Color object segmentation and tracking using flexible statistical model and level-set

Multimedia Tools and Applications - This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with...     

Mathematical Model Validation of Search Protocols in MP2P Networks

Broadcasting is a basic technique in Mobile ad-hoc network (MANET), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique, floods the network with query messages, while the random walk technique operates by contacting the subsets of every node’s neighbors at each step, thereby restricting the search space. One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource. Many earlier works have mainly focused on the simulation-based analysis of flooding, and its variants under a wired network. Although, there have been some empirical studies in peer-to-peer (P2P) networks, the analytical results are still lacking, especially in the context of P2P systems running over MANET. In this paper, we describe how P2P resource discovery protocols perform badly over MANETs. To address the limitations, we propose a new protocol named ABRW (Address Broadcast Random Walk), which is a lightweight search approach, designed considering the underlay topology aimed to better suit the unstructured architecture. We provide the mathematical model, measuring the performance of our proposed search scheme with different widely popular benchmarked search techniques. Further, we also derive three relevant search performance metrics, i.e., mean no. of steps needed to find a resource, the probability of finding a resource, and the mean no. of message overhead. We validated the analytical expressions through simulations. The simulation results closely matched with our analytical model, justifying our findings. Our proposed search algorithm under such highly dynamic self-evolving networks performed better, as it reduced the search latency, decreased the overall message overhead, and still equally had a good success rate.     

Multi-Criteria Fuzzy-Based Decision Making Algorithm to Optimize the VHO Performance in Hetnets

Despite the seemingly exponential growth of mobile and wireless communication, this same technology aims to offer uninterrupted access to different wireless systems like Radio Communication, Bluetooth, and Wi-Fi to achieve better network connection which in turn gives the best quality of service (QoS). Many analysts have established many handover decision systems (HDS) to enable assured continuous mobility between various radio access technologies. Unbroken mobility is one of the most significant problems considered in wireless communication networks. Each application needs a distinct QoS, so the network choice may shift appropriately. To achieve this objective and to choose the finest networks, it is important to select a best decision making algorithm that chooses the most effective network for every application that the user requires, dependent on QoS measures. Therefore, the main goal of the proposed system is to provide an enhanced vertical handover (VHO) decision making program by using a Multi-Criteria Fuzzy-Based algorithm to choose the best network. Enhanced Multi-Criteria algorithms and a Fuzzy-Based algorithm is implemented successfully for optimal network selection and also to minimize the probability of false handover. Furthermore, a double packet buffer is utilized to decrease the packet loss by 1.5% and to reduce the number of handovers up to 50% compared to the existing systems. In addition, the network setup has an optimized mobility management system to supervise the movement of the mobile nodes.     

An Effective Online Collaborative Training in Developing Listening Comprehension Skills

The COVID-19 outbreak severely affected formal face-to-face classroom teaching and learning. ICT-based online education and training can be a useful measure during the pandemic. In the Pakistani educational context, the use of ICT-based online training is generally sporadic and often unavailable, especially for developing English-language instructors’ listening comprehension skills. The major factors affecting availability include insufficient IT resources and infrastructure, a lack of proper online training for speech and listening, instructors with inadequate academic backgrounds, and an unfavorable environment for ICT-based training for listening comprehension. This study evaluated the effectiveness of ICT-based training for developing secondary-level English-language instructors’ listening comprehension skills. To this end, collaborative online training was undertaken using random sampling. Specifically, 60 private-school instructors in Chakwal District, Pakistan, were randomly selected to receive online-listening training sessions using English dialogs. The experimental group achieved significant scores in the posttest analysis. Specifically, there were substantial improvements in the participants’ listening skills via online training. Given the unavailability of face-to-face learning during COVID-19, this study recommends using ICT-based online training to enhance listening comprehension skills. Education policymakers should revise curricula based on online teaching methods and modules.     

A Formal Testing Model for Operating Room Control System Using Internet of Things

Technological advances in recent years have significantly changed the way an operating room works. This work aims to create a platform to solve the problems of operating room occupancy and prepare the rooms with an environment that is favorable for all operations. Using this system, a doctor can control all operation rooms, especially before an operation, and monitor their temperature and humidity to prepare for the operation. Also, in the event of a problem, an alert is sent to the nurse responsible for the room and medical stuff so that the problem can be resolved. The platform is tested using a Raspberry PI card and sensors. The sensors are connected to a cloud layer that collects and analyzes the temperature and humidity values obtained from the environment during an operation. The result of experimentations is visualized through a web application and an Android application. The platform also considers the security aspects such as authorization to access application functionalities for the Web and the mobile applications. We can also test and evaluate the system’s existing problems and vulnerabilities using the IEEE and owasp IoT standards. Finally, the proposed framework is extended with a model based testing technique that may be adopted for validating thesecurity aspects.     

Comparative Design and Study of A 60 GHz Antenna for Body-Centric Wireless Communications

In this paper performance of three different designs of a 60 GHz high gain antenna for body-centric communication has been evaluated. The basic structure of the antenna is a slotted patch consisting of a rectangular ring radiator with passive radiators inside. The variation of the design was done by changing the shape of these passive radiators. For free space performance, two types of excitations were used—waveguide port and a coaxial probe. The coaxial probe significantly improved both the bandwidth and radiation efficiency. The center frequency of all the designs was close to 60 GHz with a bandwidth of more than 5 GHz. These designs achieved a maximum gain of 8.47 dB, 10 dB, and 9.73 dB while the radiation efficiency was around 94%. For body-centric applications, these antennas were simulated at two different distances from a human torso phantom using a coaxial probe. The torso phantom was modeled by taking three layers of the human body—skin, fat, and muscle. Millimeter waves have low penetration depth in the human body as a result antenna performance is less affected. A negligible shift of return loss curves was observed. Radiation efficiencies dropped at the closest distance to the phantom and at the furthest distance, the efficiencies increased to free space values. On the three layers human body phantom, all three different antenna designs show directive radiation patterns towards off the body. All three designs exhibited similar results in terms of center frequency and efficiency but varied slightly by either having better bandwidth or maximum gain.     

Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

In recent years, progressive developments have been observed in recent technologies and the production cost has been continuously decreasing. In such scenario, Internet of Things (IoT) network which is comprised of a set of Unmanned Aerial Vehicles (UAV), has received more attention from civilian to military applications. But network security poses a serious challenge to UAV networks whereas the intrusion detection system (IDS) is found to be an effective process to secure the UAV networks. Classical IDSs are not adequate to handle the latest computer networks that possess maximum bandwidth and data traffic. In order to improve the detection performance and reduce the false alarms generated by IDS, several researchers have employed Machine Learning (ML) and Deep Learning (DL) algorithms to address the intrusion detection problem. In this view, the current research article presents a deep reinforcement learning technique, optimized by Black Widow Optimization (DRL-BWO) algorithm, for UAV networks. In addition, DRL involves an improved reinforcement learning-based Deep Belief Network (DBN) for intrusion detection. For parameter optimization of DRL technique, BWO algorithm is applied. It helps in improving the intrusion detection performance of UAV networks. An extensive set of experimental analysis was performed to highlight the supremacy of the proposed model. From the simulation values, it is evident that the proposed method is appropriate as it attained high precision, recall, F-measure, and accuracy values such as 0.985, 0.993, 0.988, and 0.989 respectively.     

Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

Industrial internet of things (IIoT) is the usage of internet of things (IoT) devices and applications for the purpose of sensing, processing and communicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-related processes to attain more profits. However, such IoT based smart industries need internet connectivity and interoperability which makes them susceptible to numerous cyber-attacks due to the scarcity of computational resources of IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoT environment. In this paper, we propose a hyperelliptic curve cryptography (HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with the aim of improving security while lowering computational and communication overhead in IIoT environment. HECC with 80-bit smaller key and parameters sizes offers similar security as elliptic curve cryptography (ECC) with 160-bit long key and parameters sizes. We assessed the IIoT-CS scheme security by applying formal and informal security evaluation techniques. We used Real or Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formal security analysis and proved that the IIoT-CS scheme provides resistance to various attacks. Our proposed IIoT-CS scheme is relatively less expensive compared to the current state-of-the-art in terms of computational cost and communication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead, respectively, compared to the most recent protocol.     

Optimized Resource Allocation for IoT-D2D Communication Using WSN

The need for a strong system to access radio resources demands a change in operating frequency in wireless networks as a part of Radio Resource Management (RRM). In the fifth-generation (5G) wireless networks, the capacity of the system is expected to be enhanced by Device-to-Device (D2D) communication. The cooperation and Resources Allocation (RA) in the development of Internet of Things (IoT) enabled 5G wireless networks are investigated in this paper. Developing radio RA methods for D2D communication while not affecting any Mobile Users’ (MU) communication is the main challenge of this research. Distinct performance goals such as practising equality in the rates of user data, increasing Network Throughput (NT), and reducing End-to-End Delay (EED) are achieved by RA. The study undertaken on optimising performance for various wireless networks is focused on in this research work. Proposing a polynomial-time Proportional Fair Resource Allocation Method (PFRAM), which considers the MU’s rate requirements, is the prime objective of this paper. Any Resource Allocation Method (RAM) can be used by the proposed method for MU, and the time and differing location channel conditions are the factors to be adapted with. Allotting more than one resource block is allowed by our PFRAM to a D2D pair. The automatic maintenance of battery-less IoT wireless devices’ energy level is done potentially using an Extensible Energy Management System (EEMS). Finally, the device’s Node Transmission Power (NTP) can be managed using an Energy-Saving Algorithm (ESA) designed in this work for Node Uplink Data Transmission (NUDT). The trade-off between the Packet Loss Rate (PLR) and NTP is balanced by the algorithm. The cost of NUDT’s average Energy Consumption (EC) is reduced by locating the optical NTP. In order to free much bandwidth for wireless information, NUDT conserves the harvested energy for minimising Radio Frequency (RF) Energy Transmission (ET). MATLAB simulations are used to assess the proposed EEMS. The IoT device’s NTP is managed using ESA designed for NUDT. The significant minimisation of channel hopping EED and the selection of the premium quality communication channel by the proposed framework are indications of the simulation results. 67.19% is the bandwidth to transmit DPs with the Bandwidth Allocation Algorithm (BAA), which is greater than the cases in its absence.     

Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver ailment. Signs of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s condition. Those changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis, suggesting that the cause may be alcohol instead of liver disease. Existing Two-Dimensional (2D) ultrasound data sets contain an accuracy rate of 85.9% and a 2D Computed Tomography (CT) data set of 91.02%. The most recent work on designing a Three-Dimensional (3D) ultrasound imaging system in or close to real-time is examined. In this article, a Deep Learning (DL) model is implemented and modified to fit liver CT segmentation, and a semantic pixel classification of road scenes is recommended. The architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers. A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training phase. For the normal class, we obtained 100% precision for chronic cirrhosis hepatitis (73%), offset cirrhosis (59.26%), and offensive cirrhosis (91.67%) for chronic hepatitis or cirrhosis (73,0%). The aim is to develop a Computer-Aided Detection (CAD) screening tool to detect steatosis. The results proved 98.33% exactness, 94.59% sensitivity, and 92.11% case with Convolutional Neural Networks (CNN) classification. Although the classifier’s performance did not differentiate so clearly at this level, it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve (AUC) and accuracy.