Non-Contact Physiological Measurement System for Wearing Masks During the Epidemic

Physiological signals indicate a person’s physical and mental state at any given time. Accordingly, many studies extract physiological signals from the human body with non-contact methods, and most of them require facial feature points. However, under COVID-19, wearing a mask has become a must in many places, so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research. In this study, RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate, blood pressure, respiratory rate, and forehead temperature for people wearing masks due to the pandemic. Using the green (G) minus red (R) signal in the RGB image, the region of interest (ROI) is established in the forehead and nose bridge regions. The photoplethysmography (PPG) waveforms of the two regions are obtained after the acquired PPG signal is subjected to the optical flow method, baseline drift calibration, normalization, and bandpass filtering. The relevant parameters in Deep Neural Networks (DNN) for the regression model can correctly predict the heartbeat and blood pressure. In addition, the temperature change in the ROI of the mask after thermal image processing and filtering can be used to correctly determine the number of breaths. Meanwhile, the thermal image can be used to read the temperature average of the ROI of the forehead, and the forehead temperature can be obtained smoothly. The experimental results show that the above-mentioned physiological signals of a subject can be obtained in 6-s images with the error for both heart rate and blood pressure within 2%~3% and the error of forehead temperature within ±0.5°C.     

Efficient bit-parallel multipliers over finite fields GF(2)

Hardware implementation of multiplication in finite field GF(2^m) based on sparse polynomials is found to be advantageous in terms of space-complexity as well as the time-complexity. In this paper, we present a new permutation method to construct the irreducible like-trinomials of the form (x + 1)^m + (x + 1)ⁿ + 1 for the implementation of efficient bit-parallel multipliers. For implementing the multiplications based on such polynomials, we have defined a like-polynomial basis (LPB) as an alternative to the original polynomial basis of GF(2^m). We have shown further that the modular arithmetic for the binary field based on like-trinomials is equivalent to the arithmetic for the field based on trinomials. In order to design multipliers for composite fields, we have found another permutation polynomial to convert irreducible polynomials into like-trinomials of the forms (x² + x + 1)^m + (x² + x + 1)ⁿ + 1, (x² + x)^m + (x² + x)ⁿ + 1 and (x⁴ + x + 1)^m + (x⁴ + x + 1)ⁿ + 1. The proposed bit-parallel multiplier over GF(2^4m) is found to offer a saving of about 33% multiplications and 42.8% additions over the corresponding existing architectures.     

Using ubiquitous games in an English listening and speaking course: Impact on learning outcomes and motivation

This paper reports the results of a study which aimed to investigate how ubiquitous games influence English learning achievement and motivation through a context-aware ubiquitous learning environment. An English curriculum was conducted on a school campus by using a context-aware ubiquitous learning environment called the Handheld English Language Learning Organization (HELLO). HELLO helps students to engage in learning activities based on the ARCS motivation theory, involving various educational strategies, including ubiquitous game-based learning, collaborative learning, and context-aware learning. Two groups of students participated in the learning activities prescribed in a curriculum by separately using ubiquitous game-based learning and non-gaming learning. The curriculum, entitled ‘My Campus’, included three learning activities, namely ‘Campus Environment’, ‘Campus Life’ and ‘Campus Story’. Participants included high school teachers and juniors. During the experiment, tests, a survey, and interviews were conducted for the students. The evaluation results of the learning outcomes and learning motivation demonstrated that incorporating ubiquitous games into the English learning process could achieve a better learning outcomes and motivation than using non-gaming method. They further revealed a positive relationship between learning outcomes and motivation.     

Development of high throughput microfluidic cell culture chip for perfusion 3-dimensional cell culture-based chemosensitivity assay

This study reports a microfluidic cell culture chip encompassing 36 microbioreactors for high throughput perfusion 3-dimensional (3D) cell culture-based chemosensitivity assays. Its advantages include the capability for multiplexed medium delivery, and the function for both efficient and high throughput micro-scale 3D culture construct preparation and loading. The results showed that the proposed medium pumping mechanism was able to provide a uniform pumping rates ranging from 1.2 to 3.9 μl h⁻¹. In addition, the simple cell/hydrogel loading scheme has been proven to be able to carry out 3D cell culture construct preparation and loading precisely and efficiently. Furthermore, a chemosensitivity assay was successfully demonstrated using the proposed cell culture chip. The results obtained were also compared with the same evaluation based on a conventional 2D monolayer cell culture. It can be concluded that the choice of cell culture format can result in different chemosensitivity evaluation results. Overall, because of the nature of miniaturized perfusion 3D cell culture, the cell culture chip not only can provide stable, well-defined and more biologically relevant culture environments, but it also features low consumption of research resources. All these traits are found particularly useful for high-precision and high-throughput 3D cell culture-based assays.     

The speaker identification by using genetic wavelet adaptive network based fuzzy inference system

In this paper, an intelligent speaker identification system is presented for speaker identification by using speech/voice signal. This study includes both combination of the adaptive feature extraction and classification by using optimum wavelet entropy parameter values. These optimum wavelet entropy values are obtained from measured Turkish speech/voice signal waveforms using speech experimental set. It is developed a genetic wavelet adaptive network based on fuzzy inference system (GWANFIS) model in this study. This model consists of three layers which are genetic algorithm, wavelet and adaptive network based on fuzzy inference system (ANFIS). The genetic algorithm layer is used for selecting of the feature extraction method and obtaining the optimum wavelet entropy parameter values. In this study, one of the eight different feature extraction methods is selected by using genetic algorithm. Alternative feature extraction methods are wavelet decomposition, wavelet decomposition – short time Fourier transform, wavelet decomposition – Born–Jordan time–frequency representation, wavelet decomposition – Choi–Williams time–frequency representation, wavelet decomposition – Margenau–Hill time–frequency representation, wavelet decomposition – Wigner–Ville time–frequency representation, wavelet decomposition – Page time–frequency representation, wavelet decomposition – Zhao–Atlas–Marks time–frequency representation. The wavelet layer is used for optimum feature extraction in the time–frequency domain and is composed of wavelet decomposition and wavelet entropies. The ANFIS approach is used for evaluating to fitness function of the genetic algorithm and for classification speakers. It has been evaluated the performance of the developed system by using noisy Turkish speech/voice signals. The test results showed that this system is effective in detecting real speech signals. The correct classification rate is about 91% for speaker classification.     

Ant colony search algorithms for optimal polygonal approximation of plane curves

This paper presents a new polygonal approximation method using ant colony search algorithm. The problem is represented by a directed graph such that the objective of the original problem becomes to find the shortest closed circuit on the graph under the problem-specific constraints. A number of artificial ants are distributed on the graph and communicate with one another through the pheromone trails which are a form of the long-term memory guiding the future exploration of the graph. The important properties of the proposed method are thoroughly investigated. The performance of the proposed method as compared to those of the genetic-based and the tabu search-based approaches is very promising.     

A comprehensive model of the effects of online store image on purchase intention in an e-commerce environment

The aim of this study is to identify structural relationships between aspects of online store image and purchase intention. Responses from 211 website visitors were analyzed using structural equation modeling (SEM) to examine the research hypotheses. The results demonstrated that enjoyment and familiarity are predictors of ease-of-use and settlement performance, respectively. Settlement performance and usefulness are positively related to purchase intention. The results provide some suggestions for online store owners to help them arrange budget priorities for website design. Moreover, it is important to manage image familiarity for an online store through image-enhancing techniques, such as advertising and publicity.     

Interface traps effect on the charge transport mechanisms in metal oxide semiconductor structures based on silicon nanocrystals

The transport phenomena in Metal-Oxide-Semiconductor (MOS) structures having silicon nanocrystals (Si-NCs) inside the dielectric layer has been investigated by high frequency Capacitance-Voltage (C-V) method and the Deep-Level Transient Spectroscopy (DLTS). For the reference samples without Si-NCs, we observe a slow electron trap for a large temperature range, which is probably a response of a series electron traps having a very close energy levels. A clear series of electron traps are evidenced in DLTS spectrum for MOS samples with Si-NCs. Their activation energies are comprised between 0.28 eV and 0.45 eV. Moreover, we observe in this DLTS spectrum, a single peak that appears at low temperature which we attributed to Si-NCs response. In MOS structure without Si-NCs, the conduction mechanism is dominated by the thermionic fast emission/capture of charge carriers from the highly doped polysilicon layer to Si-substrate through interface trap-states. However, at low temperature, the tunneling of charge carriers from highly Poly-Si to Si-substrate trough the trapping/detrapping mechanism in the Si-NCs contributed to the conduction mechanism for MOS with Si-NCs. These results are helpful to understand the principle of charge transport of MOS structures having a Si-NCs in the SiO_x = 1.5 oxide matrix.     

Scalable Gaussian Normal Basis Multipliers over GF(2<Superscript><Emphasis Type="Italic">m</Emphasis></Superscript>) Using Hankel Matrix-Vector Representation

This work presents a novel scalable multiplication algorithm for a type-t Gaussian normal basis (GNB) of GF(2^m). Utilizing the basic characteristics of MSD-first and LSD-first schemes with d-bit digit size, the GNB multiplication can be decomposed into n(n + 1) Hankel matrix-vector multiplications. where n = (mt + 1)/d. The proposed scalable architectures for computing GNB multiplication comprise of one d × d Hankel multiplier, four registers and one final reduction polynomial circuit. Using the relationship of the basis conversion from the GNB to the normal basis, we also present the modified scalable multiplier which requires only nk Hankel multiplications, where k = mt/2d if m is even or k = (mt − t + 2)/2d if m is odd. The developed scalable multipliers have the feature of scalability. It is shown that, as the selected digit size d ≥ 8, the proposed scalable architectures have significantly lower time-area complexity than existing digit-serial multipliers. Moreover, the proposed architectures have the features of regularity, modularity, and local interconnection ability. Accordingly, they are well suited for VLSI implementation.