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The purpose of this paper is to design and analyze the performance of live model of Bessel beamformer for thorough comprehension of beamforming in adaptive environment and compared with live model of least mean square (LMS) in terms of gain and mean square error (MSE). It presents the principal elements of communication system. The performance of designed live model is tested for its efficiency in terms of signal recovery, directive gain by minimizing MSE using the “wavrecord” function to bring live audio data in WAV format into the MATLAB workspace. These adaptive techniques are illustrated by appropriate examples.

The proposed algorithm framework relies on MATLAB software with the goal to obtain high efficiency in terms of signal recovery, directive gain by minimizing MSE using the “wavrecord” function to bring live audio data in WAV format. It is assumed that this audio signal is only the message or the baseband signal received by the computer. Here the authors consider computer (laptop) as a base station containing adaptive signal processing algorithm and source (mobile phone) as a desired user, so the experiment setup is designed for uplink application (user to base station) to differentiate between desired signal, multipath and interfering signals as well as to calculate their directions of arrival.

The presented adaptive live model is reliable, robust and lead to a substantial reduction of MSE, signal recovery in comparison with the LMS technique. The paper contains experimental data. Obtained results are presented clearly and the conclusion comes directly from the presented experimental data. The paper shows that the presented method leads to superior results in comparison with the popular LMS method and can be used as a better alternative in many practical applications.

The adaptive processes described in the paper are still limited to simulation. It is because of the non-availability of real system for testing, therefore chosen research approach that is platform of MATLAB is opted for simulation. Therefore, researchers are encouraged to test the proposed algorithms on real system if possible.

The paper contains experimental data. The paper's impact on the society is acceptable. These implications are consistent with the findings and the conclusions of the paper. However, there is a need to extend this paper to a next level by implementing the proposed algorithms in the real time environment using FPGA technology.

This research will improve the signal quality of wireless cellular system by increasing capacity and will reduce the total cost of the system so that cost toward subscribers be decreased.

The live model presented in this paper is shown to provide better results. It is the original work and can provide scientific contribution to signal processing community.

The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS) algorithm, can be named as Bessel LMS (BLMS) algorithm. Its performance is compared in adaptive environment with LMS in terms of two important performance parameters, namely; convergence and mean square error. The proposed BLMS algorithm is implemented on digital signal processor along with antenna array to make it smart in wireless sensor networks.

Convergence analysis is theoretically developed and verified through MatLab Software.

Theoretical model is verified through simulation and its results are shown in the provided table.

The theoretical model can obtain validation from well-known result of Wiener filter theory through principle of orthogonality.

The purpose of this paper is to compare the low‐cost switched‐beam antennas and the more expensive adaptive antennas in a wireless mesh network (WMN) scenario.

A mathematical framework is adopted in order to determine, for the two antenna systems, the number of sustainable simultaneous communications and the achievable throughput of a backbone WMN in a realistic outdoor environment.

The paper includes a thorough examination of benefits and crucial implementation issues concerning the adoption multiple antennas on mesh routers.

The paper investigates the relationship between the antenna system and the adopted channel coding technique, to determine the set‐up that may offer the best compromise between performance and complexity.

The purpose of this paper is to present and solve the problem of adaptive beamforming (ABF) for a uniform linear array (ULA) as an optimization problem. ABF mainly concerns with estimation of weights of antenna array so as to direct the major lobe in the direction of desired user and nulls in the direction of interfering signals with reduced side lobe level (SLL).

The potential of gravitational search algorithm is explored to optimize multi-objective fitness function for ABF using MATLAB software.

The performance of the algorithm has been compared by considering different number of interference signals at different power levels. The proposed algorithm presents good convergence rate and accurate steering of main lobe and nulls with reduced SLL compared to the well-known ABF technique, namely, minimum variance distortionless response (MVDR) and previously reported results. The simulation results are presented in tabular form.

The present work is limited to simulation. The researchers are encouraged to solve the problem of ABF using the proposed approach in hardware.

The application of proposed algorithm is to optimize multi-objective function for ABF with reduced SLL in linear antenna arrays.

When a sensor device is packaged together with a CPU, it is called a “smart sensor.” The sensors really become smart when the tight integration of sensing and processing results in an adaptive sensing system that can react to environmental conditions and consistently deliver useful measurements to a robotic system even under the harshest of the conditions. We illustrate this point with an example from our recent work on illumination‐adaptive algorithm for dynamic range compression that is well suited for an on‐chip implementation resulting in a truly smart image sensor. Our method decides on the tonal mapping for each pixel based on the signal content in pixel's local neighborhood.

The researchers and policy makers worldwide have proposed many ideas for smart cities and homes in urban areas. The extensive work done for urban smart homes neglects the unique constraints of homes at remote mountain tops and deserts and rural village homes. The purpose of this paper is to propose a smart energy management system for a self-sustained home of any type situated in any geographical location with the availability of renewable energy sources like solar, etc. The purpose is mainly to highlight the importance and advantages of direct current (DC) homes with DC loads rather than a conventional alternating current (AC) home with both AC and DC loads. An attempt has been made to evolve a multi-agent coordinated control for the low voltage direct current (LVDC) smart home system.

LVDC supply systems with in situ power generation are providing an efficient solution for the energy needs of a DC smart home. The individual sub-systems of the LVDC system have their unique functions and priorities and hence require both coordinated and independent control. The entire DC smart home system is modeled in the Matlab and codes are implemented for each agent of the home. LVDC grid is operating either in battery connected mode or utility grid-connected mode, and the DC link voltage is held constant in both the cases. Energy imported from the utility grid is minimized by load shedding during the rectifier mode of the bidirectional converter. In addition, load shedding is also done when the battery is discharging to increase the discharge time of the battery. Load shedding is done on the basis of a fixed priority of loads. A 48 s simulation is performed on the Matlab model to bring out the 24-hour operation of the proposed system. Various modes are simulated and the corresponding actions of the agents are tested.

A new control strategy with agents for each sub-system of the LVDC system is presented. Each individual agent works in tandem with other agents and meets its own control imperatives without compromising the requirements of the overall system. Unlike the centralized control system, the proposed control strategy is a distributed control system. The control algorithm for each of the agents is developed, and the pseudo code is presented. The results of the simulation of the proposed scheme are presented to confirm the usefulness of the new control approach.

The multi-agent concept for an energy management system is less addressed and thus its potential for efficient home energy management is presented. The proposed multi-agent strategy for a complete DC smart home with exclusive DC loads is not done earlier and is reported for the first time. The success of this strategy can be extended to other DC micro-grid systems like telecom power systems, ships, aircraft, datacentres, server rooms, residential complexes and commercial malls.

Starting from the premises that Internet of Things (IoT) applications can be used in museums as an aid to visiting systems, the purpose of this paper is to see how recommendation systems can be developed to provide advanced services to museum visitors.

The research methodology employs a qualitative exploratory multi-case study: the method used has consisted in crossing the information currently known on the most advanced communication technologies (ICT) with the requirements of enhancing museum services, in order to determine the possible trajectories of applying the former to the latter.

The implementation of recommender system outlines the main implications and effects of an advanced market-driven digital orientation, as the system’s users are the starting point for innovation and the creation of value. For a museum, it will be possible to access to an additional system of knowledge alongside that of its scientific staff. This process has profound implications in the way in which a museum presents itself and how it is perceived by its visitors and, in a wider sense, by the potential demand.

The paper consists in an exploratory effort to introduce an analytical framework for an evolved adaptive museum orientation system; the empirical investigation can be structured in the inductive-predictive view of assessing this promising debate further.

Implementing the IoT blueprint entails introducing a plethora of new products, services and business models, opening new routes to guide and direct cultural events. Now, more than ever, sustainable development involves an intrinsic balancing act between the pluralism of data and that of customer needs, which is achieved through the elaboration of digital data.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

The purpose of this paper is to use the proposed algorithm for the fast adaptation of the antenna array radiation pattern on the particular scenario of the incoming signals. The fitness function to be minimized includes the precise estimation of signals’ arrival angles, setting the deep nulls in the directions of the interfering signal, the reduction of the main lobe’s width and the reduction of side lobes.

Unlike conventional adaptive algorithms, the proposed algorithm allows synthesis of radiation patterns in the case of a larger number of incident desired and interfering signals. The proposed method also reduces the width of the dead zone.

In this paper a comparison of the results obtained from the chaotic beamforming algorithm with the results obtained by using the Sequential Quadratic Programming method is presented.

The chaotic beamforming algorithm is proposed here. It is based on the optimization of the least mean square and on the variable step-size least mean square algorithms, using chaos theory for synthesis of the radiation pattern of the linear antenna array.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.