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The patient’s heterogeneous data in IoT-based healthcare system are gathered using various sensor nodes. the existing healthcare and monitoring systems are mostly based on ontology or type-1 fuzzy logic which is insufficient due to inconsistency and uncertainty in the sensed data. in this paper a novel data fusion scheme is proposed which is based on type-2 fuzzy logic (T2FL) incorporated with Dempster–Shafer theory (DST) to extract precise information and correctly infer the result. in the proposed scheme the membership values of the patient data are effectively decided by type-2 fuzzy logic, and the evidence obtained from the membership values are properly fused and processed by the DST in the decision-making system. extensive computer simulation with heart disease and diabetes dataset reveals that the proposed scheme considerably outperforms the existing schemes based on ontology and type-1 fuzzy logic with respect to the decision accuracy.

Multisensor data fusion is extensively used to merge data from heterogeneous sensors in a smart environment. However, sensors provide noisy and uncertain information which is a big challenge for researchers. Since uncertainty in the data is a central constraint for data fusion and decision-making systems. Dempster-Shafer’s evidence theory is an appropriate method for modeling and fusing uncertain information. In this paper, a novel data fusion scheme is proposed based on the modified belief entropy of the basic probability assignments (BPAs) to quantify the uncertainty in the information and fused them by Dempster-Shafer evidence theory. The proposed DFUDS (data fusion based on measuring uncertainty in Dempster-Shafer) scheme considers the available redundant information in the body of evidence (BoEs). The BoEs obtained from the sensor data are processed by proposed belief entropy, and fuse all pieces of evidence by Dempster’s rule of combination to transfer the conflicting data into decision-making results. Extensive computer simulation results show that the proposed scheme outperforms in terms of the degree of uncertainty, evidence, reasoning, and decision accuracy under active contexts of the smart environment.

Nowadays, Reinforcement Learning (RL) is applied to various real-world tasks and attracts much attention in the fields of games, robotics, and autonomous driving. It is very challenging and devices overwhelming to directly apply RL to real-world environments. Due to the reality gap simulated environment does not match perfectly to the real-world scenario and additional learning cannot be performed. Therefore, an efficient approach is required for RL to find an optimal control policy and get better learning efficacy. In this paper, we propose federated reinforcement learning based on multi agent environment which applying a new federation policy. The new federation policy allows multi agents to perform learning and share their learning experiences with each other e.g., gradient and model parameters to increase their learning level. The Actor-Critic PPO algorithm is used with four types of RL simulation environments, OpenAI Gym’s CartPole, MoutainCar, Acrobot, and Pendulum. In addition, we did real experiments with multiple Rotary Inverted Pendulum (RIP) to evaluate and compare the learning efficiency of the proposed scheme with both environments.

In this paper, we have given the design and development of a new wearable device that assists visually impaired individuals to travel independently and confidently. The newly proposed device is based on range-based sensors and would work effectively in both indoor and outdoor conditions. It is constructed in the form of two separate modules; one module is designed such that it can be attached to the waist belt of the user, and the other module is designed to wear it on ankle of the user. Both the modules communicate with each other using wireless communication and can cover the full front environment of the user. The information about the front environment is transmitted to the user, via headphone, by sending a set of voice instructions, stored in a memory card added in the belt module. In order to use the device in crowd mode, appropriate networking techniques were also implemented in the prototype such that the interference of two or more devices in the close vicinity can be avoided. In the end, effectiveness of the device is analyzed and proved by conducting experiments and obtaining statistical results.

Efficient data collection and communication are key tasks in smart IoT environment consisting of a large number of devices. Here imprecise data are generated due to the interferences between the devices and harsh operation condition, and therefore data fusion is needed to gather and extract useful data from multiple sources. A number of approaches for data fusion have been proposed which are based on probability, artificial intelligence, or evidence theory to efficiently aggregate the data. The techniques allow the system to be cognitive and intelligent in terms of decision-making under the uncertainty of data and limited resource. In this paper a comprehensive survey on the data fusion techniques for smart IoT system is presented. The challenges and opportunities with data fusion are also delineated. It will be useful for the researchers in developing the applications and services based on smart IoT environment, which require intelligent decision making.

The rapid evolution of Internet of Things and cloud computing have endorsed a novel computing paradigm called edge computing. Here tasks are processed by edge devices before sent to the cloud to reduce the computational latency and overhead of cloud server. In edge computing efficient classification and distribution of the tasks among the constituent nodes is a challenging issue because of their resource limitedness and heterogeneity. In this paper a novel scheme named KTCS (K-means Clustering-based Task Classification and Scheduling) is proposed which classifies the task based on the type of resource requirement in terms of CPU, I/O, or COMM before distributed to the edge node. Using the K-means algorithm modeled with the M/M/c queuing theory, the proposed scheme efficiently schedules and assigns the task so that the utilization of the edge devices can be increased. The simulation result reveals that the proposed scheme significantly improves the performance of edge nodes in terms of task execution time and resource utilization.

Lithium-ion batteries are the most used technology in portable electronic devices. High energy density and high power per mass battery unit make it preferable over other batteries. The existing constant-temperature and constant-voltage charging technique (CT–CV), with a closed loop, lacks a detailed design of control circuits, which can increase charging speed. This article addresses this research gap in a novel way by implementing a simpler feedback proportional integral and differential (PID) control to a closed-loop CT–CV charging circuit. Voltage-mode control (VMC) and average current-mode control (ACM) methods were implemented to maintain the battery voltage, current, and temperature at safe limits. As per simulation results, 23% faster charging is achieved by implementing VMC and almost 50% faster charging is attained by employing the ACM technique in the PID controller. Our proposed control strategy is validated experimentally, which yields up to 25% faster charging of a battery than the reference battery.

This paper presents a novel fixed time sliding mode disturbance observer (FTSMDO) based second-order fixed time sliding mode control (FTSMC) for small scale unmanned helicopter to do hover operations in the presence of external disturbances. Sliding mode control is insensitive to matched uncertainties but sensitive to mismatched uncertainties. The novel FTSMDO exactly estimates the total mismatched uncertainties effecting the sliding mode control performance. To counter the mismatched uncertainties a new sliding surface augmented by the total mismatched disturbance approximated by the FTSMDO is defined. The second-order FTSMC forces the system states to reach the sliding surface in fixed time and then on the sliding surface the system states exponentially converge to the desired equilibrium point. The control performance is compared with the disturbance observer based sliding mode controller (DOB-SMC) and shows superior performance.

In Pakistan, hydroelectric power is one of the reliable sources of electricity with a capacity of 8,713 MW, which is 29% of the total energy mix. Hence, with such a vast resource capacity of hydroelectric power, its optimization and dispatch planning will have a great significance. This research work discusses the importance of hydroelectric power generation planning for both storage and run-of-river (ROR) hydropower plants, as well a; a solver-based optimization technique is proposed for the first time to resolve the intricate job of generation planning for hydroelectric power plants in MATLAB. A mathematical-optimization model is also developed, which uses a Mixed-Integer Linear-Programming (MILP) algorithm, based on the objective function of profit maximization, which considers a random varying revenue plan as model input. Three hydropower generators of different capacities and efficiencies are considered for the optimization problem. MILP based solution is proposed for both storage and ROR hydropower plants with two dispatch schedules, i.e., Normal dispatch schedule and optimum dispatch schedule. The objective functions are solved, and the profit (in dollars) from each dispatch schedule is calculated and compared. The preliminary optimization results show an increase of $22,000 and $29,130 in the profit for storage and ROR hydropower plants, respectively, which is 19% more than average income. Hence, ensuring the credibility of the proposed algorithm for maximizing the revenue ($), is aimed to facilitate and assist better planning for electric power producers.

Faults detection and analysis in PV system are considered critical for ensuring safety and increasing output power of PV arrays. PV faults do not only reduce output power and efficiency but also lessen the working life time of a system. Most common and chronic PV faults are line to line, line to ground, shadowing fault, and arc fault while less common and acute faults are hotspot, degradation, bypass diode, and connection faults. Event of PV fault detection failures, such as most recent in Mount Holly, USA in 2011 evinced the improvement in current fault detection and mitigation techniques to shrink such failures. There are various limitations in the existing fault detection techniques, as identified in this paper, which may cause misdetection of the faults. This paper is focused on mathematical formulation of various PV faults and lead to the latter’s critical analysis in terms of efficiency, accuracy, complexity, and reliability. The presented work also helps to identify nature and causes of occurrence of a PV fault. This research work serves as a special set of references and recommendations for researchers and PV manufacturing industry to improve fault detection prospects in solar PV systems.

This paper contributes to the fabrication of a snake-like robot in which the motion can be achieved through active wheels. The robot is constructed in such a way that its size can be increased and decreased, as well as it can undulate into a sine wave-like shape. The snake robot with wheels consists of chain of links attached to each other with the help of the passive prismatic and revolute joints. A neural oscillator-based central pattern generator (CPG) algorithm is applied to the robot so that rhythmic serpentine as well as rectilinear motions can be generated in it. In addition, a novel smooth transition mechanics of robot motion, from stationary position to serpentine or rectilinear motion as well as transition between these two gaits, is also suggested in the paper. The working of the formulated CPG motion algorithm is realized through experimental setup equipped with a motion capture system as well as through simulations.

In this work, an improved interactive multiple model-based smoothing algorithm is proposed for tracking and interception of a maneuvering target. The smoothed states are obtained at some past scan ($$k-N$$), but the target interception happens at the current scan. This helps to obtain the real-time benefits of the smoothing. Fixed-lag smoothing interactive multiple model algorithm is employed to follow the target more precisely. Real-time benefit of smoothing is used to improve the decision making in order to achieve better target interception before it reaches its desired destination. Three different simulation environments have been used to analyze the performance and validity of the proposed algorithm. The simulation results are further extended to compare each case in terms of hit error and root-mean-square error.

Network Virtualization (NV) technology allows multiple virtual network requests to share resources on the same subtract network. In network virtualization, Virtual Network Embedding (VNE) is one of the main techniques used to map a virtual network to the substrate network. The effectiveness and efficiency of the virtual network are determined by the performance of the embedding algorithm. Hence, an efficient embedding algorithm is required to reduce the rejection rate and embed the maximum number of virtual networks which best fit the subtract network. In this article, we propose Ego Network-based Virtual Network Embedding (EN-ViNE) algorithm which aims to improve the performance of the embedding to accept more VNRs and increase the long-term revenue. We utilize the ego-network technique to search the nearest subtract nodes for embedding virtual nodes and found the shortest path between them for link embedding. The proposed scheme attempts to minimize the rejection of virtual network requests (VNRs) that are intended to maximize the long-term revenue for the substrate network provider. Extensive computer simulation reveals that the proposed scheme considerably outperforms the existing algorithms, topology-aware, and baseline for the long-term average revenue, acceptance ratio, and revenue/cost ratio.

This paper presents a novel incentive-based load shedding management scheme within a microgrid environment equipped with the required IoT infrastructure. The proposed mechanism works on the principles of reverse combinatorial auction. We consider a region of multiple consumers who are willing to curtail their load in the peak hours in order to gain some incentives later. Using the properties of combinatorial auctions, the participants can bid in packages or combinations in order to maximize their and overall social welfare of the system. The winner determination problem of the proposed combinatorial auction, determined using particle swarm optimization algorithm and hybrid genetic algorithm, is also presented in this paper. The performance evaluation and stability test of the proposed scheme are simulated using MATLAB and presented in this paper. The results indicate that combinatorial auctions are an excellent choice for load shedding management where a maximum of 50 users participate.

Automatic catheter and guidewire segmentation plays an important role in robot-assisted interventions that are guided by fluoroscopy. Existing learning based methods addressing the task of segmentation or tracking are often limited by the scarcity of annotated samples and difficulty in data collection. In the case of deep learning based methods, the demand for large amounts of labeled data further impedes successful application. We propose a synthesize and segment approach with plug in possibilities for segmentation to address this. We show that an adversarially learned image-to-image translation network can synthesize catheters in X-ray fluoroscopy enabling data augmentation in order to alleviate a low data regime. To make realistic synthesized images, we train the translation network via a perceptual loss coupled with similarity constraints. Then existing segmentation networks are used to learn accurate localization of catheters in a semi-supervised setting with the generated images. The empirical results on collected medical datasets show the value of our approach with significant improvements over existing translation baseline methods.

In a Convolutional Neural Network, each neuron in the output feature map takes input from the neurons in its receptive field. This receptive field concept plays a vital role in today's deep neural networks. However, inspired by neuro-biological research, it has been proposed to add inhibitory neurons outside the receptive field, which may enhance the performance of neural network models. In this paper, we begin with deep network architectures such as VGG and ResNet, and propose an approach to add lateral inhibition in each output neuron to reduce its impact on its neighbours, both in fine-tuning pre-trained models and training from scratch. Our experiments show that notable improvements upon prior baseline deep models can be achieved. A key feature of our approach is that it is easy to add to baseline models; it can be adopted in any model containing convolution layers, and we demonstrate its value in applications including object recognition and semantic segmentation of aerial images, where we show state-of-the-art result on the Aeroscape dataset. On semantic segmentation tasks, our enhancement shows 17.43% higher mIoU than a single baseline model on a single source (the Aeroscape dataset), 13.43% higher performance than an ensemble model on the same single source, and 7.03% higher than an ensemble model on multiple sources (segmentation datasets). Our experiments illustrate the potential impact of using inhibitory neurons in deep learning models, and they also show better results than the baseline models that have standard convolutional layer.

Wireless sensor network (WSN) is used for data collection and transmission in IoT environment. Since it consists of a large number of sensor nodes, a significant amount of redundant data and outliers are generated which substantially deteriorate the network performance. Data aggregation is needed to reduce energy consumption and prolong the lifetime of WSN. In this paper a novel data aggregation scheme is proposed which is based on modified radial basis function neural network to classify the collected data at cluster head and eliminate the redundant data and outliers. Additionally, cosine similarity is used to cluster the nodes having the most similar data. The radial basis function (RBF) is adapted by Mahalanobis distance to support the outlier’s detection and analysis in the multivariate data. The data collected from the sensor node at the cluster head are processed by mahalanbis distance-based radial basis function neural network (MDRBF-NN) before transferred to the based station. Extensive computer simulation with real datasets shows that the proposed scheme consistently outperforms the existing representative data aggregation schemes in terms of data classification, outlier detection, and energy efficiency.

Posting information on social media platforms is a popular activity through which personal and confidential information can leak into the public domain. Consequently, social media can contain information that provides an indication that an organization has been compromised or suffered a data breach. This paper describes a technique for inferring if an organization has been compromised from information posted on social media. The proposed strategy forms the basis of an alarm system which generates an alert for possible unreported cybercrime incidents. The proposed strategy used two social media cybercrime related datasets that were collected from the Irish and New York regions from financial organizations’ Twitter accounts. The Tweets are labelled as either containing cybercrime indicators or not, and then the cybercrime Tweets were labelled further into crime categories. A deep dense pyramidal Neural Network model is used to classify the Tweets. This approach achieves an AUC of \(~0.85 \pm 0.03\) which outperforms the baseline of deep convolutional neural networks.

Diabetic Retinopathy is the leading cause of blindness in the last 100 years. The traditional screening process for DR and its stages takes a lot of time, and it is not practical. Using machine learning techniques and image processing, we can automate detecting diabetic retinal disease and disease stage with acceptable performance. In this work, we have used multiple deep convolution neural networks (CNN) with the same architecture of InceptionV3. Each of the pre-trained Inception V3 architecture is retrained with 2200 preprocessed and leveled images. The dataset is preprocessed using multiple high performing and effective image processing techniques. Then the newly trained models are used for identifying the level of DR. In the final stage, we use a voting scheme for classifying the level of DR from the output of each model. We have achieved 90.5% accuracy in binary classification (Normal/DR) and 81.1% accuracy in 5-class classification.

Apple defect detection using hyperspectral image has become an interesting research focus since the last decade. It is important for wide range of applications, such as agricultural sector, food processing, and automatic fruits grading system. However, this task is a challenging one due to calyx and stem, their different types and orientation, and similar in their visual appearance. The proposed method based on a deep learning approach using SqueezeNet architecture. However, the apple images are extracted and fed into a deep network for training and testing. The proposed SqueezeNet architecture utilizes convolution neural network to regress a bypass connection between the fire modules across the images. It has been evaluated our own created dataset. The excremental result shows that our proposed methods are efficient and effective. The general detection rate was 92.23%.

Scalability and ease of implementation make Peer-to-Peer (P2P) infrastructure an attractive option for live video streaming. Peer end-users or peers in these networks have extremely complex features and exhibit unpredictable behavior, i.e. any peer may join or exit the network without prior notice. Peers' dynamics is considered one of the key problems impacting the Quality of Service (QoS) of the P2P based IPTV services. Since, peer dynamics results in video disruption to consumer peers, for smooth video distribution, stable peer identification and selection is essential. Many research works have been conducted on stable peer identification using classical statistical methods. In this paper, a model based on machine learning is proposed in order to predict the length of a user session on entering the network. This prediction can be utilized in topology management such as offloading the departing peer before its exit. Consequently, this will help peers to select stable provider peers, which are the ones with longer session duration. Furthermore, it will also enable service providers to identify stable peers in a live video streaming network. Results indicate that the SVR based model performance is superior to an existing Bayesian network model.

Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same, consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD).

In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing, (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.

Wireless sensor network allows efficient data collection and transmission in IoT environment. Since it usually consists of a large number of sensor nodes, a significant amount of redundant data and outliers are generated which deteriorate the network performance. In this paper, a novel data aggregation scheme is proposed which is based on self-organized map neural network to reduce redundant data and eliminate outliers. In addition, cosine similarity is used to improve the clustering process of sensor nodes based on the density and similarity of the data, and interquartile analysis is adopted to remove outliers. It allows to significantly reduce the energy consumption and enhance the network performance. Extensive simulation with real dataset shows that the proposed scheme consistently outperforms the existing representative data aggregation schemes in term of data reduction rate, network lifetime, and energy efficiency.

Studies are proceeded to stabilize cardiac surgery using thin micro-guidewires and catheter robots. To control the robot to a desired position and pose, it is necessary to accurately track the robot tip in real time but tracking and accurately delineating the thin and small tip is challenging. To address this problem, a novel image analysis-based tracking method using deep convolutional neural networks (CNN) has been proposed in this paper. The proposed tracker consists of two parts; (1) a detection network for rough detection of the tip position and (2) a segmentation network for accurate tip delineation near the tip position. To learn a robust real-time tracker, we extract small image patches, including the tip in successive frames and then learn the informative spatial and motion features for the segmentation network. During inference, the tip bounding box is first estimated in the initial frame via the detection network, thereafter tip delineation is consecutively performed through the segmentation network in the following frames. The proposed method enables accurate delineation of the tip in real time and automatically restarts tracking via the detection network when tracking fails in challenging frames. Experimental results show that the proposed method achieves better tracking accuracy than existing methods, with a considerable real-time speed of 19ms.

Cross-border power trading through grid interconnection has gained significant attention in South Asia to support energy deficit states of the South Asian Association for Regional Cooperation (SAARC) region. Nonetheless, cross border grid interconnection requires technical standardization and grid code synchronization to ensure technically feasible, reliable, safe and economical power exchange among the SAARC states. Importantly, grid code harmonization necessitates the uniformity of technical data and codes among the member states. In this paper, we investigate the relevant factors such as regional barriers, trade potential, a gap in the grid codes, standardization of technical data, energy pricing, load profile, the load factor of different states, and regulatory framework of different states. All these factors are found to be helpful in formulating a set of recommendations for seamless power grid interconnections and power trading. We have developed a power pool model for the SAARC region with an objective to effectively realize the objectives of cross-border power trade in the region. The presented model takes account of transparency in market-clearing price, bidding of data, season-wise load profile, market volume, best case practices, institutional setup and rigorous case studies to ensure seamless grid interconnection and reliable power trading within the region.

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset., 18 pages

This paper presents a business model for energy storage trading in a small neighborhood. A region composed of multiple households with a common energy storage system is considered, the capacity of which is shared amongst the households. The households submit bids to an auctioneer to get storage capacity in the shared energy storage system. This paper introduces a combinatorial auction mechanism for such trades to happen. The participants are allowed to bid in single bid format or with combinations of bids called packages. It also presents a novel winner determination solution for combinatorial auctions that uses an evolutionary algorithm by combining the genetic algorithm with particle swarm optimization. Furthermore, the price determination of each trade is also explained. Using MATLAB, performance evaluation and stability tests of the proposed auction technique are performed and presented in this paper.

Data security, complete system control, and missed storage and computing opportunities in personal portable devices are some of the major limitations of the centralized cloud environment. Among these limitations, security is a prime concern due to potential unauthorized access to private data. Biometrics, in particular, is considered sensitive data, and its usage is subject to the privacy protection law. To address this issue, a multimodal authentication system using encrypted biometrics for the edge-centric cloud environment is proposed in this study. Personal portable devices are utilized for encrypting biometrics in the proposed system, which optimizes the use of resources and tackles another limitation of the cloud environment. Biometrics is encrypted using a new method. In the proposed system, the edges transmit the encrypted speech and face for processing in the cloud. The cloud then decrypts the biometrics and performs authentication to confirm the identity of an individual. The model for speech authentication is based on two types of features, namely, Mel-frequency cepstral coefficients and perceptual linear prediction coefficients. The model for face authentication is implemented by determining the eigenfaces. The final decision about the identity of a user is based on majority voting. Experimental results show that the new encryption method can reliably hide the identity of an individual and accurately decrypt the biometrics, which is vital for errorless authentication.

Nowadays, wireless sensor network (WSN) is an important component in IoT environment, which enables efficient data collection and transmission. Since WSN consists of a large number of sensor nodes, network congestion can easily occur which significantly degrades the performance of entire network. In this paper a novel scheme called SMQP (Statistical Multipath Queue-wise Preemption) routing is proposed to balance the load and avoid the congestion for ZigBee-based WSN. This is achieved by employing statistical path scheduling and queue-wise preemption with multiple paths between any source and destination node. NS2 simulation reveals that the proposed scheme significantly improves the QoS in terms of delivery ratio, end-to-end delay, and packet delivery ratio compared to the representative routing schemes for WSN such as ad hoc on-demand distance vector and ad hoc on-demand multipath distance vector scheme.

The unidentified number of unmanned aerial vehicles (UAVs) can execute aggressive maneuvers in the restricted and the cluttered environment. Therefore, it is difficult to detect and track the uncertain motion of the UAV target in such complex environment. In addition, multi-target tracking (MTT) algorithms such as joint data association approach faces various computational complexities that could exceeds the available computation resources. This paper develops a novel smoothing data association idea in a linear multi-target (LM) tracking based on integrated probabilistic data association (sLM-IPDA) algorithm that acts like a single target tracker in the MTT situation. The significant detection and tracking performance of a UAV are validated without a-prior information of the UAV’s initial position. The forward and backward tracks are initialized separately using sensor measurements received in each scan. The sLM-IPDA estimates the backward multi-tracks simultaneously associating backward tracks in a subsequent predicted forward track for fusion. Thus, a forward track state estimate is obtain using the smoothing (fusion) measurements. This significantly improves estimation accuracy for large number of cross-over targets in heavy clutter. Numerical assessments of the sLM-IPDA are verified using both simulation and experiment to demonstrate the application of the proposed algorithm.

In this paper, we propose simple and effective compression of CSLBP (Center Symmetric Local Binary Pattern) descriptors, which is a textured based operator and mostly used as key point descriptor. With default parameters for computation, it is 256-length descriptor for each keypoint or affine patch. CSLBP is an extended form of LBP (Local Binary Patterns). The calculation of CSLBP descriptor is effective, robust, and straightforward for different image transformations for instance; image blurring and illumination alteration. However, an improvement in time and space consumption of CSLBP can be attained by means of simple compression. For this reason, CSLBP is a smart choice for smart phones as well as large databases. We reduce the descriptor length (dimensions) upto 50% without applying any techniques of dimensionality reduction like PCA (Principle Component Analysis) or LDA (Linear Discriminant Analysis). The compressed CSLBP descriptor is denoted as C-CSLBP. The performance of C-CSLBP is evaluated on state-of-the-art datasets using standard metrics. It is quantitatively shown by experiments that C-CSLBP is equivalently effective compared to CSLBP despite of reduced dimensions

The information technology’s new revolution is Internet of Things (IoT), where physical world is connected to the internet. IoT applications are diversified such as smart health, smart industry, smart homes and most importantly smart logistics for perishable items conservation. Although IoT devices have revolutionized world but due to their constrained nature they are at the cost of a greater risk of personal data loss, security breaches and misuse. In this research, a review is presented for proposing two state of the art modules i.e. communication model of smart logistics and security of IoT devices in Smart Logistics. As the smart logistics have perishable items i.e. fruits, vegetables, meat, medicine, chemicals, and cosmetics etc. which are sensitive to temperature, humidity and pressure etc. If the sensors monitoring these items have been handled abnormally due to security attacks, it will give huge loss. Therefore, security of smart logistics is of paramount importance in this research. In order to expedite research in this emergent area of IoT, in this research paper a brief review on progress of IoT in smart logistics, and its security has been presented.

Fault analysis in photovoltaic (PV) arrays is considered important for improving the safety and efficiency of a PV system. Faults do not only reduce efficiency but are also detrimental to the life span of a system. Output can be greatly affected by PV technology, configuration, and other operating conditions. Thus, it is important to consider the impact of different PV configurations and materials for thorough analysis of faults. This paper presents a detailed investigation of faults including non-uniform shading, open circuit and short circuit in different PV interconnections including Series-Parallel (SP), Honey-Comb (HC) and Total-cross-Tied (TCT). A special case of multiple faults in PV array under non-uniform irradiance is also investigated to analyze their combined impact on considered different PV interconnections. In order to be more comprehensive, we have considered monocrystalline and thin-film PV to analyze faults and their impact on power grids. Simulations are conducted in MATLAB/Simulink, and the obtained results in terms of power(P)–voltage(V) curve are compared and discussed. It is found that utilization of thin-film PV technology with appropriated PV interconnections can minimize the impact of faults on a power grid with improved performance of the system.

The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO 2 ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%.

In the current era of neural networks and big data, higher dimensional data is processed for automation of different application areas. Graphs represent a complex data organization in which dependencies between more than one object or activity occur. Due to the high dimensionality, this data creates challenges for machine learning algorithms. Graph convolutional networks were introduced to utilize the convolutional models concepts that shows good results. In this context, we enhanced two of the existing Graph convolutional network models by proposing four enhancements. These changes includes: hyper parameters optimization, convex combination of activation functions, topological information enrichment through clustering coefficients measure, and structural redesign of the network through addition of dense layers. We present extensive results on four state-of-art benchmark datasets. The performance is notable not only in terms of lesser computational cost compared to competitors, but also achieved competitive results for three of the datasets and state-of-the-art for the fourth dataset., 10 pages

Doubly-fed induction generator (DFIG) now a days is widely used in wind energy system while the research is also going on to use it for small scale hydro power systems on sites, where there is low variations in water flow in different seasons to improve overall efficiency of the system. Since the stator circuit of DFIG is connected directly to the grid making it sensitive to any grid disturbances. According to the grid codes, the generation system must remain connected to the grid in case of dips and should support the grid during voltage dips by supplying reactive power to the grid to recover the voltage soon after the dip and helps stabilizing the system. A lot of research work is going on since last several years to revamp low-voltage ride through capability of the generation system. In this paper DFIG behaviour under symmetrical voltage dips are scrutinized.Furthermore, a new technique known as “Controlling Generator Magnetizing Current(CGMC)” is developed to augment the response of the system in case of symmetrical dips in voltage and decay the stator flux faster to avoid losing control of the system. Finally simulation results are verified, presented and discussed in terms of its practical implementations.

IP Multimedia Subsystem (IMS) is a next generation network that provides the hypermedia services as data, voice and video to users. Due to high level requirements for IMS services, new kinds of network attacks are endlessly emerging. Thus, it is of paramount importance to protect the networks from attacks. Consequently, the Intrusion Detection Systems (IDS) are quickly becoming a popular requirement in building a network security infrastructure. Securing service and signalization is a vital feature in the IMS network. Signaling is generally based on Session Initiation Protocol (SIP) which offers numerous challenges regarding security which causes issues in IMS network. This work presents a study of SIP protocol and discovers the critical security vulnerabilities in the course of registration phase. We focused on DDoS attacks on IMS server using SIP particularly with REGISTER message and proposed a scheme based on multi agent systems for intrusion detection which takes the advantage of the distributed paradigm to implement an efficient distributed system, as well as the integration of existing techniques, i.e., the well-known IDS SNORT.

With the passage of time the trend of technology usage by farmers has been increasing to improve the quantity and quality of crop production. The multi parameter monitoring system is presented in this research where farmers/users will be updated with the help of internet. In this research two different technologies, Internet of things (IoT) and wireless sensor network are combined in an innovative way for smart remote monitoring system of crops. Sensor nodes are deployed in fields which gather data about different parameters. At transmission side these values are displayed and then transmitted to the base station using a network of XBee sensors. A database is established for maintaining sensor values which could be helpful for research and analysis of environmental factors such as soil moisture, UV index, rain, air pressure and humidity on crop production.

In recent years, research has been carried out using a micro-robot catheter instead of classic cardiac surgery performed using a catheter. To accurately control the micro-robot catheter, accurate and decisive tracking of the guidewire tip is required. In this paper, we propose a method based on the deep convolutional neural network (CNN) to track the guidewire tip. To extract a very small tip region from a large image in video sequences, we first segment small tip candidates using a segmentation CNN architecture, and then extract the best candidate using shape and motion constraints. The segmentation-based tracking strategy makes the tracking process robust and sturdy. The tracking of the guidewire tip in video sequences is performed fully-automated in real-time, i.e., 71 ms per image. For two-fold cross-validation, the proposed method achieves the average Dice score of 88.07% and IoU score of 85.07%.

Demand side management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via home energy management systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, evolutionary algorithms-based (binary particle swarm optimization, genetic algorithm, and cuckoo search) DSM model for scheduling the appliances of residential users is presented. The model is simulated in time of use pricing environment for three cases: 1) traditional homes; 2) smart homes; and 3) smart homes with renewable energy sources. Simulation results show that the proposed model optimally schedules the appliances resulting in electricity bill and peaks reductions.

In the recent few years, a lot of advancement has been made in Urdu linguistics. There are many portals and news websites that are generating a huge amount of data every day. However, there is still no publicly available dataset nor any framework available for automatic Urdu extractive summary generation. In an automatic extractive summary generation, the sentences with the highest weights are given importance to be included in the summary. The sentence weight is computed by the sum of the weights of the words in the sentence. There are two famous approaches to compute the weight of the words in the English language: local weights (LW) approach and global weights (GW) approach. The sensitivity of the weights depends on the contents of the text, the one word may have different weights in a different article, known as LW based approach. Whereas, in the case of GW, the weights of the words are computed from the independent dataset, which implies the weights of all words remain the same in different articles. In the proposed framework, LW and GW based approaches are modeled for the Urdu language. The sentence weight method and the weighted term-frequency method are LW based approaches that compute the weights of the sentences by the sum of important words and the sum of frequencies of the important words, respectively. Whereas, vector space model (VSM) is GW based approach, that computes the weight of the words from the independent dataset, and then remain the same for all types of the text; GW is widely used in the English language for various applications such as information retrieval and text classification. The extractive summaries are generated by LW and GW based approaches and evaluated with ground-truth summaries that are obtained by the experts. The VSM is used as a baseline framework for sentence weighting. Experiments show that LW based approaches are better for extractive summary generation. The F-score of the sentence weight method and the weighted term-frequency method are 80% and 76%, respectively. The VSM achieved only 62% accuracy on the same dataset. Both, the datasets with ground-truth, and the code are made publicly available for the researchers.

The increased trend toward multisensor target tracking system is driving an interest for distributed tracks fusion algorithm. In such a system, local tracks formed by each sensor may arrive in the fusion center with temporal out-of-sequence because of different sensor data processing time and random communication delay, moreover, the priori information on the origins of tracks to be fused is usually unknown due to clutter disturbance and presence of multitargets. This paper considers the problem of fusing out-of-sequence tracks (OOSTs) with track origin uncertainty in a distributed fusion setup, and proposes a novel all neighbor fusion-integrated forward prediction fusion and decorrelation (ANF-IFPFD) method. The proposed ANF-IFPFD enumerates and probabilistically evaluates all feasible track-to-track association events, and fuses the central tracks with extracted information purely contributed by the local OOSTs through an information decorrelation process. Furthermore, it enables to operate the false track discrimination (FTD) in the fusion center by using the fused probability of target existence as a track quality measure. Additionally, two implementation configurations of the proposed ANF-IFPFD method are also designed to provide a trade-off between the tracking performance and the system communication bandwidth consumption. Monte Carlo simulations are carried out to validate the superiority of the proposed ANF-IFPFD over the enhanced state-of-art methods, in terms of both tracking performance and also computation-storage requirement. Compared to the configuration without any information feedback, the partial information feedback configuration of the ANF-IFPFD method is verified to deliver intensive improvements for both local tracking and tracks fusing.

In the recent past, a significant increase has been observed in the use of underwater wireless sensor networks for aquatic applications. However, underwater wireless sensor networks face several challenges including large propagation delays, high mobility, limited bandwidth, three-dimensional deployments, expensive manufacturing, and energy constraints. It is crucial for underwater wireless sensor networks to mitigate all these limitations primarily caused by the harsh underwater environment. To address some of the pertinent challenges, adaptive hop-by-hop cone vector-based forwarding routing protocol is proposed in this article which is based on the adaptive hop-by-hop vector-based forwarding. The novelty of adaptive hop-by-hop cone vector-based forwarding includes increasing the transmission reliability in sparse sensor regions by changing the base angle of the cone according to the network structure. The number of duplicate packets and end-to-end delay is also reduced because of the reduced base angle and a smart selection criterion for the potential forwarder node. The proposed routing protocol adaptively tunes the height and opening of the cone based on the network structure to effectively improve the performance of the network. Conclusively, this approach significantly reduces energy tax, end-to-end delay, and packet delivery ratio.

The paper reports a new model based on the understanding and encompassing intelligence from brain i.e. biological pyramidal neurons, tailored for emotion recognition. Our objective is to introduce and utilize usage of non-Convolutional layers in models and show comparable or state-of-the-art performance for multi-class emotion recognition problem. We open-sourced the optimized code for researchers. Our model shows state-of-the-art performance on two emotion recognition datasets (eNTERFACE and Youtube) enhancing previous best result by \(9.47\%\) and \(20.8\%\), respectively.

Researchers proposed various visual based methods for estimating the fruit quantity and performing qualitative analysis, they used ariel and ground vehicles to capture the fruit images in orchards. Fruit yield estimation is a challenging task with environmental noise such as illumination changes, color variation, overlapped fruits, cluttered environment, and branches or leaves shading. In this paper, we proposed a learning free fast visual based method to correctly count the apple fruits tightly overlapped in a complex outdoor orchard environment. We first carefully build the color based HS model to perform the color based segmentation. This step extracts the apple fruits from the complex orchard background and produces the blobs representing apples along with the additional noisy regions. We used the fine tuned morphological operators to refine the blobs received from the previous step and remove the noisy regions fol-lowed by the Gaussian smoothing. Finally we treated the circular shaped blobs with Hough Transform algorithm to calculate the center coordinates of each apple edge and the method correctly locates the apples in the images. The results ensures the proposed algorithm successfully detects and count apple fruits in the images captured from apple orchard and outperforms the standard state of the art contoured based method.

Accurate localization of catheters or guidewires in fluoroscopy images is important to improve the stability of intervention procedures as well as the development of surgical navigation systems. Recently, deep learning methods have been proposed to improve performance, however these techniques require extensive pixel-wise annotations. Moreover, the human annotation effort is equally expensive. In this study, we mitigate this labeling effort using generative adversarial networks (cycleGAN) wherein we synthesize realistic catheters in flouroscopy from localized guidewires in camera images whose annotations are cheaper to acquire. Our approach is motivated by the fact that catheters are tubular structures with varying profiles, thus given a guidewire in a camera image, we can obtain the centerline that follows the profile of a catheter in an X-ray image and create plausible X-ray images composited with such a centerline. In order to generate an image similar to the actual X-ray image, we propose a loss term that includes perceptual loss alongside the standard cycle loss. Experimental results show that the proposed method has better performance than the conventional GAN and generates images with consistent quality. Further, we provide evidence to the development of methods that leverage such synthetic composite images in supervised settings.