Industry 4.0: Hand Recognition on Assembly Supervision Process

In the assembly industry, the process of assembling components is very important in order to produce a quality product. Assembly of components should be carried out sequentially based on the standards set by the company. For companies that still operate the assembly process manually by employee, sometimes errors occur in the assembly process, which can affect the quality of production. In order to be carried out the assembly process according to the procedure, a system is needed that can detect employee hands when carrying out the assembly process automatically. This study proposes an artificial intelligence-based real-time employee hand detection system. This system will be the basis for the development of an automatic industrial product assembly process to welcome the Industry 4.0. To verify system performance, several experiments were carried out, such as; detecting the right and left hands of employees and detecting hands when using accessories or not. From the experimental results it can be concluded that the system is able to detect the right and left hands of employees well with the resulting FPS average of 15.4.Pada industri perakitan, proses merakit komponen merupakan hal yang sangat penting guna menghasilkan produk yang berkualitas. Perakitan komponen hendaklah dilakukan secara urut berdasarkan standar yang telah ditentukan oleh perusahaan. Bagi perusahaan yang masih menggunakan proses perakitan secara manual yakni dengan menggunakan tenaga manusia, terkadang terjadi kesalahan dalam proses perakitan, sehingga dapat mempengaruhi kualitas produksi. Agar proses perakitan dapat dilakukan sesuai prosedur, maka diperlukan sebuah sistem yang dapat mendeteksi tangan karyawan ketika melakukan proses perakitan secara otomatis. Penelitian ini mengusulkan sistem pendeteksian tangan karyawan secara real-time berbasis kecerdasan buatan. Sistem ini akan menjadi dasar untuk pengembangan proses perakitan produk industri secara otomatis untuk menyambut industri 4.0. Untuk memverifikasi kinerja sistem, beberapa percobaan dilakukan yaitu mendeteksi tangan kanan dan kiri karyawan serta mendeteksi tangan ketika menggunakan aksesoris atau tidak. Dari hasil percobaan dapat disimpulkan bahwa sistem mampu mendeteksi tangan kanan dan kiri karyawan dengan baik dengan rata-rata FPS yang dihasilkan adalah 15.4

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).This work is funded by FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020)

DNA-based client puzzle for WLAN association protocol against connection request flooding

In recent past, Wireless Local Area Network (WLAN) has become more popular because of its flexibility. However, WLANs are subjected to different types of vulnerabilities. To strengthen WLAN security, many high security protocols have been developed. But those solutions are found to be ineffective in preventing Denial of Service (DoS) attacks. A ‘Connection Request Flooding’ DoS (CRF-DoS) attack is launched when an access point (AP) encounters a sudden explosion of connection requests. Among other existing anti CRF-DoS methods, a client puzzle protocol has been noted as a promising and secure potential solution. Nonetheless, so far none of the proposed puzzles satisfy the security requirement of resource-limited and highly heterogeneous WLANs. The CPU disparity, imposing unbearable loads on legitimate users, inefficient puzzle generation and verification algorithms; the susceptibility of puzzle to secondary attacks on legitimate users by embedding fake puzzle parameters; and a notable delay in modifying the puzzle difficulty – these are some drawbacks of currently existing puzzles. To deal with such problems, a secure model of puzzle based on DNA and queuing theory is proposed, which eliminates the above defects while satisfying the Chen puzzle security model. The proposed puzzle (OROD puzzle) is a multifaceted technology that incorporates five main components include DoS detector, queue manager, puzzle generation, puzzle verification, and puzzle solver. To test and evaluate the security and performance, OROD puzzle is developed and implemented in real-world environment. The experimental results showed that the solution verification time of OROD puzzle is up to 289, 160, 9, 3.2, and 2.3 times faster than the Karame-Capkun puzzle, the Rivest time-lock puzzle, the Rangasamy puzzle, the Kuppusamy DLPuz puzzle, and Chen's efficient hash-based puzzle respectively. The results also showed a substantial reduction in puzzle generation time, making the OROD puzzle from 3.7 to 24 times faster than the above puzzles. Moreover, by asking to solve an easy and cost-effective puzzle in OROD puzzle, legitimate users do not suffer from resource exhaustion during puzzle solving, even when under severe DoS attack (high puzzle difficulty)

Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care

Background: Mobile and wearable technology presents exciting opportunities for monitoring behavior using widely available sensor data. This could support clinical research and practice aimed at improving quality of life among the growing number of people with dementia. However, it requires suitable tools for measuring behavior in a natural real-life setting that can be easily implemented by others. Objective: The objectives of this study were to develop and test a set of algorithms for measuring mobility and activity and to describe a technical setup for collecting the sensor data that these algorithms require using off-the-shelf devices. Methods: A mobility measurement module was developed to extract travel trajectories and home location from raw GPS (global positioning system) data and to use this information to calculate a set of spatial, temporal, and count-based mobility metrics. Activity measurement comprises activity bout extraction from recognized activity data and daily step counts. Location, activity, and step count data were collected using smartwatches and mobile phones, relying on open-source resources as far as possible for accessing data from device sensors. The behavioral monitoring solution was evaluated among 5 healthy subjects who simultaneously logged their movements for 1 week. Results: The evaluation showed that the behavioral monitoring solution successfully measures travel trajectories and mobility metrics from location data and extracts multimodal activity bouts during travel between locations. While step count could be used to indicate overall daily activity level, a concern was raised regarding device validity for step count measurement, which was substantially higher from the smartwatches than the mobile phones. Conclusions: This study contributes to clinical research and practice by providing a comprehensive behavioral monitoring solution for use in a real-life setting that can be replicated for a range of applications where knowledge about individual mobility and activity is relevant

New methods of partial transmit sequence for reducing the high peak-to-average-power ratio with low complexity in the ofdm and f-ofdm systems

The orthogonal frequency division multiplexing system (OFDM) is one of the most important components for the multicarrier waveform design in the wireless communication standards. Consequently, the OFDM system has been adopted by many high-speed wireless standards. However, the high peak-to-average- power ratio (PAPR) is the main obstacle of the OFDM system in the real applications because of the non-linearity nature in the transmitter. Partial transmit sequence (PTS) is one of the effective PAPR reduction techniques that has been employed for reducing the PAPR value 3 dB; however, the high computational complexity is the main drawback of this technique. This thesis proposes novel methods and algorithms for reducing the high PAPR value with low computational complexity depending on the PTS technique. First, three novel subblocks partitioning schemes, Sine Shape partitioning scheme (SS-PTS), Subsets partitioning scheme (Sb-PTS), and Hybrid partitioning scheme (H-PTS) have been introduced for improving the PAPR reduction performance with low computational complexity in the frequency-domain of the PTS structure. Secondly, two novel algorithms, Grouping Complex iterations algorithm (G-C-PTS), and Gray Code Phase Factor algorithm (Gray-PF-PTS) have been developed to reduce the computational complexity for finding the optimum phase rotation factors in the time domain part of the PTS structure. Third, a new hybrid method that combines the Selective mapping and Cyclically Shifts Sequences (SLM-CSS-PTS) techniques in parallel has been proposed for improving the PAPR reduction performance and the computational complexity level. Based on the proposed methods, an improved PTS method that merges the best subblock partitioning scheme in the frequency domain and the best low-complexity algorithm in the time domain has been introduced to enhance the PAPR reduction performance better than the conventional PTS method with extremely low computational complexity level. The efficiency of the proposed methods is verified by comparing the predicted results with the existing modified PTS methods in the literature using Matlab software simulation and numerical calculation. The results that obtained using the proposed methods achieve a superior gain in the PAPR reduction performance compared with the conventional PTS technique. In addition, the number of complex addition and multiplication operations has been reduced compared with the conventional PTS method by about 54%, and 32% for the frequency domain schemes, 51% and 65% for the time domain algorithms, 18% and 42% for the combining method. Moreover, the improved PTS method which combines the best scheme in the frequency domain and the best algorithm in the time domain outperforms the conventional PTS method in terms of the PAPR reduction performance and the computational complexity level, where the number of complex addition and multiplication operation has been reduced by about 51% and 63%, respectively. Finally, the proposed methods and algorithms have been applied to the OFDM and Filtered-OFDM (F-OFDM) systems through Matlab software simulation, where F-OFDM refers to the waveform design candidate in the next generation technology (5G)

Neurophysiological assessment of an innovative maritime safety system in terms of ship operators' mental workload, stress, and attention in the full mission bridge simulator

The current industrial environment relies heavily on maritime transportation. Despite the continuous technological advances for the development of innovative safety software and hardware systems, there is a consistent gap in the scientific literature regarding the objective evaluation of the performance of maritime operators. The human factor is profoundly affected by changes in human performance or psychological state. The difficulty lies in the fact that the technology, tools, and protocols for investigating human performance are not fully mature or suitable for experimental investigation. The present research aims to integrate these two concepts by (i) objectively characterizing the psychological state of mariners, i.e., mental workload, stress, and attention, through their electroencephalographic (EEG) signal analysis, and (ii) validating an innovative safety framework countermeasure, defined as Human Risk-Informed Design (HURID), through the aforementioned neurophysiological approach. The proposed study involved 26 mariners within a high-fidelity bridge simulator while encountering collision risk in congested waters with and without the HURID. Subjective, behavioral, and neurophysiological data, i.e., EEG, were collected throughout the experimental activities. The results showed that the participants experienced a statistically significant higher mental workload and stress while performing the maritime activities without the HURID, while their attention level was statistically lower compared to the condition in which they performed the experiments with the HURID (all p < 0.05). Therefore, the presented study confirmed the effectiveness of the HURID during maritime operations in critical scenarios and led the way to extend the neurophysiological evaluation of the HFs of maritime operators during the performance of critical and/or standard shipboard tasks

Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature.This work was supported by FCT project UID/EEA/50008/2013. The authors would also like to acknowledge the contribution of the COST Action IC1303–AAPELE–Architectures, Algorithms and Protocols for Enhanced Living Environments