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A miniaturized attitude control system (ACS), which is capable of attaining a high spin rate and good pointing accuracy, is an integral part of any small spacecraft. In this regard, embedded magnetorquers are gaining significant attention because they are inserted in printed circuit board (PCB) internal layers with no extra space occupation and result in almost no mass and development cost. In this article, an innovative embedded magnetorquer with asymmetric nonuniform geometric structure is presented. The proposed magnetorquer is optimized for various parameters of interest, i.e., magnetic moment, resultant generated torque, power consumed, and heat dissipation for a CubeSat standard nanosatellite by means of employing diverse nonunity track-width-ratio geometrical arrangements. The magnetorquer driver and reconfigurable circuit is implemented with commercial-off-the-shelf (COTS) components which are low cost and easily available from the market. Embedded coils with variable trace width were simulated in order to predict the accuracy and to achieve the maximum torque versus power dissipation ratio of the developed models. Time-varying rotational analyses are performed to evaluate the angular spin rates driven by the asymmetric coils. Due to embedded coil structure, a detailed thermal analysis is conducted for various coil configurations (i.e., series, parallel, hybrid, etc.) to preserve thermal limits and to ensure the efficacy of the system. The simulation results were validated through a comprehensive set of experimental measurements which also establishes the framework for selecting the optimal coils configuration based on specific mission requirements.

The purpose of this study is to offer an adaptive hybrid controller for the formation control of multiple unmanned aerial vehicles (UAVs) leader-follower configurations with communication delay. Although numerous studies about the control of the formation exist, very few incorporate the delay in their model and are adaptive as well. The motivation behind this article is to bridge that gap. The strategy consists of an adaptive fuzzy logic controller and a Proportional, Integral, and Derivative (PID) controller where the logic controller fines/tunes the PID controller gains. The controller also consists of an integrator that raises the order of the system which helps reduce the noise and steady-state errors. The simulations confirm that the proposed technique is robust and satisfies mission requirements. Moreover, the flying formations of the swarm were created by a B-spline curve based on a simple waypoint. The main contribution of this study is to present a model where the formation remains stable during the whole flight, errors are within the optimal range, and the time delays are manageable.

This study proposes a novel hybrid strategy for formation control of a swarm of multiple unmanned aerial vehicles (UAVs). To enhance the fitness function of the formation, this research offers a three-dimensional formation control for a swarm using particle swarm optimization (PSO) with Cauchy mutant (CM) operators. We use CM operators to enhance the PSO algorithm by examining the varying fitness levels of the local and global optimal solutions for UAV formation control. We establish the terrain and the fixed-wing UAV model. Furthermore, it also models different control parameters of the UAV as well. The enhanced hybrid algorithm not only quickens the convergence rate but also improves the solution optimality. Lastly, we carry out the simulations for the multi-UAV swarm under terrain and radar threats and the simulation results prove that the hybrid method is effective and gives better fitness function.

This article reviews remotely operated underwater vehicle (ROUV) and its different types focusing on the control systems. This study offers a brief introduction of unmanned underwater vehicle (UUV) together with ROUV. Underwater robots are designed to work as an alternative to humans because of a difficult and hazardous underwater environment. The applications and demand of marine robots are increasing with the passage of time. There are several research articles and publications available on these topics but, a complete review of old and recent research about this technology is still hard to find. This article also assesses some recently published research papers on underwater systems. It presents the comparison of different control systems and designs of underwater vehicles. There have been major developments in marine technology depending on the needs, applications and cost of different missions. Scientists design many remotely operated vehicles based on the educational or industrial purposes. This article is presented in order to help and assist the future researchers as a massive review of the field of remotely operated underwater vehicles and their possible future developments are presented.

Skin-like sensors that transduce tactile pressures and vibrations with minimal environment variation on performance are crucial in robotic sensing and prosthetic skins. However, sensor performance ...

Cooperative path planning of multiple unmanned aerial vehicles is a complex task. The collision avoidance and coordination between multiple unmanned aerial vehicles is a global optimal issue. This research addresses the path planning of multi-colonies with multiple unmanned aerial vehicles in dynamic environment. To observe the model of whole scenario, we combine maximum–minimum ant colony optimization and differential evolution to make metaheuristic optimization algorithm. Our designed algorithm, controls the deficiencies of present classical ant colony optimization and maximum–minimum ant colony optimization, has the contradiction among the excessive information and global optimization. Moreover, in our proposed algorithm, maximum–minimum ant colony optimization is used to lemmatize the pheromone and only best ant of each colony is able to construct the path. However, the path escape by maximum–minimum ant colony optimization and it treated as the object for differential evolution constraints. Now, it is ensuring to find the best global colony, which provides optimal solution for the entire colony. Furthermore, the proposed approach has an ability to increase the robustness while preserving the global convergence speed. Finally, the simulation experiment results are performed under the rough dynamic environment containing some high peaks and mountains.

Remotely operated underwater vehicle (ROUV) provides an interesting and extensible platform to search the wanted objects and for the inspection of deep sea. The basic goal of this research is to control the position and stabilize the dynamic behavior of ROUV. In this study, we design the dual controller approach design for controlling the overall responses of ROUV. The design dual controller consists of model reference adaptive control (MRAC) along with proportional integral derivative (PID) controller and an integral use for the feedback of the design scheme. The dynamic moments and disturbances in the system is dealt by MRAC controller and PID controller is responsible for tuning the adaptive gains of the system. However, Lyapunov stability criterion is responsible for the stability of the system. The inclusion of integrator as a feedback in the system increases the order of the system model, but it helps to eliminate the steady state error and also improves the convergence rate of the system. The designed control algorithm is tested and confirmed its validity using experiment and simulations by tracking the reference path of the ROUV. It is evidence that the designed control system shows quick convergence, improved steady state error and better robustness in the presence of disturbances.

Small UAVs are susceptible to the external disturbance, especially the wind field disturbance in the atmosphere environment. As a result, UAV’s states including attitude, speed, and position are usually unable to track the desired control commands. In this paper, different types of wind fields which easily affect the UAV are summarized; furthermore, the mechanism of their wind fields affecting the UAV is first strictly analyzed. Next, a novel “reject external disturbance” flight mode for UAV is put forward to offset the trajectory deviation caused by side wind, which makes use of the wind speed information obtained by airspeed and ground speed of UAV. In order to implement the “reject external disturbance” flight mode, the Lyapunov stability theory-based variable model reference adaptive control (VMRAC) system is proposed, and it could also deal with the adverse effects of wind shear and turbulence on UAV flight. Finally, simulation results show that the proposed strategy can significantly improve the trajectory following quality of the UAV under wind disturbance.

Unmanned aerial vehicles (UAV) equipped with a manipulator offer an additional flexibility and smart way to grasp the desired objects from inaccessible locations where the access of ground vehicles (GV) are not possible. In this research, we design an adaptive control based regulation, pole-placement and tracking (RST) control scheme for controlling the nonlinear behavior of an under-actuated quad rotor aerial vehicle. The overall performance of the system dealt by MIT rules. The aerial vehicle is equipped with a camera and a gripper that helps us to locate the wanted object from inaccessible location. The model of quad rotor UAV has six degrees of freedom (6-DOF) and the equipped gripper is about (2-DOF). For a successful flight operation UAV requires a reliable controller to stabilize the aerodynamic effects, disturbances that produced by the gripper. Due to aforementioned issue, design an adaptive RST controller, to control the dynamic behavior of the highly nonlinear complex system. Moreover, the effectiveness of the designed controller proven by applying computer-based simulation and it will verify experimentally. Lastly, it observes that the designed controller shows better robustness and good steady state performance to accomplish the given task.

The power distribution module (PDM) is one of the vital modules onboard small satellites and its design governs the performance of all the subsystems including communication, attitude determination & control, navigation, payload, etc. The paper presents the design of the PDM developed for the Pakistan National Student Satellite-1 (PNSS-1). The main focus of the design is modularity, redundancy, design reuse and miniaturization using commercial off the shelf (COTS) components. PDM is the sub-module of electric power system (EPS) which converts the power distribution bus (PDB) voltage level (unregulated 28V) into the secondary voltage levels and to distribute these voltages to loads according to their requirements. In order to improve reliability and to overcome various failures, redundancy is provided at all levels. No single component failure in PDM disables or degrades its intended functions that may lead to mission failure. Latch-up protection circuit is designed for the CMOS processor and at the end a thermal model is presented for the PDM unit.

Pigeon Inspired Optimization (PIO) algorithm is gaining popularity since its development due to faster convergence ability with great efficiencies when compared with other bio-inspired algorithms. The navigation capability of homing pigeons has been precisely used in Pigeon Inspired Optimization algorithm and continuous advancement in existing algorithms is making it more suitable for complex optimization problems in various fields. The main theme of this survey paper is to introduce the basics of PIO along with technical advancements of PIO for the motion planning techniques of dynamic agents. The survey also comprises of findings and limitations of proposed work since its development to help the research scholar around the world for particular algorithm selection especially for motion planning. This survey might be extended up to application based in order to understand the importance of algorithm in future studies.

An active attitude control system using a novel six-layer electromagnetic embedded printed magnetorquer for a 3-U nanosatellite is presented in this paper. The proposed design was optimized...

Underactuated systems are very difficult to control and stabilize due to fewer number of control inputs as compared to degrees of freedom (DOF). Thus, this research manuscript presents a comparative analysis of two major control schemes for an underactuated air cushion vehicle (ACV) commonly known as hovercraft. By studying the translational and angular dynamics of proposed underactuated mechatronic system, the mathematical model had been derived using Newton Euler formalism. The validity and effectiveness of proportional integrated differentiator (PID) control design is compared with the Fuzzy based PID (F-PID) scheme. Thus, with provided simulation results, paper concludes that the proposed algorithm of fuzzy based PID (F-PID) is better solution for achieving robust transient and steady state performances than simple PID control scheme even in the availability of bounded uncertainties with quick convergence rate.

These days, the usage of healthcare applications in the secure and authenticate blockchain network has been growing progressively. The distributed healthcare applications can store and shared data with another node without any centralized authority by exploiting the blockchain technology. However, existing proof of work inside blockchain did not consider the anomaly detection in healthcare networks and mobility of the workload widely ignored in the literature studies. In this paper, the study investigates the travelling salesman problem with the scheduling, threshold, and anomaly detection constraints in distributed fog nodes. The fog nodes are local servers and implementing inside the hospital to facilitate the users from different healthcare services. The study devises the mobility scheduler anomaly detection (MSAD) schemes which consist of two phases, e.g., the initial assignment of healthcare workloads to optimal fog nodes and anomaly detection and validation in the healthcare blockchain network. Simulation results show that MSAD outperformed in terms of scheduling, threshold, anomaly detection in the healthcare blockchain network as compared to baseline studies.

This paper addresses the path planning and control of multiple colonies/clusters that have unmanned aerial vehicles (UAV) which make a network in a hazardous environment. To solve the aforementioned issue, we design a new and novel hybrid algorithm. As seen in the mission requirement, to combine the Maximum-Minimum ant colony optimization (ACO) with Vicsek based multi-agent system (MAS) to make an Artificially Intelligent (AI) scheme. In order to control and manage the different colonies, UAVs make a form of a network. The designed method overcomes the deficiencies of existing algorithms related to controlling and synchronizing the information globally. Furthermore, our designed architecture bounds, lemmatizes the pheromone, and finds the best ants which then make the most optimized path. The key contribution of this study is to merge two unique algorithms into a hybrid algorithm that has superior performance than both algorithms operating separately. Another contribution of the designed method is the ability to increase the number of individual agents inside the colony or the number of colonies with a good convergence rate. Lastly, we also compared the simulation results with the non-dominated sorting genetic algorithm II (NSGA-II) in order to prove the designed algorithm has a better convergence rate.

Gripper mounted remotely operated underwater vehicle (ROUV) delivers more attractive and flexible solution to grasp the wanted objects inside the deep water. The main aim of this research is to stabilize and control the position of fully actuated (ROUV) equipped with a gripper. The nonlinear model based observer along with proportional, integral and derivative (PID) controller designed to stabilize and control the position of fully actuated ROUV along with the motion of attached gripper in it. However, the PID controller utilize to control the altitude of the vehicle and nonlinear-based observer with PID controller designed to control and stabilize the attitude of ROUV. The designed control algorithm applied on the model of six degrees of freedom (6 DOF) ROUV attached with a gripper that has (2 DOF) that makes total (8 DOF) to control. The hydrodynamic behavior, instability that produces by the arm that is the main reason to design two different controllers to control and stabilize the altitude and attitude of ROUV. Hence, it observes that designed control scheme has better stability and better transient behavior.

In this paper, we presented an automated system for identification and classification of fish species. It helps the marine biologists to have greater understanding of the fish species and their habitats. The proposed model is based on deep convolutional neural networks. It uses a reduced version of AlexNet model comprises of four convolutional layers and two fully connected layers. A comparison is presented against the other deep learning models such as AlexNet and VGGNet. The four parameters are considered that is number of convolutional layers and number of fully-connected layers, number of iterations to achieve 100% accuracy on training data, batch size and dropout layer. The results show that the proposed and modified AlexNet model with less number of layers has achieved the testing accuracy of 90.48% while the original AlexNet model achieved 86.65% over the untrained benchmark fish dataset. The inclusion of dropout layer has enhanced the overall performance of our proposed model. It contain less training images, less memory and it is also less computational complex.

Optical network on chip (ONoC) has evolved as an innovative technology for on-chip interconnects that can fulfill the upcoming requirements of manycore processors used in UWASN. The objective of this paper is two-fold, first we assessed the performance of electrical and optical NoC mesh and torus topologies with 64 and 144 nodes. We explored the impact of different packet and network sizes on average latency and throughput of NoCs. Furthermore, we investigated the effect of application mapping on crosstalk noise, laser power consumption and SNR for optical mesh/torus architectures under real time benchmark applications. The experimental outcomes revealed that ONoC has advantages of improved average latency and improved throughput for large packet size. Second, we proposed a hybrid optical–electrical NoC topology based on multi write single read serpentine optical bus architecture aiming to minimize the communication latency and energy consumption. We present an optimized routing algorithm for the proposed topology that exploits the advantage of processing parallelism level to reduce latency in hybrid network. The proposed topology proved to be 48% and 53% efficient in latency, 12% and 17% higher in throughput and provides 54% and 23% reduction in energy consumption under uniform random and hotspot traffic patterns respectively as compared to other NoC architectures.

Attitude, speed, and position of unmanned aerial vehicles are susceptible to wind disturbance. The types, characteristics, and mathematical models of the wind, which have great influence on unmanned aerial vehicle in the low-altitude environment, are summarized, including the constant wind, turbulent flow, many kinds of wind shear, and the propeller vortex. Combined with the mathematical model of the unmanned aerial vehicle, the mechanism of unmanned aerial vehicle movement in the wind field is illustrated from three different kinds of viewpoints including velocity viewpoint, force viewpoint, and energy viewpoint. Some simulation tests have been implemented to show the effects of different kinds of wind on unmanned aerial vehicle’s path and flight states. Finally, some proposals are presented to tell reader in which condition, which wind model should be added to simulation, and how to enhance the stability of unmanned aerial vehicle for different kinds of wind fields.

The dead zone in the buck converter can be eliminated by integrating flyback converter with it. However, its input Power Factor (PF) is low when operated with Constant On-Time Control (COTC). In this paper, a Variable On-Time Control (VOTC) strategy is proposed for Integrated Buck-Flyback Converter (IBFC) with simple structure to obtain unity PF. By feed forwarding input and output voltage to change the on-time of both switches, high PF can be achieved. The operating principle of the IBFC is discussed with both types of control schemes and for verifying the effectiveness of VOTC scheme; simulations are carried out by using Saber simulator. Keywords: Constant On-Time Control (COTC), Integrated Buck-Flyback Converter (IBFC), Variable On-Time Control (VOTC) Scheme

Arm mounted unmanned aerial vehicles provide more feasible and attractive solution to manipulate objects in remote areas where access to arm mounted ground vehicles is not possible. In this research, an under-actuated quadrotor unmanned aerial vehicle model equipped with gripper is utilized to grab objects from inaccessible locations. A dual control structure is proposed for controlling and stabilization of the moving unmanned aerial vehicle along with the motions of the gripper. The control structure consists of model reference adaptive control augmented with an optimal baseline controller. Although model reference adaptive control deals with the uncertainties as well as attitude controlling of unmanned aerial vehicle, baseline controller is utilized to control the gripper, remove unwanted constant errors and disturbances during arm movement. The proposed control structure is applied in 6-degree-of-freedom nonlinear model of a quadrotor unmanned aerial vehicle equipped with gripper having (2 degrees of freedom) robotic limb; it is applicable for the simulations to desired path of unmanned aerial vehicle and to grasp object. Moreover, the efficiency of the presented control structure is compared with optimal baseline controller. It is observed that the proposed control algorithm has good transient behavior, better robustness in the presence of continuous uncertainties and gripper movement involved in the model of unmanned aerial vehicle.

In this paper, a framework of smart transportation system is proposed, aiming to address the transportation problem in Karachi city. In modern day world, the mega cities and urban areas are on the edge of transformation into smart cities. With the advancement of engineering and technology, smart cities are designed to integrate and utilize these scientific innovations to provide smart solutions and social innovations for sustainable infrastructure, thus they are able to provide its resident highest quality of life by utilizing its resources effectively. One of the major application of smart cities is the Smart Transportation System, which provides safer, quick, environment friendly service to the residents. Thus, this study highlights the current traffic situation of Karachi and propose a framework to transform it into a smart transportation system. In order to have a smart transportation system, it is necessary to have in-depth knowledge and information about the city dynamics and its traffic related issues. Therefore, this study also highlights current traffic situation of Karachi, its road conditions and capacity, vehicles condition, alternate mean of transport (other than road-based system) and its present condition, and finally proposes a framework to develop a smart transportation system while keeping in mind the aforesaid traffic problems.

This research proposes ASC (Adaptive Synergetic Controller) for the nonlinear model of MH (Mini Helicopter) to stabilize the desired altitude and angle. The model of MH is highly nonlinear, underactuated and multivariable in nature due to its dynamic uncertainties and restrictions of velocities during the flight. ASC can force the tracking errors of the system states converges to zero in a finite interval of time. The MH system requires smooth controller and fast precise transition response from initial state till the desired state, therefore the parametric calculations and simulations can be done by the proposed ASC algorithm. It is validated that the above simulated results of the proposed controller have a better convergence rate and smoother stability response in order to track the desired altitude and angle when compared with SMC (Sliding Mode Controller). Moreover, it does not need any linearization, transformation and variations in the system model.

The ever-decreasing size of the small satellites with more demanding payloads has opened new research areas to investigate innovative onboard data-handling solutions. This paper presents the use of optical intra-satellite communication using discretely designed transceivers. The transceiver can handle large variations in the received photocurrents and implements filtration against the ambient light. The designed transceiver has been tested with selected optical components including infrared diodes, photo detectors, and optical guides to validate its functionality. The displacement damage testing on selected optical components was performed to validate their suitability for use in space. The experimental communication schemes have been presented for free space as well as glass fiber-based intra-spacecraft communication.

This paper presents a novel application of data mining techniques to guide academic programs design and assessment. More specifically, it propose using association rule mining techniques to discover a set of rules that govern the relationship between two core components of an academic program, program educational objectives (PEOs) and students outcomes(SOs). As a case study, this paper demonstrates how association rule mining techniques are applied to mine mapping rules between the PEOs and a predefined set of SOs adopted by the American Board for Engineering and Technology-Engineering Accreditation Commission (ABET-EAC) for engineering programs. To this end, a set of 152 ABET-EAC accredited engineering programs’ self-study reports have been collected and the mapping data between their PEOs and ABET-EAC SOs have been extracted. This dataset has been pre-processed and transformed into a representation suitable for applying association rule mining techniques. This involves identifying a set of PEOs labels, annotating data instances with PEOs labels, and projecting each multi-label data instance into a set of single-label instances. Apriopi algorithm is then applied to discover the rules that govern the mapping between PEOs and ABET-EAC SOs. The discovered rules are of particular importance for guiding the design and assessment of engineering academic programs. Besides that, the discovered rules unveil of a number of interesting correlations between PEOs and ABET-EAC SOs that need further investigation by pedagogists.

In modern electrical systems, solar energy extracted is integrated into electrical grid using power converters. In most cases, several inverters are connected across the same DC link so that the circulating currents between the inverter are made zero and the input power is shared among the available inverters. Because of the nonlinear nature of grid-tied photovoltaic (PV) system and the inherent modelling uncertainties, conventional control schemes cannot provide satisfactory performances under all operating conditions. Nonlinear control techniques for gridtied inverter have been explored in the literature. However, the design of nonlinear controllers for the control of grid-tied parallel inverters has been rarely reported. Therefore, the purpose of this research work is to design a nonlinear controller for the control of grid-tied parallel inverter system (PIS). A novel control strategy is devised that utilizes backstepping inspired integral sliding mode control (ISMC) to maintain a constant DC link voltage and control power flow to the grid. An algorithm is developed, which determines the number of inverters connected across the DC link. The control strategy employs Lyapunov approach to ensure stability in an event of disturbance and guarantees robustness. With the proposed control scheme, better and superior performance is observed in transient response, total harmonic distortion (THD) minimization and integrating power into the grid at unity power factor. Furthermore, with the control strategy, the available power from the PV array is successfully distributed among multiple inverters operating in parallel.

This paper explores the effectiveness of Particle Swarm Classification (PSC) for a classification task in the field of educational data mining. More specifically, it proposes PSC to design a classification model capable of classifying questions into the six cognitive levels of Bloom's taxonomy. To this end, this paper proposes a novel specialized initialization mechanism based on Rocchio Algorithm (RA) to mitigate the adverse effects of the curse of dimensionality on the PSC performance. Furthermore, in the design of the RA-based PSC model of questions classification, several feature selection approaches are investigated. In doing so, a dataset of teachers' classroom questions was collected, annotated manually with Bloom's cognitive levels, and transformed into a vector space representation. Using this dataset, several experiments are conducted, and the results show a poor performance of the standard PSC due to the curse of dimensionality. However, when the proposed RA-based initialization mechanism is used, a significant improvement in the average performance, from 0.243 to 0.663, is obtained. In addition, the results indicate that the feature selection approaches play a role in the performance of the RA-based PSC (average performance ranges from 0.535 to 0.708). Finally, a comparison between the performance of RA-based PSC (average performance = 0.663) and seven machine learning approaches (best average performance = 0.646) confirms the effectiveness of the proposed RA-based PSC approach. Keywords: Particle swarm classification, Rocchio Algorithm, Educational data mining, Questions classification, Bloom's taxonomy

The intent of study is to establish a criterion for the experimental setup of rasterstereography, one that is more efficient, simple, accurate and precise to examine and analyse the curvature of the object or the subject. Firstly, the setup is needed to be calibrated and for this purpose we considered a reference plane, eleven cylinders of different diameter ranging from 30-119mm were used for calibration of curvature maps of cylinders and cylindrical objects through the regression line. Further this model was tested on subjects and for that we reckoned cylindrical body parts of boys and a total of 30 college students were involved as subjects in this process. The body parts to be measured had curvature like cylinders of different diameters ranging from 42-120mm. The distortion of raster grid was observed, quantified and recorded with the help of different tools and results were established. The regression line was obtained between the measured (x) and computed (y). The standard error of the cylindrical object was 0..04 and that of cylindrical body parts of subjects was 0.0407. This technique was validated through the coefficient of correlation for objects and subjects which was found to be 0.99982 and 0.999141 respectively. Accuracy and Precision were also calculated for this model, which were 99.60% accurate and 99.49% precise. Curvature maps were found more than 99%. This technique is inexpensive, reliable, reproducible and may be installed easily in hospitals to examine appraise body shape, composition and deformities of young men.

The curse of dimensionality is a well-known problem in data classification. In this paper, the Center-Based Sampling (CBS) theory is utilized to develop a new variant of Particle Swarm Optimization (PSO), dubbed CBS-PSO, capable of dealing with the curse of dimensionality problem in text classification. More specifically, the CBS is exploited to equip PSO with two specialized mechanisms to attract the search toward the center region of the search space. The first mechanism estimates the coordinates of the center point of the search space using Rocchio Algorithm (RA), whereas the second mechanism uses the RA-based estimation to generate informed particles, located at the center region, and incorporate them in the swarm to gradually attract the search for the optimal classifiers toward this promising region. The performance of the CBS-PSO is evaluated against three Machine Learning (ML) approaches on three classification tasks of textual datasets from UC Irvine ML repository. The results indicate that the CBS-PSO can be regarded as a very competitive and promising text classifier with much space for improvement.

In tertiary education, program education objects (PEDs) are a core component around which all program's activities revolve. This paper presents a data-driven approach to uncover an important aspect of this component that is its correlation to program majors (PMs). It does so by applying three well-known data correlation metrics, namely Pointwise Mutual Information, Correlation Coefficient, and Odds Ratio, to a dataset extracted from self-study reports of a set of Engineering programs. The collected dataset has undergone a preprocessing step to transform it into a suitable representation. This involves data cleaning, data annotation using a set of PEDs labels, and data projection to break down each multi-PEDs label data instances into a number of single PEDs data instances. The results obtained from the application of the three correlation metrics show a remarkable consistency among the three metrics in their evaluation of the correlation between PMs and PEDs. In a subsequent step, a ranking procedure of the PEDs within each PM, based on the obtained PMs-PEDs correlation strength, is applied and then a majority vote among the ranks of the three metrics is performed to obtain an overall rank of the PEDs within each PM. The obtained results show that each PM has a unique pattern of PEDs ranks, which suggests that PM nature plays a key role in determining the PM-PEDs correlation pattern. As a general conclusion, although the obtained results need further investigation on their causality correlation, the obtained quantitative correlations are very beneficial to the academicians particularly when designing new programs or reviewing existing ones.

To improve the performance of multi-unmanned aerial vehicle path planning in plateau narrow area, a control strategy based on Cauchy mutant pigeon-inspired optimization algorithm is proposed in this article. The Cauchy mutation operator is chosen to improve the pigeon-inspired optimization algorithm by comparing and analyzing the changing trend of fitness function of the local optimum position and the global optimum position when dealing with unmanned aerial vehicle path planning problems. The plateau topography model and plateau wind field model are established. Furthermore, a variety of control constrains of unmanned aerial vehicles are summarized and modeled. By combining with relative positions and total flight duration, a cooperative path planning strategy for unmanned aerial vehicle group is put forward. Finally, the simulation results show that the proposed Cauchy mutant pigeon-inspired optimization method gives better robustness and cooperative path planning strategy which are effective and advanced as compared with traditional pigeon-inspired optimization algorithm.

Data storage on local devices provides fast, secure and complete access to the users. However, it needs sufficient storage, which is not feasible for light-weight clients’ environments. In such scenarios, the usage of external devices makes the system vulnerable to data tampering, privacy leakage, and other data security issues. Wireless Sensor Networks (WSNs) consist of resource constrained devices, where external storage is preferred due to the lack of storage capacity in local devices. Therefore, using a centralized storage mechanism in WSNs causes slow data retrieval, which affects further operations on data. Relying on trusted parties also cause certain privacy and trust issues. Various data trading mechanisms are introduced to efficiently utilize the stored data. However, they still lack in certain aspects as discussed above. Therefore, we propose and implement a Secure Incentive-based Data Storage and Trading Model (SIDSTM), which provides a secure and efficient distributed storage mechanism. The proposed scheme uses AES-128 encryption scheme to encrypt the data for privacy purposes. An elliptic curve Diffie-Hellman key exchange is used for securely exchanging the private keys among the network peers. In WSNs, there are fewer data trading models so far. Anyone can access the data if he has access to the hash provided by the IPFS in the InterPlanetary File System (IPFS). Thus, IPFS provides efficient data access and storage mechanism. However, it deletes the data after a certain time interval due to the limited storage. Therefore, the proposed model provides incentives to motivate IPFS’s peers for data storage. It also eliminates the need for the third-party involvement in data trading. The simulations are conducted to prove the effectiveness of the proposed model by evaluating its efficiency and scalability.

Devices in the Internet of Things (IoT) produce large amounts of sensitive data. However, the use of the public Internet for data transfer by IoT devices makes them susceptible to cyber attacks. Among these attacks, data tampering or modification attacks to disrupt or bias the states of applications using these data may result in widespread damage and outages. To detect such attacks, this paper proposes an efficient and simple technique to detect data tampering in IoT systems. The proposed mechanism uses a random time hopping sequence and random permutations to hide validation information. We also present a formal security analysis of the proposed protocol. Performance analysis of the proposed protocol shows that it has low computational complexity and is suitable for IoT systems.

Performance of wireless sensor network are highly prone to network anomalies particularly to misdirection attacks and blackhole attacks. Therefor intrusion detection system has a key role in WSN and it’s essential in security application. However the identification of active attacks is cumbersome in many cases particularly for remote sensing applications. This paper proposes hybrid anomaly detection method for misdirection and blackhole attacks by employing K-medoid customized clustering technique. A synthetic dataset was established by defining network parameters and threshold values were obtained to detect the anomalies. Experimental work was performed on network simulator (NS-2) and R studio. The proposed algorithm successfully detect the hybrid anomalies with high accuracy. This work is suitable for hybrid anomaly detection including misdirection and blackhole attacks in wireless environment.

Recently, Wireless sensor networks (WSNs) have turned out to be a dominant subject of research due to its application areas. Moreover, with the integration of Unmanned Aerial Vehicle (UAV) in the loop, WSN becomes more valuable and interested topic among research community. With the addition of UAV in WSN, life span of WSN is increased and communication between WSN and Network Control Centre becomes more efficient. UAVs not only increased the life of the network, but also reduce the data processing and memory related issues in the network. In this paper, we address the problem of data gathering from a WSN with altitude controlled UAV. Simulation results verify the effectiveness of our proposed method.

This paper proposes highly compact and high speed hardware architectures of 64-bit KASUMI block cipher for wide range of wireless applications. A novel methodology is adopted for low area KASUMI implementation employing a single combined substitution block (CSB) for S9 and S7 substitution functions in FI/FO function. The CSB performs S9/S7 transformations and re-utilizes the common combinational logic of AND and AND–XOR gates whereas computations of CSB logic expressions are performed using parallel hierarchy of ANDs/AND-XORs. This scheme substantially reduces the area generating moderate throughput values for compact KASUMI architecture. FPGA implementation with Xilinx Virtex 7 and ASIC implementation using IC Design Compiler, 0.18 µm at 1.8 V constituted 126 CLB slices/2.64 k gates having throughput values of 180/122 Mbps respectively. For high speed KASUMI implementation, the optimized combinational logic methdodology of compact architecture is extended to individual S9/S7 substitution functions and 2 × pipeline schemes are proposed. The odd and even round functions FO and FL are configured for simultaneous operations and path delays are reduced using XOR logic modifications thereby producing high throughput and high efficiency values. Hardware implementations yielded an area of 1619 CLB slices/52.7 k gates attaining throughput values of 16.9/16.1 Gbps for high speed KASUMI architecture I whereas KASUMI architecture II constituted 1847 CLB slices/59.8 k gates attaining very high throughput of 27.5/26.6 Gbps with FPGA and ASICs respectively. A detailed design and performance analysis of proposed KASUMI architectures is described.

In the presented paper, an innovative method to address the problem of synchronous speed in the network connected DC motors is presented by utilizing the linear quadratic regulator control along with the consensus algorithm of the leader following multi-agent system (MAS). From MAS perspective, each chopper fed DC motor is considered as single agent connected through fixed and undirected network. In this study, common lyapunov function is used to ensure switch system stability in such a way that if the $$\mathscr{i}$$ th agent is controllable and observable, then all the agents in the multi-motor system network can reach the consensus i.e. synchronous speed. To validate the proposed method, simulation results are presented using MATLAB by considering the application of the load on the motors and without load too.

When the on-time is constant during a line cycle, the input power factor (PF) of critical conduction mode buck-buck/boost power factor correction converter is low. A variable on-time control (VOTC) scheme is put forward in this paper to realize high PF. By utilizing the input and output voltage to modulate the on-time of buck and buck/boost switch, we can realize high-input PF with low-total harmonic distortion within the universal input voltage range. The operating principles and the comparative analysis of the converter with conventional constant on-time control (COTC) and VOTC are analyzed. The proposed control scheme achieves high PF and efficiency as compared with the COTC. A 100-W prototype was built up to verify the effectiveness of the proposed control strategy.

This paper proposes a Sliding Mode Controller to improve the dynamic performance of buck converter. Proposed controller comprises of a linear part and a nonlinear part. Linear and non-linear parts of the controller consist of PID and hysteresis control respectively. PID controller is applied to the output voltage loop of buck converter and hysteresis control is implemented through inductor current ripple, which is used to select ON and OFF points of the hysteresis control. Proposed controller for buck converter is simulated using MATLAB®/SIMULINK® software. Simulation analysis shows improved response with proposed SMC. Little and no overshoot in output voltage and inductor current is observed respectively for initial transient, however settling time is drastically improved and there is negligible overshoot in output voltage during line and load variation. Keywords: Buck Converter, Line and Load Variation, Sliding Mode Controller, Transient Response

In outcome-based academic programs, Program Education Objects (PEOs) are the key pillars on which program components are built. They are articulated linguistically as broad statements of graduates’ professional and career accomplishments within a few years of graduation. Moreover, PEOs are mapped into a set of skills and attributes known as Program Learning Outcomes (PLOs). It goes without saying that a profound understanding of the PEOs is a key factor in the success of an academic program. For this sake, this paper proposes a data analytics-based approach to examine the correlations among PEOs. More specifically, it applies a data similarity-based approach to analyze the correlations among the PEOs of engineering programs. To this end, a dataset of PEOs–PLOs mapping of a set of engineering programs has been extracted from their self-study reports. The collected dataset has undergone preprocessing steps to transform it into a suitable representation. This involves data cleaning, data annotation using a developed set of PEOs labels, and removal of data instances with multiple PEO labels. Each PEO is then represented as a vector space model whose dimensions are the PLOs, and their values are the relative frequencies of PLOs computed from all data instances of that PEO. After that, three data similarity measures, namely Euclidean distance, cosine measure, and Manhattan distance, are applied to measure the similarity between PEOs vector space models. The resultant similarity matrices are then analyzed at the level of a specific measure, an agreement between measures, and average similarity across all measures. The analysis results contribute to a better understanding of the PEOs correlations and provide very useful actionable insights for empowering decision making toward systemization and optimization of academic programs processes.

The underactuated quadrotor unmanned aerial vehicle (UAV) is one of the nonlinear systems that have few actuators as compared to the degree of freedom (DOF), thus, it is a strenuous task to stabilize its attitude and positions. Moreover, an induction of unmodelled dynamic factors and uncertainties make it more difficult to control its maneuverability. In this paper, a model-free based single-dimension fuzzy sliding mode control (MFSDF-SMC) is proposed to control the attitude and positions of underactuated quadrotor UAV. The paper discusses the kinematic and dynamic models with unmodelled dynamic factors and unknown external disturbances. These unmodelled factors and disturbances may lead the quadrotor towards failure in tracking specific trajectory and may also generate some serious transient and steady-state issues. Furthermore, to avoid the problem of gimbal lock, the model is amalgamated with hyperbolic function to resolve the singularity issues dully developed due to Newton Euler’s dynamic modeling. The simulation results performed for MFSDF-SMC using MATLAB software R2020a are compared with conventional sliding mode control, fuzzy-based sliding control and single-dimension fuzzy-based sliding mode control without a model-free approach. The design and implementation of the model-free single dimension-based fuzzy sliding mode control (MFSDF-SMC) with an updated Lyapunov stability theorem is presented in this work. It is observed that MFSDF-SMC produces robust trajectory performance therefore, and the manuscript suggests the experimental setup to test the proposed algorithm in a noisy environment keeping the same conditions. The verification of the equipment used and its effective demonstration is also available for the reader within the manuscript.

In recent years, there has been a growing interest in applying Particle Swarm Optimization (PSO) to data classification. Nonetheless, due to the curse of dimensionality, the effectiveness of the PSO applied to high dimensional data classification becomes questionable. This paper proposes a novel specialized PSO initialization mechanism, developed specifically for PSO applications to high dimensional data classification. The proposed initialization mechanism is inspired by the center-based sampling theory, which argues that the center of the search space is a promising region for the initialization step in evolutionary algorithms. Furthermore, the proposed initialization mechanism is based on an information retrieval algorithm called Rocchio Algorithm (RA); that identifies the center region of the search space of data classification. To validate the proposed mechanism, RA-based PSO has been applied to a high dimensional classification task in educational data mining. More specifically, RA-based PSO has been applied to classify a dataset of teachers' classroom questions into Bloom's taxonomy cognitive levels. To do so, a dataset of teachers' classroom questions has been collected and annotated manually with Bloom's taxonomy cognitive levels. Pre-processing steps have been applied to convert questions into a representation suitable for classification. Using this dataset, the standard PSO, PSO with generic initialization mechanisms, and RA-based PSO have been experimented and compared. The results show a poor performance of the standard PSO and the PSO with the generic initialization mechanisms, as well as a significant improvement in the performance of RA-based PSO. These results indicate that a proper task-specific PSO initialization mechanism is crucial for effective PSO performance in high dimensional data classification. Furthermore, a comparison between RA-based PSO and pure RA classification provide a quantitative estimation of the role of initialization mechanism and PSO search for the classification of the dataset. On the other hand, the comparison between RA-based PSO approach and three conventional machine learning approaches, experimented on the same dataset confirms the effectiveness of RA-based PSO for high dimensional data classification. Moreover, the comparison between RA-based PSO approach and machine learning approaches, in terms of computational time efficiency, shows that they are comparable in classification time. However, as the learning of PSO is a time-consuming process, its effectiveness is significantly affected if the learning time is a matter.