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The nanogrids are the basic unit to develop more complex and stronger distributed systems. DC nanogrids allow a better performance and higher efficiency than the AC nanogrids. The lack of DC home appliances in the local market has resulted in a slow development of DC Home Nanogrids (DCHN). The Compact Fluorescent Lamp (CFL) and LED are used in power electronics as high efficiency and low cost lightning components. However, these devices represent a source of emission of harmonics in Alternating Current (AC) and Direct Current (DC) systems, due to their nonlinear behavior. In this paper a mathematical model for the nonlinear loads and experimental results of the voltage and current ripple in a DCHN are presented. The CFL and LED lamps are used as our nonlinear loads for test the model. The model explains well the experimental results of how the ripple amplitude is reduced. We also present the effect of decreasing the ripple amplitude when it is incorporated an induction stove in standby mode to the DCHN, which also is explained by the model.

Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.

This paper focuses on the face detection problem of three popular animal cat-egories that need control such as horses, cats and dogs. To be precise, a new Convolutional Neural Network for Animal Face Detection (CNNAFD) is actu-ally investigated using processed filters based on gradient features and applied with a new way. A new convolutional layer is proposed through a sparse feature selection method known as Automated Negotiation-based Online Feature Selection (ANOFS). CNNAFD ends by stacked fully connected layers which represent a strong classifier. The fusion of CNNAFD and MobileNetV2 constructs the newnetwork CNNAFD-MobileNetV2 which improves the classification results and gives better detection decisions. Our work also introduces a new Tunisian Horse Detection Database (THDD). The proposed detector with the new CNNAFD-MobileNetV2 network achieved an average precision equal to 99.78%, 99% and 98.28% for cats, dogs and horses respectively.

In this work, we have proposed and experimentally demonstrated a true full-duplex IO (TFD-IO) for high-speed high-density interfaces. The proposed TFD-IO can be used as an independent module that converts a unidirectional IO/interconnect to a fully bidirectional IO/interconnect, to ideally double the throughput of the high-speed interface. The TFD-IO uses a correlation-based technique to cancel the self-interference (SI) adaptively in the background. The signals transmitted from the near-end and the far-end can use independent baud-rates and signaling schemes in a TFD-IO. A proof-of-concept design of the TFD-IO module has been fabricated in a 65 nm CMOS technology, and demonstrated with bidirectional throughputs of up to 12.8\,Gb/s.

We have compared two Neural network models with Wiener filtering technique for Speech signal enhancement. Our paper intends to suggest the best method suitable for speech denoising and quality enhancement. We have utilized MATLAB software with most advanced toolboxes for building the models. For comparing our models, we computed PSNR and SNR values.

Today, mobile devices like smartphones are supported with various wireless radio interfaces including cellular (3G/4G/LTE) and Wi-Fi (IEEE 802.11) [42]. The legacy devices can only communicate with only one interface. The Transmission Control Protocol, or TCP, has a limitation inability to change connection settings without breaking the connection. In this paper, we explain how multi-path TCP (MPTCP) protocol has been proposed to solve TCP single-interface limitation and provides a huge improvement on application performance by using multiple paths transparently (auto path changing). We discuss the last mile, which is the final networking segment that carried all network traffic. Indeed, the available bandwidth in last-mile link can effectively harm the network throughput as it limits the amount of transmitted data. We found that the quality of the last mile networks significantly determines the reliability and quality of the carrying network. We believe MPTCP can provide a convenient solution for the last mile problem. We provide a holistic view of the challenges and potential enablers in details.

In this paper, we propose a novel method to improve the shape sensing accuracy of FBG for catheter by fusing FBG-based sensed shape with sparse fluoroscopic images. The main advantage of the new proposed method compared to other methods are the limited number in fluoroscopic image used during procedure while it still maintains high precision real-time 3D visualization of the catheter. To demonstrate the performance of the proposed method 2D and 3D dynamic experiments were carried out and they shows promising results. For a catheter with an embedded fiber length of 170 mm, the proposed approach can reconstruct the 3D shape with a median root mean square error of 0.54 mm were seen in the 3D experiments compared to the traditional approach of using FBG alone of 0.86 mm.

Number of embedded devices connected to the Internet is rapidly increasing, especially in the era of the Internet of Things (IoT). The growing number of IoT devices communicating wirelessly causes a communication-parameters selection problem, implying the increasing number of communication collisions. Multiple factors of IoT networks signify this problem, such as inability to communication-channel listening prior to the transmission (due to longer distances), energy constrains (due to inability of powering devices from the grid), or limitation of duty cycle and high interference (due to usage of unlicensed band in communication technologies). This article is focused on alleviating this problem in LoRa networks, which is one of the most promising technology for long-range and low-power communication. We utilize the existing LoRa@FIIT protocol to achieve energy-efficient communication. The scalability of the LoRa network is increased by modifying the communication-parameters selection algorithm. By ensuring of quality of service mechanism at each node in the infrastructure, the application domain of the proposed architecture is widened. The simulation-based experimental results showed a significantly reduced number of collisions for mobile nodes, which reduces the channel congestion and the wasted energy by retransmissions.

Offline Handwritten Text Recognition (HTR) is one of the most interesting challenges in today’s date in the field of Image processing. This paper introduces a novel technique to recognize the handwritten text by using Convolutional Recurrent Neural Network along with Connectionist Temporal Classification. This model makes use of the IAM dataset. Offline Signature Verification (SV) is another challenging task in the field of biometrics. This paper demonstrates a novel technique to verify the signature as an original or forged one, and makes use of the Convolutional Siamese network.

The paper presents a novel approach by using multi- step predictions to address the adaptive sampling problem in a resources and obstacles constrained mobile robotic sensor network to efficiently monitor environmental spatial phenomena. It is first proposed to employ the Gaussian process (GP) to represent the spatial field, which can then be used to predict the field at unmeasured locations. The adaptive sampling problem aims to drive the mobile sensors to optimally navigate the environment where the sensors adaptively take measurements of the spatial phenomena at each sampling step. To this end, a conditional entropy based optimality criterion is proposed, which aims to minimize prediction uncertainty of the GP model. By predicting possible measurements the mobile sensors potentially take in a horizon of multiple sampling steps ahead and exploiting the chain rule of the conditional entropy, a multi-step predictions based adaptive sampling optimization problem is formulated. The objective of the optimization problem is to find the optimal sampling paths for the mobile sensors in multiple sampling steps ahead, which then provides their benefits in terms of better navigation, deployment and data collection with more informative sensor readings. However, the optimization problem is nonconvex, complex, constrained and mixed-integer. Therefore, it is proposed to employ the proximal alternating direction method of multipliers algorithm to efficiently solve the problem. More importantly, the solution obtained by the proposed approach is theoretically guaranteed to be converged to a stationary value. Effectiveness of the proposed algorithm was extensively validated by the real- world dataset, where the obtained results are highly promising.

The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Mat´ern cross covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding univariate and multivariate sensor selection problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice.

Recent studies have shown that physiological signals such as heart beat and breathing can be remotely captured from human faces using a regular color camera under ambient light. This technology, referred to as remote photoplethysmography (rPPG), can be used to collect the physiological status of users who are in front of a camera, which may raise privacy concerns. To avoid the privacy abuse of the rPPG technology, this paper develops PulseEdit, a novel and efficient algorithm that can edit the physiological signals in facial videos without affecting visual appearance and thus protect the user’s physiological signal from disclosure. PulseEdit can either remove the trace of the physiological signal in a video or transform the video to contain a target physiological signal chosen by a user. Experimental results show that PulseEdit can effectively edit physiological signals in facial videos and prevent heart rate measurement based on rPPG. It is possible to utilize PulseEdit in adversarial scenarios against rPPG-based visual security algorithms. We present analyses on the performance of PulseEdit against rPPG-based liveness detection and rPPG-based deepfake detection, and demonstrate its ability to circumvent these visual security algorithms and its important role in supporting the design of attack-resilient systems.

Underground water pipes are important to any country's infrastructure. Overtime, the metallic pipes are prone to corrosion, which can lead to water leakage and pipe bursts. In order to prolong the service life of those assets, water utilities in Australia apply protective pipe linings. Long-term monitoring and timely intervention are crucial for maintaining those lining assets. However, the water utilities do not possess the comprehensive technology to achieve it. The main reasons for lacking such technology are the unavailability of sensors and accurate robot localization technologies. Feature based localization methods such as SLAM has limited use as the application of liners alters the features and the environment. Encoder based localization is not accurate enough to observe the evolution of defects over a long period of time requiring unique defect correspondence. This motivates us to explore accurate contact-less and wireless based localization methods. We propose a cost-effective localization method using UHFRFID signals for robot localization inside pipelines based on Gaussian process combined particle filter. Experiments carried out in field extracted pipe samples from the Sydney water pipe network show that using the RSSI and Phase data together in the measurement model with particle filter algorithm improves the localization accuracy up to 15 centimeters precision.

This paper proposes an analytical model to calculate the airgap function and inductance, which can be used to get an optimal design during the initial design phase. It investigates the relationship between the design parameters of the rotor and the motor performance. This paper is under review in IEEE Transactions on Energy Conversion.

Several types of Routing Protocols for mobile ad hoc networks have been proposed recently for their application in various areas such as military, government and many other fields. In this paper, we have proposed a comparative study of various routing protocols for mobile ad-hoc networks particularly emphasizing on its security aspect. Furthermore, we have discussed the comparison among existing routing protocols for MANET. Main factor for comparison is routing methodologies and knowledge required to make routing decisions. The comparison is made on the basis of the following factors: integrity, confidentiality, authentication, nonrepudiation and availability. Analysis of the secured version of proposed routing method has been done on the basis of the five security requirements and the same has been shown in the comparison tables.

In the frame of ongoing efforts between space agencies to define an on-off-keying-based optical low-Earth-orbit (LEO) direct-to-Earth (DTE) waveform, this paper offers an in-depth analysis of the Variable Data Rate (VDR) technique. VDR, in contrast to the currently adopted Constant Data Rate (CDR) approach, enables the optimization of the average throughput during a LEO pass over the optical ground station (OGS). The analysis addresses both critical link level aspects, such as receiver (time, frame, and amplitude) synchronization, as well as demonstrates the benefits stemming from employing VDR at system level. This was found to be around 100% compared to a CDR transmission approach.

Coronavirus (COVID-19) is an ecumenical pandemic that has affected the whole world drastically by raising a global calamitous situation. Due to this pernicious disease, millions of people have lost their lives. The scientists are still far from knowing how to tackle the coronavirus due to its multiple mutations found around the globe. Standard testing technique called Polymerase Chain Reaction (PCR) for the clinical diagnosis of COVID-19 is expensive and time consuming. However, to assist specialists and radiologists in COVID-19 detection and diagnosis, deep learning plays an important role. Many research efforts have been done that leverage deep learning techniques and technologies for the identification or categorization of COVID-19 positive patients, and these techniques are proved to be a powerful tool that can automatically detect or diagnose COVID-19 cases. In this paper, we identify significant challenges regarding deep learning-based systems and techniques that use different medical imaging modalities, including Cough and Breadth, Chest X-ray, and Computer Tomography (CT) to combat COVID-19 outbreak. We also pinpoint important research questions for each category of challenges. The challenges highlighted in this paper will call an attention to the noticeable weaknesses and problems in the existing deep learning based COVID-19 detection systems and techniques. Moreover, the research questions for each challenge will guide the researchers to come up with novel solutions in COVID-19 detection.