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<p>The next generation for mobile communication is new radio (NR) that supporting air interface which referred to the fifth generation or 5G. Long term evolution (LTE), universal mobile telecommunications system (UMTS), and global system for mobile communication (GSM) are 5G NR predecessors, also referred to as fourth generation (4G), third generation (3G) and second generation (2G) technologies. Pseudo-noise (PN) code length and modulation technique used in the 5G technology affect the output spectrum and the payload of DL-FRC specification, in this paper quadrature phase shift keying (QPSK), 16 QAM modulation approaches tested under additive white Gaussian noise (AWGN) in term of bit error rate (BER) which used with 5G technology system implemented with MATLAB-Simulink and programing and, resulting of 1672, 12296 bit/slot payload at frequency range FR1 from 450 MHz-6 GHz and 4424, 20496 bit/slot payload at frequency range FR2 from 24.25 GHz-52.6 GHz, also determining subcarrier spacing, allocated source block, duplex mode, payload bit/slot, RBW (KHz), sampling rate (MHz), the gain and the bandwidth of main, side loop where illustrated.</p>

PurposeThe aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.Design/methodology/approachThis paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.FindingsAt the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.Originality/valueBCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

AbstractCustomizable, portable, battery-operated, wireless platforms for interfacing high-sensitivity nanoscale sensors are a means to improve spatiotemporal measurement coverage of physical parameters. Such a platform can enable the expansion of IoT for environmental and lifestyle applications. Here we report a platform capable of acquiring currents ranging from 1.5 nA to 7.2 µA full-scale with 20-bit resolution and variable sampling rates of up to 3.125 kSPS. In addition, it features a bipolar voltage programmable in the range of −10 V to +5 V with a 3.65 mV resolution. A Finite State Machine steers the system by executing a set of embedded functions. The FSM allows for dynamic, customized adjustments of the nanosensor bias, including elevated bias schemes for self-heating, measurement range, bandwidth, sampling rate, and measurement time intervals. Furthermore, it enables data logging on external memory (SD card) and data transmission over a Bluetooth low energy connection. The average power consumption of the platform is 64.5 mW for a measurement protocol of three samples per second, including a BLE advertisement of a 0 dBm transmission power. A state-of-the-art (SoA) application of the platform performance using a CNT nanosensor, exposed to NO2 gas concentrations from 200 ppb down to 1 ppb, has been demonstrated. Although sensor signals are measured for NO2 concentrations of 1 ppb, the 3σ limit of detection (LOD) of 23 ppb is determined (1σ: 7 ppb) in slope detection mode, including the sensor signal variations in repeated measurements. The platform’s wide current range and high versatility make it suitable for signal acquisition from resistive nanosensors such as silicon nanowires, carbon nanotubes, graphene, and other 2D materials. Along with its overall low power consumption, the proposed platform is highly suitable for various sensing applications within the context of IoT.

This paper proposes a methodology to monitor the instantaneous value of the current and its derivative in the abc, αβ0, and dq0 reference frames to act in the detection of fault current in medium-voltage distribution systems. The method employed to calculate the derivative was Euler’s, with processing sampling rates of 10, 50, 100, and 200 μs. Using the MATLAB/Simulink platform, fault situations were analyzed on a real feeder of approximately 1.1132 km in length, fed by an 11.4 kV source, composed of 26 unbalanced loads and modeled as constant power. The simulation results show that the detection occurred in the different fault situations implemented in the feeder and that the detection speed is related to the value of the processing sampling rate (PSR) used. Considering all fault situations and regardless of the PSR value used, the total average detection time was 49 µs. Besides that, the joint action of the detection system with the Thyristor Controlled Series Capacitor (TCSC) limited the fault current in each situation. The average detection time for each fault situation analyzed was below the typical time for a recloser to act, regardless of the reference adopted for the analysis.

This paper constructs and simulates the interface circuit of a temperature sensor based on SMIC 0.18 [Formula: see text]m CMOS. The simulation results show that when the power supply voltage is 1.8 V, the chopper op-amp gain is 89.44 dB, the low-frequency noise is 71.83 nV/Hz,[Formula: see text] and the temperature coefficient of the core temperature sensitive circuit is 1.7808 mV/[Formula: see text]C. The sampling rate of 10-bit SAR ADC was 10 kS/s, effective bit was 9.0119, SNR was 59.3256 dB, SFDR was 68.7091 dB, and THD was −62.5859 dB. The measurement range of temperature sensor interface circuit is −50[Formula: see text]C[Formula: see text]C, the relative temperature measurement error is ±0.47[Formula: see text]C, the resolution is 0.2[Formula: see text]C/LSB, and the overall average power consumption is 434.9 [Formula: see text]W.

AbstractRobotic vision plays a key role for perceiving the environment in grasping applications. However, the conventional framed-based robotic vision, suffering from motion blur and low sampling rate, may not meet the automation needs of evolving industrial requirements. This paper, for the first time, proposes an event-based robotic grasping framework for multiple known and unknown objects in a cluttered scene. With advantages of microsecond-level sampling rate and no motion blur of event camera, the model-based and model-free approaches are developed for known and unknown objects’ grasping respectively. The event-based multi-view approach is used to localize the objects in the scene in the model-based approach, and then point cloud processing is utilized to cluster and register the objects. The proposed model-free approach, on the other hand, utilizes the developed event-based object segmentation, visual servoing and grasp planning to localize, align to, and grasp the targeting object. Using a UR10 robot with an eye-in-hand neuromorphic camera and a Barrett hand gripper, the proposed approaches are experimentally validated with objects of different sizes. Furthermore, it demonstrates robustness and a significant advantage over grasping with a traditional frame-based camera in low-light conditions.

After stroke, paretic arm muscles are constantly exposed to abnormal neural drive from the injured brain. As such, hypertonia, broadly defined as an increase in muscle tone, is prevalent especially in distal muscles, which impairs daily function or in long-term leads to a flexed resting posture in the wrist and fingers. However, there currently is no quantitative measure that can reliably track how hypertonia is expressed on daily basis. In this study, we propose a novel time-based surface electromyography (sEMG) measure that can overcome the limitations of the coarse clinical scales often measured in functionally irrelevant context and the magnitude-based sEMG measures that suffer from signal non-stationarity. We postulated that the key to robust quantification of hypertonia is to capture the true baseline in sEMG for each measurement session, by which we can define the relative duration of activity over a short time segment continuously tracked in a sliding window fashion. We validate that the proposed measure of sEMG active duration is robust across parameter choices (e.g., sampling rate, window length, threshold criteria), robust against typical noise sources present in paretic muscles (e.g., low signal-to-noise ratio, sporadic motor unit action potentials), and reliable across measurements (e.g., sensors, trials, and days), while providing a continuum of scale over the full magnitude range for each session. Furthermore, sEMG active duration could well characterize the clinically observed differences in hypertonia expressed across different muscles and impairment levels. The proposed measure can be used for continuous and quantitative monitoring of hypertonia during activities of daily living while at home, which will allow for the study of the practical effect of pharmacological and/or physical interventions that try to combat its presence.

Stochastic models play a crucial role in global navigation satellite systems (GNSS) data processing. Many studies contribute to the stochastic modeling of GNSS observation noise, whereas few studies focus on the stochastic modeling of process noise. This paper proposes a method that is able to jointly estimate the variances of observation noise and process noise. The method is flexible since it is based on the least-squares variance component estimation (LS-VCE), enabling users to estimate the variance components that they are specifically interested in. We apply the proposed method to estimate the variances for the dual-frequency GNSS observation noise and for the process noise of the receiver code bias (RCB). We also investigate the impact of the stochastic model upon parameter estimation, ambiguity resolution, and positioning. The results show that the precision of GNSS observations differs in systems and frequencies. Among the dual-frequency GPS, Galileo, and BDS code observations, the precision of the BDS B3 observations is highest (better than 0.2 m). The precision of the BDS phase observations is better than two millimeters, which is also higher than that of the GPS and Galileo observations. For all three systems, the RCB process noise ranges from 0.5 millimeters to 1 millimeter, with a data sampling rate of 30 s. An improper stochastic model of the observation noise results in an unreliable ambiguity dilution of precision (ADOP) and position dilution of precision (PDOP), thus adversely affecting the assessment of the ambiguity resolution and positioning performance. An inappropriate stochastic model of RCB process noise disturbs the estimation of the receiver clock and the ionosphere delays and is thus harmful for timing and ionosphere retrieval applications.

Successive Approximation Register (SAR) Analog to Digital Converter (ADC) architecture comprises of sub modules such as comparator, Digital to Analog Converter and SAR logic. Each of these modules imposes challenges as the signal makes transition from analog to digital and vice-versa. Design strategies for optimum design of circuits considering 22nm FinFET technology meeting area, timing, power requirements and ADC metrics is presented in this work. Operational Transconductance Amplifier (OTA) based comparator, 12-bit two stage segmented resistive string DAC architecture and low power SAR logic is designed and integrated to form the ADC architecture with maximum sampling rate of 1 GS/s. Circuit schematic is captured in Cadence environment with optimum geometrical parameters and performance metrics of the proposed ADC is evaluated in MATLAB environment. Differential Non Linearity and Integral Non Linearity metrics for the 12-bit ADC is limited to +1.15/-1 LSB and +1.22/-0.69 LSB respectively. ENOB of 10.1663 with SNR of 62.9613 dB is achieved for the designed ADC measured for conversion of input signal of 100 MHz with 20dB noise. ADC with sampling frequency upto 1 GSps is designed in this work with low power dissipation less than 10 mW.

Abstract Scintillation detectors are widely used for hard X-ray spectroscopy and allow us to investigate the dynamics of runaway electrons in tokamaks. This diagnostic tool proved to be able to provide information about the energy or the number of runaway electrons. Presently it has been used for runaway studies at the GOLEM and the COMPASS tokamaks. The set of scintillation detectors used at both tokamaks was significantly extended and improved. Besides NaI(Tl) (2 × 2 inch) scintillation detectors, YAP(Ce) and CeBr3 were employed. The data acquisition system was accordingly improved and the data from scintillation detectors is collected with appropriate sampling rate (≈300 MHz) and sufficient bandwidth (≈100 MHz) to allow a pulse analysis. Up to five detectors can currently simultaneously monitor hard X-ray radiation at the GOLEM. The same scintillation detectors were also installed during the runaway electron campaign at the COMPASS tokamak. The aim of this contribution is to report progress in diagnostics of HXR radiation induced by runaway electrons at the GOLEM and the COMPASS tokamaks. The data collected during the 12th runaway electron campaign (2020) at COMPASS shows that count rates during typical low-density runaway electron discharges are in a range of hundreds of kHz and detected photon energies go up to 10 MeV (measured outside the tokamak hall). Acquired data from experimental campaigns from both machines will be discussed.