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Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

This study introduces a Direct Model Reference Adaptive Control (DMRAC) algorithm in a buck–boost converter in the power distribution of an electric vehicle. In this study, DMRAC was used in order to overcome the system nonlinearity due to load demand variation, in case of different driving modes (such as acceleration, stable and regenerative braking system mode), and the presence of disturbances in the system. DMRAC receives popularity because of its robustness in the presence of nonlinearity and ensuring system stability. To evaluate the efficacy of DMRAC in the current system, its performance was compared with a PI controller in the MATLAB/Simulink environment. The simulation results show the superiority of DMRAC over a conventional PI control approach, in both variable load demand and disturbed system cases that were measured by tracking error. The improvement was seen in the DMRAC response, with smaller tracking error and faster transient and disturbance rejection. The main contribution of this work is in introducing DMRAC, particularly in a buck–boost converter, and its efficacy with a DC–DC converter for an electric vehicle, which has not been studied before.

Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method.

Solar photovoltaic (PV) systems are widely used to mitigate greenhouse gases (GHG), due to their green renewable nature. However, environmental factors such as bird drops, shade, pollution, etc., accommodation on PV panels surface reduce photons transmission to PV cells, which results in lower energy yield and GHG mitigation potential of PV system. In this study, the PV system’s energy and GHG mitigation potential loss is investigated under environmental stresses. Defects/hotspots caused by the environment on PV panel surface have unknown occurrence frequency, time duration, and intensity and are highly variable from location to location. Therefore, different concentrations of defects are induced in a healthy 12 kWp PV system. Healthy PV system has the potential to avoid the burning of 3427.65 L of gasoline by 16,157.9 kWh green energy production per annum. However, in 1% and 20% defective systems, green energy potential reduces to 15,974.3 and 12,485.6 kWh per annum, respectively. It is equivalent to lesser evasion burning of 3388.70, and 2648.64 L of gasoline, respectively. A timely solution to defective panels can prevent losses in the PV system to ensure optimal performance.

Detecting buildings from high-resolution satellite imagery is beneficial in mapping, environmental preparation, disaster management, military planning, urban planning and research purposes. Differentiating buildings from the images is possible however, it may be a time-consuming or complicated process. Therefore, the high-resolution imagery from satellites needs to be automated to detect the buildings. Additionally, buildings exhibit several different characteristics, and their appearance in these images is unplanned. Moreover, buildings in the metropolitan environment are typically crowded and complicated. Therefore, it is challenging to identify the building and hard to locate them. To resolve this situation, a novel probabilistic method has been suggested using local features and probabilistic approaches. A local feature extraction technique was implemented, which was used to calculate the probability density function. The locations in the image were represented as joint probability distributions and were used to estimate their probability distribution function (pdf). The density of building locations in the image was extracted. Kernel density distribution was also used to find the density flow for different metropolitan cities such as Sydney (Australia), Tokyo (Japan), and Mumbai (India), which is useful for distribution intensity and pattern of facility point f interest (POI). The purpose system can detect buildings/rooftops and to test our system, we choose some crops with panchromatic high-resolution satellite images from Australia and our results looks promising with high efficiency and minimal computational time for feature extraction. We were able to detect buildings with shadows and building without shadows in 0.4468 (seconds) and 0.5126 (seconds) respectively.

In this paper, a coordinated multipoint joint transmission (CoMP-JT) framework at mmWave for a cyclic prefix (CP)-free multiuser OFDM wireless communication system is developed and analyzed. The aim is to provide high-quality service to cell-edge users; otherwise, the cell-users would suffer from significant signal degradation due to undesired interference. The impact of complex Hadamard transform with block diagonalization channel precoding for multiuser interference reduction and designed subcarrier mapping for out-of-band (OOB) reduction are investigated. In addition, the paper studied the input back-off-aided high-power amplifier for peak-to-average power ratio (PAPR) reduction and forward error correction channel coding for improved bit error rate (BER) for cell-edge users at mmWave frequencies. Moreover, signal-to-interference-noise ratio and ergodic achievable rate are estimated both in the presence and absence of CoMP-JT-based transmission technique to verify their significance in terms of transmitted power. Numerical investigations showed an OOB reduction of 312 dB, PAPR reduction from 17.50 dB to 7.66 dB, and improved BER of 1×10−3 in 16-QAM for a signal-to-noise ratio of −6 dB. Hence, the simulation results demonstrated the effectiveness of the developed system.

Most traditional AC/AC power converters suffer from power quality problems and multi-stage power conversion losses. The rectifier and inverter-based AC/AC converter topology not only increases multi-stage power conversion losses, but also increases the volume, weight, and cost, and decreases the longevity of the converter due to the DC-link capacitor, line filter and electromagnetic interference (EMI) filter. High-frequency (about 10 kHz) switching advanced pulse width modulation techniques are generally used in order to compensate the power quality problems, which increase the switching losses and introduce the EMI problems. In this paper, a new generalized step-down single-stage line-frequency switching AC/AC power converter topology is proposed. The proposed converter uses line-frequency switching, and does not require any pulse width modulation techniques. The proposed topology offers promising performances in terms of lower order harmonics, total harmonic distortion, the elimination of DC-link capacitors and EMI filters, and switching losses. The circuit was designed and simulated in a MATLAB/Simulink environment. A scaled-down laboratory prototype of the proposed topology was developed in order to validate the feasibility. The experimental and simulation results reveal the feasibility of the proposed generalized step-down single-stage converter topology, and its excellent features.

A novel, rectangle-based, porous-core photonic crystal fiber (PCF) has been modeled for the efficient propagation of a THz wave. The performance of the anticipated model has been assessed using the finite element method (FEM) in the range of 0.5–1.5 THz. Both the fiber core and cladding are modeled with rectangular air holes. Numerical analysis for this model reveals that the model has a lower amount of dispersion of about 0.3251 ps/THz/cm at 1.3 THz. Compared to the other THz waveguides, the model offers an ultra-lower effective material loss of 0.0039 cm−1 at the same frequency. The confinement loss is also lower for this model. Moreover, this model has a high-power fraction of about 64.90% at the core in the x-polarization mode. However, the effective area, birefringence, and numerical aperture have also been evaluated for this model. Maintenance of standard values for all the optical parameters suggests that the proposed PCF can efficiently be applied in multichannel communication and several domains of the THz technology.