Search

Due to superior soft tissue contrast afforded by magnetic resonance imaging (MRI), there is great potential for multi-parametric MRI (mpMRI) for the detection and eventual classification of renal masses (RMs). In this study, we investigated fully automated deep learning methods for RMs detection using T2-Weighted (T2W) spin-echo and two contrast-enhanced T1-Weighted gradient-echo-corticomedullary (T1W-CM), nephrographic-phase (T1W-NG), T1-Weighted In-phase (T1W-IP) and opposed-phase (T1W-OP) images. The dataset contained mpMRI images of 108 kidney cancer patients with an average size of renal mass of $24~\pm ~7.8$ cm. In the first stage, kidneys were segmented using a 2D attention U-Net model, which was reported in a previous study. In the second stage, we tested five different state-of-the-art methods for RMs detections on mpMRI sequences. The model predictions were compared to manual annotations using precision, recall, specificity, and Dice Similarity Coefficient (DSC). The best-performing deep learning models were U-Net, U-Net++, and attention U-Net on the T2W, T1W-CM, and T1W-NG sequences respectively. Of the 5 mpMRI sequences, we also demonstrated that the T1W-CM is the most suitable for RMs detection. This automated detection of RMs in mpMRI sequences may be useful for the subsequent characterization of RMs in a fully automated artificial intelligence-based pipeline.

Remote sensing image analysis plays a vital role in achieving intelligent agricultural monitoring. However, the acquisition of high-resolution agricultural remote sensing data can be resource-intensive, resulting in an imbalance between training samples and artificial intelligence model parameters. In order to achieve accurate agricultural land recognition of limited-resolution remote sensing images, this article proposes a joint network of super-resolution and active learning (AL). The network introduces a pretrained image super-resolution model and optimizes this for remote sensing classification tasks. It effectively detects detailed features and completes the reconstruction. Based on the reconstructed data, an AL algorithm is proposed with a DBSS. It balances the contributions between interclass and boundary samples. Furthermore, we propose a semisupervisory assistance strategy based on consistency, it fully utilizes the predictive power of deep learning models aiming to reduce labeling costs. This framework is proved effective by experiments on an agricultural remote sensing image dataset, it reduces the cost of agricultural data annotation and improves the efficiency of model learning for low-resolution agricultural remote sensing.

Uncertainty is a key issue in thermal analysis and management for permanent magnet (PM) synchronous machines (PMSMs) and always occurs in both conduction and convection heat transfers. However, this issue has not been evaluated thoroughly. Therefore, this paper presents a comprehensive sensitivity analysis with uncertainty quantification, and the influences of more than 20 uncertain factors on temperature rises of winding and PM are evaluated. The investigated uncertainties include the convection coefficients under different cooling methods, the main machine losses, the interface gaps, and the materials’ thermal conductivities, where a 3-kW outer rotor PMSM is taken as an example for illustration. Furthermore, the most critical uncertain factors are determined based on a well-calibrated finite element analysis (FEA) model. Finally, the employed FEA model is validated experimentally under different cooling conditions.

The semiconductor industry has adopted heterogeneous integration (HI), incorporating modular intellectual property (IP) blocks (chiplets) into a unified system-in-package (SiP) to overcome the slowdown in Moore’s Law and Dennard scaling and to respond to the increasing demand for advanced integrated circuits (ICs). Despite the manifold benefits of HI, such as enhanced performance, reduced area overhead, and improved yield, this transformation has also led to security vulnerabilities in the SiP supply chain and in-field operations, ranging from chiplet piracy and SiP reverse engineering (RE) to information leakage. Although conventional countermeasures provide the desired robustness for monolithic ICs, they are insufficient for addressing these challenges in the context of HI. To address these concerns, this paper presents a novel root-of-trust architecture, augmenting the process of integration using a centralized chiplet hardware security module (CHSM), aiming to provide comprehensive and robust protection throughout the SiP supply chain and in-field operations. Also, the proposed architecture equipped with the CHSM effectively addresses potential security breaches while providing robust protection against zero-day attacks through its reconfigurable capabilities. Throughout five detailed case studies, this paper performs a comprehensive security analysis to illustrate the resilience of CHSM against contemporary attack scenarios in the HI domain.

The quality of images and videos is an important research topic due to their wide applications. The research should match the human subjective evaluation of quality. There are three types of objective image quality assessment: full-reference, reduced-reference, and no-reference. No-reference image quality assessment is the most realistic because distorted images often have no reference. In this article, we propose an algorithm that uses the discrete cosine transform (DCT) of patches to extract a feature vector. We apply the Self-Organizing Map (SOM) at two levels. In the first level, we classify the patches into three types: good quality, noisy, and blurred. In the second level, we further classify the noisy and blurred patches into weak and severe distortions using two separate SOMs. Then, we used a straightforward neural network with supervised back-propagation to adjust the number of distorted patches of five classes in one image to assign a quality score. Our method of training Self-Organizing Maps (SOMs) with reference, noisy, and blurred image patches allowed us to estimate the quality scores of other types of distortions that matched subjective scores equally well. The Spearman Rank Order Correlation Coefficient (SROCC) performance of our method, when measured against the subjective scores of degraded images that were used in training, lies in the range of 0.88-0.92. Similarly, for other degradation types that were examined (10 in total), the SROCC performance is in the range of 0.76-0.89, which is higher than other methods. The experiments show that our scores are consistent with the subjective scores.

Internet-of-Things (IoT) is rapidly growing in wireless technology, aiming to connect vast numbers of devices to gather and distribute vital information. Despite individual devices having low energy consumption, the cumulative demand results in significant energy usage. Consequently, the concept of ultra-low-power tags gains appeal. Such tags communicate by reflecting rather than generating the radio frequency (RF) signals by themselves. Thus, these backscatter tags can be low-cost and battery-free. The RF signals can be ambient sources such as wireless-fidelity (Wi-Fi), cellular, or television (TV) signals, or the system can generate them externally. Backscatter channel characteristics are different from conventional point-to-point or cooperative relay channels. These systems are also affected by a strong interference link between the RF source and the tag besides the direct and backscattering links, making signal detection challenging. This paper provides an overview of the fundamentals, challenges, and ongoing research in signal detection for AmBC networks. It delves into various detection methods, discussing their advantages and drawbacks. The paper’s emphasis on signal detection sets it apart and positions it as a valuable resource for IoT and wireless communication professionals and researchers.

This paper proposes an Image Contrast Enhancement (ICE) method based on using an Improved Chicken Swarm Optimization (ICSO) algorithm to enhance images while at the same time preventing over-enhancement. In the optimization process, a new practical objective function is employed to reach three main goals, preserving the main details, generating an image with a uniform histogram, and reducing the spikes in the modified histogram. In the proposed approach, the RGB color channels are optimized individually. The performance of the proposed method is suitable for enhancing the contrast of low- and high-contrast images. A subjective experiment is designed to visually evaluate and compare the results with other ICE methods. The simulation results on the CSIQ, TID2013, and SEID datasets show that the proposed method outperforms numerous traditional and state-of-the-art ICE techniques both subjectively and objectively. The most important advantage of the newly proposed technique is that there is an agreement among observers on when over-enhancement occurs regardless of whether the Initial processed image was of low or high contrast.

The interconnection of renewable energy systems, which are complex nonlinear systems, often results in power fluctuations in the interconnection line and high system frequency due to insufficient damping in extreme and dynamic loading situations. To solve this problem, load frequency control ensures nominal operating frequency and orderly fluctuation of grid interconnection power by delivering high-quality electric power to energy consumers through efficient and intelligent control systems. To introduce the frequency control of power systems, this paper presents a novel control technique of Fractional Order Integral-Tilt Derivative with Filter (FOI-TDN) controller optimized by the current soft computing technique of hybrid Sine-Cosine algorithm with Fitness Dependent Optimizer (hSC-FDO). For more realistic analysis, practical constraints with nonlinear features, such as controller dead band, communication time delay, boiler dynamics, and generation rate constraint are embedded in the given system model. The proposed approach outperforms some recently developed heuristic approaches such as fitness dependent optimizer, firefly algorithm, and particle swarm optimization for the interconnected power system of two areas with multiple generating units in terms of minimum undershoot, overshoot, and settling time. To improve the system performance, capacitive energy storage devices are used in each area and thyristor control phase shifter is used in the interconnection line of the power system. The potential of the hSC-FDO-based FOI-TDN is demonstrated by comparing it with conventional FOTID/FOPID/PID controllers for two areas with multiple power generators IPS. Finally, a robustness analysis is performed to determine the robustness of the presented control system by varying the system loads and system parameters.

Permanent magnet Vernier (PM-V) machines, at low power levels (few kWs), have shown a great potential to improve the torque density of existing direct-drive PM machines without much compromising on efficiency or making the machine structure more complicated. An improved torque density is very desirable for offshore wind power applications where the size of the direct-drive machine is an increasing concern. However, the relatively poor power factors of the PM-V machines will increase the power converter rating and hence cost. The objective of this paper is to review the benefits and challenges of PM-V machines for direct-drive offshore wind power applications. The review has been presented considering the system-level (direct-drive generator + converter) performance comparison between the surface-mounted permanent magnet Vernier (SPM-V) machines and the conventional SPM machines. It includes the indepth discussion on the challenges facing the PM-V machines when they are scaled up for multi-MW offshore wind power application. Other PM-V topologies discussed in literature have also been reviewed to asses their suitability for offshore wind power application.

The availability of access to Integrated Circuits’ scan chain is an inevitable requirement of modern ICs for testability/debugging purposes. However, leaving access to the scan chain OPEN resulted in numerous security threats on ICs. It raises challenging concerns particularly when the secret asset, like secret information, is placed within the chip, such as the keys of cryptographic algorithms, or similarly logic obfuscation key. So, to combat these threats, numerous secure scan chain architectures have been proposed in the literature to prevent any unauthorized access to the scan chain. They also keep the availability of the scan chain for testability/debugging. In this paper, we first show why a secure scan chain architecture is required when security primitives, like logic obfuscation, are in place. Then, we provide a holistic overview of all secure scan chain architectures starting from preliminary methods introduced when cryptography is in place and the adversary threat model is very limited. It is then followed by newer and more advanced methods introduced when logic obfuscation is in place and the adversary threat model is much stronger. Hence, we have more concentration on the architecture proposed more recently on logic obfuscation. We evaluate all secure scan chain architectures in terms of security and resiliency, testability/debugging time and complexity, and area/power/delay overhead.

The slower than expected adoption rate of blockchain technology has highlighted that there are barriers due to the diversity of its applications and its users. To overcome this limitation and take full advantage of the novel technology, researchers from academia as well as industry are dedicated to find different solutions, where two or more blockchains can interact with each other. As a result, several interoperability solutions have presented themselves. To investigate the functionalities and underlying mechanisms of interoperable blockchain solutions, researchers have conducted several surveys by discussing the features and innovations of these methods. However, the existing surveys tend to focus on the architectural description of the interoperability solutions and completely overlook the most promising aspect of blockchain adaptability, namely the smart contract. This paper fills the gap by exploring the role of smart contracts in blockchain interoperability solutions. Our research has classified the existing interoperability solutions into three main categories: heterogeneous blockchains and homogeneous smart contracts, homogeneous blockchains and homogeneous smart contracts, heterogeneous blockchains, and heterogeneous smart contracts. To provide a systematic overview of the smart contracts used in blockchain interoperability, each category is further divided into subcategories by identifying the functionalities of the smart contract used. Based on our survey, a taxonomy is proposed to help classify the blockchain interoperability solutions. The interoperability solutions in each category are analyzed in-depth, and the results are presented in tabular format to illustrate the characteristics of the interoperability solutions in a meaningful way. Finally, a number of open issues and research directions are discussed to overcome the limitations and improve the performance of blockchain interoperability.

Fuel cell- and electric-powered trucks are promising technologies for zero-emission heavy-duty transportation. Recently, Fuel Cell Trucks (FCT) have gained wider acceptance as the technology of choice for long-distance trips due to their lighter weight and shorter fueling time than electric-powered trucks. Broader adoption of Fuel Cell Trucks (FCT) requires planning strategies for locating future hydrogen refueling stations (HRS), especially for fleets that transport freight along intercity and inter-country highways. Existing mathematical models of HRS placement often focus on inner-city layouts, which make them inadequate when studying the intercity and intercountry FCT operation scale of FCT. Furthermore, the same models rarely consider decentralized hydrogen production from renewable energy sources, essential for decarbonizing the transportation sector. This paper proposes a mathematical model to guide the planning of the hydrogen infrastructure to support future long-haul FCTs. First, the model uses Geographic Information System (GIS) data to determine the HRS’s optimal number and location placement. Then, the model categorizes and compares potential hydrogen production sources, including off-site delivery and on-site solar-to-hydrogen production. The proposed model is illustrated through a case study of the west coastal area of the United States (from Baja California, Mexico to British Columbia, Canada). Different geospatial scenarios were tested, ranging from the current operational distance of FCEV (250km) and future releases of hydrogen FCT (up to 1,500km). Results highlight the capabilities of the model in identifying the number and location of the HRS based on operation distances, in addition to determining the optimal hydrogen production technology for each HRS. The findings also confirm the viability of green hydrogen production through solar energy, which could play a critical role in a low-carbon transportation future.

Among several approaches to privacy-preserving cryptographic schemes, we have concentrated on noise-free homomorphic encryption. It is a symmetric key encryption that supports homomorphic operations on encrypted data. We present a fully homomorphic encryption (FHE) scheme based on sedenion algebra over finite ℤn rings. The innovation of the scheme is the compression of a 16-dimensional vector for the application of Frobenius automorphism. For sedenion, we have p16 different possibilities that create a significant bijective mapping over the chosen 16-dimensional vector that adds permutation to our scheme. The security of this scheme is based on the assumption of the hardness of solving a multivariate quadratic equation system over finite ℤn rings. The scheme results in 256n multivariate polynomial equations with 256+16n unknown variables for n messages. For this reason, the proposed scheme serves as a security basis for potentially post-quantum cryptosystems. Moreover, after sedenion, no newly constructed algebra loses its properties. This scheme would therefore apply as a whole to the following algebras, such as 32-dimensional trigintadunion.

A non-intrusive data collection framework is developed to analyze the desk-level occupancy and energy use patterns of occupants in shared office spaces. The framework addresses the limitations of previous studies in the literature, which either lacked the granularity to study individual occupants' behaviors or relied on data from complex Building Management Systems (BMS). The framework is applied to a shared office space of an academic institution in the United Arab Emirates (UAE), where occupancy, lighting, and plug-load data were collected from individual desks for 6 months. The results highlight weak relationships between the occupancy status and the total electric loads, with 35% of the total electric loads consumed when the area is completely vacant, and 64% of the plug-load energy consumed when the desks were reported as unoccupied. While specific to the studied building, the results highlight the role that a high-resolution data monitoring framework plays in capturing inefficient consumption patterns. The findings also confirm the contribution of occupant behavior (OB) to the energy performance gap commonly observed between predicted and actual energy levels.

Transverse flux permanent magnet (TFPM) machines are a potential candidate for direct-drive wind power application due to high torque and power densities. The operating principle of TFPM machines is firstly described with various topologies available in literature, with the placement of magnets as a criterion for the classification of TFPM machine topologies. This review includes characteristics, power factor improvement, cogging torque minimization, material consideration, modelling techniques, and scalability. Different wind turbine concepts with direct-drive machines, control techniques and power converter topologies are also considered in this paper.

Surface mounted permanent magnet Vernier (SPM-V) machines are known for their high torque density but relatively poor power factor compared to conventional SPM machines. The high torque density feature of the SPM-V machines is desirable for direct-drive offshore wind power applications as it leads to reduced generator size, mass and cost. However, their poor power factor can negatively affect the converter cost and efficiency. This paper compares the system-level performance, including generator active and structural components and converter, between the SPM-V and the conventional SPM generator systems. Four different power ratings, i.e. 0.5MW, 3MW, 5MW and 10MW, have been considered to study the trend of system-level performance with increasing power rating. The study shows that the SPM-V generators can be lighter and cheaper than their conventional SPM counterparts. However, after the consideration of converter cost and efficiency, the conventional SPM generator exhibited slightly better overall performance. Nonetheless, with the development of novel Vernier topologies and reduction in converter costs in the future due to emerging technologies, the Vernier generators can still be competitive for direct-drive offshore wind power applications.

Random forest (RF) is a popular ensemble learning method that is widely used for the analysis of remote sensing images. RF also has connections with the kernel-based method. Its tree-based structure can generate an RF kernel (RFK) that provides an alternative to common kernels such as radial basis function (RBF) in kernel-based methods such as support vector machine (SVM). Using an RFK in an SVM has been shown to outperform both RF and SVM-RBF (i.e., using an RBF kernel in an SVM) in classification tasks with a high number of features. Here, we explore new designs of RFKs for remote sensing image classification. Different RF structural parameters and characteristics are used to generate various RFKs. In particular, we explore the use of RFs depth, the number of branches between terminal nodes of trees, and the predicted class probabilities for designing and evaluating new RFK. Two depth-based kernel are proposed: an RFK at the optimal depth, and a multiscale one created by combining RFKs at multiple depths. We evaluate the proposed kernels within an SVM by classifying a time series of Worldview-2 images, and by designing experiments having a various number of features. Benchmarking the new RFKs against the RBF shows that the newly proposed kernels outperform the RBF kernel for the experiments with a higher number of features. For the experiments with a lower number of features, RFKs and RBF kernel perform at about the same level. Benchmarking against the standard RF also shows the general outperformance of the proposed RFKs in an SVM. In all experiments, the best results are obtained with a depth-optimized RFK.

Paintings evoke certain emotions in the viewers. Colors, shape, texture, and many other factors affect the feeling conveyed by paintings, but colors seem to have a stronger effect due to a century-long study of color-emotion association in various fields of psychology, art, and color science. There are many color-emotion theories and most of them have been implemented, however, the Luscher Color Test is untouched amongst them. Based on several reasons, discussed in detail inside the paper, we believe this theory can cover problems in this domain of emotion extraction. The main motivation for choosing the Luscher test was that this method is designed for personality and mood analysis and it can better study abstract paintings. In this paper, a set of paintings from Iranian-Islamic cultural heritage is chosen as a dataset. We have proposed the L-EEP method based on Culture Technology (CT) concept to extract emotions from paintings with image processing techniques and psychology knowledge. This method extracts specific colors from paintings and by performing the Luscher test automatically, is able to determine eight emotions. For this matter, paintings are assessed in two moods: 1. The full extent of the painting 2. Cropped interest area of the painting that attracts more attention. Then, the color palette which is extracted colors ordered based on their coverage extent enters search engine. The search engine performs the searching process in the 3D knowledge base of Luscher color-emotion layers to extract relative values of emotions in both scenarios. For the evaluation of the results, three steps were taken. First, we compared the output results of ancient Persian painting with literature and text of their background stories. Then a viewer evaluation is done to compare the results with human viewpoint. Finally, a set of modern abstract paintings peer-rated in the IAPS standard system to further examine the proposed method. The results of the three forms of evaluation indicate the applicability of the L-EEP approach.

An important problem of cyber-security is malware analysis. Besides good precision and recognition rate, ideally, a malware detection scheme needs to be able to generalize well for novel malware families (a.k.a zero-day attacks). It is important that the system does not require excessive computation particularly for deployment on the mobile devices. In this paper, we propose a novel scheme to detect malware which we call Malytics. It is not dependent on any particular tool or operating system. It extracts static features of any given binary file to distinguish malware from benign. Malytics consists of three stages: feature extraction, similarity measurement, and classification. The three phases are implemented by a neural network with two hidden layers and an output layer. We show feature extraction, which is performed by tf-simhashing, is equivalent to the first layer of a particular neural network. We evaluate Malytics performance on both Android and Windows platforms. Malytics outperforms a wide range of learning-based techniques and also individual state-of-the-art models on both platforms. We also show Malytics is resilient and robust in addressing zero-day malware samples. The F1-score of Malytics is 97.21% and 99.45% on Android dex file and Windows PE files, respectively, in the applied datasets. The speed and efficiency of Malytics are also evaluated.

Cell planning (CP) is the most important phase in the life cycle of a cellular system as it determines the operational expenditure, capital expenditure, as well as the long-term performance of the system. Therefore, it is not surprising that CP problems have been studied extensively for the past three decades for all four generations of cellular systems. However, the fact that small cells, a major component of future networks, are anticipated to be deployed in an impromptu fashion makes CP for future networks vis-a-vis 5G a conundrum. Furthermore, in emerging cellular systems that incorporate a variety of different cell sizes and types, heterogeneous networks (HetNets), energy efficiency, self-organizing network features, control and data plane split architectures (CDSA), massive multiple input multiple out (MIMO), coordinated multipoint (CoMP), cloud radio access network, and millimetre-wave-based cells plus the need to support Internet of Things (IoT) and device-to-device (D2D) communication require a major paradigm shift in the way cellular networks have been planned in the past. The objective of this paper is to characterize this paradigm shift by concisely reviewing past developments, analyzing the state-of-the-art challenges, and identifying future trends, challenges, and opportunities in CP in the wake of 5G. More specifically, in this paper, we investigate the problem of planning future cellular networks in detail. To this end, we first provide a brief tutorial on the CP process to identify the peculiarities that make CP one of the most challenging problems in wireless communications. This tutorial is followed by a concise recap of past research in CP. We then review key findings from recent studies that have attempted to address the aforementioned challenges in planning emerging networks. Finally, we discuss the range of technical factors that need to be taken into account while planning future networks and the promising research directions that necessitates the paradigm shift to do so.