoRa as the future solution for IoT infrastructure.

scholarly journals Long-Term Impacts of Fair Machine Learning

2019 ◽  
Vol 28 (3) ◽  
pp. 7-11
Author(s):  
Xueru Zhang ◽  
Mohammad Mahdi Khalili ◽  
Mingyan Liu
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Human Beings ◽  
Learning Models ◽  
Real World Data ◽  
World Data ◽  
Fairness Concerns ◽  
Fairness Constraints ◽  
Machine Learning Models

Machine learning models developed from real-world data can inherit potential, preexisting bias in the dataset. When these models are used to inform decisions involving human beings, fairness concerns inevitably arise. Imposing certain fairness constraints in the training of models can be effective only if appropriate criteria are applied. However, a fairness criterion can be defined/assessed only when the interaction between the decisions and the underlying population is well understood. We introduce two feedback models describing how people react when receiving machine-aided decisions and illustrate that some commonly used fairness criteria can end with undesirable consequences while reinforcing discrimination.

A Purely Symbolic Model for Dynamic Scene Interpretation

1997 ◽  
Vol 06 (04) ◽  
pp. 635-664 ◽  
Author(s):  
Laurent Chaudron ◽  
Corine Cossart ◽  
Nicolas Maille ◽  
Catherine Tessier
Keyword(s):  
Petri Nets ◽  
Real World ◽  
Lattice Theory ◽  
Sensor Data ◽  
Dynamic Scene ◽  
Real World Data ◽  
Symbolic Model ◽  
Numerical Processing ◽  
Scene Interpretation ◽  
Interpretation System

The symbolic level of a dynamic scene interpretation system is presented. This symbolic level is based on plan prototypes represented by Petri nets whose interpretation is expressed thanks to 1st order cubes, and on a reasoning aiming at instantiating the plan prototypes with objects delivered by the numerical processing of sensor data. A purely symbolic meta-structure, grounded on the lattice theory, is then proposed to deal with the symbolic uncertainty issues. Examples on real world data are given.

scholarly journals Special Issue on Intelligent Integrated Systems for Robotics

1994 ◽  
Vol 6 (2) ◽  
pp. 119-119
Author(s):  
Michitaka Kameyama ◽  
Keyword(s):  
Real World ◽  
Integrated Circuit ◽  
Integrated Systems ◽  
Next Generation ◽  
Special Issue ◽  
Robot Systems ◽  
Scale Integration ◽  
Near Future ◽  
Circuit Technology

The new area of ""intelligent integrated systems"" has been proposed to develop one of the generic technologies for next-generation electronics and information systems. Although the interpretation may be different for individual persons, I think the area is the integration of the three concepts as shown in Figure. One is the concept of ""system on silicon"" using the integrated circuit technology. Giga-scale integration will be available in near future, so that we have to develop hardware and software architecture related to ultra highly parallel processing. Another is the concept of intelligence including physical model based computations as well as AI technology. The other is the concept of real-world applications just different from computer-world applications. The signal flow is passed through a real world, so that the performance should be evaluated as the response time or delay time. The examples are robotics, car electronics, home electronics, factory automation and so on. This special issue is planned to demonstrate the above important area, especially dedicated for robotics which is a typical example of the intelligent integrated systems. I believe that the contents of this issue give great impact on' the next-generation robot systems, and it will be a memorial publication. Finally, I would like to express my appreciation to the authors for their efforts and contributions to this special issue and also to the members of the Editorial Board for their useful comments.

Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers

2013 ◽  
pp. 154-163
Author(s):  
Jan Žižka ◽  
Vadim Rukavitsyn
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Classification Accuracy ◽  
Large Data ◽  
Classification Problem ◽  
Experimental Results ◽  
Human Beings ◽  
Real World Data ◽  
Natural Languages ◽  
Automatic Categorization

E-shopping customers, blog authors, reviewers, and other web contributors can express their opinions of a purchased item, film, book, and so forth. Typically, various opinions are centered around one topic (e.g., a commodity, film, etc.). From the Business Intelligence viewpoint, such entries are very valuable; however, they are difficult to automatically process because they are in a natural language. Human beings can distinguish the various opinions. Because of the very large data volumes, could a machine do the same? The suggested method uses the machine-learning (ML) based approach to this classification problem, demonstrating via real-world data that a machine can learn from examples relatively well. The classification accuracy is better than 70%; it is not perfect because of typical problems associated with processing unstructured textual items in natural languages. The data characteristics and experimental results are shown.

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