5D Parameter Estimation of Near-Field Sources Using Hybrid Evolutionary Computational Techniques

Hybrid evolutionary computational technique is developed to jointly estimate the amplitude, frequency, range, and 2D direction of arrival (elevation and azimuth angles) of near-field sources impinging on centrosymmetric cross array. Specifically, genetic algorithm is used as a global optimizer, whereas pattern search and interior point algorithms are employed as rapid local search optimizers. For this, a new multiobjective fitness function is constructed, which is the combination of mean square error and correlation between the normalized desired and estimated vectors. The performance of the proposed hybrid scheme is compared not only with the individual responses of genetic algorithm, interior point algorithm, and pattern search, but also with the existing traditional techniques. The proposed schemes produced fairly good results in terms of estimation accuracy, convergence rate, and robustness against noise. A large number of Monte-Carlo simulations are carried out to test out the validity and reliability of each scheme

An Application of Artificial Intelligence for the Joint Estimation of Amplitude and Two-Dimensional Direction of Arrival of Far Field Sources Using 2-L-Shape Array

An easy and efficient approach, based on artificial intelligence technique, is proposed to jointly estimate the amplitude, elevation, and azimuth angles of far field sources impinging on 2-L-shape array. In these proposed artificial intelligence techniques, the metaheuristics based on genetic algorithm and simulated annealing are used as global optimizers assisted with rapid local version of pattern search for optimization of the adaptive parameters. The performance metric is employed on a fitness evaluation function depending on mean square error which is optimum and requires single snapshot to converge. The proposed approaches are easy to understand, and simple to implement; the genetic algorithm specifically hybridized with pattern search generates fairly good results. The comparison of the given schemes is carried out with 1-L-shape array, as well as, with parallel-shape array and is found to be in good agreement in terms of accuracy, convergence rate, computational complexity, and mean square error. The effectiveness and efficiency of the given schemes are examined through Monte Carlo simulations and their inclusive statistical analysis

Hepatitis B virus infection among different sex and age groups in Pakistani Punjab

<p>Abstract</p> <p>Background</p> <p>Hepatitis B virus (HBV) infection is a serious health problem in the developing countries including Pakistan. Various risk factors are responsible for the spread of this infectious disease. Prevalence of HBV infection in apparently suspected individual of Punjab province of Pakistan was analyzed during January 2008 to December 2010. Current study was aimed to investigate the epidemiology and risk factors of HBV infection.</p> <p>Methodology</p> <p>Four thousand eight hundred and ninety patients suffering from chronic liver disease were screened for the presence of HBV DNA using qualitative Real Time PCR methodology to confirm their status of infection. A predesigned standard questionnaire was filled for all the patients that included information about the possible risk factors.</p> <p>Results</p> <p>A total of 4890 ELISA positive patients were screened for Hepatitis B virus infection. Of these 3143 were positive for HBV, includes 68.15% males and 31.85% females. Male were observed to be more frequently infected as compared to the female with a positivity ratio of 2.14: 1. The rate of infection increases with the passage of time in the course of three years. Highest frequency of infection was found in the age of 21-30 was 34.93% followed by 23.83% in 31-40. Only (13.39%) were belonging to the age group 11-20 year. The rate of infection declines with increasing age as shown by age groups 41-50 (16.13%) and 51-60 (7.09%). While children aged 0-10 and very old >60 age groups were very less frequently 1.49% and 1.65% infected respectively. Important risk factors contributing to HBV spread include barber risk (23.60%), blood transfusion (4.04%), History of injection 26.19%, Reuse of syringes 26.60%, dental risk (11.20%) and surgical procedure (4.26%). Among the entire respondents trend sharing personal items was very common. History of injection, barber risk, surgery and dental procedure and reuse of syringes appear as major risk factors for the transmission.</p> <p>Conclusion</p> <p>Male were more frequently exposed to the risk factors as compared to female. Similarly the younger age group had high rate of infection as compared to the children's and the older age groups. Reuse of syringes', barber risk and History of injection were main risk identified during the present study. To lower HBV transmission rate Government should take aggressive steps towards massive awareness and vaccination programs to decrease the burden of HBV from the Punjab province of Pakistan.</p

Synchronizing data stream processing

Synchronization of data stream processing has a significant impact on performance of systems where processing of long sequences of data items needs to be done simultaneously. In earlier works on stream processing, synchronization has been discussed to a limited extent or has been completely overlooked. This work describes a formal model of synchronization in a data stream processing network. We use a notation of data stream processing networks to identify circumstances that necessitate synchronization. We also express processing of groups of data items in terms of database transactions within a data stream processing network. A technique similar to timestamp ordering of database transactions is used to solve the problems. A solution is presented as a set of rules that govern processing of groups of data items. A proof of correctness has been provided for the strategy used to solve the problems

Sidelobe Reduction in Non-Contiguous OFDM-Based Cognitive Radio Systems Using a Generalized Sidelobe Canceller

In orthogonal frequency division multiplexing (OFDM), sidelobes of the modulated subcarriers cause high out-of-band (OOB) radiation, resulting in interference to licensed and un-licensed users in a cognitive radio system environment. In this work, we present a novel technique based on a generalized sidelobe canceller (GSC) for the reduction of sidelobes. The upper branch of the GSC consists of a weight vector designed by multiple constraints to preserve the desired portion of the input signal. The lower branch has a blocking matrix that blocks the desired portion and preserves the undesired portion (the sidelobes) of the input signal, followed by an adaptive weight vector. The adaptive weight vector adjusts the amplitudes of the undesired portion (the sidelobes) so that when the signal from the lower branch is subtracted from the signal from the upper branch, it results in cancellation of the sidelobes of the input signal. The effectiveness and strength of the proposed technique are verified through extensive simulations. The proposed technique produces competitive results in terms of sidelobe reduction as compared to existing techniques

Suppression of Mutual Interference in Noncontiguous Orthogonal Frequency Division Multiplexing Based Cognitive Radio Systems

Orthogonal frequency division multiplexing (OFDM) is a favourable technology for dynamic spectrum access (DSA) due to the flexibility in spectrum shaping. In spite of that, high sidelobes of OFDM subcarriers bring in considerable interference to the nearby users, particularly in OFDM based cognitive radio (CR) networks, where the secondary users (SUs) are capable of accessing the spectrum opportunistically. In this paper, two new techniques for the suppression of high sidelobes are proposed. The proposed techniques composed of an optimization scheme are followed by generalized sidelobe canceller. The proposed techniques can be considered as a two-level suppression technique in the sense that in the first level the sidelobe is reduced by using cancellation carriers (CCs), whose amplitudes are determined using genetic algorithm (GA) and differential evolution (DE), while in the second level further reduction of sidelobe is achieved using generalized sidelobe canceller (GSC). Simulation results show the power spectral density (PSD) performance of the proposed techniques in comparison with already existing techniques, demonstrating that the proposed techniques minimize the out-of-band radiation (OOBR) significantly, thus qualifying for more effective spectrum sharing

Multiple Target Localization with Bistatic Radar Using Heuristic Computational Intelligence Techniques

We assume Bistatic Phase Multiple Input Multiple Output radar having passive Centrosymmetric Cross Shape Sensor Array (CSCA) on its receiver. Let the transmitter of this Bistatic radar send coherent signals using a subarray that gives a fairly wide beam with a large solid angle so as to cover up any potential relevant target in the near field. We developed Heuristic Computational Intelligence (HCI) based techniques to jointly estimate the range, amplitude, and elevation and azimuth angles of these multiple targets impinging on the CSCA. In this connection, first the global search optimizers, that is,are developed separately Particle Swarm Optimization (PSO) and Differential Evolution (DE) are developed separately, and, to enhance the performances further, both of them are hybridized with a local search optimizer called Active Set Algorithm (ASA). Initially, the performance of PSO, DE, PSO hybridized with ASA, and DE hybridized with ASA are compared with each other and then with some traditional techniques available in literature using root mean square error (RMSE) as figure of merit

Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review

The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI’s capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an “AI scrutiny” phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI’s role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI’s involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI’s capabilities while upholding stringent academic standards can be envisioned

A Novel Deceptive Jamming Approach for Hiding Actual Target and Generating False Targets

Deceptive jamming is a popular electronic countermeasure (ECM) technique that generates false targets to confuse opponent surveillance radars. This work presents a novel approach for hiding the actual target while producing multiple false targets at the same time against frequency diverse array (FDA) radar. For this purpose, the modified FDA radar is assumed to be mounted on the actual aircraft. It intercepts the opponent’s radar signals and transmits back to place nulls in the radiation pattern at the desired range and direction to exploit FDA radar’s range-dependent pattern nulling capability. The proposed deceptive jammer produces delayed versions of the intercepted signals to create false targets with multiple ranges to confuse the opponent’s radar system. The novel mathematical model is proposed whose effectiveness is verified through several simulation results for different numbers of ranges, directions, and antenna elements