This paper theoretically investigates the asymptotic properties of maximum likelihood estimates of GaGLM, and discusses some properties about Gamma distribution. It can provide theoretical foundation for expanding the application scope of gamma distribution based regression model, and benefit the further interval estimates, hypothesis tests and stochastic control design.The existence of the Gamma function in Gamma distribution makes correlation analysis certain specificity, and few researchers do relevant theoretical research. To complement this part, we established the asymptotic properties and the application condition of maximum likelihood estimates of GaGLM. In addition to this, we also discussed the propertis of the Fisher information matrix ,and n-order moment of Z and log(Z).

This work sheds light on the dynamics of aqueous droplets in hydrophobic environment as well as their distant interactions, reasoned by well-studied Cheerios effect.

Since E-Mobility is on the rise worldwide, large Charging Infrastructure (CI) networks are required to satisfy the upcoming Charging Demand (CD). Understanding this CD with its spatial and temporal uncertainties is important for grid operators to quantify the grid impact of Electric Vehicle integration and for Charging Station (CS) operators to assess long-term CI investments. Hence, we introduce an Agent-based E-Mobility Model assessing regional CI systems with their multi-directional interactions between CSs and vehicles. A Global Sensitivity Analysis (GSA) is applied to quantify the impact of 11 technical levers on 17 relevant charging system outputs. The GSA evaluates the E-Mobility integration in terms of grid impact, economic viability of CSs and Service Quality of the deployed Charging Infrastructure (SQCI). Based on this impact assessment we derive general guidelines for E-Mobility integration into regional systems. We found, inter alia, that CI policies should aim at allocating CSs across different types of locations to utilize cross-locational effects such as CSs at public locations affecting the charging peak in residential areas by up to 18%. Additionally, while improving the highway charging network is an effective lever to increase the SQCI in urban areas, public charging is an even stronger lever in rural areas.

Analog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network.

This paper is motivated by the need in certain engineering contexts to construct approximations for irrational functions in the complex variable z. The main mathematical tool that will be used is a special continued fraction expansion that cause the rational approximant to collocate the irrational function at specific important values of z. This paper introduces two theorems that facilitate the construction of the rational approxima- tion.

This paper presents a novel and compact bandgap comparator (BGRCOMP) for under-voltage lockout (UVLO) and thermal shutdown (TSD) protection circuits. The proposed BGRCOMP is self-referenced and combines the advantages of both a high-accuracy bandgap reference and a comparator into one single circuit. A latch-controlled biasing technique is also presented, which reduces static power consumption of the proposed BGRCOMP. The proposed BGRCOMP is used for the design of compact and low power UVLO and TSD circuits. The post-layout simulation results using a 0.18 µm BCD-on-SOI technology prove the attractive performance of the UVLO and TSD with a static current (IQ) of 7.76 µA and 5.4 µA from a 5 V supply, respectively. The deviations of UVLO thresholds are less than 3 mV in the temperature range of -40~85 °C.

Make use of battery energy storage system (BESS) to regulate frequency measures after introducing wind power source. The secondary frequency control is the main application of utilizing the BESS

Recently, Power Quality is attracting the attention of researchers focused on distribution network reconfiguration. Mainly, harmonic distortion as an emerging issue of distribution networks plays an essential role in power quality-oriented network reconfiguration. Commonly, harmonic analysis of distribution networks is being performed while considering non-linear loads, capacitor banks, sub-transmission transformers, and line impedances. However, in harmonic analysis, the presence of linear loads and their effects are being neglected. Consideration of linear loads in harmonic analysis affects the impedance characteristic of the distribution network. Consequently, it changes the total harmonic distortion of bus voltages. In this paper, for the first time, exhaustive research is performed on the effect of linear loads in distribution network reconfiguration by considering five conventional linear load models. The evaluations are conducted on two balanced and unbalanced distribution networks: IEEE 69-bus and 95-bus practical Iranian distribution network using particle swarm optimization aiming to find the minimum total losses of the network. Comparison results confirm that the linear load modelling is very effective in the optimum configuration.

This article presents development of an innovative approach to identify spectrally significant wavelength bands, for a given environment, to tune hyperspectral sensor acquisition before UAV borne surveys. As several programmable hyperspectral sensors are now available, it is often a challenge to consider the suitable wavelengths of interest. Researchers often conduct a thorough field survey to identify the composition of target endmembers in an area to identify suitable wavelengths before UAV survey, which is difficult and cumbersome. Otherwise, the selection of wavelengths by trial-and-error is error-prone. To our knowledge, this is the first time a technique for optimal hyperspectral band (or feature) selection has been proposed to pre-tune UAV-hyperspectral sensors before the survey. A metaheuristic evolutionary workflow using Particle Swarm Optimisation was used for this. The method is easy in the field and efficient to identify optimal bands before UAV-hyperspectral surveys.

In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, training gesture and gesture recognition using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis for hand tracking are being used to obtain motion descriptors and hand region. It is fairy robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, the techniques have been proposed to improve the performance of the recognition and the accuracy using the approaches like selection of the training images and the adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. In the experiments, it has been tested to the vocabulary of 36 gestures including the alphabets and digits, and results effectiveness of the approach.

This paper introduces Generalized Fourier transform (GFT) that is an extension or the generalization of the Fourier transform (FT). The Unilateral Laplace transform (LT) is observed to be the special case of GFT. GFT, as proposed in this work, contributes significantly to the scholarly literature. There are many salient contribution of this work. Firstly, GFT is applicable to a much larger class of signals, some of which cannot be analyzed with FT and LT. For example, we have shown the applicability of GFT on the polynomially decaying functions and super exponentials. Secondly, we demonstrate the efficacy of GFT in solving the initial value problems (IVPs). Thirdly, the generalization presented for FT is extended for other integral transforms with examples shown for wavelet transform and cosine transform. Likewise, generalized Gamma function is also presented. One interesting application of GFT is the computation of generalized moments, for the otherwise non-finite moments, of any random variable such as the Cauchy random variable. Fourthly, we introduce Fourier scale transform (FST) that utilizes GFT with the topological isomorphism of an exponential map. Lastly, we propose Generalized Discrete-Time Fourier transform (GDTFT). The DTFT and unilateral $z$-transform are shown to be the special cases of the proposed GDTFT. The properties of GFT and GDTFT have also been discussed.

Age of Information (AoI) measures the freshness of data in mission critical Internet-of-Things (IoT) applications i.e., industrial internet, intelligent transportation systems etc. In this paper, a new system model is proposed to estimate the average AoI (AAoI) in an ultra-reliable low latency communication (URLLC) enabled wireless communication system with decodeand- forward relay scheme over the quasi-static Rayleigh block fading channels. Short packet communication scheme is used to meet both reliability and latency requirements of the proposed wireless network. By resorting finite block length information theory, queuing theory and stochastic processes, a closed-form expression for AAoI is obtained. Finally, the impact of the system parameters, such as update generation rate, block length and block length allocation factor on the AAoI are investigated. All results are validated by the numerical results.

Data collection is an essential part of sensor devices, particularly in such technologies Like Internet of Things (IoT), wireless sensor networks (WSN), and sensor cloud (SC). In recent years, various literature had been published in these research areas to propose different models, architectures, and contributions in the domains. Due to the importance of efficient data collection regarding reducing. energy consumption, latency, network lifetime, and general cost, a momentous literature volume has been published to facilitate data collection. Hence, review studies have been conducted on data collection in these domains in isolation. However, a lack of comprehensive review collectively identifies and analyzes the differences and similarities among the data collection proposals in IoT, WSN, and SC. The main objective of this research is to conduct a comprehensive survey to explore the current state, use cases, contributions, performance measures, evaluation measures, and architecture in the IoT, WSN, and SC research domains. The findings indicate that studies on data collection in IoT, WSN, and SC are relatively consistent with stable output in the last five years. Nine novel contributions are found with models, algorithms, and frameworks being the most utilized by the selected studies. In conclusion, key research challenges and future research directions have been identified and discussed.

We report world record high data transmission over standard optical fiber from a single optical source. We achieve a line rate of 44.2 Terabits per second (Tb/s) employing only the C-band at 1550nm, resulting in a spectral efficiency of 10.4 bits/s/Hz. We use a new and powerful class of micro-comb called soliton crystals that exhibit robust operation and stable generation as well as a high intrinsic efficiency that, together with an extremely low spacing of 48.9 GHz enables a very high coherent data modulation format of 64 QAM. We achieve error free transmission across 75 km of standard optical fiber in the lab and over a field trial with a metropolitan optical fiber network. This work demonstrates the ability of optical micro-combs to exceed other approaches in performance for the most demanding practical optical communications applications.

We present an alternative inference framework for the Gaussian process-based extended object tracking (GPEOT) models. The method provides an approximate solution to the Bayesian filtering problem in GPEOT by relying on a new measurement update, which we derive using variational Bayes techniques. The resulting algorithm effectively computes approximate posterior densities of the kinematic and the extent states. We conduct various experiments on simulated and real data and examine the performance compared with a reference method, which employs an extended Kalman filter for inference. The proposed algorithm significantly improves the accuracy of both the kinematic and the extent estimates and proves robust against model uncertainties.

Renweable energy resources degrade network inertia, thereby frequency stability is worsened. Grid-forming converter aids to stabelize the frequency measures after disturbance by applying virtual inertia to counter the reduced system inertial response. However, some challenges are encountered due to the PID control parameters setting and the virtual inertia value selection

Enabling higher levels of autonomy requires an increased ability to identify and handle internal faults and unforeseen changes in the environment. This article presents an approach to improve this ability for a robotic system executing a series of independent tasks by using a dynamic decision network (DDN). A case study of an industrial inspection drone performing contact-based inspection is used to demonstrate the capabilities of the resulting system. The case study demonstrates that the system is able to infer the presence of internal faults and the state of the environment by fusing information over time. This information is used to make risk-informed decisions enabling the system to proactively avoid failure and to minimize the consequence of faults. Lastly, the case study demonstrates that evaluating past states with new information enables the system to identify and counteract previous sub-optimal actions.

The solar tracking system detects the astronomical position of the sun during the day and increases the output power of the PV panel by placing it in a suitable position relative to the angle of the sun's rays. Many solar tracking systems have been developed so far that either have not been able to move on two axes or have been based on geometric and astronomical equations and artificial intelligence, which are expensive. This study presented a new DAST based on LDRs, which adjusts the PV panel relative to the angle of the sun's rays by moving simultaneously on two axes. DAST is a very simple and cost-effective control system that utilizes Wheatstone bridge circuit function and LDRs. If this controller is used, it is possible to control PV panels on the metal structure both individually and in an integrated manner. Therefore, the experimental findings of this solar tracking system can help develop solar energy applications.