This brief proposes a recursive constrained sine second-order error promoting adaptive (RCSSOEPA) algorithm. Compared with classical recursive method, the RCSSOEPA algorithm can achieve better steady state performance in impulsive noise. In general, the sine second-order error (SSOE) is constructed to devise a new recursive constrained adaptive-filtering within the least-squares framework for solving linear constrained optimization problems. The mean-square (MS) stability of the RCSSOEPA and its theoretical instantaneous MS deviation under Gaussian and non Gaussian noise are analyzed, numerically investigated and discussed in detail. Simulated results are reported to give a comfirmation of the theoretical analysis, and show that the RCSSOEPA outperforms recent developed constrained adaptive filtering algorithms in the estimation misalignment and when used for system identification under impulsive-noise.

This paper introduces a new CMOS implementation of a spiking Leaky Integrate-and-Fire (LIF) neuron. The circuit does not require any analog circuit block such as comparators or current sources thanks to the use of a ring-oscillator as an integrator and a phase delay detector as threshold logic. The circuit admits both excitatory and inhibitory input spiking signals whose pulse width does not affect the computation. Instead of powering the ring oscillators from a power supply, the input spikes charge a capacitor bank which powers the ring oscillator. The paper describes the operation of the neuron analytically for the input integration and the membrane voltage decay. Also, a circuit-level simulation in 65nm CMOS technology has been performed, achieving a power efficiency of <40fJ/spike.

In this letter, an ultra-broadband rectifier with expanded dynamic input power range (IPR) for both wireless power transfer (WPT) and radio frequency energy-harvesting (RFEH) is proposed and analyzed. Expanded dynamic IPR and broadband impedance matching are realized by utilizing a microstrip transmission line impedance compression circuit (MTLICC) with the topology of the paralleled voltage doubler. A proof-of-concept prototype shows a dynamic IPR of 8.5–23.5 dBm, a frequency range of 0.33–2.61 GHz (fractional bandwidth of 155%), PCE is greater than 50%, and a peak PEC of 79.7% (achieved at an input-power level of 19 dBm). The measurement results are in good agreement with the simulation. Covering most 3G/4G/5G/ISM frequency bands, this design is promising for RF power transmission and energy harvesting in the age of the Internet of Things systems.

The final paper of this paper has been accepted at USENIX Annual Technical Conference (ATC) 2024, with the title: "TileClipper: Lightweight Selection of Regions of Interest from Videos for Traffic Surveillance". The final version of this paper can be found here.

This paper uses X wave and Bessel pulses for multi-channel ultrasound data communications. The method can be extended to optical and electromagnetic data communications and other areas. Implications of X wave in other areas such as quantum mechanics (deBroglie waves) are discussed.

This paper proposes an adaptive-output-voltage regulation (AOVR) based solution to alleviate the dc-link undervoltage for grid-forming (GFM) inverters. First, it is shown that large disturbances may cause the dc-link undervoltage for GFM inverters threatening the safe operation of the system. To tackle this issue, an AOVR-based control strategy is proposed, where the main idea is to reduce the output voltage of the GFM inverter and also speed up the dynamic process of the power angle by intentionally enlarging the frequency deviation of the active power control loop. By doing so, the dc-link output energy will reduce to avoid possible undervoltage. The parameter design of the proposed method is based on the overmodulation constraints, and an auxiliary module is configured for the low-voltage-ride-through (LVRT) requirements. The applicability of the proposed solution is also checked when considering the overcurrent limitation. The effectiveness of the proposed control strategy is finally confirmed by the experimental tests.

Cloudified mobile networks, such as 5G, are expected to deliver a multitude of services to several slices in parallel, while having reduced capital and operating expenses. The 5G mobile systems, therefore, need to ensure that the SLAs of customized end-to-end sliced services are met. This requires monitoring the resource usage and characteristics of data flows at the virtualized network components and interfaces of its cloud mobile network, as well as tracking the performance at its radio interfaces and UEs. A centralised monitoring architecture can not scale to support millions of UEs though. This paper, proposes a distributed telemetry framework in which UEs act as early warning sensors. Upon flagging an anomaly, the cloudified mobile network activates a machine learning model to attribute the cause of the anomaly. We employ active, passive and in-band telemetry in our monitoring framework and achieve an impressive performance of 85% F1 score in detecting anomalies caused by different bottlenecks, and an overall 89% F1 score in attributing these bottlenecks. Our distributed framework achieves almost same bottleneck attribution accuracy to that of acentralized monitoring system but with no overhead of transmitting UE-based telemetry data to the centralized controller.

With the decreasing number of directly grid-connected synchronous machines (SMs) forming the grid voltage, the risk of power system instability increases, especially regarding the grid frequency due to the decreasing power system inertia. Grid-forming (GFM) control of inverter based resources (IBRs) with inertia emulation capability is required to face these challenges and to guarantee reliable operation of the future power system. This paper proposes virtual synchronous machine (VSM) control for doubly-fed induction machine (DFIM) based wind energy conversion systems (WECS) that (i) enables feedforward torque control (FTC) and thus, does not degrade the WECS performance during normal operation, (ii) adapts the VSM damping based on the DFIM operating point, and (iii) protects the VSM against overloading to avoid loss of synchronism.

In this paper, a rectangular-shaped wideband dielectric resonator Graphene inspired electronic band gap (EBG) based complementary split ring resonator (CSRR) loaded miniaturized THz MIMO antenna is designed having a dimension of 56×56×3.6μm3 is designed on a Roggers RO3035 substrate with a relative permittivity of 3.6 and loss tangent 0.0015. This antenna works in the range of 6.0THz-12.5THz (70.76%) with a peak gain of 7.68 dBi. This composite DRA materials having Silicon (εr = 11.1) based cylindrical dielectric and cylindrical Graphene disk to be stacked on rectangular radiated antenna having height of 13.5µm and 0.2µm to improve the impedance bandwidth as well as gain of the antenna from 5.24dBi to 7.68dBi. Firstly single-element gold plated rectangular shaped antenna is transformed into a two-element MIMO antenna with mushroom-shaped EBG structure is deployed for improving the isolation between two antenna elements as well as to improve the front-to-back ratio (FBR). CSRR is loaded to obtained band notch characteristics for avoiding the interference between near wireless devices in the span of 10.6-10.8THz. Graphene disk having 3µm height put on Silicon (εr = 11.1) based cylindrical DRA provides high gain and improved the impedance bandwidth for achieving wide bandwidth from 6.0THz-12.5THz. The proposed MIMO Antenna performance has been judged by antenna parameters like gain, return loss, isolation between two antenna elements, diversity parameters like ECC, DG, TARC, Mean Effective Gain (MEG), Channel Capacity Loss (CCL) and found proposed DRA-MIMO antenna suitable for wide band nano/optical communication in 6G for IoT and bio-medical sensing application.

Multidimensional channel sounding measures the geometrical structure of mobile radio propagation. The parameters of a multipath data model in terms of directions, time-of-flight and Doppler shift are estimated from observations in frequency, time and space. A maximum likelihood estimation framework allows joint high-resolution in all dimensions. The prerequisite for this is an appropriate parametric data model that represents the multipath propagation correctly. At the same time, a device data model is necessary that typically results from calibration measurements. The used model should be as simple as possible since its structure has a considerable effect on the estimation effort. For instance, the inherent effort in parameter search is reduced if the influence of the parameters is kept orthogonal. Therefore, the data model is characterized by several approximations. The most important is the “narrowband assumption” which assumes a low relative bandwidth and also avoids considering any frequency response in magnitude and phase. We extend the well-known multidimensional \gls{rimax} parameter estimation framework by including proper frequency responses. The advantage reveals most clearly with high bandwidth in the mmWave and sub-THz range. It allows for a more realistic modeling of antenna arrays. It breaks with the usual narrowband model and allows a better modeling of mutual coupling and time delay effects. If the interacting object extends over several delay bins (hence an extended target in radar terminology) we propose a model that assigns a short delay spread, respectively a frequency response to the propagation path that associates itto the respective object. We verify the validity of the device model by numerical experiments on simulated and measured antenna data and compare it to a state-of-the-art method. Additionally, we use synthetic data based on raytracing results and measurements both ranging from \SI{27}{\giga\hertz} up to \SI{33}{\giga\hertz} with known ground truth information and show that the proposed estimator not based on the narrowband assumption delivers better performance for higher relative bandwidths than the conventional \gls{rimax} implementation.

Synchronous machines (SMs) like reluctance synchronous machines (RSMs) or interior permanent magnet synchronous machines (IPMSMs) are characterized by nonlinear stator flux linkages which are crucial for optimal control and operation management. This paper presents a novel disturbance observer that allows to estimate the nonlinear flux linkages online for any SM (with constant or no excitation). Moreover, a simple identification sequence with ramp-like voltage signals is proposed to extract key information of the nonlinear stator flux linkage maps within a short period of time and without the need of any controller, torque sensor or prime mover. Finally, the proposed observer with identification sequence is implemented and validated for a real and highly nonlinear IPMSM by realistic and detailed simulation results.

Global Warming has been a problem at the heart of Earth’s environmental issues for nearly 5 decades, with the potential to affect a significant portion of the global population and cause catastrophic irreversible damage to the planet’s future. Changes in Earth’s climate due to the rise in global temperatures will have an enormous impact on communities around the world, along with a drastic displacement of humans and an extreme loss in natural biodiversity. Current methods of combating this issue have proven to be ineffective, requiring a more comprehensive and innovative approach. This project aims to propose a potential solution to mitigate the effects of global warming and limit temperatures to sustainable levels through the use of stratospheric aerosols. Through a process of data collection, experimentation, and modeling, I was able to correlate the presence of aerosols in the stratosphere to a consequent drop in temperatures and utilize regression prediction to forecast a 45% drop in global temperatures after examining the effects of volcanic ash in the stratosphere. I was also able to compare monthly aerosol concentration levels to declines in the growth of temperatures and conclude that by keeping aerosol optical thickness over 0.185, we can stabilize global temperatures and achieve climate change goals set to protect the Earth. By implementing the changes to Earth’s atmosphere, we can reflect heat from the Sun and create a cooling effect for the planet, potentially stopping climate change and saving billions of people.

An Artificial Neural Network (ANN) based Optimal Feedforward Torque Control (OFTC) strategy for electrically excited synchronous machines (EESMs) is proposed. After design, data set creation, training and validation of the ANN, the analytical computation of the optimal stator and exciter currents is achieved which allows to minimize copper and iron losses and to produce the desired (or maximally feasible) machine torque. Voltage and currents constraints of stator and exciter are considered as well. In contrast to conventional OFTC, the proposed ANN-based OFTC strategy does not require iterations nor a decision tree to find the optimal current triple while machine nonlinearities, magnetic cross-coupling, saturation and speed-dependent iron losses are taken into account. In addition, the proposed ANN design procedure allows to consider measurable OFTC goals and computational resources that ensures a real-time capable implementation. Comprehensive simulation results for a real and nonlinear EESM clearly show these benefits by comparing the proposed ANN-based OFTC with results of a nonlinear optimization problem (NLP) solver (which cannot be used in real-time).

Large-scale processing of heterogeneous datasets in interdisciplinary research often requires time-consuming manual data curation. Ambiguity in the data layout and preprocessing conventions can easily compromise reproducibility and scientific discovery, and even when detected, it requires time and effort to be corrected by domain experts. Poor data curation can also interrupt processing jobs on large computing clusters, causing frustration and delays. We introduce DataCurator , a portable software package that verifies arbitrarily complex datasets of mixed formats, working equally well on clusters as on local systems. Human-readable TOML recipes are converted into executable machine-verifiable templates, enabling users to easily verify datasets using custom rules without writing code. Recipes can be used to transform and validate data, for pre- or post-processing, selection of data subsets, sampling, and aggregation, such as summary statistics. Processing pipelines no longer need to be burdened by laborious data validation, with data curation and validation replaced by human and machine verifiable recipes specifying rules and actions. Multithreaded execution ensures scalability on clusters, and existing Julia, R, and Python libraries can be reused. DataCurator enables efficient remote workflows, offering integration with Slack and the ability to transfer curated data to clusters using OwnCloud and SCP. Code available at: https://github.com/bencardoen/DataCurator.jl.

This publication proposes a parametric data model and a gradient-based maximum likelihood estimator suitable for the description of delay-dispersive responses of multiple dynamic UWB-radar targets. The target responses are estimated jointly with the global target parameters range and velocity. The large relative bandwidth of UWB has consequences for model-based parameter estimation. On the one hand, the Doppler effect leads to a dispersive response in the Doppler spectrum and to a coupling of the target parameters which both need to be considered during modeling and estimation. On the other hand, the shape of an extended target results in a dispersive response in range which can be resolved by the radar resolution. We consider this extended response as a parameter of interest, e.g., for the purpose of target recognition. Hence, we propose an efficient description and estimation of it by an FIR structure only imposing a restriction on the target’s dispersiveness in range. We evaluate the approach on simulations, compare it to state of the art solutions and provide a validation on measurement data. © 2023 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works .

Many people have lost their lives in weapon incidents every year. According to CDC report 124 people died every day in 2020 due to gun violence. Research has been carried out in this field in recent years for object detection using machine learning methodologies. In the following research methodology, we have carried out research on deep learning based automated weapon detection system which allows system to detect weapons such as Knife, Handgun, and rifle automatically. We have implemented object detection model You Only Look Once YOLO V4 and trained it on our datasets. Training showed that You Only Look Once YOLO V4 outperformed convolutional neural network CNN. By using this model for closed circuit televisions CCTV, we can help people in saving their lives and reduce human mass slaughter. Moreover, we proposed a method to improve accuracy of Convolutional Neural Network CNN by adding N times N number of layers. It will reduce the complexity of the model and improve accuracy.

The most enriched cell types in the breast tumor microenvironment are cancer cells, cancer-associated fibroblasts, and tumor-associated macrophages. To recapitulate the cellular dynamics of the breast tumor microenvironment in vitro, the most abundant cell types need to be incorporated. Laser direct write bioprinting offers a precise, gentle, and reproducible method to print disparate cell types in user-defined geometries. Herein, we develop novel laser direct write cell printing protocols -- first as a customizable generalized framework, which is then adapted to print homotypic and heterotypic cancer-stromal arrays, and human macrophages. We demonstrate the ability to fabricate in vitro heterocellular constructs for studying cell-cell signaling in healthy and diseased microenvironments, as well as the capability to print human immune cells with high fidelity to pave the way for bioprinting immunocompetent tissue models going forward.

The origin of electromagnetism has been a recurring question since the discovery of electricity. In the 19th century, Maxwell unified electricity and magnetism into a single electrodynamic theory described by Maxwell’s equations. This was followed by the discovery of the electrodynamic force on a moving charge by Lorentz. Later, Einstein demonstrated that magnetism and the Lorentz force can be considered a relativistic effect. However, despite these important theories, there is at present no physical theory that identifies the single fundamental cause of all known classical electromagnetism. In an attempt to identify this single fundamental cause, this paper shows that classical electromagnetism can be considered as effect of scalar waves emanating from charges. It is shown in this paper that Coulomb’s law, Maxwell’s equations, the magnetic and electric field, the Lorentz force, and the Lienard-Wiechert potentials, can all be derived only from this single hypothesis that scalar waves are emitted by the charges. In addition, it was shown that the Lorentz transformation can also be derived from this hypothesis of scalar waves. To achieve this, an important new quantity was introduced: the momentum of the scalar waves. It was shown that the force caused by these scalar waves is simply the time derivative of this momentum. It was also shown that both the Lorentz force and the Coulomb force can be represented as the time derivative of the scalar wave momentum.