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THE YEAR IS 2028. IT'S 8 P.M. ON A Wednesday night and you're famished. You're staring wistfully at the only remaining item in your refrigerator: a package of sausages with an unappetizing grayish hue. Ugh. Did they always look like that? Are they still safe to eat? In 2018, you'd have to rely on your sense of smell and take a gamble. But in 2028, you might simply wave your smartphone over the package. The smartphone interrogates the package's embedded sensor, which measures the concentration of gases associated with meat decomposition. The smartphone displays the message "Safe to eat within the next 20 hours," and then offers a list of recipes for cooking with sausages. Too hungry to bother with the recipes, you tear open the package, toss the sausages into a frying pan, and discard the package-along with its sensor technology.

Millions of new research papers are published each year, making it extremely difficult for researchers to find out what they really want. Existing paper recommendation algorithms cannot effectively address the recommendation of newly published papers due to lack of historical information(e.g., citation information; view log), the so-called cold start problem. Furthermore, in most of these studies, papers are considered in homogeneous or bipartite networks. However, in a real bibliographic network, there are multiple types of objects (e.g., researchers, papers, venues, topics) and multiple types of links among these objects. In this paper, we study the problem of new paper recommendation in the heterogeneous bibliographic network, and a new method called HIPRec, i.e., meta-graph based recommendation model, is proposed to solve this problem. First, the top-K most interesting meta-paths are selected based on the training data. Secondly, a greedy method is proposed to select the most significant meta-graphs generated by merging the meta-paths, which can describe more sophisticated semantics between researchers and papers than simple meta-paths. In the meantime, meta-path and meta-graph based topological features are systematically extracted from the network. Lastly, a supervised model is used to learn the best weights associated with different topological features in deciding the researcher-new paper recommendations. We present experiments on a real bibliographic network, the DBLP network, which show the effectiveness of our approach compared to state-of-the-art new paper recommendation methods.

As a tool for human technological advancement, the peer-review system acts as a gateway for ensuring academic paper qualities. However, the system has proven to be slow and expensive. Also, biasedness remains an unsolved problem. Such issues could become a major bottleneck, which can adversely impact research progress and dissemination of knowledge. This paper aims to propose a double-blind paper review system to preserve the authors and reviewers anonymity. This system also addresses issues concerning the reviewers payment, inconsistent review metrics, and biased reviews. The proposed solution utilizes the Hyperledger Fabric blockchain with the InterPlanetary File System (IPFS). The blockchain smart contracts provide a base for financial transactions between paper publishers and the reviewers. Hence, we introduce AcadCoin, a novel cryptocurrency used for supporting said financial transactions. Also, the Hyperledger blockchain provides user access control to achieve double-blindness in reviews. Along with the Hyperledger blockchain, the IPFS is used to store the paper documents, review documents and open metrics documents to reduce the storage requirement of the blockchain. A broad system architecture is constructed to combine the blockchain and the file storage system. This system architecture distributes nodes of the system to related parties. Finally, the blockchain network is implemented and tested using the Hyperledger Composer Playground environment.

This paper considers a cell-free massive MIMO system with multiple-antenna access points and single-antenna users. The APs use conjugate beamforming to beamform the data to all users in the network. Total energy efficiency maximization is investigated. This optimization problem is nonconvex and thus difficult to solve. Existing solutions are based on second-order optimization methods in connection with convex approximations. These methods have been shown to perform very well but their complexity does not scale favorably with the network size. To tackle this issue, in this paper we propose to use a first-order method for nonconvex programming to our energy efficiency problem. Compared to the second-order methods, the proposed method achieves the same performance, while its run time is much faster. Thus, it is considered as a feasible solution for resource allocation in cell-free massive MIMO systems.

In this paper, we describe a distributed ledger re-cycling system to encourage responsible disposal of paper cups. A complete working prototype is described. Real measurements are presented to illustrate the potential suitability of the IOTA based distributed ledger for this application.

First launched nearly three decades ago, PDF, which stands for Portable Document Format, has become one of the most common formats for text documents. Nowadays, PDF is widely used by individuals, companies, and organizations. At most notable academic conferences, including IEEEs, papers are required to be submitted in PDF format. Along with its increasing popularity in recent times, PDF has been a target for many attacks, some of which are called content-masking attacks. Content-masking attacks are based on the idea of causing underlying text, which is extracted by text-mining softwares and read by machines, to be displayed differently to humans. Inspired by the same idea but is designed and implemented differently, our proposed attack is against the paper submission process at large academic conferences, which consists of two subprocesses: plagiarism detection and automatic assignment of reviewers to papers. Our attack overcomes many weaknesses of previous attacks. In addition, we introduce a method to avoid content masking attacks against the automatic reviewers' assignment process by preventing attackers from getting true topic information from PDF documents.

Scientific retrieval systems need to be given domain search terms for searching publications, however, as natural language, search terms provided by users are often fuzzy and limited and some relevant terms are always overlooked in searching. Meanwhile, users always desire to be given domain related keywords to enlighten themselves what other terms can be used for their searching. This paper presents a concepts recommendation model in scientific paper retrieval, in which concepts are extracted from keyword in scientific papers, and some data mining algorithms are used to calculate the similarity between search terms and concepts and do recommendation for users. This model is simple and can be used with small dataset, in which all training data used is from meta data of papers that is easy to acquired. Experimental result hold good precision, which shows that this research not only simplifies searching step and improves the searching quality for users, but also lays the foundation for semantic search.

Computer vision has been studied from many persective. It expands from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The wide usage has attracted many scholars to integrate with many disciplines and fields. This paper provide a survey of the recent technologies and theoretical concept explaining the development of computer vision especially related to image processing using different areas of their field application. Computer vision helps scholars to analyze images and video to obtain necessary information, understand information on events or descriptions, and scenic pattern. It used method of multi-range application domain with massive data analysis. This paper provides contribution of recent development on reviews related to computer vision, image processing, and their related studies. We categorized the computer vision mainstream into four group e.g., image processing, object recognition, and machine learning. We also provide brief explanation on the up-to-date information about the techniques and their performance.

Pervasive Social Networks of Strangers are available constantly, connecting users directly with no preexisting relationships. Such networks demand a high level of privacy, as users should be anonymous and their behaviour unlinkable. However, the anonymity of users could lead to abusive messages, spam, or fraud. A possible solution is to keep track of users' reputations, based on feedback from other users. However, the need to collate feedback on user behaviour to form reputations is at odds with the need for privacy. Anonymous and unlinkable feedback is also required, whilst multiple feedbacks given on the same item should be detected. To resolve this, we use group signatures and direct anonymous attestation, to give a reputation-based messaging scheme.

We argue that as email attacks continue to increase in sophistication, error rates and filter processing times are both likely to increase. We address the problem at its root by introducing the notion of open quarantine, an approach that avoids tradeoffs between filtering precision and delivery delays. This is achieved using a multi-phase filtering approach, combined with the neutralization of messages with undetermined security posture.

Although the Domain Name System (DNS) was designed as a naming system, its features have made it appealing to repurpose it for the deployment of novel systems. One important class of such systems are security enhancements, and this work sheds light on their deployment. We show the characteristics of these solutions and measure reliability of DNS in these applications. We investigate the compatibility of these solutions with the Tor network, signal necessary changes, and report on surprising drawbacks in Tor’s DNS resolution.

We1 propose a real-time system for indoor/outdoor localization of first responders in emergency situations based on networking of wearable devices and cloud-based GNSS (Global Navigation Satellite Systems) augmentation. We present outdoor tests that validate this concept. With good network geometry, accuracy of the indoor positioning is similar to the accuracy commonly achieved outdoors by GNSS.

In this paper, we consider the problem of transmit signal and receive filter design for a phase modulated continuous wave (PMCW) monostatic single-input single-output (SISO) radar system when low-resolution analog-to-digital-convertor (ADC) is used at the receiver. By applying Bussgang decomposition model, the effect of low-resolution ADC is considered in the model, and two cases of colored and white noise are investigated. In the white noise case, it is concluded that the matched filter (MF) is the best filter maximizing the signal-to-interference-plus-noise-ratio (SINR) of the receive filter output. In the presence of colored noise, we propose a cyclic design procedure for maximizing the output SINR. Numerical examples illustrate the superiority of the proposed method in comparison with the well-known m- sequence probing signal with matched or designed receive filter. The effect of different parameters is also verified.

Speech Emotion Recognition (SER) is the technique for determining underlying emotions from speech samples. Usually text transcripts supplement vocal cues and contains additional information that boosts the SER process. An unsupervised Fuzzy Inference System (FIS) is developed for SER, in this paper, that incorporates audio and text features. The extracted features are: pitch, energy and textual sentiment score. The proposed system is based on Mamdani Fuzzy Inference model and is capable of determining four emotions: happy, sad, angry and neutral. Our FIS has three variants based on the sentiment lexicon- AFINN, SentiWordNet and VADER selected for computing textual sentiment score as the text feature. The main highlights of this work are: i) formulation of eleven novel fuzzy rules based on audio and text cues for SER ii) comparative analysis of all variants of our proposed unsupervised FIS with six state-of-the-art supervised machine learning approaches for SER iii) both speaker independent SER and speaker dependent SER are executed iv) investigations reveal that for few speakers in speaker-dependent SER have received higher accuracies than the others and v) the proposed unsupervised FIS can handle multiple datasets without any training while the supervised machine learning algorithms fail for cross-dataset evaluation. The experiments conducted on speech datasets: SAVEE and RAVDESS, indicate that our FIS has achieved higher accuracy and f1-scores as compared to the other state-of-the-art-methods.

With the increasing popularity of optimization algorithms in electromagnetic engineering, it is clear that mixed-variable design problems prevail. This paper shows that the Cross-Entropy (CE) optimization method is intrinsically versatile to handle these and other types of problems. We provide implementation details of two antenna examples optimized by the CE method to demonstrate its elegance and efficiency.

Prof. Mojgan Daneshmand made seminal contributions to the advancements of substrate integrated waveguide (SIW) technology together with her students and colleagues. In this paper, we present our recent work on miniaturized SIW diplexer in memory of Mojgan and her family. The proposed diplexer is centered at 3.45 and 4.9 GHz for 5G sub-6 GHz communications, which is enabled by a common dual half-mode substrate integrated rectangular cavity (HMSIRC) coupled with singlemode cavities in each channel. Besides the elimination of T-junction, HMSIRCs are also introduced for the miniaturization of sub-6 GHz circuits. Additionally, by employing the first two modes in the dual-HMSIRC, a large frequency ratio of 1.42 between 3.45 and 4.9 GHz can be realized, which is difficult to implement with the original whole cavity scheme because of spurious passband problems. A 2nd-order example with ripple fractional bandwidths of 2% and 1.6% for the two channels is synthesized, designed, fabricated, and tested for demonstration.

The main interest of this paper is concept of Compressive Sensing and its application in 1D biomedical signals. The accent is on analyzing several types of biomedical signals and finding domain in which they are sparse. Therefore, we use gradient-based algorithm to reconstruct these biomedical signals with different number of samples. By working with gradient-based algorithm, we presented reconstruction of Electrocardiogram, Electroencephalogram and Electromyogram biomedical signals and drew a conclusion about differences between them. Several sparsifying basis are tested in order to find the most suitable one, for specific signal type, and we verified the whole theory by experimental results.

Speech imitation, that is, the method of converting a source speaker’s utterance in a manner such that it sounds as though it were uttered by the target has been an active and volatile area of research in current times. The paper focuses on the techniques of modifying sounds emitted by the original speaker (also called the source). Speech Imitation involves metamorphosing the source speaker’s speech to present it in a way as though it were produced by the selected speaker (also called the target). Most systems specializing in speech imitation compulsorily necessitate the usage of parallel data to construct natural or unprocessed sounding speech. Examples of such systems would include RBM [1]. However, there are various obstacles associated with parallel data such as it being an arduous task to gather in real-time and the requirement for the vocabulary to be an exact match which perhaps might not be available to train the model. To remove such dependency on parallel data, a recently innovated model, GANs [3] are being explored.

A fully-digital wideband joint communication-radar (JCR) at the millimeter-wave (mmWave) band will simultaneously enable high communication and radar performances with enhanced design flexibility. In this paper, we present a measurement platform with a software-defined architecture to evaluate and demonstrate the performance of these JCR systems using real channel measurements. We develop this platform by extending a mmWave communication set-up with an additional full-duplex radar receiver and by capturing the MIMO JCR channel using a moving antenna on a sliding rail. To characterize the JCR performance, we conduct indoor experiments and apply traditional/advanced processing algorithms on the measured data. The results demonstrate that our testbed at 73 GHz with 2 GHz bandwidth can capture the JCR channel with high range/direction estimation accuracy. The comparison between the communication and radar channel shows the potential for improving JCR performance by exploiting the antenna diversity due to widely-separated communication and radar receivers.

Undeniably, the Internet of Things (IoT) ecosystem continues to evolve at a breakneck pace, exceeding all growth expectations and ubiquity barriers. From sensor to cloud, this giant network keeps breaking technological bounds in several domains, and wireless sensor nodes (motes) are expected to be predominant as the number of IoT devices grows towards the trillions. However, their future in the IoT ecosystem still seems foggy, where several challenges, such as (i) device&rsquo, s connectivity, (ii) intelligence at the edge, (iii) security and privacy concerns, and (iv) growing energy needs, keep pulling in opposite directions. This prospective paper offers a succinct and forward-looking review of recent trends, challenges, and state-of-the-art solutions of low-end IoT motes, where reconfigurable computing technology plays a key role in tomorrow&rsquo, s IoT devices.

Data confidentiality, integrity and authentication are security properties which are often enforced with the generation of secure channels, such as Virtual Private Networks, over unreliable network infrastructures. Traditionally, the configuration of the systems responsible of encryption operations is performed manually. However, the advent of software-based paradigms, such as Software-Defined Networking and Network Functions Virtualization, has introduced new arms races. In particular, even though network management has become more flexible, the increased complexity of virtual networks is making manual operations unfeasible and leading to errors which open the path to a large number of cyber attacks. A possible solution consists in reaching a trade-off between flexibility and complexity, by automatizing the configuration of the channel protection systems through policy refinement. In view of these considerations, this paper proposes a preliminary study for an innovative methodology to automatically allocate and configure channel protection systems in virtualized networks. The proposed approach would be based on the formulation of a MaxSMT problem and it would be the first to combine automation, formal verification and optimality in a single technique.

During the past decade or so, knowledge graphs have stealthily made way into our daily lives, whether through voice assistants (the likes of Alexa, Google Assistant or Siri), spontaneous search results or customized personal shopping experiences through online stores recommenders. Today, we are surrounded and constantly interacting with knowledge graphs on a regular basis. The scope and impact of knowledge graphs and underlying graph databases are still a mystery to people. Considering its smooth entry into our lives, most of us are unaware of how dependent we actually are on the technology. In this paper, we have shown the working of the Google knowledge graph and how the knowledge graph works as the most effective recommendation system. We have discussed how it finds its way through daily life in various fields like Banking, Social media (Facebook, LinkedIn, Netflix), Food application (Uber Eats) and the Healthcare sector.

Botnets continue to be a threat to organizations, thus various machine learning-based botnet detectors have been proposed. However, the capability of such systems in detecting new or unseen botnets is crucial to ensure its robustness against the rapid evolution of botnets. Moreover, it prolongs the effectiveness of the system in detecting bots, avoiding frequent and time-consuming classifier re-training. We present BOTection, a privacy-preserving bot detection system that models the bot network flow behavior as a Markov Chain. The Markov Chain state transitions capture the bots' network behavior using high-level flow features as states, producing content-agnostic and encryption resilient behavioral features. These features are used to train a classifier to first detect flows produced by bots, and then identify their bot families. We evaluate our system on a dataset of over 7M malicious flows from 12 botnet families, showing its capability of detecting bots' network traffic with 99.78% F-measure and classifying it to a malware family with a 99.09% F-measure. Notably, due to the modeling of general bot network behavior by the Markov Chains, BOTection can detect traffic belonging to unseen bot families with an F-measure of 93.03% making it robust against malware evolution.

This paper studies the dirty-paper coding (DPC) based secure transmission in a multiuser broadcast channel. Since the encoding order of DPC determines which information-bearing signals must be treated as noise by potential eavesdroppers, adopting DPC enables the accurate characterization of the intrinsic secrecy as well as secrecy outage of multiuser broadcasting. Furthermore, the information-bearing signals can be designed to provide secrecy in addition to supporting normal (unclassified) transmission. To show this, we consider the scenario where one user requests secure transmission and the other users request normal transmission, and propose a hybrid secure transmission scheme which combines zero-forcing DPC and artificial noise (AN). By solving the secrecy rate maximization problem under constraints on the secrecy outage probability and the normal communication rates, we find that in addition to supporting the normal transmission, the proposed scheme has the potential to achieve a secrecy rate close to that of the traditional AN-based beamforming.

Web of Things (WoT) is the most promising application model of Internet of Things (IoT). Current IoT systems urgently need extendibility and loose coupling, which are easily provided by WoT. However, some concerns about WoT security have been raised by academic researchers as well as industrial engineers. This paper provides a review of WoT literature especially on security issues. Moreover, this paper proposes an architecture that regards smart gateways as ideal devices to achieve WoT security. Smart gateways are classified into five types, and security functions are suggested for each type.

International audience; With the increasing development of handheld devices, Location Based Services (LBSs) became very popular in facilitating users' daily life with a broad range of applications (e.g. traffic monitoring, geo-located search, geo-gaming). However , several studies have shown that the collected mobility data may reveal sensitive information about end-users such as their home and workplaces, their gender, political, religious or sexual preferences. To overcome these threats, many Location Privacy Protection Mechanisms (LPPMs) were proposed in the literature. While the existing LPPMs try to protect most of the users in mobility datasets, there is usually a subset of users who are not protected by any of the existing LPPMs. By analogy to medical research, there are orphan diseases, for which the medical community is still looking for a remedy. In this paper, we present MooD, a fine-grained multi-LPPM user-centric solution whose main objective is to find a treatment to mobile users' orphan disease by protecting them from re-identification attacks. Our experiments are conducted on four real world datasets. The results show that MooD outperforms its competitors, and the amount of user mobility data it is able to protect is in the range between 97.5% to 100% on the various datasets. CCS Concepts • Security and privacy → Pseudonymity, anonymity and untraceability.

This artifact includes source code, scripts and datasets required to reproduce the experimental figures in the evaluation of the MM'18 paper, which is entitled "MiniView Layout for Bandwidth-Efficient 360-Degree Video". The artifact reports the comparison results among the standard cube layout (CUBE), the equi-angular layout (EAC), and the MiniView layout (MVL) in terms of compressed video size, visual quality of views and decoding and rendering time.

In this paper we propose a statistical approach for the optimization of the phased codes used in radar. By considering each code as a realization of a random variable we show that we can build good codes if the distribution of the random variable is suitably chosen. We then consider sequences generated by the skew tent map and we show that for some values of the bifurcation parameter the invariant probability density coincides with the desired one. To confirm this idea we compute the maximal peak of the ambiguity function outside zero relative to the bifurcation parameter of the skew tent map.

This paper outlines the importance of gaining a good knowledge of the underwater channel. The channel is discussed in terms of absorption and scattering with an explanation of the processes involved in both. Then a review of the various techniques used to model these processes is presented. Some results obtained via a Monte-Carlo simulation of the channel are presented and discussed, showing how channels with similar parameters but different scattering conditions can have very different responses.

Spectrum auction has been considered a promising solution to release the under-utilized spectrum from primary spectrum licensee to potential secondary users. In this paper, we introduce an auction-based resource allocation scheme between a spectrum broker and base stations (BS)s that belong to a wireless service provider (WSP). Each BS submits the proportion of resources it wishes to lease from each auctioned spectrum band based on its true demand and the auctioneer's offered price. The spectrum broker leases the resource blocks (RB)s of its under-utilized spectrum bands to the WSP base stations using an iterative resource allocation algorithm. The proposed algorithm is based on a fair spectrum auction. The spectrum broker who manages the under-utilized spectrum resources plays the role of an auctioneer. First, we establish utility functions for the auctioneer and each participating BS. Then we maximize these utilities in iterations in order to achieve optimal price and allocated resources. Furthermore, we introduce a cheat-prevent auction mechanism based on ascending-bid auction to prevent greedy BSs from cheating activities to obtain lower prices for the auctioned resources. Finally we present simulation results on the performance of the proposed resource leasing mechanism.

Information retrieval is the process of obtaining and presenting more related information from the largest collection of information resources according to the user’s need. The tremendous growth in information resources on the Internet makes the information retrieval process a tedious and difficult task for users. Due to information overloading, there is a need for better techniques to retrieve most relevant information from web. This paper presents the information retrieval system by using the PSO algorithm. In presented system, to extract the text from web documents, all html tags are removed. After that stop words and special characters are removed from extracted text for recovering only meaningful contents. TF-IDF concept is used for feature selection. Now PSO optimization technique is used for identifying and refining the features set, these selected features are stored in a database which is used for information retrieval process. In other hand input query is converted into more than one similar semantic query strings. These query strings are compared with the obtained feature sets in the database by using the cosine similarity function. The most similar text is retrieved as an outcome of the information retrieval system.

In order to realize next generation digital terrestrial television broadcasting (DTTB) systems, the currently deployed Integrated Services Digital Broadcasting Terrestrial (ISDB-T) is still essential due to the spectrum-scarcity. This paper proposes a layered division multiplexing (LDM) scheme to simultaneously transmit ISDB-T stream and a secondary data stream over a common frequency band. The next generation DTTB system can be obtained by combining the ISDB-T and the secondary data streams. In the proposed scheme, we deploy the primary layer for transmission of ISDB-T signal and the secondary layer for transmission of secondary data stream both employed space time block coding. In addition, we only adopt partial LDM to reduce the interference between two systems, since the transmission capacity of the secondary layer exceeded the minimum requirement due to reuse of primary layer.

Several general DFE algorithms for multi-sensor systems are reviewed.Some random phenomena induced by networks are summarized.The advances of DFE algorithms for networked systems are reviewed.Some future topics on DFE algorithms for networked systems are provided. In this paper, the advances of distributed fusion estimation (DFE) algorithms for multi-sensor networked systems (NSs) are reviewed. First, several general DFE algorithms are reviewed. Then, some random phenomena in NSs are discussed, including data quantization, random transmission delays, packet dropouts, fading measurements and communication disturbances. Finally, the advances of DFE algorithms for NSs are summarized.

This work presents a first of its kind high-throughput 3-D millimeter-wave imaging system for packaged goods in commerce. We use millimeter waves to image through non-metallic packaging to see the items inside, and permit automated analysis of crucial business and process metrics such as object count, anomaly detection, or estimation of void space inside packages. The presented system achieves system resolutions of δ X = 20.4 mm, δy = 4.3 mm, and δ Z = 8.4 mm, with accuracy of 1 mm in the y direction and 2.5 mm in the z direction. It is shown that the system is not only capable of producing 3-D images of the products inside an optically-opaque corrugated cardboard shipping box, but is also sensitive enough to produce an image of the cardboard itself.

A secure medical image watermarking framework using SVD, DE, step space filling curve and IntDCT is proposed. The proposed algorithm starts with partition of the host medical image into 8×8 blocks and then IntDCT is applied in each block. By collecting integer DC coefficients, a feeble resolution medical image is obtained. SVD is applied on this feeble resolution image. For maintaining a strong relationship between imperceptibility and robustness, the popular differential evolution (DE) technique is chosen to examine the most suitable scaling factors. The singular values of the host image are modified with the singular values of the watermark to insert the watermark in the host image. Here, DE is playing a very important role to determine the most suitable scaling factors to the embedded process for maintaining the dilemma between robustness and imperceptibility without degrading the nobility of medical images. To increase the security of the watermark image, first it is scuffled with the help of step space filling curve before the embedding process. The experimental observations and results proved the efficiency of the proposed framework. It maintains the quality of the watermarked images and the watermark can be extracted from the seriously malformed images.

Survivability is an important component in the requirements in elastic optical networks (EONs). We examine here the significance of network survivability design against multiple-link failures under dedicated protection, shared risk link group (SRLG) and bandwidth squeezing schemes. The proposed mixed integer linear programming (MILP) formulation seeks to derive some different types of protection considering routing, spectrum assignment, grooming, modulation format as well as shared risk link group constraints. The proposed MILP provides efficient survivability results and resource savings (in terms of spectrum) for a full design of modern EONs.

We deal with the waveform design problem for the radar-communications coexistence (RCC) that ensures low levels of waveform correlations and meanwhile implements efficient information embedding (IE) via the fast-time waveform transmission. To tackle the problem, we propose two unimodular waveform designs, in which the integrated sidelobe level (ISL) is evaluated to find the optimal waveform correlations for the radar transmission and the attributes of waveform phases with large degrees of freedom are exploited to embed communication symbols. The first design is an offline strategy simply using the phase initialization for waveform generation to embed communication symbols. It requires only a predefined database that maps the waveform initialization to the emitted version of it, which gives the lowest ISL of the waveform. The second design serves as an online strategy employing the phase difference between any two waveform elements for the implementation of IE, and its accuracy to approaching desirable phases is elaborately transformed into a quadratic form and jointly designed with the minimization of ISL. We formulate this design as a nonconvex optimization problem, whose compound objective involves the outer product of the waveform vector that is to be designed. Moreover, we apply the majorization-minimization technique to solve the formulated problem, based on which we also devise an efficient algorithm that iterates with a closed-form solution. Simulation results verify the effectiveness of the proposed designs for the RCC.

We present a mathematical technique for using Hermite functions to reconstruct various types of images. Even though the functions are not orthogonal, they can be used in image processing. The functions permit us to get good localization of the signals and it is shown that we get better reconstructed image quality for a larger number of Hermite functions used. Finally, we use a Hermit function frame to decompose and reconstruct a few types of images and compare the results for various number of Hermit functions.

With the advent of Network Function Virtualization (NFV) techniques, a subset of the Internet traffic will be treated by a chain of virtual network functions (VNFs) during their journeys while the rest of the background traffic will still be carried based on traditional routing protocols. Under such a multi-service network environment, we consider the co-existence of heterogeneous traffic control mechanisms, including flexible, dynamic service function chaining (SFC) traffic control and static, dummy IP routing for the aforementioned two types of traffic that share common network resources. Depending on the traffic patterns of the background traffic which is statically routed through the traditional IP routing platform, we aim to perform dynamic service function chaining for the foreground traffic requiring VNF treatments, so that both the end-to-end SFC performance and the overall network resource utilization can be optimized. Towards this end, we propose a deep reinforcement learning based scheme to enable intelligent SFC routing decision-making in dynamic network conditions. The proposed scheme is ready to be deployed on both hybrid SDN/IP platforms and future advanced IP environments. Based on the real GEANT network topology and its one-week traffic traces, our experiments show that the proposed scheme is able to significantly improve from the traditional routing paradigm and achieve close-to-optimal performances very fast while satisfying the end-to-end SFC requirements.

A general dynamic resource allocation scheme is introduced, which iteratively reconfigures an elastic optical network according to traffic dynamism and service diversity. As an application example, the scheme is employed for impairment-aware power-efficient resource allocation in a short-haul metro elastic optical network to demonstrate a typical 5% power plus service penalty for compensating bandwidth limitations compared with the corresponding limitless counterpart.

The coverage area of terrestrial 4G/5G networks is usually limited, far from satisfying the communication demand in the remote rural, the post disaster and maritime scenarios. Both satellite and unmanned aerial vehicle (UAV) can be adopted to solve this problem in the 6G era. Towards this end, we consider a cognitive satellite-UAV network (CSUN), where satellite and UAVs are managed in a coordinated manner, and opportunistically share spectrum to alleviate the spectrum scarcity problem. Particularly, we use the UAV swarm to mitigate the satellite-UAV interference. Motivated by practical applications, the limited on-board energy and imperfectly acquired channel state information (CSI) are discussed. We propose a process-oriented optimization scheme to maximize the data transmission efficiency, which jointly optimizes the transmit power and hovering time of UAV swarm for the whole flight process. The scheme takes both energy constraints and interference power constraints into account, and performs in an iterative way. Simulation results demonstrate the superiority of the proposed algorithm, which could be an effective solution for extending the coverage performance of terrestrial 4G/5G networks.

Successive interference cancellation (SIC) decoding scheme is commonly utilized in nonorthogonal multiple access (NOMA) scheme to obtain the data of each user under different channel conditions at acceptable complexity. However, the imperfect SIC would lead to error propagation (EP) problem, deteriorating the error performance of the NOMA scheme. In this work, a SIC-free NOMA scheme is proposed in the pulse modulation-based visible light communication (VLC) systems with two users. The user under worse channel condition adopts on-off keying (OOK) and the other employs multiple pulse position modulation (MPPM). Here, we employ the soft decision decoding (SDD) scheme to decode the MPPM signal of the user under the better channel condition, which could fundamentally alleviate the adverse impact of EP on symbol error rate (SER) performance and is easier and faster to implement than the traditional SIC decoding scheme. The expressions of SER for two users are derived and the Monte Carlo (MC) simulation results are further provided to validate analytical results.

The IP is a primary data plane protocol on the Internet, which has several deficiencies when addressing the needs of modern digital society involving machine-to-machine communication and a remarkably enhanced user experience. New IP is an advanced network protocol specification to modernize the network layer without changing the fundamental Internet architecture. New IP envisions a new header format with 3 functional characteristics, i.e., shipping spec, contract spec, and payload spec. Using these fundamental blocks, New IP proposes a new data plane forwarding paradigm with far more advanced capabilities, such as ManyNets addressing, high precision services and qualitative communications.

The IoT Integration Middleware (IoTIM) assists as an integrating level for distinctive groups of Receptors, Actuators, units, and software. The broking is definitely responsible for getting information right from the linked gateway, digesting the received information, offering the received specifics to connected deals, and managing devices. A gateway can articulate at once by way of the IoT incorporation middleware if it facilitates the suitable spoken switch concept, which contains a related delivery process, like CoAP, HTTP or MQTT. additionally, supplier orientated Architectures (SOA) give favored systems and standards for finding, handling, gain access to control, invocation and structure of service which may easily become presented at the cyberspace, by techniques of leverage as well as, boosting web-affiliated protocols that feature HTTP and so open up model patterns just like XML. Proposed function can be concentrated about middleware broking to offer appropriate multimodal communique.

Full wave solvers today are capable of solving very complex electromagnetic problems and enable the design of complex antennas. They also allow engineers to confidently assess the antenna performance in a real-time environment prior to its fabrication and physical integration with the desired system. In this work, we use the power of full-wave simulators to understand several salient aspects of near-field antenna measurements so as to facilitate the measurement of advanced antennas for emerging technologies. The modeling of the measurement process in a full-wave solver is elaborated in detail, followed by an investigation of two representative aspects of near-field measurements, which are assessing the impact of non-ideal probe and improving the efficiency of phase retrieval algorithms.

The vision of Internet of Things (IoT) has evolved the needs of providing seamless connectivity between devices across the globe. A potential solution to this requirement is to launch a CubeSat constellation that can provide broadband Internet access to rural and undeveloped areas (Internet of Space (IoS)). This has escalated the need for low-cost, low-profile antennas with wide angle beam steering capabilities. In this work, we elaborate on the design of a broadband, circularly polarized reflectarray capable of dynamic beam steering in the frequency range of 17.8-20.2 GHz. The reflectarray unit cell consists of a set of four copies of rotated Archimedean spiral arms, and the desired phase shift is obtained by suitably switching between these arms through PIN diodes. Representative prototypes have been fabricated and measured, and beam scans up to 60° have been achieved.

As the complexity of hardware (sensors, components, antennas) and software (algorithms) increases, it is practical and efficient to manage and process test configuration and data analysis as close to the testbed as possible (inline) instead of offline compute platforms. We present mmMoReEdge, a mmWave modular and reconfigurable testbed inspired by a smart edge networking and communication framework, typically found in IoT devices. In mmMoReEdge, complex signal processing is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. Using a mathematical model for processing time (the proposed figure of merit), we present results which show that mmMoReEdge is 50% to 70% faster as compared to an offline general-purpose processor based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.

Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid’s operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges. This work is financially supported by the Danish project RemoteGRID, which is a ForskEL program under Energinet.dk with grant agreement no. 2016-1-12399.

We overview a recent line of work [Rozhoň and Ghaffari at STOC 2020; Ghaffari, Harris, and Kuhn at FOCS 2018; and Ghaffari, Kuhn, and Maus at STOC 2017], which proved that any (locally-checkable) graph problem that admits an efficient randomized distributed algorithm also admits an efficient deterministic distributed algorithm, thereby resolving several central and decades-old open problems in distributed graph algorithms. We present a short and self-contained version of the proofs, and conclude by discussing several related questions that remain open. This article accompanies a keynote talk of the author at the International Colloquium on Structural Information and Communication Complexity (SIROCCO) 2020. The writing is based on [24, 28, 45] and primarily targets non-experts. ISSN:0302-9743 ISSN:1611-3349

Cloud computing is the newly emerged technology adopted by many organizations due to its different benefits. Unfortunately, despite all the benefits offered by the cloud, there are certain concerns regarding the security issues related to the cloud platform which can threaten its widespread adoption. In this study, we suggest a scalable model to profile and prevent security attacks in the application layer of a cloud environment using an accurate and interpretable machine learning algorithm called regularized class association rules. The proposed model is based, first, on three additional security factors $(k, l$ and $m)$ , second, on the traditional authentication methods such as passwords and biometrics including keystroke to grant access to the cloud services/resources for an authorized user. Moreover, a case study of the proposal is given in order to validate the model and its usefulness. Eventually, a simulation was done to test the model performances.