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The simulation results show that the ICA-VMD method can effectively recover the original source from the contaminated data. It is hoped that, in the future, there will be new discoveries and advances in science and technology to solve the noise interference problem through this method.The lipid phosphoric acid di-n-decyl ester (PADE) has played an important role in the development of taste sensors. As previously reported, however, the concentration of PADE and pH of the solution affected the dissociation of H+, which made the measurement results less accurate and stable. In addition, PADE caused deterioration in the response to bitterness because PADE created the acidic environment in the membrane. To solve these problems, our past study tried to replace the PADE with a completely dissociated substance called tetrakis [3,5-bis (trifluoromethyl) phenyl] borate sodium salt dehydrate (TFPB) as lipid. To find out whether the two substances can be effectively replaced, it is necessary to perform an in-depth study on the properties of the two membranes themselves. In this study, we fabricated two types of membrane electrodes, based on PADE or TFPB, respectively, using 2-nitrophenyl octyl ether (NPOE) as a plasticizer. We measured the selectivity to cations such as Cs+,K+,Na+ and Li+, and also the membrane impedance of the membranes comprising PADE or TFPB of the different concentrations. As a result, we found that any concentration of PADE membranes always had low ion selectivity, while the ion selectivity of TFPB membranes was concentration-dependent, showing increasing ion selectivity with the TFPB concentrations. The ion selectivity order was Cs+>K+>Na+>Li+. The hydration of ions was considered to participate in this phenomenon. In addition, the membrane impedance decreased with increasing PADE and TFPB concentrations, while the magnitudes differed, implying that there is a difference in the dissociation of the two substances. The obtained results will contribute to the development of novel receptive membranes of taste sensors.The problem of characterizing the structural residual life is one of the most challenging issues of the damage tolerance concept currently applied in modern aviation. Considering the complexity of the internal architecture of composite structures widely applied for aircraft components nowadays, as well as the additional complexity related to the appearance of barely visible impact damage, prediction of the structural residual life is a demanding task. In this paper, the authors proposed a method based on detection of structural damage after low-velocity impact loading and its classification with respect to types of acting stress on constituents of composite structures using the developed processing algorithm based on segmentation of 3D X-ray computed tomograms using the rebmix package, real-oriented dual-tree wavelet transform and supporting image processing procedures. The presented algorithm allowed for accurate distinguishing of defined types of damage from X-ray computed tomograms with strong robustness to noise and measurement artifacts. The processing was performed on experimental data obtained from X-ray computed tomography of a composite structure with barely visible impact damage, which allowed better understanding of fracture mechanisms in such conditions. The gained knowledge will allow for a more accurate simulation of structural damage in composite structures, which will provide higher accuracy in predicting structural residual life.Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human-agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human-human and human-agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human-human cooperation.A robot's ability to grasp moving objects depends on the availability of real-time sensor data in both the far-field and near-field of the gripper. This research investigates the potential contribution of tactile sensing to a task of grasping an object in motion. It was hypothesised that combining tactile sensor data with a reactive grasping strategy could improve its robustness to prediction errors, leading to a better, more adaptive performance. Using a two-finger gripper, we evaluated the performance of two algorithms to grasp a ball rolling on a horizontal plane at a range of speeds and gripper contact points. The first approach involved an adaptive grasping strategy initiated by tactile sensors in the fingers. The second strategy initiated the grasp based on a prediction of the position of the object relative to the gripper, and provided a proxy to a vision-based object tracking system. It was found that the integration of tactile sensor feedback resulted in a higher observed grasp robustness, especially when the gripper-ball contact point was displaced from the centre of the gripper. These findings demonstrate the performance gains that can be attained by incorporating near-field sensor data into the grasp strategy and motivate further research on how this strategy might be expanded for use in different manipulator designs and in more complex grasp scenarios.This paper presents and discusses a Low-Band (LB) Low Noise Amplifier (LNA) design for a diversity receive module where the application is for multi-mode cellular handsets. The LB LNA covers the frequency range between 617 MHz to 960 MHz in 5 different frequency bands and a 5 Pole Single Throw (5PST) switch selects the different frequency bands where two of them are for the main and three for the auxiliary bands. The presented structure covers the gain modes from -12 to 18 dB with 6 dB gain steps where each gain mode has a different current consumption. In order to achieve the Noise Figure (NF) specifications in high gain modes, we have adopted a cascode Common-Source (CS) with inductive source degeneration structure for this design. To achieve the S11 parameters and current consumption specifications, the core and cascode transistors for high gain modes (18 dB, 12 dB, and 6 dB) and low gain modes (0 dB, -6 dB, and -12 dB) have been separated. Nevertheless, to keep the area low and keep the phase discontinuity within ±10∘, we have shared the degeneration and load inductors between two cores. To compensate the performance for Process, Voltage, and Temperature (PVT) variations, the structure applies a Low Drop-Out (LDO) regulator and a corner case voltage compensator. The design has been proceeded in a 65-nm RSB process design kit and the supply voltage is 1 V. For 18 dB and -12 dB gain modes as two examples, the NF, current consumption, and Input Third Order Intercept Point (IIP3) values are 1.2 dB and 16 dB, 10.8 mA and 1.2 mA, and -6 dBm and 8 dBm, respectively.Vancomycin (VCM) is a first-line antimicrobial agent against methicillin-resistant Staphylococcus aureus, a cause of nosocomial infections. Therapeutic drug monitoring is strongly recommended for VCM-based chemotherapy. The authors attempted to develop a simple VCM sensor based on molecularly imprinted polymer (MIP), which can be used with simple operations. Methacrylic acid (MAA), acrylamide, methylenebisacrylamide, and allylamine carboxypropionate-3-ferrocene (ACPF) were copolymerized in the presence of VCM and grafted from the surface of indium-tin oxide (ITO) to obtain MIP-coated electrodes. The MIP-grafted ITO electrode was used for differential pulse voltammetry (DPV) measurements in a buffer solution containing VCM or whole bovine blood. The obtained current depends on the VCM concentration with high linearity. The dynamic range covered the therapeutic range (20-40 μg/mL) of the VCM but was almost insensitive to teicoplanin, which has a similar structure to VCM. The ITO electrodes grafted by the same procedure except for omitting either VCM or APCF were not sensitive to VCM. The sensitivity of the MIP electrodes to VCM in whole blood and buffered saline, but the background current in blood was higher than that in saline. This high background current was also seen in the deproteinized plasma. Thus, the current is probably originated from the oxidation of low molecular weight reducing agents in the blood. The MIP-grafted ITO electrode using ACPF as a functional monomer would be a promising highly selective sensor for real-time monitoring of VCM with proper correction of the background current.Supervised training of human activity recognition (HAR) systems based on body-worn inertial measurement units (IMUs) is often constrained by the typically rather small amounts of labeled sample data. Systems like IMUTube have been introduced that employ cross-modality transfer approaches to convert videos of activities of interest into virtual IMU data. We demonstrate for the first time how such large-scale virtual IMU datasets can be used to train HAR systems that are substantially more complex than the state-of-the-art. Complexity is thereby represented by the number of model parameters that can be trained robustly. see more Our models contain components that are dedicated to capture the essentials of IMU data as they are of relevance for activity recognition, which increased the number of trainable parameters by a factor of 1100 compared to state-of-the-art model architectures. We evaluate the new model architecture on the challenging task of analyzing free-weight gym exercises, specifically on classifying 13 dumbbell execises. We have collected around 41 h of virtual IMU data using IMUTube from exercise videos available from YouTube. The proposed model is trained with the large amount of virtual IMU data and calibrated with a mere 36 min of real IMU data. The trained model was evaluated on a real IMU dataset and we demonstrate the substantial performance improvements of 20% absolute F1 score compared to the state-of-the-art convolutional models in HAR.Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human's voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition.