Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier’s parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological stages.

Multi-party authorization (MPA) typically involves multiple parties to control and grant access to shared data. MPA is used to solve the insider’s attack problem by ensuring that a single authority or party is not acting alone. Currently, almost all existing implementations of MPA are centralized and fall short in providing logs and events related to provenance of granting permissions in a trusted, secure, immutable, auditable, and decentralized manner. Moreover, for sharing data, proxy re-encryption algorithms are often used to give secure access to encrypted shared data. These schemes and algorithms are also centralized and cannot be trusted. In this paper, we propose a fully decentralized blockchain-based solution in which MPA is implemented using Ethereum smart contracts, and proxy re-encryption algorithms (which are computationally expensive) are implemented using multiple oracles to give access to encrypted shared data stored on a public and decentralized storage platform, such as the Interplanetary File Systems (IPFS). The smart contracts help to validate results based on the majority of encrypted results determined by the oracles. For this, we incorporate reputation mechanisms in the proposed smart contracts to rate the oracles based on their malicious and non-malicious behaviors. We present algorithms along with their full implementation, testing, and validation details. We evaluate the proposed system in terms of security, cost, and generalization to show its reliability and practicality. We make the smart contract source code publicly available on Github.

Wu and Verdú developed a theory of almost lossless analog compression, where one imposes various regularity conditions on the compressor and the decompressor with the input signal being modelled by a (typically infinite-entropy) stationary stochastic process. In this work we consider all stationary stochastic processes with trajectories in a prescribed set of (bi-)infinite sequences and find uniform lower and upper bounds for certain compression rates in terms of metric mean dimension and mean box dimension. An essential tool is the recent Lindenstrauss-Tsukamoto variational principle expressing metric mean dimension in terms of rate-distortion functions. We obtain also lower bounds on compression rates for a fixed stationary process in terms of the rate-distortion dimension rates and study several examples.

Job shops are an important production environment for low-volume high-variety manufacturing. Its scheduling has recently been formulated as an Integer Linear Programming (ILP) problem to take advantages of popular Mixed-Integer Linear Programming (MILP) methods, e.g., branch-and-cut. When considering a large number of parts, MILP methods may experience difficulties. To address this, a critical but much overlooked issue is formulation tightening. The idea is that if problem constraints can be transformed to directly delineate the problem convex hull in the data preprocessing stage, then a solution can be obtained by using linear programming methods without much difficulty. The tightening process, however, is fundamentally challenging because of the existence of integer variables. In this paper, an innovative and systematic approach is established for the first time to tighten the formulations of individual parts, each with multiple operations, in the data preprocessing stage. It is a major advancement of our previous work on problems with binary and continuous variables to integer variables. The idea is to first link integer variables to binary variables by innovatively combining constraints so that the integer variables are uniquely determined by the binary variables. With binary and continuous variables only, it is proved that the vertices of the convex hull can be obtained based on vertices of the linear problem after relaxing binary requirements. These vertices are then converted to tight constraints for general use. This approach significantly improves our previous results on tightening individual operations. Numerical results demonstrate significant benefits on solution quality and computational efficiency. This approach also applies to other ILP problems with similar characteristics and fundamentally changes the way how such problems are formulated and solved.

Digital healthcare technologies are transforming the face of prosthetic care. Millions of amputees around the world do not currently have access to any form of prosthetic healthcare. However, digital technologies provide a promising solution. Digital healthcare technologies have the potential to augment the range and efficiency of prosthetists so they can reach more patients. These technologies will enable affordable prostheses to be built on a scale larger than currently possible with today’s clinical practices. In this paper, we explore the social aspects of amputation as a global issue, describe current practices for designing and manufacturing prosthetic sockets, and examine shifting trends towards virtual care models. Importantly, we assess the technologies used in these virtual health workflows to understand their critical needs. Large technological gaps need to be overcome in order to enable the mass production and distribution of prostheses digitally. However, recent advances in computational methods and CAD/CAM technologies are bridging this gap faster than ever before. We foresee that these technologies will return mobility and economic opportunity to amputees on a global scale in the near future.

Knowing the physical properties and scattering mechanisms contributes to Synthetic Aperture Radar (SAR) image interpretation. For single-polarized SAR data, however, it is difficult to extract the physical scattering mechanisms due to lack of polarimetric information. Time-frequency analysis (TFA) on complex-valued SAR image provides extra information in frequency perspective beyond the ’image’ domain. Based on TFA theory, we propose to generate the sub-band scattering pattern for every object in complex-valued SAR image as the physical property representation, which reveals backscattering variations along slant-range and azimuth directions. In order to discover the inherent patterns and generate a scattering classification map from single-polarized SAR image, an unsupervised hierarchical deep embedding clustering algorithm based on time-frequency analysis (HDEC-TFA) is proposed to learn the embedded fea- tures and cluster centers simultaneously and hierarchically. The polarimetric analysis result for quad-pol SAR images is applied as reference data of physical scattering mechanisms. In order to compare the scattering classification map obtained from single- polarized SAR data with the physical scattering mechanism result from full-polarized SAR, and to explore the relationship and similarity between them in a quantitative way, an information theory based evaluation method is proposed. We take Gaofen- 3 quad-polarized SAR data for experiments and the results and discussions demonstrate that the proposed method is able to learn valuable scattering properties from single-polarization complex- valued SAR data, and to extract some specific targets as well as polarimetric analysis. At last, we give a promising prospect to future applications.

A document by Tanweer Alam. Click on the document to view its contents.

The wireless communication is making it easier for smart devices to communicate with one another in terms of the network of the Internet of Things. Smart devices are automatically linked and built up a network on their own. But there are more obstacles to safe access within the network itself. Mobile devices such as smart home automation access point, smart washing machines, mobile boards, temperature sensors, color-changing smart lighting, smartphones, wearable devices, and smart appliances, etc. are widespread in our daily lives and is becoming valuable tools with wireless communication abilities that are using specific wireless standards that are commonly used with IEEE 802.11 access points. On the realism of the Internet, security has been perceived as a prominent inhibitor of embracing the cloud paradigm. It is resource storage and management that may lay in any since the cloud environment is a distributed architecture, which place of the world, many concerns have been raised over its vulnerabilities, security threats and challenges. The involvement of various parties has widened these concerns based on each party’s perspective and objective. The Cloud point of view we mainly discuss the causes of obstacles and challenges related to security, reliability, privacy and service availability. The wireless communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in constant growth in the cloud’s use and popularity. Our purpose of this study is to create a framework of mobile ad hoc network mobility model using cloud computing for providing secure communication among smart devices network for the internet of things in 5G heterogeneous networks. Our main contribution links a new methodology for providing secure communication on the internet of smart devices in 5G. Our methodology uses the correct and efficient simulation of the desired study and can be implemented in a framework of the Internet of Things in 5G.

This paper presents an electromagnetic inversion algorithm for the design of cascaded metasurfaces that enables the design process to begin from more practical output field specifications such as a desired power pattern or far-field performance criteria. Thus, this method combines the greater field transformation support of multiple metasurfaces with the flexibility of the electromagnetic inverse source framework. To this end, two optimization problems are formed: one associated with the interior space between two metasurfaces, and the other for the exterior space. The cost functionals corresponding to each of these two optimization problems are minimized using the nonlinear conjugate gradient algorithm with analytic expressions for the gradient operators. A total variation regularizer is incorporated into the optimization procedure to favour smooth field variations from one unit cell to the next. The numerical implementation of the developed design procedure is presented in detail along with several two-dimensional (2D) simulated examples to demonstrate the capabilities of the method.

Tucker decomposition is a standard method for processing multi-way (tensor) measurements and finds many applications in machine learning and data mining, among other fields. When tensor measurements arrive in a streaming fashion or are too many to jointly decompose, incremental Tucker analysis is preferred. In addition, dynamic basis adaptation is desired when the nominal data subspaces change. At the same time, it has been documented that outliers in the data can significantly compromise the performance of existing methods for dynamic Tucker analysis. In this work, we present Dynamic L1-Tucker: an algorithm for dynamic and outlier-resistant Tucker analysis of tensor data. Our experimental studies on both real and synthetic datasets corroborate that the proposed method (i) attains high basis estimation performance, (ii) identifies/rejects outliers, and (iii) adapts to nominal subspace changes.

We present a method that operates an electrolyzer to meet the demand of a hydrogen refueling station in a cost-effective manner by solving a model-based optimal control problem. To formulate the underlying problem, we first conduct an experimental characterization of a Siemens SILYZER 100 polymer electrolyte membrane electrolyzer with \SI{100}{\kilo \watt} of rated power. We run experiments to determine the electrolyzer's conversion efficiency and thermal dynamics as well as the overload-limiting algorithm used in the electrolyzer. The resulting detailed nonlinear models are used to design a real-time optimal controller, which is then implemented on the actual system. Each minute, the controller solves a deterministic, receding-horizon problem which seeks to minimize the cost of satisfying a given hydrogen demand, while using a storage tank to take advantage of time-varying electricity prices and photovoltaic inflow. We illustrate in simulation the significant cost reduction achieved by our method compared to others in the literature, and then validate our method by demonstrating it in real-time operation on the actual system.

This paper introduces a new equivalent circuit for linear direct current networks consisting of independent voltage and current sources, and resistors, which represents all the power dissipated internally in the resistors. It is demonstrated that the internal losses of any network have two components. One is variable and dependent on the internal resistances of the actual circuit and the power transferred to the pair of accessible terminals. The other is constant and dependent only on the internal voltage and current sources and the resistances of the actual network. It is also demonstrated theoretically and validated by numerical simulation that the traditional Thevenin and Norton equivalent circuits are particular cases of the proposed equivalent circuit in this paper. The proposed equivalent circuit can be used to analyze power and efficiencies of the actual network.

Next word prediction is an input technology that simplifies the process of typing by suggesting the next word to a user to select, as typing in a conversation consumes time. A few previous studies have focused on the Kurdish language, including the use of next word prediction. However, the lack of a Kurdish text corpus presents a challenge. Moreover, the lack of a sufficient number of N-grams for the Kurdish language, for instance, five grams, is the reason for the rare use of next Kurdish word prediction. Furthermore, the improper display of several Kurdish letters in the Rstudio software is another problem. This paper provides a Kurdish corpus, creates five, and presents a unique research work on next word prediction for Kurdish Sorani and Kurmanji. The N-gram model has been used for next word prediction to reduce the amount of time while typing in the Kurdish language. In addition, little work has been conducted on next Kurdish word prediction; thus, the N-gram model is utilized to suggest text accurately. To do so, R programming and RStudio are used to build the application. The model is 96.3% accurate.

The COVID-19 is an exponentially growing disease that has intentioned nations to use technologies to detect the coronavirus infection. Several nations are working greatly to fight against COVID-19. Many nations have been using a range of devices to combat the pandemic, seeking information about growth, monitoring as well as the leaking the confidential information of the residents. This research aims to assist infected people online using the Internet of Things (IoT) and Blockchain technologies through smart devices. IoT-based healthcare devices gather useful information, provide additional insight through symptoms and behaviors, allow remote monitoring, and simply give people better self - determination and healthcare. Blockchain allows the secure transfer of patient health information, regulates the medical distribution network. A four-layer architecture is proposed using IoT and Blockchain to detect and prevent individuals to be COVID 19. This research provides a framework for patients with COVID-19 infectious disease and recognizes health issues and diagnoses online. Smart devices such as smartphones can install any mobile apps such as Aarogya Setu, Tawakkalna, and so on. These applications can track COVID-19 patients properly. The installation of mobile apps on smart devices focuses to reduce the time and cost and increase the performance of the infectious patient’s condition. A four-layer architecture is proposed using IoT and Blockchain technologies. Many research works focus on investigating, analyzing, and highlighting the affected individuals through guiding the COVID-19 infection. Eventually, various mobile apps are recognized and addressed in this paper.

Hybrid digital and analog beamforming is an emerging technique for high-data-rate communication at millimeter-wave (mm-wave) frequencies. Experimental evaluation of such techniques is challenging, time-consuming, and costly. This article presents a hardware-oriented modeling method for predicting the performance of an mm-wave hybrid beamforming transmitter. The proposed method considers the effect of active circuit nonlinearity as well as the coupling and mismatch in the antenna array. It also provides a comprehensive prediction of radiation patterns and far-field signal distortions. Furthermore, it predicts the antenna input active impedance, considering the effect of active circuit load-dependent characteristics. The method is experimentally verified by a 29-GHz beamforming subarray module comprising an analog beamforming integrated circuit (IC) and a 2 × 2 subarray microstrip patch antenna. The measurement results present good agreement with the predicted ones for a wide range of beam-steering angles. As a use case of the model, far-field nonlinear distortions for different antenna array configurations are studied. The demonstration shows that the variation of nonlinear distortion versus steering angle depends significantly on the array configuration and beam direction. Moreover, the results illustrate the importance of considering the joint operation of beamforming ICs, antenna array, and linearization in the design of mm-wave beamforming transmitters.

Corrective transmission system operation can help integrate more renewable energy sources and save redispatch costs by providing a higher utilization of the power grid. However, reliable and fast provision of flexibility are key to achieve corrective operation. This work develops a new method to determine if flexibility from distribution grids is available on transmission corridors when needed. An analysis of the German energy system in the year 2030 is performed to estimate the potential of different flexibility options and shows the potential flexibility distribution systems can contribute to a corrective transmission system operation.

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

Currently, the COVID‐19 has directly affected the millions of humans lives. The symptoms of the disease involving fever, malaise, chest infection, and breathing difficulties, were identified, and its existence is continuously becoming restructured. The World Health Organization (WHO) had mentioned the wide diagnostics test besides COVID-19 that would also assist medical facilities to recognize infectious diseases as well as currently focusing efficiently on preventing and afterward defeating this viral disease. The infection is usually transmitted among human beings in direct contact, greatest through the liquid bubbles generated through cough, sneeze, or speaking. This paper reviews the COVID 19 pandemic, its history, current updates, contact tracing applications, and use of emerging technologies like the Internet of Things (IoT) and Blockchain for stopping the spreading and provide service online to the patient from a distance.