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Purpose – The purpose of this research is to design and build a wild animal pest repellent device with combination of passive infrared (PIR) sensor and ultrasonic signal based on microcontroller as system controller. The PIR sensor is used to detect the presence of wild animal objects and ultrasonic signals to interfere with the hearing.

Design/Methodology/Approach – The design of the system is built based on microcontroller as the system controller. The system as a whole includes hardware and software. The design of hardware consists of the system design on the transmitter side and the system design on the receiver side, while the software in the of system are algorithms using C language programming.

Findings – The resulting repellent device can detect animals approaching up to a distance of 5 m and may interfere with its hearing with a 40 kHz ultrasonic frequency up to a distance of 20 m. The system also uses remote monitoring devices using 433 MHz radio frequency up to a distance of 60 m.

Research Limitations/Implications – Each animal has different hearing frequencies, as well as some wild animals, but the hearing frequencies of wild animals are generally at ultrasonic frequencies. The frequency of animal hearing may vary from audio frequency to ultrasonic frequency, so ultrasonic wave emission testing with varying frequencies is required.

Practical Implications – This research combines systems on transmitters and receivers, with real-time monitoring of wild animal positions, and it can be possible to monitor the position of more detailed animals by installing more types of sensors as well as increasing the number of sensors.

Originality/Value – This paper may provide additional insight into the hearing frequencies of animals and may also serve as comparable papers for similar studies.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

An accurate detection of overhead ground wire under open surroundings with varying illumination is the premise of reliable line grasping with the off-line arm when the inspection robot cross obstacle automatically. This paper aims to propose an improved approach which is called adaptive homomorphic filter and supervised learning (AHSL) for overhead ground wire detection.

First, to decrease the influence of the varying illumination caused by the open work environment of the inspection robot, the adaptive homomorphic filter is introduced to compensation the changing illumination. Second, to represent ground wire more effectively and to extract more powerful and discriminative information for building a binary classifier, the global and local features fusion method followed by supervised learning method support vector machine is proposed.

Experiment results on two self-built testing data sets A and B which contain relative older ground wires and relative newer ground wire and on the field ground wires show that the use of the adaptive homomorphic filter and global and local feature fusion method can improve the detection accuracy of the ground wire effectively. The result of the proposed method lays a solid foundation for inspection robot grasping the ground wire by visual servo.

This method AHSL has achieved 80.8 per cent detection accuracy on data set A which contains relative older ground wires and 85.3 per cent detection accuracy on data set B which contains relative newer ground wires, and the field experiment shows that the robot can detect the ground wire accurately. The performance achieved by proposed method is the state of the art under open environment with varying illumination.

This paper describes a two-month summer intensive course designed to introduce participants with a hands-on technical craft on robotics and to acquire experience in the low-level details of embedded systems. Attendants started this course with a brief introduction to robotics; learned to draw, design and create a personalized 3D structure for their mobile robotic platform and developed skills in embedded systems. They were familiarize with the practices used in robotics, learning to connect all sensors and actuator, developing a typical application on differential kinematic using Arduino, exploring ROS features under Raspberry Pi environment and Arduino – Raspberry Pi communication. Different paradigms and some real applications and programming were addressed on the topic of Artificial Intelligence. Throughout the course, participants were introduced to programming languages (including Python and C++), advanced programming concepts such as ROS, basic API development, system concepts such as I2C and UART serial interfaces, PWM motor control and sensor fusion to improve robotic navigation and localization. This paper describes not just the concept, layout and methodology used on RobotCraft 2017 but also presents the participants knowledge background and their overall opinions, leading to focus on lessons learned and suggestions for future editions.

Values of parameters such as temperature, humidity, number of plastic products and the location of plastic injection moulds are required to determine the efficiency of plastic injection moulds with a view to improving the quality of the outputs. This article determined the appropriate sensors for the measurement of these essential parameters in the most suitable form of representation of the data to aid a proficient analysis of the data.

The outputs of these sensors were obtained by connecting the sensors to the general-purpose input/output (GPIO) pins of a Raspberry Pi and writing a Python programme for the connected GPIO pins. The values of the outputs of these sensors were represented in a graphical form. The connection of the Raspberry Pi and the sensors were done with a full-sized breadboard and jumper wires. A computer-aided design (CAD) of the connections was produced using Fritzing software.

The appropriate sensors determined are MLX90614 infrared thermometer sensor, DHT11 humidity sensor, pixy2 vision sensor and Neo-6m GPS sensor. This study proposed that the sensors analytic system be applied on an industrial plastic injection mould to measure and display the various parameters of the injection moulds for the purpose of understanding and improving the performance of the injection mould

An electronic system that provides the continuous values of essential parameters of a plastic injection mould in operation.

Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.

In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.

Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.

A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.

The purpose of this study is to generate knowledge about the use of smart campus tools to improve the effective and efficient use of campuses. Many universities are facing a challenge in attuning their accommodation to organisational demand. How can universities invest their resources as effectively as possible and not in space that will be poorly utilized? The hypothesis of this paper is that by using smart campus tools, this problem can be solved.

To answer the research question, previous survey at 13 Dutch universities was updated and compared with a survey of various universities and other organizations. The survey consisted of interviews with structured and semi-structured questions, which resulted in a unified output for 27 cases.

Based on the output of the cases, the development of smart campus tools at Dutch universities was compared to that of international universities and other organizations. Furthermore, the data collection led to insights regarding the reasons for initiating smart campus tools, user and management information, costs and benefits and foreseen developments.

Although the use of smart tools in practice has gained significant momentum in the past few years, research on the subject is still very technology-oriented and not well-connected to facility management and real estate management. This paper provides an overview of the ways in which universities and organizations are currently supporting their users, improving the use of their buildings and reducing their energy footprint through the use of smart tools.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

Improvement of reverse supply chains requires accurate and timely information about the patterns of consumption. In the consumer context, the ways to generate and access such use-visibility data are in their infancy. The purpose of this study is to demonstrate how the Internet of Things (IoT) may be operationalised in the domestic setting to capture data on a consumer’s use of products and the implications for reverse supply chains.

This study uses an explorative case approach drawing on data from studies of six UK households. “Horizontal” data, which reveals patterns in consumers’ use processes, is generated by combining “vertical” data from multiple sources. Use processes in the homes are mapped using IDEF0 and illustrated with the data. The quantitative data are generated using wireless sensors in the home, and qualitative data are drawn from online calendars, social media, interviews and ethnography.

The study proposes four generic measurement categories for operationalising the concept of use-visibility: experience, consumption, interaction and depletion, which together address the use of different household resources. The explorative case demonstrates how these measures can be operationalised to achieve visibility of the context of use in the home. The potential of such use-visibility for reverse supply chains is discussed.

This explorative case study is based on an in-depth study of the bathroom which illustrates the application of use-visibility measures (UVMs) but provides a limited use context. Further research is needed from a wider set of homes and a wider set of use processes and contexts.

The case demonstrates the operationalisation of the combination of data from different sources and helps answer questions of “why?”, “how?”, “when?” and “how much?”, which can inform reverse supply chains. The four UVMs can be operationalised in a way that can contribute to supply chain visibility, providing accurate and timely information of consumption, optimising resource use and eliminating waste.

IDEF0 framework and case analysis is used to identify and validate four UVMs available through IoT data – that of experience, consumption, interaction and depletion. The UVMs characterise IoT data generated from a given process and inform the primary reverse flow in the future supply chain. They provide the basis for future data collection and development of theory around their effect on reverse supply chain efficiency.

Construction organizations must maintain a productive workforce without sacrificing their health and safety. The global construction sector loses billions of dollars yearly to poor health and safety practices. This study aims to investigate benefits derivable from using wearable technologies to improve construction health and safety. The study also reports the challenges associated with adopting wearable technologies.

The study adopted a quantitative design, administering close-ended questions to professionals in the Nigerian construction industry. The research data were analysed using descriptive and inferential statistics.

The study found that the critical areas construction organizations can benefit from using WSDs include slips and trips, sensing environmental concerns, collision avoidance, falling from a high level and electrocution. However, key barriers preventing the organizations from adopting wearable technologies are related to cost, technology and human factors.

The time and cost lost to H&S incidents in the Nigerian construction sector can be reduced by implementing the report of this study.

Studies on WSDs have continued to increase in developed countries, but Nigeria is yet to experience a leap in the research area. This study provides insights into the Nigerian reality to provide directions for practice and theory.