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Visible light communication (VLC) has emerged as a secure, cost-effective, and green supplement to the existing radio frequency (RF) communication systems. However, the performance of a typical VLC link is degraded by the following factors: (a) inter-symbol interference (ISI), which arises due to limited modulation bandwidth of light-emitting diode (LED), (b) the dispersion due to multi-path reflections, and (c) nonlinear transfer characteristics of LED. To mitigate ISI and LED nonlinearity, existing blind post-distorters, like the Volterra modified cascaded multimodulus algorithm (MCMMA) and the Hammerstein improved multistage clustering (HIMSC) approaches, experience degradations from modeling errors due to abrupt polynomial-series truncation. To mitigate these degradations, we propose a novel random Fourier features (RFF) based improved multi-stage clustering (RFF-IMSC) algorithm for blind post-distortion. Simulations performed over standardized IEEE 802.15 PAN VLC channels indicate that the proposed RFF-RIMSC post-distorter delivers improved performance as compared to the existing blind post-distorters. Also, analytical guarantees are presented and error-rate performance are analyzed for the $\epsilon $ -optimality of this approach with respect to supervised loss-functions.

Reproducing Hilbert space (RKHS)-based adaptive algorithms have attracted increased attention in machine learning and nonlinear signal processing with applications in visible light communications, radar, radio frequency communications and others. However, performance of RKHS-based algorithms is highly sensitive to a suitable learning criterion. In this regard, the Versoria criterion-based adaptive filtering has gained interest in recent works due to its superior convergence characteristics as compared to the popular criterion such as minimum mean square error, and maximum correntropy criterion. Therefore, in this brief, a novel random Fourier feature (RFF)-based kernel recursive maximum Versoria criterion (KRMVC) algorithm is proposed. Convergence analysis is presented next for the proposed RFF-KRMVC algorithm as guarantees of the promised performance benefits. Lastly, the analytical results are validated by corresponding computer-simulations over practical application-scenarios considered in this brief.

Ultraviolet (UV) communication has emerged as a promising solution for providing non-line-of-sight (NLOS) wireless connectivity due to strong molecular and aerosol scattering at UV wavelength. However, performance of UV based communication systems is severely impaired due to nonlinear transfer-characteristics of light emitting diode (LED), which degrades the overall symbol error rate (SER) performance. In addition, UV based communication systems are also impaired by multiplicative distortion due to turbulence, that causes detrimental instantaneous outages. Hence, in this work, first an expression for the outage probability is derived for a nonlinear UV communication system via analytical characterization of the statistics of the additive distortion. Further, utilizing the proposed analytical model for additive distortion, the error rate of reproducing kernel Hilbert space (RKHS) based detectors is quantified for the nonlinear outdoor NLOS UV channel. Additionally, using the derived expression for error-rate, an RKHS based minimum symbol error rate (MSER) equalizer is formulated to mitigate the distortion due to LED nonlinearity, and to enhance the error-rate performance of the considered nonlinear NLOS UV link. Convergence of the proposed MSER equalizer is analyzed, and improvements in error-rate promised by the proposed equalizer are validated by computer simulations over typical NLOS UV channels.

Visible light communication (VLC) has emerged as a promising technology that facilitates high speed green communication for the next generation communication systems. However, some of the performance-limiting impairments in VLC include intersymbol interference (ISI), LED's nonlinearity, and multiplicative fading distortion due to user mobility, which present a significant gap between the promised and the achieved throughput of VLC based systems. To mitigate the aforementioned channel impairments, this paper proposes a random Fourier feature (RFF) based kernel minimum symbol error rate (KMSER) decision feedback equalizer (DFE) based post-distorter for VLC, which supports post distortion under finite memory budget. Furthermore, the feedforward, and feedback filter weights are jointly optimized by using a stronger minimum symbol error rate criterion as opposed to the conventional minimum mean square error criterion. Simulations indicate that the proposed RFF-KMSER-DFE based post-distorter exhibits superior BER performance and lower computational complexity over the existing dictionary based post-distorters. Lastly, an analytical bound for BER is derived for the proposed algorithm, and corroborated through simulations.

Visible light communications (VLC) has emerged as a viable supplement for high speed next-generation communication systems. However, performance of a VLC link is limited by the nonlinear characteristics of light emitting diodes (LEDs), ambient light, and inter-symbol interference (ISI). In this letter, the paradigm of least-squares based post-distortion over reproducing kernel Hilbert spaces (RKHS) is considered for a visible light communication (VLC) link. The performance-limiting impairments (viz. LED nonlinearity, and ISI), are mitigated by random Fourier features (RFF) based least minimum symbol error-rate post-distorter in RKHS, which facilitates post-distortion under finite memory-budget constrained scenarios. Performance of the proposed algorithm is tested over IEEE 802.15r1 PAN VLC channels, and the proposed algorithm is observed to converge significantly faster, whilst supporting higher data rates as compared to the existing RKHS based post-distorters.

Performance of massive multi-input multi-output (MIMO) visible light communication (VLC) link is limited by ill-conditioned massive-MIMO channel, and nonlinearity of light emitting diodes (LEDs), which degrades the bit error rate (BER) performance. In this letter, to circumvent the above limitations, we propose a hybrid solution by using a singular value decomposition based adaptive precoder and a kernel minimum symbol error rate based post-distorter. Simulations indicate that the proposed algorithm outperforms the existing algorithms in terms of BER and computational complexity. Further, for the first time, the effect of DC-bias fluctuations on BER and dictionary-size is quantified analytically, and verified through numerical simulations.

Neutralizing antibody-based passive immunotherapy could be an important therapeutic option against COVID-19. Herein, we demonstrate that equines hyper-immunized with chemically inactivated SARS-CoV-2 elicited high antibody titers with a strong virus-neutralizing potential, and F(ab')

Visible light communication (VLC) has emerged as a viable supplement to the existing radio frequency based communication systems. However, its performance is severely impaired by a) nonlinear characteristics of light emitting diode (LED), which reduces the Euclidean distance between neighboring constellation points, and b) intersymbol interference (ISI) due to time-dispersive nature of VLC channel, which reduces the eye opening of the received symbols and degrades the bit error rate (BER) performance. In this paper, we propose a novel kernel least mean square (KLMS) with adaptive decision feedback equalizer based post-distorter for VLC for joint mitigation of LED nonlinearity and ISI. In this work, minimum mean square error criterion based joint optimization problem is formulated in a reproducing kernel Hilbert space for optimization of the feedforward and feedback filter coefficients. Simulations indicate that the proposed post-distorter yields better BER performance as compared to existing algorithms.

Visible light communication (VLC) is a promising and eco-friendly complementary technology to the existing radio frequency (RF) based communication system. However, the performance of VLC based system is limited by dispersive characteristics of the VLC channel, and restricted modulation bandwidth of light emitting diode (LED). To mitigate the ISI, orthogonal time frequency space (OTFS) modulation is proposed in the literature. However, it has been found that the performance analysis of OTFS over multipath VLC channels has not been investigated. This paper investigates the performance of OTFS over static multipath channels for the VLC system. Simulations performed over the standardized IEEE 802.15.7 VLC channel indicate that OTFS with message passing detector delivers an approximate 6 dB SNR gain at BER of 10 −4 over the conventional orthogonal frequency division multiplexing (OFDM) scheme with minimum mean square error (MMSE) based detector.

Massive multiple input multiple output (m-MIMO) visible light communication (VLC) has emerged as a viable technology to enhance the throughput of an existing VLC based systems in order to serve the high speed data requirement for beyond 5G and 6G communication systems. However, performance of m-MIMO VLC is severely impaired by (1) columns of m-MIMO VLC channel are highly correlated which makes the channel matrix ill-conditioned, and (2) nonlinear transfer-characteristics of light emitting diode (LED). The aforementioned channel impairments collectively degrade the overall bit error rate (BER) at the receiver. In this paper, finite memory budget adaptive precoder is proposed to decrease the channel correlation/condition number of MIMO channel matrix. Reproducing kernel Hilbert space (RKHS) based post-distorters have been proposed in the literature for VLC which rely on growing dictionary of kernel evaluations, and hence it is difficult to practically implement them under finite memory budget. In this paper, random Fourier features (RFF) based kernel least mean square (RFF - KLMS) algorithm is proposed for post-distortion over m-MIMO VLC channels which alleviates the requirement of dictionary and facilitates post-distortion under finite memory budget. Computer simulations performed over m-MIMO VLC channels show that the proposed RFF based algorithm exhibit similar BER performance with reduced computational complexity as compared to the dictionary based post-distortion algorithm.

Visible light communication (VLC) is eco-friendly and low-cost supplement to the existing radio frequency (RF) based communication system. However, the performance of VLC based system is limited by dispersive characteristics of the VLC channel which leads to inter-symbol-interference (ISI) and inter-carrier-interference (ICI). To alleviate the ISI and ICI, orthogonal time frequency space (OTFS) modulation has been proposed in the literature for both RF and millimeter wave communication systems. However, the performance analysis of OTFS over mobile multipath VLC channels is still an open area to investigate. This paper investigates the performance of OTFS over mobile multipath channels for the VLC system. Simulations performed over the realistic mobile multipath VLC channel indicate that OTFS with message passing detector exhibit better bit error rate over the conventional orthogonal frequency division multiplexing scheme with minimum mean square error based detector.

Part 1: Regular Papers; International audience; The transmission reach of multi-core fiber (MCF) communication systems is severely affected by inter-core crosstalk (IC-XT), which limits its application for long-reach core optical network. One of the major factors limiting the transmission reach of MCF is nonlinear IC-XT interference, which makes the overall system nonlinear, thereby resulting in a poor bit error rate (BER) performance. Conventional Volterra series based nonlinear equalizer are computationally complex, and impaired by modeling error due to the truncation of polynomial kernel. In this paper, for the first time, we propose multivariate kernel least mean square (KLMS) based adaptive nonlinear equalizer for mitigating IC-XT impairments in MCF communication systems. The proposed scheme is inspired from reproducing kernel Hilbert space (RKHS) based machine learning algorithms. Simulations are performed for different multi-core structures, fiber lengths, and modulation schemes, which show that the proposed KLMS algorithm exhibit superior BER performance over the existing Volterra series equalizer.

Visible light communication (VLC) has emerged as a promising, green, and interference-free supplement to existing radio frequency-based communication systems. To enhance the capacity of existing VLC-based systems, using massive arrays of light emitting diodes (LEDs) at the transmitter and photodiodes at the receiver has been proposed recently, resulting in a massive multiple-input multiple-output (m-MIMO) VLC system. However, by increasing LED array pitch in m-MIMO, the VLC channel matrices are generally ill conditioned which makes them sensitive to small perturbations and degrades the overall bit error rate (BER) performance. To reduce the condition number of the overall channel matrix, a novel precoder using an exponent-based singular value decomposition is proposed for m-MIMO VLC system. Furthermore, a joint optimization problem is formulated to optimize the exponent and to detect symbols iteratively using the minimum symbol error rate criterion. Simulations show that the proposed adaptive precoding technique exhibits superior BER performance over existing techniques. Analytical upper bounds for BER are also derived and validated by simulations.

Molecular communication (MC) has emerged as a viable solution for nanonetworks, where bio-molecules are used to transmit the information. However, one of the factors that limits the throughput of diffusion based MC (DBMC) is inter-symbol interference (ISI), which arises due to long tail of channel impulse response (CIR) of the DBMC. In addition to ISI, performance of the DBMC based systems is also limited by the nonlinear CIR of DBMC, which results in nonlinear distortions at the receiver, and degrades the overall bit error rate (BER) performance. Conventional minimum mean square error (MMSE) and decision feedback equalizer (DFE) based receivers deliver suboptimal performance for nonlinear systems. In this paper, for the first time, we propose Volterra-DFE based nonlinear equalizer for mitigating the aforementioned DBMC channel impairments. Numerical results show that the proposed Volterra-DFE algorithm exhibits superior BER performance over the conventional MMSE and DFE based equalizers.

Feature compression and conditioning are some of the important enhancers for Neural Networks to classify efficiently in the least duration per epoch cycle. However, due to a large number of redundant features seen in raw images, the number of input parameters for artificial neural networks drastically increases thus increasing the data storage, processing requirements as well as the duration of training. The use of Deep Neural Networks is also an approach, but a relatively higher demand for computational resources is inevitable. Mapping the image matrix to Histogram Oriented Gradient (HOG) vectors and Local Binary Pattern (LBP) reduces the unused feature information held by the initial image by successfully being able to encompass the most necessary features such as edges or contours of vehicles in the form of vectors for further classification through artificial neural networks as mentioned earlier. Extensive validation has been performed on separate datasets with different objectives. Furthermore, investigation shows that for increased accuracy, the fusion of the two vectors should be used. Through the implementation of this process, we can easily reduce the high density of hidden neurons and still obtain better performance. Additionally, our Dataset Generator Module (DGM) has drastically improved upon the uptime of manually tagging subparts of images from a raw video from 40 minutes initially to less than 2 minutes per frame. In view of capturing natural motion, orientations and perspective of moving vehicles, building the datasets right from the video is preferred, for this reason our unique DGM also accounts for differential window sizing as well which renders the amount and variation of data much more efficient leading it to further be able to identify a threshold distance of vehicles as well.

Diffusion based molecular communication (DBMC) acts as an indispensable element of nano-networking and Internet of things based applications. In DBMC, bio-molecules serve as information carriers for nanoscale systems. However, performance of the DBMC is limited by the following factors; nonlinear channel impulse response (CIR) of the DBMC which makes the mapping between the received symbols and transmitted symbols nonlinear, inter-symbol-interference (ISI) due to long tail of CIR of the DBMC which degrades the overall bit error rate (BER) performance, and stochastic movement of the molecules which introduces noise at the receiver. Most of the prior works on receiver design for molecular communication (MC) are based on linear detection algorithms, which are sub-optimal for the nonlinear DBMC system. Thus, to improve the performance of MC system, for the first time, we propose adaptive nonlinear receiver for MC based on the kernel least mean square algorithm, in the reproducing kernel Hilbert space. Numerical results show that the proposed nonlinear receiver improves BER performance as compared to the existing linear detection algorithms-based receiver.

Although the surgical extraction of the mandibular third molar is routinely performed in dental clinics, the precise management of complications associated with it requires thorough knowledge and experience in the field of oral and maxillofacial surgery. Iatrogenic dislodgment of a tooth or its fragment is a rare complication and usually occurs when excessive, uncontrolled forces are applied via elevators. It also is possible that this rare complication may be underreported. This case report describes the retrieval, under local anesthesia, of a mandibular left third molar crown from the posterosuperior region of the pterygomandibular space after iatrogenic dislodgment.

Information theoretic learning based approaches have been combined with the framework of reproducing kernel Hilbert space (RKHS) based techniques for nonlinear and non-Gaussian signal processing applications. In particular, generalized kernel maximum correntropy (GKMC) algorithm has been proposed in the literature which adopts generalized Gaussian probability density function (GPDF) as the cost function in order to train the filter weights. Recently, a more flexible and computationally efficient algorithm called maximum Versoria criterion (MVC) which adopts the generalized Versoria function as the adaptation cost has been proposed in the literature which delivers better performance as compared to the maximum correntropy criterion. In this paper, we propose a novel generalized kernel maximum Versoria criterion (GKMVC) algorithm which combines the advantages of RKHS based approaches and MVC algorithm. Further, a novelty criterion based dictionary sparsification technique as suggested for kernel least mean square (KLMS) algorithm is proposed for GKMVC algorithm for reducing its computational complexity. Furthermore, an analytical upper bound on step-size is also derived in order to ensure the convergence of the proposed algorithm. Simulations are performed over various non-Gaussian noise distributions which indicate that the proposed GKMVC algorithm exhibits superior performance in terms of lower steady-state error floor as compared to the existing algorithms, namely the KLMS and the GKMC algorithms.

Visible light communication (VLC) has emerged as a promising supplement to the existing radio frequency (RF) technology, which facilitates high speed communication for next generation communication systems. Although promising, the performance in a practical VLC system is severely impaired by ambient light interference. Furthermore, nonlinearity of light emitting diodes (LEDs) and imperfect/oudated channel state information (CSI) are other factors that degrade the achievable bit error rate (BER) performance. In this work, an analytical framework for analyzing the expression for BER of a wide class of modulation techniques is derived for an impaired VLC system considering: (a) ambient light interference, (b) LED nonlinearity, and (c) imperfect and outdated CSI. The derived analytical expressions for BER are validated through extensive computer simulations performed over typical VLC channels. Lastly, the utility of the derived analytical framework is demonstrated in the context of high data-rate VLC systems, wherein the performance of DC biased optical orthogonal frequency division multiplexing (DCO-OFDM) is quantified for VLC links impaired by the aforementioned degradations.

Tests of statistical significance do not provide information about the clinical significance of research results. It is of utmost importance for a researcher to not only know the statistical significance of the difference, but also the magnitude of the difference and whether it is clinically significant or not. Results might be statistically significant yet it may not have some clinical relevance and vice versa. The concept of clinical significance has received little discussion or debate in the dental literature. The purpose of this paper was to compare statistical and clinical significance, to provide an overview of the two, and to discuss some implications of these concepts for dental research with the help of some suitable examples.

The design and optimization of rectangular patch antenna and comparison between H-shaped patch antenna and rectangular patch antenna have been investigated keeping in view of vital parameters such as S- parameters, VSWR, axial ratio, bandwidth & operating frequency. A novel approach is followed in the optimization process using FEM (Finite Element Method). This FEM technique gave more easy calculation in the design of Microstrip patch antenna and analysis of effect of various design parameters like dimensions (L & W) and substrate of antenna has been done. The proposed antenna is designed at 2.4 GHz of frequency and the simulation results a gain of 1.1016 with directivity of 2.2577 and bandwidth 307 MHz. The proposed ‘H’ shaped Microstrip antenna is fed by 50 Ω Microstrip feed line. The improvement in the results has been incorporated in the design. &nbsp

This paper will discuss how static Var compensator successfully been applied to control transmission system. The voltage level of the system changes when there is a change in load and the drop in load voltage lead to demand for the reactive power. Static Var Compensator is basically a shunt connected Static Var Generator whose output is controlled by adjust the value of capacitive or inductive current. One of the reasons for installing the Static Var Compensator to improve the voltage profile and increases the system lodability. When system voltage is low it generates the reactive power. When the system voltage is high it absorbs the reactive power. FACTS are technologies that increase flexibility of transmission system, control of power flow and increases transmission capacity of the system. In this paper FACT controller such as Static Var Compensator are used to maintain the voltage within limit. Simulation will be provided by using MATLAB.