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Recent advancements in the domain of smart communication systems and technologies have led to the augmented developments for very large scale integrated circuit designs in electro-magnetic applications. Increasing demands for low power, compact area and superior figure of merit–oriented circuit designs are the trends of the recent research studies. Hence, to accomplish such applications intended for optical communications, the transimpedance amplifier (TIA) was designed.

In this research work, the authors present a multi-layer active feedback structure which mainly composes a transimpedance stage and a gain stage followed by a low pass filter. This structure enables to achieve improved input impedance and superior gain. A simplified cascaded amplifier has also been designed in a hierarchical topology to improvise the noise effect further. The proposed TIA has been designed using Taiwan Semiconductor Manufacturing Company 45 nm complementary metal oxide semiconductor technology. Moreover, the thermal noise has been analyzed at −3 dB bandwidth to prove the reduction in thermal noise with increase in frequency for most of the devices used in the designed circuit.

The proposed differential TIA circuit was found to obtain the transimpedance gain of 50.1 dBO without applying any external bias current which is almost 8% improvised as compared to the conventional circuit. In addition to this, bandwidth achieved was 2.15 GHz along with only 38 W of power consumption, which is reasonably 100 times improvised in comparison of conventional circuit. Hence, the proposed differential TIA is suitable for the low power optical communications applications intended to work on low supply voltage.

The designed work is done by authors in university lab premises and is not copied from anywhere. To the best of the authors’ knowledge, it is 100% original.

Nigeria, a prominent country in Sub-Sahara Africa, is plagued with a protracted, erratic and low power supply. The purpose of this paper is to present an experimental investigation of the noise levels and pollutants’ (CO, CO₂ and particulate matter (PM_2.5)) concentrations associated with the prevalent use of diesel-powered generators in the country. It is aimed to provide information on the level of gaseous, particulate and noise pollutants that are related to diesel-powered generators that could assist in policy formulation and create public awareness on the possible health risks.

Diesel-fueled generators (105) with age and installed capacity ranging from 0.5 to 14 years and 10 to 500 kVA, respectively, were engaged in this work at Sango area of Ogun State, Nigeria. Standard measuring instruments were placed at 1 m from the diesel-powered generators to determine the noise levels and concentrations of CO, CO₂ and PM_2.5.

Ranges of 72.6–115.6 dB, 19–198 ppm, 501–5,805 ppm and 221–492 µg/m³ for the noise level, CO, CO₂ and PM_2.5 concentrations, respectively, were obtained. Both the averages and ranges of the noise levels and pollutants’ concentrations were considerably higher than the recommended maximum limits. Thus, this study substantiated the pollution of ambient noise and air because of the operation of diesel-fueled generators. Furthermore, the health risks connected to the exposure to CO and PM_2.5 as implied via the evaluation of the air quality index revealed very unhealthy and hazardous conditions, respectively.

The measurement of the pollutants’ concentrations at the tips of the exhaust pipes of the diesel-powered generators was desirable but could not be achieved using manually logged devices. Nonetheless, adequate pollutants’ concentration data that satisfactorily represent the level of air pollution associated with diesel-fueled generators’ operations were obtained at around 1 m from the exhaust pipes.

The study provided additional knowledge on the levels of noise and pollutants, and the public health risks connected to the operations of diesel-powered generators that will be beneficial to the public and policymakers.

The results revealed a considerably high level of noise and air pollution, and the inherent environmental and public health problems connected to diesel-powered generators’ usage in Nigeria. This could serve as a viable tool for formulating environmental policy and providing the necessary societal awareness in this regard.

IT does not require a very large crystal ball to envisage the growth of aviation during the next fifteen years. Sea travel has become too slow; air travel has become reliable and more independent of weather conditions; world trade has forced travel into the remotest areas; industrial countries have become more affluent, and the urge for private world travel has accompanied affluence. It is not surprising, therefore, to hear the Chairman of the British Airport Authority, Peter Masefield, predict a fifteenfold growth in air traffic in the United Kingdom during the next twenty years and to find the F.A.A. confidently anticipating a doubling of passenger traffic in five years and a nine‐fold increase in small jets in the same period.

The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a large amount of learning data, it is important to develop effective clustering approaches for user group modeling and intelligent tutoring. The paper aims to discuss these issues.

In this paper, a minimum spanning tree based approach is proposed for clustering of online learning resources. The novel clustering approach has two main stages, namely, elimination stage and construction stage. During the elimination stage, the Euclidean distance is adopted as a metrics formula to measure density of learning resources. Resources with quite low densities are identified as outliers and therefore removed. During the construction stage, a minimum spanning tree is built by initializing the centroids according to the degree of freedom of the resources. Online learning resources are subsequently partitioned into clusters by exploiting the structure of minimum spanning tree.

Conventional clustering algorithms have a number of shortcomings such that they cannot handle online learning resources effectively. On the one hand, extant partitional clustering methods use a randomly assigned centroid for each cluster, which usually cause the problem of ineffective clustering results. On the other hand, classical density-based clustering methods are very computationally expensive and time-consuming. Experimental results indicate that the algorithm proposed outperforms the traditional clustering algorithms for online learning resources.

The effectiveness of the proposed algorithms has been validated by using several data sets. Moreover, the proposed clustering algorithm has great potential in e-learning applications. It has been demonstrated how the novel technique can be integrated in various e-learning systems. For example, the clustering technique can classify learners into groups so that homogeneous grouping can improve the effectiveness of learning. Moreover, clustering of online learning resources is valuable to decision making in terms of tutorial strategies and instructional design for intelligent tutoring. Lastly, a number of directions for future research have been identified in the study.

The purpose of this paper is to improve the tracking performance of the tracking loops under high dynamic and severe jamming conditions.

First, as the two dominant measurement error sources of the tracking loops, the thermal noise jitter and the dynamic stress error are thoroughly analyzed. Second, a scheme of adaptive tracking loops, which could adaptively adjust the order and the bandwidth of tracking loops, is proposed. Third, real-time detections of the vehicle dynamics and the carrier-to-noise density ratio, and the adaptive bandwidth of the carrier loop are presented, respectively. Finally, simulations are operated to validate the excellent tracking performance of the adaptive tracking loops.

Based on the principle of minimizing the measurement errors, the loop order and bandwidth are adaptively adjusted in the proposed scheme. Thus, the anti-jamming capability and dynamic tracking performance of the tracking loops could be effectively enhanced.

This paper provides further study on the method of improving the tracking capability under complexly applied conditions of high dynamics and severe jamming.

The detections of carrier-to-noise density ratio and vehicle dynamics are used to adaptively adjusting the loop order and bandwidth, which could not only improve the measurement accuracy but also ensure the stable operation of tracking loops.

The purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.

The algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.

The first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.

Data points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.