Notes

Aftereffect of annealing temperatures about architectural, to prevent, and photocatalytic properties involving titanium dioxide nanoparticles.
Four different tests using single as well as multiple stimuli are presented. Observations on performances, noise, and taxel array behaviour are reported. The results show that the taxel array is reliable and effective in detecting the applied stimuli.IEEE 802.11ax uplink orthogonal frequency division multiple access (OFDMA)-based random access (UORA) is a new feature for random channel access in wireless local area networks (WLANs). Similar to the legacy random access scheme in WLANs, UORA performs the OFDMA backoff (OBO) procedure to access the channel and decides on a random OBO counter within the OFDMA contention window (OCW) value. An access point (AP) can determine the OCW range and inform each station (STA) of it. However, how to determine a reasonable OCW range is beyond the scope of the IEEE 802.11ax standard. The OCW range is crucial to the UORA performance, and it primarily depends on the number of contending STAs, but it is challenging for the AP to accurately and quickly estimate or keep track of the number of contending STAs without the aid of a specific signaling mechanism. In addition, the one for this purpose incurs an additional delay and overhead in the channel access procedure. Therefore, the performance of a UORA scheme can be degraded by an improper OCW range, especially when the number of contending STAs changes dynamically. We first observed the effect of OCW values on channel efficiency and derived its optimal value from an analytical model. Next, we proposed a simple yet effective OBO control scheme where each STA determines its own OBO counter in a distributed manner rather than adjusting the OCW value globally. In the proposed scheme, each STA determines an appropriate OBO counter depending on whether the previous transmission was successful or not so that collisions can be mitigated without leaving OFDMA resource units unnecessarily idle. The results of a simulation study confirm that the throughput of the proposed scheme is comparable to the optimal OCW-based scheme and is improved by up to 15 times compared to the standard UORA scheme.In this contribution, three methodologies based on temperature-sensitive paint (TSP) data were further developed and applied for the optical determination of the critical locations of flow separation and reattachment in compressible, high Reynolds number flows. Panobinostat nmr The methodologies rely on skin-friction extraction approaches developed for low-speed flows, which were adapted in this work to study flow separation and reattachment in the presence of shock-wave/boundary-layer interaction. In a first approach, skin-friction topological maps were obtained from time-averaged surface temperature distributions, thus enabling the identification of the critical lines as converging and diverging skin-friction lines. In the other two approaches, the critical lines were identified from the maps of the propagation celerity of temperature perturbations, which were determined from time-resolved TSP data. The experiments were conducted at a freestream Mach number of 0.72 and a chord Reynolds number of 9.7 million in the Transonic Wind Tunnel Göttingen on a VA-2 supercritical airfoil model, which was equipped with two exchangeable TSP modules specifically designed for transonic, high Reynolds number tests. The separation and reattachment lines identified via the three different TSP-based approaches were shown to be in mutual agreement, and were also found to be in agreement with reference experimental and numerical data.Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user's features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7°, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking systems.Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.Smart home services (SHS) should support the positive experiences of the elderly in homes with a focus on getting closer to nature. The study identified the services preferred by the elderly through a survey on the biophilic experience-based SHS, and to discuss the configuration of the sensors and devices required to provide the service. We reorganized the biophilic experience-based SHS and related sensors and devices, focusing on our previous study, and developed a survey instrument. A preference survey was conducted on 250 adults aged 20 and older, and the SPSS program was used for a factor analysis and independent two-sample T-test. We derived six factors for biophilic experience-based SHS. Compared to other age groups, the elderly preferred services that were mainly attributed to factors such as 'Immersion and interaction with nature' (A), 'Management of well-being and indoor environmental quality (IEQ)' (B), and 'Natural process and systems' (F). We proposed 15 prioritized services, along with their sensor and device configurations, in consideration of service provision regarding the elderly's preferences and universality. This study contributes to new developments in elderly-friendly smart home research by converting bio-friendly ideas into the market in the development of medical services and SHS for the elderly.Many electronic power distribution systems have strong needs for highly efficient AC-DC conversion that can be satisfied by using a buck-boost converter at the core of the power factor correction (PFC) stage. These converters can regulate the input voltage in a wide range with reduced efforts compared to other solutions. As a result, buck-boost converters could potentially improve the efficiency in applications requiring DC voltages lower than the peak grid voltage. This paper compares SEPIC, noninverting, and versatile buck-boost converters as PFC single-phase rectifiers. The converters are designed for an output voltage of 200 V and an rms input voltage of 220 V at 3.2 kW. The PFC uses an inner discrete-time predictive current control loop with an output voltage regulator based on a sensorless strategy. A PLECS thermal simulation is performed to obtain the power conversion efficiency results for the buck-boost converters considered. Thermal simulations show that the versatile buck-boost (VBB) converter, currently unexplored for this application, can provide higher power conversion efficiency than SEPIC and non-inverting buck-boost converters. Finally, a hardware-in-the-loop (HIL) real-time simulation for the VBB converter is performed using a PLECS RT Box 1 device. At the same time, the proposed controller is built and then flashed to a low-cost digital signal controller (DSC), which corresponds to the Texas Instruments LAUNCHXL-F28069M evaluation board. The HIL real-time results verify the correctness of the theoretical analysis and the effectiveness of the proposed architecture to operate with high power conversion efficiency and to regulate the DC output voltage without sensing it while the sinusoidal input current is perfectly in-phase with the grid voltage.Fluorescent markers are widely used to protect banknotes, passports, and other documents. Verification of such documents relies upon visual assessment of the markers revealed by ultraviolet (UV) radiation. However, such an explicit approach is inappropriate in certain circumstances, e.g., when discretely checking people for marks left by a pepper gel thrower. The UV light and fluorescent light must not be visible in such applications, yet reliable detection of the markers must still be performed. This problem was successfully resolved using TRIZ methodology, which led to a patent application. The main idea of the solution is to use low-intensity time-variable UV light for illuminating an object and process the image of the object acquired by a camera to detect colour changes too small to be noticed with the naked eye. This paper describes how popular graphics editors such as Adobe Photoshop Elements were used to validate the system concept devised. Simulation experiments used images taken in both visible and UV light to assess the effectiveness and perceptibility of the detection process. The advantage of such validation comes from using commodity software and performing the experiments without access to a laboratory and without physical samples, which makes this approach especially suitable in pandemic times.The CNN (convolutional neural network)-based small target detection techniques for static complex scenes have been applied in many fields, but there are still certain technical challenges. This paper proposes a novel high-resolution small-target detection network named the IIHNet (information interworking high-resolution network) for complex scenes, which is based on information interworking processing technology. The proposed network not only can output a high-resolution presentation of a small target but can also keep the detection network simple and efficient. The key characteristic of the proposed network is that the target features are divided into three categories according to image resolution high-resolution, medium-resolution, and low-resolution features. The basic features are extracted by convolution at the initial layer of the network. Then, convolution is carried out synchronously in the three resolution channels with information fusion in the horizontal and vertical directions of the network. At the same time, multiple utilizations and augmentations of feature information are carried out in the channel convolution direction.
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