A document by Chibuike Onuoha. Click on the document to view its contents.

Cosmology and prime number theory relate through the analysis of metric space in non-decelerative universe model with logarithmic integral function. It has been seen in history that the three components also exhibit relationships among each other, particularly through the zeroes of zeta functions, random matrix theory and energy levels in quantum chaotic systems. How large-scale structure of universe is related to some unknown prime number theories via mathematical tools. With this integration, new vistas are opened for refrigerators stored in mathematics and Cosmology.

Mutual coupling model is crucial in designing multiple-input-multiple-output (MIMO) antennas since mutual coupling will degrade overall MIMO performance from distorted radiation patterns and reduced antenna efficiencies. Typically, full-wave simulations have to be employed, which is often timeconsuming. Here, the artificial neural network (ANN) method is developed to reduce the modelling time. Compared to previous ANN methods that directly use model parameters as input, a novel physical preprocessing approach is proposed to incorporate antenna correlation information before the network training. As a proof of concept, the mutual coupling model of a nonuniform strongly-coupled array is realized. Furthermore, we use the trained networks for capacity estimation and power allocation with the water-filling algorithm, showing favourable model performance. The proposed physically preprocessed ANN model significantly outperforms traditional analytical solutions and direct modelling networks in terms of prediction accuracy, dataset construction costs, and network convergence, which could facilitate the fast optimization and design of advanced antenna arrays for MIMO communications.

In this comment, it is shown that Zhu et. al. had misinterpreted some double difference bases (DDBs) as twofold redundancy arrays (TFRAs). The baselines of most DDBs listed in Table I of the above communication get disrupted/broken in the event of single-element failures, thereby failing on the very promise of TFRAs to be robust against single-sensor failures. The use of robust minimum redundancy arrays (RMRAs) is suggested as an alternative solution.

A document by Bro Gester. Click on the document to view its contents.

This paper describes the design and implementation of a gas mixer for mechanical ventilators using a system of PID controllers in multiloop and parallel configuration. The theoretical principles of gas mixing by partial volumes and their relationship with mechanical ventilation parameters are addressed. The design of the electro-pneumatic system of the mixer, the control system, and its implementation are presented in detail. All electronic designs, 3D components, and the complete programming code are provided. Finally, the performance results obtained through experimental tests are presented.

The effective training of supervised deep learning models requires the labeling of extensive datasets, a process that is often costly and labor-intensive. Such models also face significant challenges with overfitting on the training data with true labels, leading to suboptimal performance on new datasets with slight variations in capturing sources or regions. This paper introduces Multi-class Unsupervised Curriculum Learning (Multi-class UCL), a novel deep learning framework. We demonstrate the effectiveness of this framework on the case study of land use and cover classification that bypasses the need for labeled data, thereby improving adaptability across different datasets. Multi-class UCL leverages pseudo-labels generated from a clustering technique to train the model and incorporates a selection process that ensures an equal representation of samples from each cluster, addressing the issue of class imbalance. The study evaluates the effectiveness of Multi-class UCL through comprehensive experiments on four diverse publicly available datasets: EuroSAT, SAT-6, RSSCN7, and UCMerced. These datasets have varying resolutions, come from different capturing sources, and encompass different geographical areas. The results demonstrate that the framework effectively learns and generalizes important features from the data, showing superior adaptability and performance across various datasets compared to traditional supervised models.

Emerging applications in the field of robotics require tactile sensors with low dynamic errors. Such sensors are often made of elastomers, which exhibit time-dependent drift and hysteresis, leading to lower sensing accuracy. In this work, we systematically study the dominant material-inherent sources for the dynamic error within a tactile sensor, based upon a diverse range of commercially available elastomers. We demonstrate that the initial error associated with the Mullins effect can be completely eliminated, that the residual transient force error related to viscoelastic drift is reduced by 57%, and that the hysteresis force error is reduced by 20% with an optimized selection of suitable elastomers.

Magnetoelastic torque sensors are prevalent in ebikes. The full-scale magnetic field is typically below 1 mT and read out via discrete fluxgates in commercial sensor modules. To achieve a more compact integration, we explore in this paper Hall-based sensors. Using a multiphysics simulation model, we analyze the system design trade-off between sensitivity and robustness against mechanical misalignment. Our model shows that Gallium Arsenide Hall-based sensors can provide a noise-free resolution of 10 bits in a 500-Hz bandwidth, with a full-scale upper limit of 200 N•m. Experimental validation confirms our simulation results. These findings establish a theoretical foundation for developing high-performance miniaturized magnetoelastic sensors for e-bikes and beyond.

Typical e-bikes magnetoelastic torque sensors employ bulky fluxgate magnetometers to resolve few μT/Nm. Instead, we propose the use of a miniaturized gradiometric arrangement of GaAs Hall plates sensitive to the radial magnetic field and a design approach that reduces the impact of axial misalignment. The proposed architecture provides comparable performance to commercial solutions, with reduced sizing and simplified magnetization requirements. Experiments conducted on a prototype with a full-scale (FS) upper limit of 200 Nm show a resolution of 7 mNm/ √ Hz with a combined standard error of 1.85%FS.

Future particle physics experiments such as nEXO and ARGO have will dedicated state-of-the-art electronics for their detector. As this electronics will most likely be in the cryogenic environment, major challenges arise for their design from the detector readout to data communication. Their requirements for radiopurity and chemical constitution add further constraints on the type of usable materials. This paper presents a silicon photonics based communication system for those experiments. This system aims to be fully integrated within a photodetection module, connecting large arrays of photon-to-digital converters to a large-scale data acquisition system. The novelty of the design relies on having no laser source in the cryostat, a major benefit for power consumption. This approach leads to a set of considerations for the design of each component of the system and the need of designing a system in a comprehensive « top-down » approach to ensure the compatibility of the components. Key challenges related to cryogenic operation of the devices are highligthed alongside our approach to move from the prototype stage towards a complete system. We present the design of the system architecture, the design of the SiP chip (AMF foundry), the design and simulation results of 65 nm TSMC CMOS SiP Driver, the design and bandwidth results of the FPGA DAQ integration and the outline of a packaging solution for optical communication within cryogenic experiments with an overview of the remaining challenges to overcome.

A document by Naga Venkata Sai Raviteja Chappa. Click on the document to view its contents.

In modern software development, choosing the right branching strategy is essential for optimizing team collaboration, managing production releases, handling hotfixes, and minimizing merge conflicts. This paper presents a comprehensive analysis of three prominent Git branching models-Gitflow, Gitflow Workflow, and Git OneFlow-each offering unique approaches to streamline development workflows and maintain code quality. Gitflow provides a structured framework for managing parallel development, releases, and hotfixes, but its complexity can introduce management overhead. Gitflow Workflow offers a more flexible adaptation of Gitflow, balancing structure and simplicity, while Git OneFlow simplifies the branching model to focus on continuous integration and deployment practices. This study explores the key features, advantages, and complexities of each model, and proposes optimizations to reduce complexity and enhance the development process. The insights provided guide development teams in selecting the most appropriate branching strategy based on project complexity, team size, and codebase ownership, ultimately leading to more efficient and effective software delivery

Previous works in array processing have proposed two types of snapshot models for the angle of arrival (AoA) estimation problem in multi-antenna systems. The deterministic model assumes that the source waveforms are non-random, while the random sensor noise is white Gaussian with a known covariance matrix. The stochastic model assumes that both the waveforms and the noise are zero-mean Gaussian. Interestingly, the performance of these two models have rarely been compared in integrated sensing and communication (ISAC) systems. Therefore, in this paper, we consider the uplink of a bistatic ISAC system that uses unitary constant envelope signaling and pilot-based channel estimation while transmitting a sensing signal simultaneously with the communication signals. The base station uses both the pilot and data signals to estimate the angle of a passive source and the transmitted data symbol by an active (connected) user equipment device. For this system, we derive the classical Cramér-Rao bound for unbiased estimators of the AoA and the transmitted symbol, along with the Bayesian Cramér-Rao bound, which bounds the error of all estimators. We also derive the ISAC-aware minimum mean squared error receiver for both the deterministic and stochastic models. We study the tradeoff between sensing and communication under the deterministic and stochastic waveform assumptions. Specifically, we show that the fundamental trade-off between sensing and communication power allocations is expressed differently in the deterministic and stochastic models and argue that the results serve as basic considerations when designing pilot and sensing signals for ISAC systems.

Satellite operators worldwide are in a race to deploy and enhance connectivity supporting diverse 5G applications and services, with success contingent upon delivering superior Quality of Experience (QoE) tailored to each service within the constraints of limited network capacity (Mbts). However, this endeavor's success is hindered by unpredictably fluctuating traffic demands, distinct packet arrival distributions across services, and evolving stochastic quality expectations of users. This paper addresses these challenges by formulating a statistical optimization problem that minimizes allocated capacity (intending to accommodate more users) while meeting specific QoE requirements, such as queuing delay. To achieve this, we leverage packet queuing analysis within the buffer system of the SatCom gateway's forward link. Given the complexity of directly addressing the formulated problem, we resort to approximating its constraints using the probability of occurrence. We propose a multi-agent Double Deep Q-Network (DDQN) algorithm, enabling a more precise representation of queue-length states and facilitating more informed decisionmaking by the agents. The approach leverages episodic training, ensuring agents are thoroughly prepared and optimized through simulations before being deployed in a real-time environment. Extensive simulation campaigns validate the efficiency of our method, showcasing its superiority over benchmark algorithms.

Long-term stability of output characteristics of piezoresistive pressure sensor chips in the form of microelectromechanical system (MEMS) is one of the most important parameters. The research analyzes the variation of useful signal of high sensitive pressure sensor chips and its errors in mechanical and temperature characteristics, which are also time-dependent. The main feature of this study is the application of the previously developed construction of mechanical part, which allows to achieve a balance between extremely high sensitivity and low error rates for ultra-low pressure range. The research demonstrates the changes after 5 years of aging for 24 pressure sensor chip samples (chip dimension 6.15x6.15 mm 2) for pressure range of 0.5 kPa with sensitivity S0,5 after = (34.5 ± 6.0) mV/V/kPa, nonlinearity 2KNL 0.5 after = (0.71 ± 0.47) %/FS and temperature hysteresis up to 0.5% in the temperature range from-30 ⁰C to +60 ⁰C. The results of long-term stability of useful signal shows that the errors for 10 out of 24 samples does not exceed the values of 1.5%, which mostly connects with influence of residual mechanical stresses (RMS) from the assembly design of pressure sensor.

Gait analysis contributes to the identification of structural and functional limitations, as well as to the development of treatment plans and the evaluation of their effect on the patient. For this purpose, spatial and temporal characteristics of the patient's gait are studied and compared with reference values taken from control subjects. This paper examines the use of trajectories described by the instantaneous center of rotation (ICR) of the lower extremity segments as a biomechanical measure for assessing human gait. Gait trials were performed in which subjects walked on the ground and on a treadmill using a wearable inertial motion capture system (MVN Awinda, Xsens), while sports cameras recorded motion in the sagittal plane. Kinematic data from gait trials were acquired by video processing using an open source sports analysis tool (Kinovea) and directly through the Xsens Analyze software. The position of the ICRs of the lower extremity segments in the sagittal plane was calculated for each time sample and the determined trajectories were analyzed in time, frequency, and state space.

The grid impedance is crucial for the stability analysis of grid-tied converters and also maintaining voltage robustness of a grid segment. However, in meshed distribution grids, it is essential to separate the feeder impedance from the overall grid impedance for an accurate assessment of power transfer capabilities and overload risks. Traditional research in power flow and converter control often treats the grid impedance as one lumped value. Beyond that, they treat this one lumped value as a known constant, overlooking its time-varying nature, which needs regular updates. Furthermore, low-voltage grids, in particular, are still only poorly monitored in real time. Thus, any practical interventions and devices, e.g., power-flow controllers and soft open points (SOP), ideally characterise their grid widely autonomously without the need of many distributed sensors. This paper introduces a novel impedance detection method to distinguish feeder impedance from total grid impedance. Through exploitation of the asymmetry of the circuit, the method can even separate the impedances of each of the transformers if there is more than one and the bare feeders. The method leverages a series-parallel direct-injection SOP circuit for reactive current injection for a precise, real-time impedance detection without extra equipment. This approach contrasts with conventional methods that typically focus on a single grid's impedance, as it estimates the impedance of both feeders in a two-bus system. It is particularly effective in differentiating mediumvoltage/low-voltage (MV/LV) transformers and cable impedance, thus offering significant insights for both stability and power transfer analysis.