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.

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.

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.

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

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.

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.

This paper presents a new dual-inductor hybrid boost converter (DI-HBOC) with two inductors located at the output. This structure allows continuous current delivered to the load, thus, reducing the output filtering capacitor size and the output voltage ripple. By relocating the inductor at the output, which is the lower current path, the conduction loss on the inductor can be significantly reduced. The right half plane zero (RHPZ) in the control-to-output transfer function can also be eliminated; therefore, a simple pulse-width modulation (PWM) voltage-mode controller can be used for the proposed DI-HBOC while still achieving high closed-loop bandwidth and fast transient response. The distinct features of the proposed converter are analytically demonstrated. A 12-to 24 V DI-HBOC and a conventional BOC (CBOC) using low-RON GaN switches with PWM voltage-mode controller are also implemented in PSIM for verification and comparison. The simulated peak power efficiency is 97.4 % that is 1.17 % higher than the CBOC. At 3 A load current, the power efficiency is improved by 9.7 % and the output ripple is only 17.5 mV, 6x lower than in CBOC.

This paper presents the capacity analysis of unmanned aerial vehicles (UAVs) communications supported by flying intelligent reflecting surfaces (IRSs). In the considered system, some of the UAVs are equipped with an IRS panel that applies certain phase-shifts to the incident waves before being reflected to the receiving UAV. In contrast to existing work, this letter considers the effect of imperfect phase knowledge on the system capacity, where the phase error is modeled as a von Mises random variable with parameter k. Analytical results, corroborated by Monte Carlo simulations, show that the achievable capacity is dependent on the phase error, however, the capacity loss becomes negligible at high signal-to-noise ratio (SNR) and when k>6.

We consider the capacity optimization of submarine links when including a realistic model of the gain-flattened constant-pump erbium doped fiber amplifiers (EDFA). While Perin et al. [1] numerically attacked this optimization for Constant-Gain (CG) amplified links, we extend the analysis also to more realistic submarine constant power-spectral-density (CPSD) links. As in [1], we concentrate on a single spatial mode of a spatial division multiplexed (SDM) link at low EDFA pump power Pp, and thus consider only the impairments of amplified spontaneous emission noise. Here we adopt a novel semi-analytical approach which consists of fixing the inversion x1 of the first EDFA (the state-variable of the link) and analytically finding capacity C(x1) by searching over the x1-feasible input wavelength division multiplexed (WDM) PSD distributions. Then the optimum inversion x1 that maximizes C(x1) is numerically obtained. This approach enables us to get both approximate (for CG links) and exact (for CPSD links) capacity-maximizing WDM input distributions, which vary inversely with the EDFA gain profile. For CG links the optimal WDM allocation is called the gain-shaped water-filling. Other practical allocations are analyzed, such as the signal to noise ratio equalizing allocation (CSNR), and the constant input power (CIP) allocation which uses a flat WDM distribution. We find that, for typical submarine span attenuations around 10dB and when the link works at the optimal inversion x1, CIP and CSNR achieve essentially the same capacity as the optimal allocation. At sufficiently large pump Pp (>= 30 mW) the optimal inversion x1 is such that the EDFA gain at 1538nm equals the span attenuation, for EDFA emission and absorption as in [1]. When span attenuations increase to 20dB, then we start seeing an advantage of the optimal allocation. Another key finding is that optimized CG and CPSD links behave roughly the same, with a slightly superior capacity for CPSD.

An equivalent-circuit topology for two-port lossy non-symmetric reciprocal networks is proposed. The circuit topology is based on the eigenstate decomposition. The proposed circuit consists of two immittances and two transformers with a single complex turns ratio, all obtained from the eigenvalues and eigenvectors of the admittance or impedance matrix of the network in a straightforward way. The interconnection of the circuit elements is simple and compact, and the real parts of its immittances are always positive. To verify its behavior, the equivalent circuit of two different leaky-wave antenna unit cells is extracted: a series-fed coupled patch radiating element, and the complementary strip-slot. Simulated and measured data are provided for the study cases, highlighting the satisfactory behavior of the equivalent circuit over a very broad bandwidth. A simple asymmetry model is also proposed that can be used advantageously in the analysis and design of non-symmetric two-ports, like unit cells of leaky-wave antennas.

The role of the source geometry in the the radiation of focusing beams by conformal antennas is examined by the comparison of their directivity functions at different maximum directions. An inverse source problem approach is adopted, where solutions stable with respect to data uncertainties are to be found by relying on the analysis of the pertinent operator by the Singular Values Decomposition. This general framework allows to connect the mean value of the mazimum directivity function to the Number of Degrees of Freedom of the conformal source, which depends only on its electrical length. For each source geometry the focusing far field ensuring the maximum directivity for every pointing direction and the corresponding source current are obtained. The comparison with the ones achieved by the usual phase compensation technique of the source current reveals their optimal behavior. The usefulness of the approach as a tool in antenna synthesis is shown by comparing different geometries for those applications where identical beams are required to be rdiated for the coverage of a larga angular domain.

Deep-BEJT: A New Human Activity Recognition System basedon Beta Elliptical Joint Trajectory (BEJT) and Long Short TermMemory (LSTM) New journal paper

This paper describes an innovative e-learning project which is the development of a digital workbook that helps teaching handwriting at school. In this work, we propose a new qualitative and quantitative analysis process of cursive handwriting. This process detects automatically mistakes, gives a real-time feedback and helps teachers evaluate children’s writing skills. The main aim of this digital workbook is to help children learn how to write correctly. The proposed process is composed of five main criteria: shape,direction, stroke order, position respect to the reference lines and kinematics of the trace. It analyzes the handwriting quality and gives automatically feedback based on the Beta-Elliptic Model using similarity detection (SD) and dissimilarity distance (DD) measure. Our work apprehends dynamic and visual representation of the acquired traces and selects efficient features adapted to various handwriting styles and multiple script languages such as Arabic, Latin, digits, and symbols drawing. It demonstrates that beta-elliptic is not only a model for segmentation and recognition but also a tool to evaluate handwriting. Our application offers two interactive interfaces respectively dedicated to learners, and experts or teachers who can adapt it easily to the specificity of each child. The validation of the proposed system is done on a database collected in Tunisia primary schools with 400 children. Experimental results show that the efficiency and robustness of our suggested framework that do help teachers and children by offering positive feedback throughout the handwriting learning process using tactile digital devices.

Presenting a novel set generalizing the usual base representation of floating point numbers. This “base-prime” set was identified in patented research on nonuniform data sampling and self-stabilizing computer arithmetic.

We proposed RTP, a composite neural network model that captures knowledge from remote transportation pollution events (RTPEs) to improve the local PM2.5 prediction. To the best of our knowledge, this is the first deep learning work to include knowledge from remote pollutants for PM2.5 prediction. RTP consists of two neural network components: a pre-trained base model and STRI model. The base model captures knowledge from local factors that influence PM2.5 concentrations and STRI captures knowledge from RTPEs by learning spatial-temporal characteristics of Satellite base AOD data and weather features from remote areas. In addition, given the size of the STRI model, to facilitate training and improve results we divide the full STRI model into two components: STRI\_fe, which is used to extract spatial-temporal features from remote areas, and STRI\_p, which predicts local PM2.5 concentrations using both remote and local features. The prediction results from STRI\_p show that the prediction error is reduced when remote features are added to the model, demonstrating that the STRI model indeed captures knowledge from RTPEs. To characterize the occurrence of RTPEs in northern Taiwan, we also developed an algorithm to classify PM2.5 concentrations attributable to RTPEs. We use the STRI model for the prediction of two EPA stations located at the northern tip of Taiwan and apply the classification algorithm to the results. This yields improvements in accuracy when remote features are added to the model, which demonstrates the impact of RTPEs at the stations.