A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification

Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).

Exploring Silent Speech Interfaces Based on Frequency-Modulated Continuous-Wave Radar

Speech is our most natural and efficient form of communication and offers a strong potential to improve how we interact with machines. However, speech communication can sometimes be limited by environmental (e.g., ambient noise), contextual (e.g., need for privacy), or health conditions (e.g., laryngectomy), preventing the consideration of audible speech. In this regard, silent speech interfaces (SSI) have been proposed as an alternative, considering technologies that do not require the production of acoustic signals (e.g., electromyography and video). Unfortunately, despite their plentitude, many still face limitations regarding their everyday use, e.g., being intrusive, non-portable, or raising technical (e.g., lighting conditions for video) or privacy concerns. In line with this necessity, this article explores the consideration of contactless continuous-wave radar to assess its potential for SSI development. A corpus of 13 European Portuguese words was acquired for four speakers and three of them enrolled in a second acquisition session, three months later. Regarding the speaker-dependent models, trained and tested with data from each speaker while using 5-fold cross-validation, average accuracies of 84.50% and 88.00% were respectively obtained from Bagging (BAG) and Linear Regression (LR) classifiers, respectively. Additionally, recognition accuracies of 81.79% and 81.80% were also, respectively, achieved for the session and speaker-independent experiments, establishing promising grounds for further exploring this technology towards silent speech recognition.

Effects of Cage Position and Light Transmission on Home Cage Activity and Circadian Entrainment in Mice

Light is known to exert powerful effects on behavior and physiology, including upon the amount and distribution of activity across the day/night cycle. Here we use home cage activity monitoring to measure the effect of differences in home cage light spectrum and intensity on key circadian activity parameters in mice. Due to the relative positioning of any individually ventilated cage (IVC) with regard to the animal facility lighting, notable differences in light intensity occur across the IVC rack. Although all mice were found to be entrained, significant differences in the timing of activity onset and differences in activity levels were found between mice housed in standard versus red filtering cages. Furthermore, by calculating the effective irradiance based upon the known mouse photopigments, a significant relationship between light intensity and key circadian parameters are shown. Perhaps unsurprisingly given the important role of the circadian photopigment melanopsin in circadian entrainment, melanopic illuminance is shown to correlate more strongly with key circadian activity parameters than photopic lux. Collectively, our results suggest that differences in light intensity may reflect an uncharacterized source of variation in laboratory rodent research, with potential consequences for reproducibility. Room design and layout vary within and between facilities, and caging design and lighting location relative to cage position can be highly variable. We suggest that cage position should be factored into experimental design, and wherever possible, experimental lighting conditions should be characterized as a way of accounting for this source of variation.

Lighting conditions as the occupational health related issue – case study

The paper deals with the important element of proper lighting conditions at a workplace. The case study has been focused on the intelligent building “Energis” of Kielce University of Technology, where the experimental tests took place. Several groups of volunteers filled in the questionnaires related to their subjective feelings of lighting conditions in lecture rooms at different seasons. Simultaneously, precise measurements of lighting intensity were carried out. The comparison of the expressions of the room users and the measurements enabled to draw conclusions about the conditions provided in the intelligent building related to lighting. The study provides valuable information of lighting conditions in the modern, intelligent buildings which are more and more common throughout the world and serve various purposes such as office, educational or other public utility buildings.

Cognitive Distractions During Sonographic Procedures: Reducing Noise-Based and Light-Based Interruptions in Hospital Rooms

Sonographers experience a high cognitive load in hospital-based care. High ambient noise and frequent noise-based interruptions include knocking on the room door, questions from others in the room or through communication technology, alarms, alerts from personal devices, and carts and people passing in the hallway. In addition, other providers turning on the overhead light is distracting for exams that need to be conducted in reduced lighting conditions. This article suggests strategies to improve working conditions for sonographers conducting exams on a patient in the hospital room. Our strategies emerge from human factors methods and principles, which derive from communication principles and theory. These strategies are organized by reducing noise-based and light-based interruptions in the hospital room and hallway, primarily through changes to the built environment and communication technology settings and reducing the use of speech during cognitively challenging time periods through training. Most of the strategies are low-cost and can be implemented within the current built environment and communication technology infrastructure. We anticipate that these strategies could enhance patient outcomes, increase patient satisfaction, improve sonographers’ job satisfaction, protect provider health, and increase procedural efficiency.

Effect of Ambient Lighting on Frequency Dependence in Transcranial Electrical Stimulation-Induced Phosphenes

Inconsistencies have been found in the relationship between ambient lighting conditions and frequency-dependence in transcranial electric current stimulation (tECS) induced phosphenes. Using a within-subjects design across lighting condition (dark, mesopic [dim], photopic [bright]) and tECS stimulation frequency (10, 13, 16, 18, 20 Hz), this study determined phosphene detection thresholds in 24 subjects receiving tECS using an FPz-Cz montage. Minima phosphene thresholds were found at 16 Hz in mesopic, 10 Hz in dark and 20 Hz in photopic lighting conditions, with these thresholds being substantially lower for mesopic than both dark (60% reduction) and photopic (56% reduction), conditions. Further, whereas the phosphene threshold-stimulation frequency relation was linear in the dark (increasing with frequency) and photopic (decreasing with frequency) conditions, a quadratic function was found for the mesopic condition (where it followed the linear increase of the dark condition from 10-16 Hz, and the linear decrease of the photopic condition from 16-20 Hz). The results clearly demonstrate that ambient lighting is an important factor in the detection of tECS-induced phosphenes, and that mesopic conditions are most suitable for obtaining overall phosphene thresholds.

A Decision Support System for Power Components Based on Improved YOLOv4-Tiny

The traditional image object detection algorithm applied in power inspection cannot effectively position power components, and the accuracy of recognition is low in scenes with some interference. In this research, we proposed a data-driven power detection method based on the improved YOLOv4-tiny model, which combined the ResNet-D module and the adjusted Res-CBAM to the backbone network of the existing YOLOv4-tiny module. We replaced the CSPOSANet module in the YOLOv4-tiny backbone network with the ResNet-D module to reduce the FLOPS required by the model. At the same time, the adjusted Res-CBAM whose feature fusion ways were replaced with stacking in the channels was combined as an auxiliary classifier. Finally, the features of five different receptive scales were used for prediction, and the display of the results was optimized by merging the prediction boxes. In the experiment, 57134 images collected on the power inspection line were processed and labeled, and the default anchor boxes were re-clustered, and the speed and accuracy of the model were evaluated by video and validation set of 3459 images. Processing multiple pictures and videos collected from the power inspection projects, we re-clustered the default anchor box and tested the speed and accuracy of the model. The results show that compared with the original YOLOv4-tiny model, the accuracy of our method that can position objects under occlusion and complex lighting conditions is guaranteed while the detection speed is about 13% faster.

Benzene and NOx photocatalytic assisted removal using indoor lighting conditions

Modern life-style is creating an indoor generation: human beings spend approximately 90% of their time indoors, almost 70% of which is at home – this trend is now exacerbated by the lockdowns/restrictions imposed due to the COVID-19 pandemic. That large amount of time spent indoors may have negative consequences on health and well-being. Indeed, poor indoor air quality is linked to a condition known as sick building syndrome. Therefore, breathing the freshest air possible it is of outmost importance. Still, due to reduced ventilation rates, indoor air quality can be considerably worse than outdoor. HVAC, air filtration systems and a well-ventilated space are a partial answer. However, these approaches involve only a physical removal. Photocatalytic mineralisation of pollutants into non-hazardous, or at least less dangerous compounds, is a more viable solution for their removal. Titanium dioxide, the archetype photocatalytic material, needs UVA light to be “activated”. However, modern household light emitting diode lamps irradiate only in the visible region of the solar spectrum. In this short-communication, we show that the surface of titanium dioxide nanoparticles modified with copper oxide(s) and graphene shows promise as a viable way to remove gaseous pollutants (benzene and NOx) by using a common light emitting diode bulb, mimicking real indoor lighting conditions. Titanium dioxide, modified with 1 mol% CuxO and 1 wt% graphene, proved to have a stable photocatalytic degradation rate, three times higher than that of unmodified titania. Materials produced in this research work are thus strong candidates for offering a safer indoor environment.