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Goal-driven networks trained to perform a task analogous to that performed by biological neural populations are being increasingly utilized as insightful computational models of motor control. The resulting dynamics of the trained networks are then analyzed to uncover the neural strategies employed by the motor cortex to produce movements. However, these networks do not take into account the role of sensory feedback in producing movement, nor do they consider the complex biophysical underpinnings of the underlying musculoskeletal system. Moreover, these models can not be used in context of predictive neuromechanical simulations for hypothesis generation and prediction of neural strategies during novel movements. In this research, we adapt state-of-the-art deep reinforcement learning (DRL) algorithms to train a controller to drive a developed anatomically accurate monkey arm model to track experimentally recorded kinematics. We validate that the trained controller mimics biologically observed neural strategies to produce movement. The trained controller generalizes well to unobserved conditions as well as to perturbation analyses. The recorded firing rates of motor cortex neurons can be predicted from the controller activity with high accuracy even on unseen conditions. Finally, we validate that the trained controller outperforms existing goal-driven and representational models of motor cortex in single neuron decoding accuracy, thus showing the utility of the complex underpinnings of anatomically accurate models in shaping motor cortex neural activity during limb movements. The learned controller can be used for hypothesis generation and prediction of neural strategies during novel movements and unobserved conditions.

The technological transformation has led to the introduction of mini computers as wearable computers which facilitate users to use the Internet, different applications, messaging, and for calls all on one platform. The focus of these wearable computers in earlier studies has been on consumers and technical aspects, and their application has not been explored from an organizational perspective. This means that the recent introduction of wearable technology has great potential, which is untapped in the organizational context, especially for supply chain collaboration (SC). In this study, wearable technology is thought to offer huge potential for organizations in SC. Therefore, the purpose of this study is to empirically examine the role of wearable technology i.e. Smartwatch in enhancing collaboration among supply chain members with mediating roles of green training (GT) and emotional intelligence (EI). The population of this study is the pharmaceutical industry in Pakistan. Simple random sampling method has been used to collect data from 150 sample size. Data have been collected through offline and online survey method. Data have been analyzed through partial least square-structure equation modeling (PLS-SEM) technique. The results of this study have empirically tested the established conceptual framework. Continuance intention to use smartwatch has a positive effect on SC. Furthermore, GT and EI have mediating roles between continuance intention to use smartwatch and SC. It is concluded that the continuance intention to use smartwatch will increase SC. GT and EI will strengthen the relationship between continuance intention to use smartwatch and SC. The use of wearable technology will motivate employees to communicate rapidly and to monitor their supply chain activities using web-based and other applications. This study has practical implications in organizations and theoretical contribution to literature.

Increasing supply chain performance (SCP) has always been a common phenomenon in the history of organizations. Recent technological advancements [including, radio frequency identification (RFID) in combination with big data predictive analytics (BDPA)] have been able to significantly leverage SCP. In this respect, there is an urgent need for acceptance of RFID technology as well as BDPA to be empirically investigated in order to better understand the enhancement of SCP. Therefore, this paper aims at establishing and empirically investigating the relationship between BDPA acceptance, RFID acceptance, and SCP, respectively. The population of this paper comprises the pharmaceutical logistics industry in China. The sample was obtained from employees through the use of a simple random sampling method. Data were collected through an adapted questionnaire. SmartPLS 3.0 software was used for structural equation modeling analysis. Partial least square-structural equation modeling (PLS–SEM), the value of path coefficient, was found to be 0.700. This showed a direct positive effect of BDPA acceptance on SCP. Moreover, RFID acceptance was also tested through the “variance accounted for” method of mediation. Results showed a partial mediation of 57% between BDPA and SCP. Theoretically, this paper fills the knowledge gap by finding relationships between BDPA and SCP via a mediation role of RFID technology. Practically, it would be beneficial for supply chain managers in the pharmaceutical industry to adopt and implement BDPA and RFID technology so as to enhance SCP.

Liver tumor segmentation is the most significant and challenging task due to high variation in size, location, depth of tumor and intimate boundaries of other organs around the liver. Computed Tomography (CT) scans of the liver differ in size and other parameters, including the number of layers in each CT scan. Thus, liver CT scans from different parameters make it a challenging task. Thus, we propose an efficient liver tumor segmentation method with Mask Region-Convolutional Neural Network (Mask-RCNN) with resnet 101 as the backboneto come upwith these challenges. The proposed method based on the Mask-RCNN approach effectively identifies tumor in liver through generating proposals about the tumor region. The proposed method converts CT volumes to normalized CT image slices in preprocessing step to get a cardinal outline of liver. Then normalized CT images are feed to resnet 101 for feature extraction. Then finally, Mask-RCNN is appliedto segment tumors in the liver. Our experiment dataset comprises 130 CT scans of different patients taken from different clinical sites, and it is freely available atLiTS challenge website. The proposed method is trained on converted CT image slices. Our proposed method hasimproved the segmentation rate by providing segmentation accuracy with average dice coefficient, JC, VOE and RVD of 0.95, 0.41, 0.12, and 0.15, respectively and lesser training time due to converted CT image slices instead of training on CT volumes.

The rapid development in the industrial sector has caused severe damage to the environment, which resulted in the occurrence of catastrophic disasters of high magnitude on earth. Moreover, the efforts for providing better early warnings and rescue operations during and after the disasters largely depend on communication protocol utilized for disaster management. Therefore, researchers are working to devise an efficient communication protocol for detection and communication of disasters. The communication protocol must comprise a methodology to transmit images of the disaster site effectively and facilitates communication between disaster site and disaster management centers. Presently, the disaster management is performed using terrestrial networks (using the Internet and mobile networks), space-ground networks (using satellites in different orbits), and air-ground networks (using Un-manned Aerial Vehicle and airships). Besides, the utilization of all devices in space, air, and ground can help to enhance the services for disaster management. In this work, Space-Air-Ground Integrated Network (SAGIN) is proposed to be used for disaster management. Moreover, a framework is proposed for the realization of the enhanced services using Internet Protocol (IP) for the detection and communication of disasters. Also, a comparison with the presently deployed networks is provided for comparison of services provided for disaster management.

The satellite networks are used for technological enhancement in many fields including mobile communication (i.e. 5G technology). Similarly, the aviation industry is in process of adopting the satellite network for effective and efficient air traffic control for enhancing the safety of passengers and aircrafts. Recently, the space-based Automatic Dependent Surveillance-Broadcast (ADS-B) system is introduced for aviation industry as an advancement of existing radar-based surveillance. Moreover, this system has its limitations such as inefficient satellite network utilization, global adaptability, and many more; which advocates its ineffectiveness as an efficient surveillance system. The challenges faced by the aviation industry can be resolved by using heterogeneous networks such as the Space-Air-Ground Integrated Network (SAGIN). Therefore, in this work, challenges faced by aviation industry and lagging in current and under-development systems are discussed, besides, a framework based on SAGIN is proposed for air traffic control. Moreover, the Satellite Communication Protocol Specifications (SCPS) is proposed for air traffic control communication and the proposed framework is simulated.

The need to explore outer and deep space and make it more beneficial for mankind is emphasized in the space industry. But such exploration missions demand intensive attitude control even under a harsh and challenging environment. Due to these external interruptions, the actuators installed at the spacecraft may suffer degradation in its performance with time and may cause partial or complete mission failure. This paper presents a fixed-time robust, yet simple, passive fault-tolerant control for a class of rigid spacecraft with partial loss of actuator effectiveness, external disturbance, and inertial uncertainties. A quaternion presents the spacecraft's attitude dynamics. The error trajectories of angular velocity and attitude are made to converge rapidly to a smaller region around the equilibrium in a fixed time. The trajectories then asymptotically converge to equilibrium. Simulation results endorse the effectiveness of the presented robust fixed-time passive fault-tolerant controller.

The increase in the flow of information and high-speed communication has compelled researchers to use satellite networks for different applications. The satellite networks comprise different satellites in different orbits that require the data security algorithms for the providing security and privacy of consumers' information. The data security primarily consists of data confidentiality service, which is provided by data encryption algorithms. The data encryption algorithm for example AES algorithm is utilized in many applications, including the satellites. The challenges for AES algorithm utilization in satellite applications are high-speed computation, lightweight implementation, and catering the radiations in the space environment. In this work, optimizations are proposed for coping with the challenges in a data encryption algorithm for the space environment. Also, the high-throughput optimization for the AES algorithm is realized on FPGA, and it equated with previous work. Moreover, the recent security attacks, for instance the nonce misuse attack is challenging for the security of encryption algorithms. The high-throughput optimized encryption algorithm protects against the recent attacks by the modifications in the AES algorithm. Moreover, the security analysis is provided against the latest attacks for the validation of high-throughput optimizations.

Power semiconductor switches such as MOSFET are widely used in electric vehicles, electric trains, and power conversion systems. The prognostics and health monitoring (PHM) and reliability of the MOSFETS are critical in power electronic systems. The ON-state resistance of the MOSFETS is the primary and significant precursor of failure. In this paper, we present a data-driven approach to predict the change in ON-state resistance using Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN). The experimental data are obtained by power cycling the MOSFET in thermal stress. The LSTM network's hyperparameters are optimized using Bayesian Optimization, which improves results and reduces time to develop the model. Our proposed Bayesian Optimized LSTM RNN (BO-LSTM RNN) gives good prediction accuracy using less number of layers. The model's performance is also analyzed using a small percentage of data used to train the LSTM RNN. The proposed model is also tested on different MOSFETS degradation data to prove the universality of the model using the same hyperparameters.

This paper compares the performance of a robust model predictive control (MPC) with a nominal MPC. The robust scheme is applied to a nonlinear system with input constraints, bounded unknown additive disturbances, and uncertainties on the system states. The design of the robust controller relies on the linearized version of the nonlinear model along its trajectory. It comprises two components, one of which is obtained by a continuous-time MPC approach with tightened constraints for stabilizing the nominal system. The other one is chosen as standard LQR gain, ensuring that the error system trajectories lie in a particular invariant set. The controller is implemented in a digital environment and utilizes a continuous-time model and equations, vastly reducing the computational burden. Furthermore, with some plausible assumptions, the closed-loop system is demonstrated to have regional exponential stability around the origin. Simulation results verify the improvement in disturbance rejection property of the employed controller over nominal MPC.

The concept of augmented reality markerless tracking is introduced. This concept provides a flexible and practical framework and is especially relevant for video game augmented reality applications. Markerless AR has been comprised of the universal communal. Markerless augmented reality is the encounter proficiency that’s now prepared for the primary time. The perception of this report is that the handler’s aspect at the live appearance of the camera of their portable phone in three dimensional and the passage that they see that is the certainty is developed by assimilated three- dimensional virtual objects. Augmented reality games have the authority to outspread virtual gaming into real-world circumstances with real people, while improving the senses of the user. This paper designates the AR-zombie game established with the purpose of detected and unindustrialized mobile augmented reality submissions, precisely for gaming, using ground plane detection collaboration techniques. The objective of the AR zombie player is to kill zombies that are identified through the demonstration of the device. Instead of using indicators as a mean of tracking zombies, this game integrates a ground plane detection system, which will augment the user experience by improving the interface of performers with the real world. As the player travels around the environment, the game will show virtual zombies on the display if the detected ground plane is predictable as belonging to the class of the zombies. AR zombie was tested with users to evaluate the interaction with the real world and its types of tracks were evaluated concerning the presentation to confirm a better gaming familiarity.

In this advanced time, innovations have been indulged deeply and has brought draconian change in one’s lifestyle, adding with it the current era of technologies-network connectivity and Wi-Fi connections has raised an extensive platform which has risen to on-demand services like Ridesharing. Being inclined by the Ridesharing /Carpooling App-based services such as Easy Taxi, Metro Cab, etc. which superintends bookings by means of Request. Uber, one of the most leading competitors in the ridesharing business have grabbed a traction on the market and serving citizens effectively but the underlying contest is none of them workings contained by a distinct association. Therefore, we have portrayed the concept of ridesharing for an organization, an IOS Application version 12.3.1, XCode 9 as an IDE for the coding of the Application. For our endeavor contemplating the present database which will be administered across MySQL and Firebase for real-time that supports student/teachers to acquiesce their request and within moments unites you with the driver. Front-end devised on WordPress and Servers will be connected using PHP Web Servers. We are considering Algorithms of Careem, Uber for supervision of the ride’s charge and the perception of E-Wallet has also been gathered whose set of rules will be designed. We hope to lay such a groundwork to make better appreciate that how this alternative can diminish the proportion of transit systems and upcoming augmentations may start it on Android Application with more upgradations.

The need for high-speed communication has led the research towards designing parallel architectural algorithms for data security. The utilization of the Advanced Encryption Standard in Counter mode (AES-CTR) in cipher-based Authenticated Encryption (AE) algorithms has realized the importance of a unique Initialization Vector (IV) for data security. The Synthetic Initialization Vector (SIV) is an improvement of a generic IV used in the AES-CTR algorithm for nonce misuse and key wrapping attacks. In this work, we have proposed a new AE algorithm with parallel architecture named as Parallel Cipher-based Message Authentication Code with SIV Algorithm (PCMAC-SIV). The PCMAC-SIV AE algorithm is consists of an AES-CTR algorithm and parallel implementation of the Cipher-based Message Authentication Code (CMAC) algorithm with SIV algorithm for avoiding nonce misuse. The proposed algorithm is implemented on FPGA for showing its utility for high throughput applications. In this work, we compared the proposed algorithm implementation with the AES-GCM -SIV algorithm. The experimental results show that the throughput of the proposed algorithm show higher throughput of 1.629 Gbps for single plaintext and 13.06 Gbps for eight plaintexts.

The major concern and basic requirement for control engineers, besides stability and trajectory tracking, is to consider and utilize available resources efficiently. It involves restriction of the energy consumption and associated costs in the presence of system constraints. Recently, such optimal control schemes and techniques that are constructed on the mathematical model of the system have been productively realized, both for linear and nonlinear systems, and are termed as Model Predictive Control (MPC). This paper deals with an Adaptive MPC for a nonlinear nonminimum phase single-link flexible joint manipulator (SFJM) system with constraints on the input control effort, states, and output. The non-linear system model is linearized along its trajectory. At each sampling interval, the quadratic optimization problem is solved. The optimum input control effort is implemented in Receding Horizon fashion. This meaningfully diminishes the computational burden involved in solving complex nonlinear differential equations and the associated non-convex optimization problem. The adaptive MPC offers performance which is comparable to Nonlinear Model Predictive Control (NMPC). In this paper, the closed-loop stability of the overall control scheme is presented, and its performance is matched to the general Linear MPC and NMPC. The simulation results show the superiority of the performance of adaptive MPC over linear MPC with a significant decrease in computational burden as compared to NMPC.

Anemia affects more than 2 billion people worldwide, which is about 25% of the world’s population. Anemia has numerous causes ranging from nutritional deficiencies, drugs, chronic conditions that indirectly cause anemia as well as primary hematologic diseases. Among the various causes of anemia world-wide, hemoglobinopathies, including Sickle Cell Disease (SCD) and Thalassemia, are the 3rd most prevalent after iron-deficiency anemia and hookworm disease. Anemia and SCD diagnosis/monitoring are challenging in low and middle income countries due to lack of laboratory infrastructure and skilled personnel as well as insufficient financial resources. We extended our previously established HemeChip system to add total hemoglobin quantification and anemia testing capability. HemeChip+ is mass-producible at low cost and offers the first and only single test point-of-care (POC) platform for portable, affordable, and accurate, hemoglobin quantification, anemia detection, and hemoglobin variant identification.

The advancements in communication technology have evolved the algorithms used for communication security. Recently, the Authenticated Encryption (AE) algorithms are employed for providing security services for data communication. The Advanced Encryption Standard in Counter mode (AES-CTR) with Cipher-block Chaining Message authentication code (AES-CCM) algorithm is used for providing the data security for various applications. But the AES-CCM algorithm provides limited throughput for data communication. Therefore, in this work, new architecture is proposed for increase in throughput for provision of data security for communication application. The proposed algorithm uses the AES-CTR algorithm with the Cipher-based Message Authentication Code (CMAC) algorithm for providing AE. The proposed AE algorithm is implemented on FPGA and it’s compared with the FPGA implementation of AES-CCM algorithm. The comparison results of the proposed algorithm and the AES-CCM algorithm shows that the proposed algorithm provides improvement in the consumption of the area, processing time, and throughput. The implementation of proposed AES-CMAC AE algorithm on FPGA provides a throughput of 4.30 Gbps.

Although, in Pakistan the electric motor industry is indigenous one, meeting all its demand and requirement through local production, but the percentage share of motors exported is quite minimal. The primary reason for this is that in international market there are specific standards and minimum efficiency criteria that should be met before exporting the motors. But the local Small-Medium Enterprises (SMEs) of Pakistan are unable to produce standard complaint motors because there is no such facility available in Pakistan that can give compliance certificates. This paper presents the design of a three-phase induction motor test bed which measures the efficiency of the motor according to the International standards. The standard experimental procedure for measuring the efficiency of 175 W induction motor according to IEEE 112 standard is discussed and results are presented. Moreover, simulation results of Electric drive are also discussed and presented.

The performance of Single Phase Grid Connected Inverters (SPGCIs) get degraded due to several detrimental factors such as the grid voltage harmonics, the dead time effect and the turn ON/OFF of the switches and so on, which cause the harmonics at the output of Grid Connected Inverters (GCIs). Therefore, it is not easy to satisfy the harmonic standards such as IEEE 519 and P1547 without the help of harmonic compensator. In this paper, the Digital Lock-in Amplifier (DLA) based harmonic compensation is proposed, which makes it possible to extract the amplitude and phase information of the harmonics from the output current and compensate it by using a simple PI controller in a feedforward manner. In order to show the superior performance of the proposed harmonic compensation technique, it is compared with those of conventional harmonic compensation methods in terms of the effectiveness of harmonic elimination, complexity and implementation. The validity of the proposed harmonic compensation techniques is verified through the PSIM simulation and experiments with a 5kW SPGCI.

Due to the recent increasing use of nonlinear loads and Grid Connected Inverters (GCIs), the harmonics are also increasing in the grid. The detrimental effects of the harmonics in the grid include the heating in the equipment and conductors, the misfiring in variable speed drives, the torque pulsations in motors and the asynchronization of the GCIs. Therefore, the standard such as IEEE 519 and P1547 forces the GCIs to meet a certain level of output quality in terms of harmonics, phase and frequency variation. In order to inject the pure active power to the grid, the synchronization between the grid and a GCI by a highperformance Phase Locked Loops (PLLs) is essential. Several kinds of PLL methods to synchronize the GCI under the distorted grid condition have been proposed. However, their performances are significantly degraded under the distorted grid condition, especially with a DC off-set and harmonics. In this paper a novel Digital Lock-in Amplifier (DLA) based PLL is proposed to improve the performance of the PLL under the highly distorted grid condition including the power quality events. Since the proposed DLA-PLL is composed of a robust Phase Sensitive detector (PSD), it is immune to any frequency component except the fundamental and the DC off-set present in the grid can be completely rejected. The superiority of the proposed DLA-PLL is proved by the simulation and experiments by comparing it to the results obtained with the six kinds of conventional PLL methods widely used under distorted grid condition.

Fire presents significant threat to life due to its severe hazards and ability to spread rapidly. Fire detection systems, specially vision-based system offers flame detection prior to any loss or destruction. In this present model, new vision-based device is designed which works on Raspberry Pi and detects flame (visible part of fire) over remote regions. An immediate alert is generated on android application. Here in, HSV color combination is used for proposed model and changes in flame color and texture is studied.

The operation and growth of interconnected power systems has given rise to many complexities. The power transfer capabilities are becoming limited day by day. It has become important to develop the strategies to cope with such problems and utilize transmission systems at their maximum thermal capacity. This thesis presents SIMULINK model of Unified Power Flow Controller (UPFC) device to analyze the operation of single and double line transmission systems of 132/220kV. UPFC model is a very important member of family of Flexible AC Transmission System (FACTS) devices. It is a microcontroller based solid state converter capable of controlling active and reactive power in transmission line and controls power flow and real power loss. This work will reveal the effects of installing UPFC model for single and double transmission line in MATLAB/Simulink to observe voltage regulation, power loss minimization and improvement in power transfer capability. Simulation results of three cases for single and double line transmission systems with and without UPFC device are examined in this work to observe the effects of installing this device in power system.

The structure and dynamics of software development project team have the potential to influence and impact the knowledge sharing process or mechanism within the project team that may affect the timely and successful completion of the project milestones. In order to confirm and validate this hypothesis, we carried out a comprehensive study in which 271 employees of 47 software development companies of Pakistan have been interviewed to share their experiences and views on the impact of project team structure and dynamics on the Knowledge Management Process (KMP) of their respective organizations. The included organizations have come under the category of Very Small Entities (VSEs). The results of the study revealed that team size, commitment, time constraint, level of motivation, communication, trust, reward system, language, multiple roles of team members and complex hierarchical organizational structure possess an enormous potential in disturbing the KMP within the software development team. Based on these factors, a knowledge sharing framework within software development project teams has been proposed.

HemeChip is the first miniaturized, single-use cartridge-based microchip electrophoresis system for identifying and quantifying hemoglobin (Hb) variants from a drop of blood at the point-of-care (POC). It is estimated that 7% of world's population lives with a hemoglobin variant, and one of the most common and severe one being the recessively transmitted sickle hemoglobin which results in Sickle Cell Disease (SCD). HemeChip (Fig. 1A,C) separates Hb variants on a strip of cellulose acetate (CA) paper that is housed in the HemeChip cartridge. This HemeChip cartridge is a mass-producible, single-use cartridge that maintains a controlled environment and a constant electric field (applied through the electrodes, Fig. 1B) for the Hb separation process. The cartridge also contains an in-chip blotting mechanism and an anti-fogging feature. The anti-fogging feature is essential for real-time imaging and tracking of Hb band(s) during the separation process. HemeChip is integrated with a portable reader (Fig. 1D) to detect the type(s) and fraction of hemoglobin types present in a blood sample. HemeChip technology has been designed and developed to offer a robust, user-friendly platform to detect, identify, and quantify Hb variants (responsible for SCD variants) even in a limited resource settings.