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The purpose of chopper amplifier is to provide the wideband frequency to support biomedical signals.

This paper proposes a chopper-stabilized amplifier with a cascoded operational transconductance amplifier. The high impedance loop is established using an MOS pseudo resistor and with a tunable MOS capacitor.

The total power consumption is 451 nW with a supplied voltage of 800 mV. The Gain and common mode rejection ratio are 48 dB and 78 dB, respectively.

All kinds of real time data analysis was not carried out, only few test samples related to EEG signals are validated because the real time chip was not manufactured due to funding issues.

The proposed work was validated with Monte-Carlo simulations. There is no external funding for the proposed work. So there is no fabrication for the design. But post simulations are performed.

The high impedance loop is established using an MOS pseudo resistor and with a tunable MOS capacitor. To the best of the author’s knowledge, this concept is completely novel and there are no publications on this work. All the modules designed for chopper amplifier are new concepts.

Attitude and heading are very important measurements on board aircraft. In modern solutions they are measured by the attitude and heading reference system (AHRS). In some small unmanned systems, the GPS track angle is used for heading corrections instead of the magnetometer; then, the system measures the track angle instead of heading. With a temporary lack of correction signals, the measurement error increases very quickly. Similarly, a quick increase in the measurement error is observed when a magnetic heading sensor used for correction stops working properly. This study aims to propose measurement of the roll angle for yaw angle correction.

AHRS algorithms were designed; typical maneuvers were analyzed. The method was verified by simulation and in flight testing analysis. For quantitative analyses, a performance index was proposed.

The method enables reduction of the yaw angle error caused by the gyros bias error. This study presents the idea, results of simulations and flight testing data analysis and discusses advantages and limitations of the presented method.

The presented methodology can be implemented in AHRS systems for manned and unmanned aircraft.

This study enables more accurate measurement of the yaw angle in the case of missing correction signals.

The purpose of this paper is to derive a new fractal active low-pass filter (LPF) within the local fractional derivative (LFD) calculus on the Cantor set (CS).

To the best of the author’s knowledge, a new fractal active LPF within the LFD on the CS is proposed for the first time in this work. By defining the nondifferentiable (ND) lumped elements on the fractal set, the author successfully extracted its ND transfer function by applying the local fractional Laplace transform. The properties of the ND transfer function on the CS are elaborated in detail.

The comparative results between the fractal active LPF (for γ = ln2/ln3) and the classic one (for γ = 1) on the amplitude–frequency and phase–frequency characteristics show that the proposed method is correct and effective, and is expected to shed light on the theory study of the fractal electrical systems.

To the best of the author’s knowledge, the fractal active LPF within the LFD calculus on the CS is proposed for the first time in this study. The proposed method can be used to study the other problems in the fractal electrical systems, and is expected to shed a light on the theory study of the fractal electrical systems.

This study aims to design a compact filtering monopole antenna for 5G communication. The design is most suited for various applications within the frequency range of 2.2–3.8 GHz. It offers enhanced bandwidth and reasonable gain with wide-stopband performance.

A low-pass filter (LPF) of complementary split ring resonator (CSRR) with short-circuited stub lines is integrated with a compact defected coplanar waveguide fed truncated circular monopole ultrawideband (UWB) antenna. The reference UWB antenna etched on an FR4 substrate was coupled to the designed LPF to transform the UWB antenna into a wideband antenna. The effect of coupling is analyzed based on the real and imaginary responses of the terminal impedance (ZT) curve. Three short-circuited stub lines of asymmetric lengths are added to the CSRR LPF to suppress harmonics, thereby enhancing the stopband performance and impedance matching between the elements. The proposed filtering antenna is fabricated using a photolithography process, and the corresponding results are measured using a network analyzer (N9951A). The radiation parameters of the proposed filtering monopole antenna are tested in the anechoic chamber. The simulated/measured results are compared and are found in agreement with each other.

The proposed design suppresses 6.5f0 harmonics, resulting in wide stopband performance and increased gain selectivity at the transition edge. A peak suppression of −41 dB and an average suppression of −18 dB were attained throughout the stopband. An operating fractional bandwidth of 54.5%/143% with a peak gain of 3 dBi/5 dBi was obtained. The proposed filtering antenna supports 5G applications such as WiMAX, WLAN, n7, n38 IMT-E, n30 WCS, n40 TDD, n41 TDD, n48 TDD, n78 TDD and n90 TDD.

The proposed design is novel and compact and has a wide application in 5G communication. With the filter, the antenna operates in wideband, and without the filter, it operates in UWB. Besides, it offers enhanced stopband performance with high gain selectivity at the transition edge. Comparatively, a 50% improvement in bandwidth, 52% improvement in size reduction and 33% improvement in harmonic suppression are attained.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

Multilevel inverters play a major role in the development of high-power industrial applications. In traditional low-level inverters (e.g. 2-level), the switching frequency is restricted and the harmonic spectrum of the system is hard to meet power requirements. Similarly, high-level inverters consist of a large number of switches, complex modulation techniques and complex hardware architecture, which results in high power loss and a significant amount of harmonic distortion. Furthermore, it is a must to ensure that every switch experiences the same stress of voltage and current. The purpose of this paper is to present an inverter topology with lower conduction and switching losses via reduced number of switches and equal voltage source-sharing technique.

Herein, the authors present a cascaded multilevel inverter having less power switches, a simple modulation technique and an equal voltage source-sharing phenomenon implementation.

The modulation technique becomes more complex when equal voltage source-sharing is to be implemented. In this study, a novel topology for the multilevel inverter with fewer switches, novel modulation technique, equal voltage source-sharing and Inductor-Capacitor-Inductor filter implementation is demonstrated to the reduce harmonic spectrum and power losses of the proposed system.

The nine-level inverter design is validated using software simulations and hardware prototype testing; the power losses of the proposed inverter design are elaborated and compared with the traditional approach.

Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.

This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.

The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.

This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

Recent advancements in the domain of smart communication systems and technologies have led to the augmented developments for very large scale integrated circuit designs in electro-magnetic applications. Increasing demands for low power, compact area and superior figure of merit–oriented circuit designs are the trends of the recent research studies. Hence, to accomplish such applications intended for optical communications, the transimpedance amplifier (TIA) was designed.

In this research work, the authors present a multi-layer active feedback structure which mainly composes a transimpedance stage and a gain stage followed by a low pass filter. This structure enables to achieve improved input impedance and superior gain. A simplified cascaded amplifier has also been designed in a hierarchical topology to improvise the noise effect further. The proposed TIA has been designed using Taiwan Semiconductor Manufacturing Company 45 nm complementary metal oxide semiconductor technology. Moreover, the thermal noise has been analyzed at −3 dB bandwidth to prove the reduction in thermal noise with increase in frequency for most of the devices used in the designed circuit.

The proposed differential TIA circuit was found to obtain the transimpedance gain of 50.1 dBO without applying any external bias current which is almost 8% improvised as compared to the conventional circuit. In addition to this, bandwidth achieved was 2.15 GHz along with only 38 W of power consumption, which is reasonably 100 times improvised in comparison of conventional circuit. Hence, the proposed differential TIA is suitable for the low power optical communications applications intended to work on low supply voltage.

The designed work is done by authors in university lab premises and is not copied from anywhere. To the best of the authors’ knowledge, it is 100% original.

This paper aims to propose a new approach to testing distributed energy resources (DERs) in compliance with the IEEE 1547-2018 standard and describes a new, integrated testing and validation system.

The system is built on the virtual instrumentation paradigm, using acquisition modules to measure physical quantities, while signal processing, including intensive calculations of required parameters, data processing, manipulation and reporting are performed on a computing device.

Intensive laboratory measurements were performed on a laboratory prototype of a microgrid that emulates DERs. The results obtained using the system described were compared with the measurements obtained by the reference instruments. As all the results match, the usability of the system was verified.

This approach to the realization of the testing and validation system has obvious advantages compared to the classical instruments and provides significant flexibility in multiple aspects. First, the system described integrates all the functions of different instruments into one measuring system, making the entire testing and validation process significantly cheaper and faster. Second, the implementation of the system is possible on different computing platforms depending on specific needs. Third, the software implementation of the system functions enables simple upgrading and the introduction of new functions or changes to existing ones according to changes in the standard. Finally, the system described is designed to automatically provide reports on compliance with the standard.

This paper emphasizes the advantages of the proposed approach over classical testing. The value of the paper is reflected in the applicability and practical implications of the proposed and described hardware and software technical solutions.

This paper aims to optimize the stiffness coefficient of the elastic element for a passive waist assistive exoskeleton (WAE). There is a tradeoff between stiffness coefficient of elastic element of the exoskeleton and work efficiency of the wearer, because elastic element affects original bending motion of the wearer and the force requirement of erector spinae is compensated by the other muscles. However, there is no accepted conclusion on how to determine the proper stiffness coefficient, especially with respected to the effort of groups of muscles, not only erector spinae.

In this study, a consumption indicator based on muscle fatigue of seven muscles is proposed and a Bayesian-based human-in-the-loop optimization approach is adopted to optimize the stiffness coefficient. Pneumatic artificial muscles are used to replace the mechanical elastic part to adjust the assistive force automatically. The proposed optimization method is verified by the way of load-lifting experiments with three different conditions: without exoskeleton, with fixed air pressure and with optimized air pressure. Six subjects participated in the experiment and each experiment is performed in different day.

Compared with No-Exo condition and static assistance condition, the parameter-optimized waist exoskeleton averagely reduces muscle fatigue of the six subjects by 45.30 ± 29.14% and 30.94 ± 30.29%, respectively. The experimental results indicate that the proposed method is effective to reduce muscle fatigue during stoop lifting task.

This paper provides a novel cost function construction method based on muscle fatigue and muscle synergy for passive WAE stiffness optimization.