Notes

Treating electroplating wastewater employing an incorporated tactic of internal microelectrolysis as well as Fenton along with reuse ferrite.
In this work, we study and analyze the reconstruction of hyperspectral images that are sampled with a CASSI device. The sensing procedure was modeled with the help of the CS theory, which enabled efficient mechanisms for the reconstruction of the hyperspectral images from their compressive measurements. In particular, we considered and compared four different type of estimation algorithms OMP, GPSR, LASSO, and IST. Furthermore, the large dimensions of hyperspectral images required the implementation of a practical block CASSI model to reconstruct the images with an acceptable delay and affordable computational cost. In order to consider the particularities of the block model and the dispersive effects in the CASSI-like sensing procedure, the problem was reformulated, as well as the construction of the variables involved. For this practical CASSI setup, we evaluated the performance of the overall system by considering the aforementioned algorithms and the different factors that impacted the reconstruction procedure. Finally, the obtained results were analyzed and discussed from a practical perspective.Single-pixel imaging, with the advantages of a wide spectrum, beyond-visual-field imaging, and robustness to light scattering, has attracted increasing attention in recent years. Fourier single-pixel imaging (FSI) can reconstruct sharp images under sub-Nyquist sampling. However, the conventional FSI has difficulty balancing imaging quality and efficiency. To overcome this issue, we proposed a novel approach called complementary Fourier single-pixel imaging (CFSI) to reduce the number of measurements while retaining its robustness. The complementary nature of Fourier patterns based on a four-step phase-shift algorithm is combined with the complementary nature of a digital micromirror device. CFSI only requires two phase-shifted patterns to obtain one Fourier spectral value. Four light intensity values are obtained by loading the two patterns, and the spectral value is calculated through differential measurement, which has good robustness to noise. The proposed method is verified by simulations and experiments compared with FSI based on two-, three-, and four-step phase shift algorithms. CFSI performed better than the other methods under the condition that the best imaging quality of CFSI is not reached. The reported technique provides an alternative approach to realize real-time and high-quality imaging.This paper proposes a screen-shooting resilient watermarking scheme via learned invariant keypoints and QT; that is, if the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the photo. A screen-shooting resilient watermarking algorithm should meet the following two basic requirements robust keypoints and a robust watermark algorithm. In our case, we embedded watermarks by combining the feature region filtering model to SuperPoint (FRFS) neural networks, quaternion discrete Fourier transform (QDFT), and tensor decomposition (TD). First we applied FRFS to locate the embedding feature regions which are decided by the keypoints that survive screen-shooting. Second, we structured watermark embedding regions centered at keypoints. Third, the watermarks were embedded by the QDFT and TD (QT) algorithm, which is robust for capturing process attacks. In a partial shooting scenario, the watermark is repeatedly embedded into different regions in an image to enhance robustness. Finally, we extracted the watermarks from at least one region at the extraction stage. The experimental results showed that the proposed scheme is very robust for camera shooting (including partial shooting) different shooting scenarios, and special attacks. Moreover, the efficient mechanism of screen-shooting resilient watermarking could have propietary protection and leak tracing applications.The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. selleck During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.The continuous, accurate and reliable estimation of gait parameters as a measure of mobility is essential to assess the loss of functional capacity related to the progression of disease. Connected insoles are suitable wearable devices which allow precise, continuous, remote and passive gait assessment. The data of 25 healthy volunteers aged 20 to 77 years were analysed in the study to validate gait parameters (stride length, velocity, stance, swing, step and single support durations and cadence) measured by FeetMe® insoles against the GAITRite® mat reference. The mean values and the values of variability were calculated per subject for GAITRite® and insoles. A t-test and Levene's test were used to compare the gait parameters for means and variances, respectively, obtained for both devices. Additionally, measures of bias, standard deviation of differences, Pearson's correlation and intraclass correlation were analysed to explore overall agreement between the two devices. No significant differences in mean and variance between the two devices were detected. Pearson's correlation coefficients of averaged gait estimates were higher than 0.98 and 0.8, respectively, for unipedal and bipedal gait parameters, supporting a high level of agreement between the two devices. The connected insoles are therefore a device equivalent to GAITRite® to estimate the mean and variability of gait parameters.With the emerging of wearable robots, the safety and effectiveness of human-robot physical interaction have attracted extensive attention. Recent studies suggest that online measurement of the interaction force between the robot and the human body is essential to the aspects above in wearable exoskeletons. However, a large proportion of existing wearable exoskeletons monitor and sense the delivered force and torque through an indirect-measure method, in which the torque is estimated by the motor current. Direct force/torque measuring through low-cost and compact wearable sensors remains an open problem. This paper presents a compact soft sensor system for wearable gait assistance exoskeletons. The contact force is converted into a voltage signal by measuring the air pressure within a soft pneumatic chamber. The developed soft force sensor system was implemented on a robotic hip exoskeleton, and the real-time interaction force between the human thigh and the exoskeleton was measured through two differential soft chambers. The delivered torque of the hip exoskeleton was calculated based on a characterization model. Experimental results suggested that the sensor system achieved direct force measurement with an error of 10.3 ± 6.58%, and torque monitoring for a hip exoskeleton which provided an understanding for the importance of direct force/torque measurement for assistive performance. Compared with traditional rigid force sensors, the proposed system has several merits, as it is compact, low-cost, and has good adaptability to the human body due to the soft structure.Dissolved-oxygen concentration and temperature are amongst the crucial parameters required for the precise monitoring of biological and biomedical systems. A novel hybrid nanocomposite probe for real-time and contactless measurement of both dissolved-oxygen concentration and temperature, based on a combination of downconverting phosphorescent molecules of platinum octaethylporphyrin and lanthanide-doped upconverting nanoparticles immobilized in a host of polystyrene, is here introduced. Chlorella algae are employed here as a model to demonstrate the hybrid nanophotonic sensor's capability to monitor the aforementioned two parameters during the photosynthesis process, since these are among the parameters impacting their production efficiency. These algae have attracted tremendous interest due to their potential to be used for diverse applications such as biofuel production; however, feasibility studies on their economic production are still underway.Analog signals from gas sensors are used to recognize all types of VOC (Volatile Organic Compound) substances, such as toxic gases, tobacco or ethanol. The processes to recognize these substances include acquisition, treatment and machine learning for classification, which can all be efficiently implemented on a Field Programmable Gate Array (FPGA) aided by Low-Voltage Differential Signaling (LVDS). This article proposes a low-cost 11-bit effective number of bits (ENOB) sigma-delta Analog to Digital Converter (ADC), with an SNR of 75.97 dB and an SFDR of 72.28 dB, whose output is presented on screen in real time, thanks to the use of a Linux System on Chip (SoC) system that enables parallelism, high-level programming and provides a working environment for the scientific treatment of gas sensor signals. The high frequency achieved by the implemented ADC allows for multiplexing the capture of several analog signals with an optimal resolution. Additionally, several ADCs can be implemented in the same FPGA so several analog signals can be digitalized in parallel.The traffic accident occurrence rate is increasing relative to the increase in the number of people using personal mobility device (PM). This paper proposes an airbag system with a more efficient algorithm to decide the deployment of a wearable bike airbag in case of an accident. The existing wearable airbags are operated by judging the accident situations using the thresholds of sensors. However, in this case, the judgment accuracy can drop against various motions. This study used the long short-term memory (LSTM) model using the sensor values of the inertial measurement unit (IMU) as input values to judge accident occurrences, which obtains data in real time from the three acceleration-axis and three angular velocity-axis sensors on the driver motion states and judges whether or not an accident has occurred using the obtained data. The existing neural network (NN) or convolutional neural network (CNN) model judges only the input data. This study confirmed that this model has a higher judgment accuracy than the existing NN or CNN by giving strong points even in "past information" through LSTM by regarding the driver motion as time-series data.
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