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To investigate whether a humanoid robot in a clinical radiological setting is accepted as a source of information in conversations before MRI examinations of patients. In addition, the usability and the information transfer were compared with a tablet. Patients were randomly assigned to a robot or tablet group with their consent prior to MRI. The usability of both devices was compared with the extended System Usability Scale (SUS) and the information transfer with a knowledge query. Reasons for refusal were collected by a non-responder questionnaire. At the University Hospital Halle 117 patients were included for participation. There was no statistically significant difference in gender and age. Of 18 non-responders, 4 refused to participate partly because of the robot; for another 3 the reason could not be clarified. The usability according to SUS score was different with statistical significance between the groups in the mean comparison and was one step higher for the tablet on the adjective scale. There was no statistically significant difference in knowledge transfer. On average, 8.41 of 9 questions were answered correctly. This study is the first application, in a clinical radiological setting, of a humanoid robot interacting with patients. Tablet and robot are suitable for information transfer in the context of MRI. In comparison to studies in which the willingness to interact with a robot in the health care sector was investigated, the willingness is significantly higher in the present study. This could be explained by the fact that it was a concrete use case that was understandable to the participants and not a hypothetical scenario. Thus, potentially high acceptance for further specific areas of application of robots in radiology can be assumed. The higher level of usability perceived in the tablet group can be explained by the fact that here the interface represents a form of operation that has been established for years in all population groups. More frequent exposure to robots could also improve the response in the future.· patients accept humanoid robots in clinical radiologic situations. · at present they can only convey information as well as an inexpensive tablet. · future systems can relieve the burden on personnel..· Stoevesandt D, Jahn P, Watzke S et al. Comparison of Acceptance and Knowledge Transfer in Patient Information Before an MRI Exam Administered by Humanoid Robot Versus a Tablet Computer: A Randomized Controlled Study. Fortschr Röntgenstr 2021; 193: 947 - 954.ZIEL: Untersucht wurde, ob ein humanoider Roboter im klinisch-radiologischen Setting als Informationsgeber im Gespräch vor MRT-Untersuchungen von Patienten akzeptiert wird. Ergänzend wurden die Benutzerfreundlichkeit und die Informationsvermittlung mit einem Tablet verglichen. Patienten wurden bei Einwilligung vor einem MRT randomisiert einer Roboter- oder Tablet-Gruppe zugeteilt. Die Handhabbarkeit beider Geräte wurde mit der erweiterten System Usability Scale (SUS) und die Informationsvermittlung mit einer Wissensabfrage verglichen. Ablehnungsgründe wurden per Non-Responder-Fragebogen erhoben. Im Universitätsklinikum Halle wurden 117 Patienten zur Teilnahme eingeschlossen. Bei Geschlecht und Alter gab es keinen statistisch signifikanten Unterschied. Von 18 Non-Respondern lehnten 4 die Teilnahme teilweise wegen des Roboters ab; bei weiteren 3 konnte der Grund nicht geklärt werden. Die Handhabbarkeit nach SUS-Score war zwischen den Gruppen im Mittelwertvergleich statistisch signifikant unterschiedlich und war für das Tablet um eine Stufe auf der Adjektiv-Skala höher. Bei der Wissensvermittlung gab es keinen statistisch signifikanten Unterschied; im Mittel wurden 8,41 von 9 Fragen korrekt beantwortet. Diese Studie ist der erster Anwendungsfall im klinisch-radiologischen Setting eines humanoiden Roboters, der mit Patienten interagierte. Tablet und Roboter sind zur Informationsvermittlung im Kontext eines MRT geeignet. Im Vergleich zu Studien, in denen nach der Bereitschaft zur Interaktion mit einem Roboter im Gesundheitswesen gefragt wurde, ist diese Bereitschaft in der hier vorliegenden Studie deutlich höher. Dies könnte dadurch erklärt werden, dass es sich um einen konkreten, für die Teilnehmer begreiflichen Anwendungsfall handelte und nicht um ein hypothetisches Szenario. Damit ist von einer potenziell hohen Akzeptanz für weitere spezifische Einsatzgebiete von Robotern in der Radiologie auszugehen. Der in der Tablet-Gruppe wahrgenommene höhere Bedienkomfort lässt sich dadurch erklären, dass hier das Interface eine seit Jahren in allen Bevölkerungsgruppen etablierte Bedienform darstellt. Eine häufigere Exposition mit einem Roboter könnte auch hier die Response zukünftig verbessern.· humanoide Roboter werden von Patienten in klinisch radiologischen Situationen akzeptiert. · aktuell können sie Informationen dabei jedoch nur so gut wie ein preisgünstiges Tablet vermitteln. · zukünftige Systeme können das Personal entlasten..· Stoevesandt D, Jahn P, Watzke S et al. Comparison of Acceptance and Knowledge Transfer in Patient Information Before an MRI Exam Administered by Humanoid Robot Versus a Tablet Computer: A Randomized Controlled Study. Fortschr Röntgenstr 2021; 193: 947 – 954.

Purpose This paper aims to propose a space-oriented solution as an interface enabling the knowledge transfer between the building and the facilities management (FM) industry. Design/methodology/approach The research gap is explored based on practical investigations in six large-scale hospitals in China. The theory of engineering systems integration inspires the proposed solution. A practical scenario is demonstrated to explain the workflow of this solution. Findings It is founded that lagging information updates of FM systems in hospital project are one of the main reasons for inefficient and costly FM workflow. Building information modelling (BIM) model could provide accurate building information to the FM systems at the building handover stage. However, few researchers focus on the continuous information transfer solution from the BIM model to FM systems during the building in-use phase. An interface should be established for the “conversation” between the frequent changes of building and the FM systems in the post-construction period. Practical implications The information updates in three FM systems due to space changes in a hospital project is considered as a practical scenario in this paper. It is presented with the workflow and the data logic chain. Originality/value The originality of this paper is to propose a solution to integrate the space information provided by the BIM model with the parameters of particular FM systems. This solution deploys a BIM model for the FM industry. The solution could allow the FM personnel to ease operations and maintenance workflow by updating the space information in the BIM model.

Corticomuscular coupling reflects nonlinear interactions and multi-layer neural information transmission between the motor cortex and effector muscle in the sensorimotor system. Transfer spectral entropy (TSE) method has been used to describe corticomuscular coupling within single scale. As an extension of TSE, multiscale transfer spectral entropy (MSTSE) is proposed in this paper to depict multi-layer of neural information transfer between two coupling signals. The reliability and effectiveness of MSTSE were verified on data generated by nonlinear numerical models and those of a force tracking task. Compared with TSE, MSTSE is more robust to the embedding dimension and performs optimally in the detection of the coupling properties. Further analysis of the physiological signals showed that the MSTSE provided more detailed band characteristics than the single scale TSE measurement. MSTSE indicates significant coupling scattered in alpha, beta and low gamma bands during the force tracking task. Besides, the coupling strength in the descending direction of the beta band was significantly higher than that in the ascending direction. This study constructs multi-scale coupling information to provide a new perspective for exploring corticomuscular interaction.

Event causality identification is an important research task in natural language processing . Existing methods largely focus on identifying explicit causal relations , and give poor performance in implicit causalities, especially in the document level. In this paper, we formalize event causality identification as a graph-based edge prediction problem and propose a novel document-level context-based graph inference mechanism. Specifically, we use attention-based neural networks to automatically extract document-level contextual information, and a direction-sensitive graph inference mechanism to achieve information transfer and interaction among event causalities. Experimental results on the EventStoryLine v1.5 dataset show that our approach outperforms previous methods and baseline systems by a large margin in F1-score metrics (2.45% improvement on intra-sentence causalities and 3.08% improvement on cross-sentence causalities). Further analysis demonstrates that our model can effectively capture the document-level contextual information and latent causal information among events.

The automatic semantic segmentation of the huge amount of acquired remote sensing data has become an important task in the last decade. Images and Point Clouds (PCs) are fundamental data representations, particularly in urban mapping applications. Textured 3D meshes integrate both data representations geometrically by wiring the PC and texturing the surface elements with available imagery. We present a mesh-centered holistic geometry-driven methodology that explicitly integrates entities of imagery, PC and mesh. Due to its integrative character, we choose the mesh as the core representation that also helps to solve the visibility problem for points in imagery. Utilizing the proposed multi-modal fusion as the backbone and considering the established entity relationships, we enable the sharing of information across the modalities imagery, PC and mesh in a twofold manner: (i) feature transfer and (ii) label transfer. By these means, we achieve to enrich feature vectors to multi-modal feature vectors for each representation. Concurrently, we achieve to label all representations consistently while reducing the manual label effort to a single representation. Consequently, we facilitate to train machine learning algorithms and to semantically segment any of these data representations – both in a multi-modal and single-modal sense. The paper presents the association mechanism and the subsequent information transfer, which we believe are cornerstones for multi-modal scene analysis. Furthermore, we discuss the preconditions and limitations of the presented approach in detail. We demonstrate the effectiveness of our methodology on the ISPRS 3D semantic labeling contest (Vaihingen 3D) and a proprietary data set (Hessigheim 3D).

The paper considers the problem of analysis of the information transmission process by multi-element communication systems in presence of a multipath signal propagation channel. To generalize the propagation effects, the model of the κ–μ fading channel with correlated shadowing was assumed, and the technology used for organizing a multi-element system was the SIMO system, equipped with the maximum-ration combiner of the signal on the receiving side. To describe the characteristics of the information transfer process, an approach based on the higher-order statistics of the ergodic capacity was used. Closed-form analytical expressions for arbitrary-order capacity higher-order statistics were obtained for the channel model under consideration. The behavior of the first four statistics (ergodic capacity, its reliability, skewness and kurtosis coefficients) is analyzed depending on the channel parameters (the number of multipath propagation clusters, the ratio of power of the dominant components to the total power of multipath waves, the degree of shadowing of the dominant components, and the shadowing correlation coefficient). Within the framework of the study, 4 distinct situations of the assumed channel model behavior were considered, which significantly differ in their properties. It is noted that, in contrast to the capacity, its higher-order statistics are significantly more sensitive to the channel parameters and, as a result, are more significant indicators of fluctuations in the information transfer rate within the communication channel. The existence of a pronounced extremum (minimum) of the reliability ergodic capacity dependence from the signal-to-noise ratio was established. It should be accounted for in practical applications, when the requirements of the signal-to-noise ratio that guarantees the desired communication link quality are set.

Less-than-truckload (LTL) transportation offers fast, flexible and relatively low-cost transportation services to shippers. In order to cope with the effects of economic recessions, the LTL industry implemented ideas such as reducing excess capacity and increasing revenues through better yield management. In this paper, we extend these initiatives beyond the reach of individual carriers and propose a collaborative framework that facilitates load exchanges to reduce the operational costs. Even though collective solutions are proven to provide benefits to the participants by reducing the inefficiencies using a system-wide perspective, such solutions are often not attainable in real-life as the negotiating parties are seeking to maximize their individual profits rather than the overall profit and also they are unwilling to share confidential information. Therefore, a mechanism that enables collaboration among the carriers should account for the rationality of the individual participants and should require minimal information transfer between participants. Having this in mind, we propose a mechanism that facilities collaboration through a series of load exchange iterations and identifies an equilibrium among selfish carriers with limited information transfer among the participants. Our time-efficient mechanism can handle large instances with thousands of loads as well as provide significant benefits over the non-collaborative management of LTL networks.

This article proposes a novel and computationally efficient explicit intertask information transfer strategy between optimization tasks by aligning the subspaces. In evolutionary multitasking, the tasks might have biases embedded in function landscapes and decision spaces, which often causes the threat of predominantly negative transfer. However, the complementary information among different tasks can give an enhanced performance of solving complicated problems when properly harnessed. In this article, we distill this insight by introducing an intertask knowledge transfer strategy implemented in the low-dimension subspaces via a learnable alignment matrix. Specifically, to unveil the significant features of the function landscapes, the task-specific low-dimension subspaces is established based on the distribution information of subpopulations possessed by tasks, respectively. Next, the alignment matrix between pairwise subspaces is learned by minimizing the discrepancies of the subspaces. Given the aligned subspaces by applying the alignment matrix to subspaces’ base vectors, the individuals from different tasks are then projected into aligned subspaces and reproduce therein. Moreover, since this method only considers the leading eigenvectors, it turns out to be intrinsically regularized and noise-insensitive. Comprehensive experiments are conducted on the synthetic and practical benchmark problems so as to assess the efficacy of the proposed method. According to the experimental results, the proposed method exhibits a superior performance compared with existing evolutionary multitask optimization algorithms.

In the next generation of mobile communication networks, unprecedented challenges are required to be met, such as much higher data rates and spectrum efficiency, lower latency, and massive connectivity. Non-orthogonal multiple access (NOMA) has recently been proposed as a promising technology to achieve much superior spectral efficiency compared to conventional orthogonal multiple access techniques employed in present communication systems. A salient feature of NOMA is its use of successive interference cancellation (SIC) to decode users’ information when multiple users are allowed to transmit in same time/frequency/code domain. In this paper, we aim to exploit an aspect of SIC, namely the availability of other users’ data to realize a cooperative NOMA system. EXtrinsic information transfer (EXIT) charts are utilized to examine the performance of proposed system in terms of user fairness while employing IRregular convolutional codes (IRCC)s. The EXIT chart using IRCC evaluates the convergence analysis for the proposed system. Further, to evaluate the system performances in cooperative NOMA system, we have derived the expressions for the achievable rates which are obtained independently and utilized them in evaluating the experimental data for the proposed NOMA model.

One of the main issues regarding the implementation of source-separation-based sanitation systems is user acceptance, which is related directly to the state of knowledge of citizens. A communication platform for the exhibition and interactive information on the topic of resource-oriented sanitation (ROS) was developed as part of the P-BANK transdisciplinary project. The P-BANK platform is based on a toilet in the public space. The communication concept focusses on the actual use of the toilets and the surrounding nutrient cycle. The central object of the communication at the P-BANK, as its name suggests, is the macronutrient phosphorus. This simplification creates a symbolic effect which is supported by the ambiguity of the sound of the chemical symbol “P” for phosphorus in connection with urinating (to pee). Furthermore, this reduction aims at shortening the messages and simplifying the information transfer. In the first project phase, the transportable system was field-tested and evaluated in the context of the 100th anniversary of the Bauhaus in Weimar. After positive reviews and experiences concerning the performance of the technical facilities and the success of the communication concept, the P-BANK will be tested and evaluated in further awareness-raising campaigns.

In order to alleviate the scale variation problem in object detection, many feature pyramid networks are developed. In this paper, we rethink the issues existing in current methods and design a more effective module for feature fusion, called multiflow feature fusion module (MF3M). We first construct gate modules and multiple information flows in MF3M to avoid information redundancy and enhance the completeness and accuracy of information transfer between feature maps. Furtherore, in order to reduce the discrepancy of classification and regression in object detection, a modified deformable convolution which is termed task adaptive convolution (TaConv) is proposed in this study. Different offsets and masks are predicted to achieve the disentanglement of features for classification and regression in TaConv. By integrating the above two designs, we build a novel feature pyramid network with feature fusion and disentanglement (FFAD) which can mitigate the scale misalignment and task misalignment simultaneously. Experimental results show that FFAD can boost the performance in most models.

When glancing at an image, human can infer what is hidden in the image beyond what is visually obvious, such as objects’ functions, people’s intents and mental states. However, such a visual reasoning paradigm is tremendously difficult for computer, requiring knowledge about how the world works. To address this issue, we propose Commonsense Knowledge based Reasoning Model (CKRM) to acquire external knowledge to support Visual Commonsense Reasoning (VCR) task, where the computer is expected to answer challenging visual questions. Our key ideas are: (1) To bridge the gap between recognition-level and cognition-level image understanding, we inject external commonsense knowledge via multi-level knowledge transfer network , achieving cell-level, layer-level and attention-level joint information transfer. It can effectively capture knowledge from different perspectives, and perceive common sense of human in advance. (2) To further promote image understanding at cognitive level, we propose a knowledge based reasoning approach , which can relate the transferred knowledge to visual content and compose the reasoning cues to derive the final answer. Experiments are conducted on the challenging visual commonsense reasoning dataset VCR to verify the effectiveness of our proposed CKRM approach, which can significantly improve reasoning performance and achieve the state-of-the-art accuracy.

To develop sustainable fifth generation (5G) wireless networks and utilize the unused spectrum, this paper focuses on cognitive radio (CR) based wireless information and energy transmissions from an unmanned aerial vehicle (UAV) to multiple low-power ground terminals (GTs). By practically considering the location-dependent air-to-ground (A2G) channel states and the non-linear energy harvesting (EH), we propose a dynamic fly-hover-transmit scheme, where the UAV successively flies between GTs, and hovers close to each GT for efficient wireless energy transfer (WET) or wireless information transfer (WIT) when the primary user (PU) is idle. By causally and optimally determining the UAV’s mobility and transmit power for each selected transmission mode (WIT, WET, or being silent), we formulate the UAV’s sum-throughput maximization over all GTs as a constrained Markov decision process (MDP) problem with battery energy constraints at all GTs and the UAV. Due to the infinitely large MDP system state space, this problem is difficult to solve. We then decompose this problem into two subproblems, by first deciding the UAV’s transmission mode and power above a given GT, and then optimizing the UAV movement policy over multiple GTs. In the first subproblem, we propose an approximate to the complicated MDP value function of low complexity in closed-form, and then analytically derive the threshold-based suboptimal transmission policies. In the second subproblem, we optimally solve a simple-but-fundamental two-GT case, and then extend the general location-dependent GT weight design to an efficient suboptimal UAV movement policy. Simulation results show the significantly improved system performance under the proposed suboptimal policies over various benchmarks in dynamic networks.

This paper addresses the issue of developing a computerized system for processing information in the construction of the trajectory of an unmanned aircraft vehicle (UAV), a remotely-piloted aviation system (RPAS), or another robotic system. Resolving this task involves the neural network learning algorithms based on the mathematical model of movement. The construction of such a trajectory between two specified destinations has been considered that provides for the possibility of bypassing static and dynamic obstacles. The specified trajectory is divided into several smaller parts. The possibility of restructuring when changing the position of obstacles in space has been considered. A UAV flight control algorithm has been developed, which implies training a neural network for bypassing obstacles of different sizes. To predict the development of the situation when an object moves between two specified points in space, it is proposed to use the Q-Learning algorithm. It has been shown that the smallest number of steps required for moving along a specified trajectory is 18, the largest is 273 steps. In case of distortion during data transmission, the training of the neural network makes it possible to reduce the possibility of collision with obstacles by improving the accuracy and speed of information transfer between the on-board computer and operator. A system of the video support to moving objects was modeled; dependence charts of the normalized frame size at different parameter values were built. Using the charts makes it possible to determine the function of the maneuver intensity. Existing neural network learning methods such as CNN and LSTM were compared. It has been proven that the success rate reaches 74 % when using CNN only, while it amounts to 92 % at the hybrid application of CNN+LSTM. The simulation results have demonstrated the high efficiency of the developed algorithm

Information about invasive species needs to be spread rapidly across a wide geographic area following an invasion. However, in-person events can be time-consuming and costly for the participants, organizers, and presenters. Online programming like webinars can bridge this gap, but there is limited published data on how best to run these programs. We report on a 10-yr webinar program, Emerald Ash Borer University, and offer suggestions for improving their effectiveness as a communication tool. Webinar participants viewed the webinars positively and undertook recommended management actions. In addition, most of our survey respondents extended the reach of this program by widely sharing the information from the webinars. Posting the webinars on popular streaming platforms greatly extended their reach long after the live viewing event. Despite their longevity, viewers of recorded videos watched them differently than those viewing live events. We suggest modifying the format of future webinar presentations to accommodate these differences to improve information transfer.

Due to the low efficiency of energy harvest (EH) for the traditional wireless powered communication network (WPCN), we propose a communication scheme based on unmanned aerial vehicle (UAV)-enabled WPCN, which can greatly improve the EH efficiency problem. In this paper, the UAV trajectory, time allocation, transmit power of UAVs, scheduling of wireless information transfer (WIT) and wireless power transfer (WPT) are jointly optimized to minimize the whole energy consumption. To solve the formulated non-convex problem with multiple constraints and attain locally optimal solution, an iterative algorithm is proposed. Simulations show that our proposed algorithm is superior to other benchmark algorithms.

A spectral power-based real-time BCI neuro-feedback (decision machine) system has been proposed in this paper. It is used to reflect the intended task of the movement imagination of hand movements. As a consequence of the cost-effective and greater temporal resolution, the proposed decision machine considered the electroencephalogram (EEG) signal. EEG data modeling is therefore an important step in seeking a rational solution for a specific application. The most potential temporal relative spectral power (TRSP) based feature extraction algorithm, and probabilistic Bayesian classifier has been used to model the EEG data for the reproduction of the thoughts. In this paper, the author has considered two channels of information for command generation. This paper also focuses on the maximization of information retrieval by analyzing incoming two channels' brain signals. This initiates to devise an appropriate reactive frequency band for each subject before applying the feature extraction algorithm. In this direction, this paper uses relative power spectral intensity (RPSI) for reactive frequency band estimation. The spectral power-based feature extraction method is responsible for the improvement of the accuracy, reducing the computational complexity, lowers the computational time, and improves the information transfer rate. The performance of the proposed BCI system shows better results compared to the different well established conventional EEG based BCI systems. The proposed real-time neuro-feedback system is a generalized BCI system that will be readily applicable for controlling any external devices. The proposed system shows 90.37% accuracy over the BCI Competition II dataset and 81% average accuracy with 64 ms computational time in the real-time application.

The control loop model and a new method for reproducing information transfer processes in the digital platform ecosystem are developed and proposed. The presented innovative solutions are based on a semantic model created for the functional architecture of a component in the ecosystem of a digital platform that provides information interaction of organizational systems. The novelty of the method is to provide the possibility of coordinated centralized management in a single information management environment of the digital platform ecosystem.

Optical lithography is a critical technique to fabricate nano-scale semiconductor devices by replicating the layouts of integrated circuits from the lithography mask onto the silicon wafer. As the critical dimension of integrated circuits continuously shrinks, source and mask optimization (SMO) methods are extensively used to improve the resolution and image fidelity of lithography patterning. However, the theoretical lower bound of the lithography pattern error in the SMO framework is not yet understood. This paper introduces an informational lithography approach to unveil the information transmission mechanism in lithography systems under freeform illumination configurations. The lithography system is regarded as an information channel, where the mask pattern and the print image on the wafer are modeled as the statistical input and output signals, respectively. Subsequently, we derive the optimal information transfer (OIT) of the lithography system, which represents the best information transfer strategy rendering the least image distortion. Based on the OIT, we derive a lower bound for the lithography pattern error, and the corresponding optimal source pattern and optimal mask probability distribution. Finally, we propose a new SMO algorithm based on the information theoretical framework to effectively improve the lithography image fidelity compared to the existing gradient-based SMO algorithm.

Brain Computer Interfaces (BCIs) allow a user to control a computer application by acquiring brain activity using Electro Enchephalo Gram (EEG). Non-invasive practical EEG headset available in the market brings more and more research in the field of BCI. BCI applications such as speller device is used both in entertainment field and health industry for typing using brain without using hands. There are a variety of paradigms available for designing a BCI based speller device. Here the primary design goals are to analyze the following parameters: speed in terms of the information transfer rate (ITR) and accuracy of word selection and increasing user friendliness. The common requirement for each method is stimulus design. In most cases the stimulus designs are responsible for the performance factors. This paper analyses the design of those stimuli used in the speller device and the associated Event Related Potential (ERP). The design parameters common to all the methodologies and performance parameters are also systematically discussed. The character selection time with each type of stimuli also has been compared.

Artificial synapses are memristor-based devices mimicking biological synapses, and they are used in neuromorphic computing systems that process information in a parallel, energy efficient way and store information in an analog, non-volatile form. The next generation of computing systems are anticipated to use memristive circuits, as they can overcome the shortcomings of the von Neumann computer architecture in which the levels of memory and the CPU are separated, creating a bottleneck that causes energy-loss during information transfer. Memristors are utilized to build Resistive Random Access Memory (RRAM) that allows for multi-level data storage and construction of self-correcting, autonomous learning systems that can solve complex computational tasks that have historically required super-computing hardware. Artificial synapses have received attention since HP Labs fabricated the first practical memristor device. In this review we summarize the working principles, device architectures, fabrication and processing techniques, as well as the strategies for materials selection including binary metal oxide, perovskite, polymer, and organic materials. We also discuss the applications and challenges of using artificial synapses in artificial intelligence tasks such as image recognition, tactile sensing and speech recognition.

Simultaneous lightwave information and power transfer (SLIPT) has been regarded as one of the enabling technologies to enhance the battery-life and data-rate for mobile electronics in the Internet of things (IoT). The existing SLIPT schemes face the challenge of achieving narrow beam transmission and mobile receiver positioning simultaneously. In this paper, we propose a simultaneous mobile information and power transfer (SMIPT) approach using the energy-concentrated resonant beam, which can realize self-alignment with the mobile receiver. To reveal the mobility mechanism of SMIPT, we present a geometric model of the mobile transmission channel and an analytical model of mobile energy and information transfer. Refraction is ignored in the geometric model and Fresnel/paraxial approximations are adopted in the above analytical model. Numerical evaluation illustrates that the exemplary SMIPT system can deliver 5 W charging power and enable 9.5 bps/Hz spectral efficiency with the moving range of 40-degree field of view (FOV) over 3 m distance. Thus, SMIPT exhibits a viable solution of simultaneous narrow beam transmission and mobile receiver positioning for high-power and high-rate SLIPT.

In this paper, we propose a joint beamforming and power-splitter optimization technique for simultaneous wireless power and information transfer in the downlink transmission of a multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system. Accordingly, each user employs a power splitter to decompose the received signal into two parts, namely, the information decoding and energy harvesting. The former part is used to decode the corresponding transmitted information, whereas the latter part is utilized for harvesting energy. For this system model, we solve an energy harvesting problem with a set of design constraints at the transmitter and the receiver ends. In particular, the beamforming vector and the power splitting ratio for each user are jointly designed such that the overall harvested power is maximized subject to minimum per-user rate requirements and the available power budget constraints at the base station. As the formulated problem turns out to be non-convex in terms of the design parameters, we propose a sequential convex approximation technique and demonstrate a superior performance compared to a baseline scheme.

A correctly selected positioning system for controlling the mobile robotic means movement ensures high positioning accuracy of the robotic platform in the garden, allows to automate precise operations in the garden and systematize route planning algorithms.(Research purpose) To substantiate the rational choice of a positioning system for controlling the mobile robotic device movement.(Materials and methods) The author formulated requirements for the positioning system to perform precise operations in the garden: mechanized collection of fruits and berries, diff erentiated application of fertilizers and chemical plant protection products. The main ones were: the positioning error was no more than 5 centimetres, the stability of information transfer to the server for building traffi c maps, the movement of a robotic device along a given trajectory, equipping beacons with a mobile power source with a capacity of at least 800 milliampere-hour, information exchange between the beacon and the built-in robotic means with a microprocessor controller according to the RS-485 standard, the signal coverage area was at least 100 square meter.(Results and discussion) The six most relevant positioning systems of the following manufacturers were described: RealTrac, Rusoft CKT, Neomatic, ISBC, Avtosensor, Marvelmind. The author compared their technical and operational parameters: operating frequencies, range, data transfer interface, location accuracy and cost of ready-made kits. He showed that Marvelmind provided uninterrupted operation at frequencies of 433 and 915 megahertz with a positioning error of no more than 2 centimetres. The tests were carried out on a small robotic vehicle with the following characteristics: maximum transport speed – 30 kilometre per hour, operating weight – 500 kilograms, length 2 metres, width – 1.2 metres, height – 1.6 metres.(Conclusions) The author substantiated the choice of the most suitable and aff ordable Marvelmind positioning system and experimentally confi rmed the positioning accuracy declared by the manufacturer. When driving in a loop-free and looped turn, the positioning accuracy did not exceed 1.5 centimetres, which met the agrotechnical requirements for mechanized collection of fruits and berries, for diff erentiated application of fertilizers and chemical plant protection products

The aim of the study was to indicate by the author the possibility of using wireless technologies as part of improving information transfer, with particular reference to activities in the field of logistics. The article is a review in which the theoretical aspects of logistics and information logistics are highlighted. A review and analysis of domestic and foreign literature was used as the theoretical scope. To determine the examples of the use of appropriate technologies in information logistics, an Internet database was used as the source of enterprises presenting selected technologies. The author focused on 5 main technological aspects used in information transfer in logistics, such as: short-range technologies, local networks, wide-area networks, low power wide-area networks and global computer networks. The analysed application allowed conclusions to be drawn clearly indicating that the use of appropriate wireless technologies allows for a more effective provision of appropriate information resources in logistics.

We introduce a framework for the evaluation of multiclass classifiers by exploring their confusion matrices. Instead of using error-counting measures of performance, we concentrate in quantifying the information transfer from true to estimated labels using information-theoretic measures. First, the Entropy Triangle allows us to visualize the balance of mutual information, variation of information, and the deviation from uniformity in the true and estimated label distributions. Next, the Entropy-Modified Accuracy allows us to rank classifiers by performance while the Normalized Information Transfer rate allows us to evaluate classifiers by the amount of information accrued during learning. Finally, if the question rises to elucidate which errors are systematically committed by the classifier, we use a generalization of Formal Concept Analysis to elicit such knowledge. All such techniques can be applied either to artificially or biologically embodied classifiers—e.g., human performance on perceptual tasks. We instantiate the framework in a number of examples to provide guidelines for the use of these tools in the case of assessing single classifiers or populations of them—whether induced with the same technique or not—either on single tasks or in a set of them. These include well-known UCI tasks and the more complex KDD cup 99 competition on Intrusion Detection.

Quantum-dot cellular automata (QCA) is one of the most prominent technologies in nanometer-scale with appreciable reduction of size and power consumption and high switching frequency to overcome the scaling limitations of complementary metal-oxide semiconductor. On the other hand, code converters play a key role in signal processing and efficient network designs. The researchers have focused on emerging nano-devices that can identify errors throughout information transfer. Therefore, in this research, a new QCA-based 4-bit binary to gray converter circuit employing the appropriate configuration of the XOR gate as a basic building block has been suggested. The layout has been generated using the QCADesigner simulation tool to test the functionality of the code converter. The performance results indicated that the proposed converter works properly and has optimum performance parameters such as latency, complexity, and consumed area as compared to the current schemes.

A method of estimating the effective data rate in channels of the Standard 802.11 was proposed. It provides for the measurement of the main energy parameter using the software and hardware of the subscriber device. This method is based on the empirical models of statistical relationships between the main parameters of the channel which are obtained on the basis of experimental studies using monitoring algorithms. The solutions obtained during the implementation of this method make it possible to take into account the maximum possible number of destabilizing factors and significantly reduce the time of assessment of the effective data rate. It should be noted that this method can be used for technical diagnostics of wireless networks of Standards 802.11x at the stages of network design and operation.It was established that when using the coefficient of energy efficiency, a significant error in the displacement of the points of intersection of the linear and logarithmic mathematical model occurs. This can lead to a discrepancy between the mathematical estimates of the effective data rate and real values. The statistical relationship gives a smaller error; however, it increases requirements for empirical studies to obtain the maximum possible reliability.One of the features of the proposed method is the reliability of assessment of the effective data rate. This reliability depends on three main factors: accuracy of assessing the results based on which the mathematical model was obtained; estimation of fluctuation intervals and characteristics of the Standard 802.11 equipment of different manufacturers. The last factor can be considered as a disadvantage that involves the creation of a database of parameters of the model of statistical relationship for different devices with correction coefficients, Предложен метод оценки эффективной скорости передачи информации в каналах стандарта 802.11, который предусматривает измерение основного энергетического параметра с помощью программно-аппаратных средств абонентского устройства. Основой такого метода являются эмпирические модели статистической связи между основными параметрами канала, полученные на основе экспериментальных исследований с применением алгоритмов мониторинга. Полученные при реализации данного метода решения позволяют учитывать максимально возможное количество дестабилизирующих факторов и значительно уменьшить время оценки эффективной скорости передачи информации. Следует отметить, что такой метод может использоваться для технической диагностики беспроводных сетей стандарта 802.11х на этапах проектирования и эксплуатации.Установлено, что, используя коэффициент энергетической эффективности, возникает значительная погрешность при смещении точек пересечения линейной и логарифмической математической модели. Это может приводить к отклонению математической оценки эффективной скорости передачи информации и реальных значений. Статистическая связь дает меньшую погрешность, но повышает требования к эмпирическим исследованиям для получения максимально-возможной достоверности.Одной из особенностей предложенного метода можно выделить достоверность оценки эффективной скорости передачи информации. Такая достоверность зависит от трех основных факторов: точность оценки результатов на основе которых получена математическая модель; оценка интервалов флуктуаций и характеристики оборудования стандарта 802.11 разных производителей. Последний фактор можно выделить как недостаток, предусматривающий создание базы данных параметров модели статистической связи для различных устройств с корректирующими коэффициентами, Запропоновано метод оцінювання ефективної швидкості передачі інформації у каналах стандарту 802.11, який передбачає вимірювання основного енергетичного параметра за допомогою програмно-апаратних засобів абонентського пристрою. Основою такого методу є емпіричні моделі статистичного зв’язку між основними параметрами каналу, які отримано на основі експериментальних досліджень із застосуванням алгоритмів моніторингу. Отриманні при реалізації даного методу рішення дозволяють враховувати максимально-можливу кількість дестабілізуючих факторів та значно зменшити час оцінювання ефективної швидкості передачі інформації. Слід відзначити, що такий метод може використовуватись для технічної діагностики безпровідних мереж стандартів 802.11х на етапах проектування та експлуатації мереж.Встановлено, що використовуючи коефіцієнт енергетичної ефективності виникає значна похибка при зміщенні точок перетину лінійної та логарифмічної математичної моделі. Це може приводити до відхилення математичної оцінки ефективної швидкості передачі інформації та реальних значень. Статистичний зв’язок дає меншу похибку, але підвищує вимоги до емпіричних досліджень для отримання максимально-можливої достовірності.Однією із особливостей запропонованого методу, можна виділити достовірність оцінювання ефективної швидкості передачі інформації. Така достовірність залежить від трьох основних факторів: точність оцінювання результатів, на основі яких отримана математична модель; оцінювання інтервалів флуктуацій та характеристики обладнання стандарту 802.11 різних виробників. Останній фактор можна виділити як недолік, що передбачає створення бази даних параметрів моделі статистичного зв’язку для різних пристроїв із корегуючими коефіцієнтами

Memetic computing is a blooming research area, which treats memes as the fundamental building blocks of information transfer. Evolutionary multitasking is an emerging topic in memetic computation, which applies evolutionary algorithm to optimize multiple tasks at a time. A famous class of algorithms for evolutionary multitasking is the multi-factorial evolutionary algorithm (MFEA). Nevertheless, current MFEAs only consider problems with small number of tasks, resulting in a lack of effective information transfer strategy. This study proposes a framework for evolutionary multitasking, called the evolution of biocoenosis through symbiosis with fitness approximation (EBSFA). The EBSFA incorporates evolution of biocoenosis through symbiosis (EBS) with fitness approximation to ameliorate the information transfer. The improvement of EBSFA is three-fold, including (1) the adaptive control of information transfer among tasks, (2) the selection of individuals from the universal offspring pool for evaluation based on fitness approximation, and (3) an ensemble method for improving the accuracy of fitness approximation through k nearest neighbors. Experimental analysis verifies the effectiveness and efficiency of the proposed EBSFA, by comparison with an advanced single-tasking method, the covariance matrix adaptation evolution strategy (CMAES), an illustrious multitasking optimization method, the MFEA-II, and an evolutionary many-tasking method, the EBS on a set of many-tasking benchmark problems. The results show that EBSFA can gain nice solution quality and fast convergence speed. Further analysis validates the effectiveness of the proposed components on improving the information transfer.

P300 speller-based brain–computer interface (BCI) is an immediate correspondence between the human brain and computer that depends on the translation of mind reactions produced by the stimulus of a subject utilising the P300 speller. No muscle movements are required for this communication. As a P300 paradigm, a novel 2 × 3 matrix consisting of visual home appliances is proposed, which helps disabled people ease their lives by accessing mobile, light, fan, door, television, electric heater etc. In most of the current P300-based BCIs, 5–15 trials work better and the low information transfer rate (ITR) is a major issue in its adaptation in real-time. The objective of this Letter is to improve accuracy as well as an ITR for real-time home appliance control applications. To address this, the authors proposed a single trial weighted ensemble of compact convolution neural network and obtained an ITR of 46.45 bits per minute and an average target appliance accuracy of 93.22% for the BCI-based home environment system. The experimental findings confirmed the feasibility of the proposed method and thus can provide guidance for future use of the system for paralysed patients.

Objective: Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that can deliver a high information transfer rate (ITR) usually require subject’s calibration data to learn the class- and subject-specific model parameters (e.g. the spatial filters and SSVEP templates). Normally, the amount of the calibration data for learning is proportional to the number of classes (or visual stimuli), which could be huge and consequently lead to a time-consuming calibration. This study presents a transfer learning scheme to substantially reduce the calibration effort. Methods: Inspired by the parameter-based and instance-based transfer learning techniques, we propose a subject transfer based canonical correlation analysis (stCCA) method which utilizes the knowledge within subject and between subjects, thus requiring few calibration data from a new subject. Results: The evaluation study on two SSVEP datasets (from Tsinghua and UCSD) shows that the stCCA method performs well with only a small amount of calibration data, providing an ITR at 198.18±59.12 (bits/min) with 9 calibration trials in the Tsinghua dataset and 111.04±57.24 (bits/min) with 3 trials in the UCSD dataset. Such performances are comparable to those from using the multi-stimulus CCA (msCCA) and the ensemble task-related component analysis (eTRCA) methods with the minimally required calibration data (i.e., at least 40 trials in the Tsinghua dataset and at least 12 trials in the UCSD dataset), respectively. Conclusion: Inter- and intra-subject transfer helps the recognition method achieve high ITR with extremely little calibration effort. Significance: The proposed approach saves much calibration effort without sacrificing the ITR, which would be significant for practical SSVEP-based BCIs.

An instrument for remote control of the robot by wearable brain–computer interface (BCI) is proposed for rehabilitating children with attention-deficit/hyperactivity disorder (ADHD). Augmented reality (AR) glasses generate flickering stimuli, and a single-channel electroencephalographic BCI detects the elicited steady-state visual evoked potentials (SSVEPs). This allows benefiting from the SSVEP robustness by leaving available the view of robot movements. Together with the lack of training, a single channel maximizes the device’s wearability, fundamental for the acceptance by ADHD children. Effectively controlling the movements of a robot through a new channel enhances rehabilitation engagement and effectiveness. A case study at an accredited rehabilitation center on ten healthy adult subjects highlighted an average accuracy higher than 83%, with information transfer rate (ITR) up to 39 b/min. Preliminary further tests on four ADHD patients between six- and eight-years old provided highly positive feedback on device acceptance and attentional performance.

The existing Devanagari-script-input-based P300 speller (DS-P3S) performs better mostly with 3–15 trials. This leads to poor information transfer rate (ITR) and a major concern in its real-time adaptation. In DS-P3S, the display paradigm is a matrix of $8\times 8$ size which has 28 more characters than the $6\times 6$ English paradigm. The increased number of characters leads to user-related issues such as a crowding effect, double flashing, adjacency distraction, task difficulty, and fatigue which increases the false detection rate. To tackle this, we propose an efficient single-trial character detection approach for DS-P3S using weighted ensemble of deep convolution neural networks (WE-DCNNs). The weighted strategy is constructed based on measured ensemble diversity to counter the instability by the individual classifier. Additionally, to reduce the false detection rate arising from a single trial, a new channel dropout-based character detection approach is introduced first time in this article. The ITR of 55.45 b/min and an average P300 classification accuracy of 92.64% achieved are comparatively higher than existing methods of DS-P3S. The significant reduction in tradeoff between bias and variance for the different subjects affirms the ease of applicability of the proposed model with just a single trial.

With rapid technological development, mobile computing and wireless transmission have become mature. These two technologies can be combined for sharing medical records on social networks, but doing so depends on the secure sharing of a patient’s data between Pervasive Social Network (PSN) nodes. Previous research has revealed that most human sensors cannot support very heavy computation, so the computation on the sensor node must be light. When a legitimate node is intercepted, an attacker may steal shared data of a third party through this intercepted node, causing security problems. Based on the aforementioned security and performance considerations, this study proposes a blockchain-based medical data preservation scheme for telecare medical information systems (TMISs), which consists of a medical sensor area authentication protocol and a social network information transfer protocol. The former protocol uses elliptic curve point multiplication to achieve secure data transfer between human sensors and mobile devices. The latter protocol stores data collected and transferred by mobile devices. The scheme herein is secure against several possible attacks and reduces the number of communication rounds below those of previously developed approaches. The social network information transfer protocol stores data using blockchain technology so that a data owner can authorize access to data by relevant users. Therefore, the proposed scheme improves upon not only the computing performance but also the security of previous approaches.

The importance of facility management (FM) has gradually been recognized in the life cycle of a building. Both new and existing buildings require a more efficient approach and advanced technology to improve the efficiency of the FM. The study is aimed to enhance the facility repair process. Thus, the study presents a building information modeling (BIM)-based facility repair platform framework, which enables expedited reporting for repairs and a comprehensive listing of the status, location, and maintenance record facilities. To effectively facilitate the FM information transfer among stakeholders, firstly, the study presents a facility traceability coding structure for building facilities, the proposed platform framework. The proposed facility traceability information coding in QR code format can help general users and FM personnel identify and locate the specific or abnormal facility quickly. Secondly, the study demonstrates that how to apply the proposed code in QR code format in the BIM-based facility repair platform with a case test. The research findings showed that the proposed method enables FM personnel to significantly improve the efficiency of FM, which can help FM personnel make preliminary facility repair judgments and schedule maintenance plan to track the status of the facility repair which can help improve the efficiency of facility management and simplify the repair process.

The semantic segmentation of the huge amount of acquired 3D data has become an important task in recent years. We propose a novel association mechanism that enables information transfer between two 3D representations: point clouds and meshes. The association mechanism can be used in a two-fold manner: (i) feature transfer to stabilize semantic segmentation of one representation with features from the other representation and (ii) label transfer to achieve the semantic annotation of both representations. We claim that point clouds are an intermediate product whereas meshes are a final user product that jointly provides geometrical and textural information. For this reason, we opt for semantic mesh segmentation in the first place. We apply an off-the-shelf PointNet++ to a textured urban triangle mesh as generated from LiDAR and oblique imagery. For each face within a mesh, a feature vector is computed and optionally extended by inherent LiDAR features as provided by the sensor (e.g. intensity). The feature vector extension is accomplished with the proposed association mechanism. By these means, we leverage inherent features from both data representations for the semantic mesh segmentation (multi-modality). We achieve an overall accuracy of 86:40% on the face-level on a dedicated test mesh. Neglecting LiDAR-inherent features in the per-face feature vectors decreases mean intersection over union by ∼2%. Leveraging our association mechanism, we transfer predicted mesh labels to the LiDAR point cloud at a stroke. To this end, we semantically segment the point cloud by implicit usage of geometric and textural mesh features. The semantic point cloud segmentation achieves an overall accuracy close to 84% on the point-level for both feature vector compositions.

Conventional wireless communication architecture, a backbone of our modern society, relies on actively generated carrier signals to transfer information, leading to important challenges including limited spectral resources and energy consumption. Backscatter communication systems, on the other hand, modulate an antenna’s impedance to encode information into already existing waves but suffer from low data rates and a lack of information security. Here, we introduce the concept of massive backscatter communication which modulates the propagation environment of stray ambient waves with a programmable metasurface. The metasurface’s large aperture and huge number of degrees of freedom enable unprecedented wave control and thereby secure and high-speed information transfer. Our prototype leveraging existing commodity 2.4 GHz Wi-Fi signals achieves data rates on the order of hundreds of Kbps. Our technique is applicable to all types of wave phenomena and provides a fundamentally new perspective on the role of metasurfaces in future wireless communication., Leveraging the large aperture and huge number of degrees of freedom offered by a programmable metasurface, the authors modulate the propagation environment of existing background commodity Wi-Fi signals to implement a secure and high-speed massive-backscatter communication link.

Zero-shot learning (ZSL) aims to recognize novel object categories by means of transferring knowledge extracted from the seen categories (source domain) to the unseen categories (target domain). Recently, most ZSL methods concentrate on learning a visual-semantic alignment to bridge image features and their semantic representations by relying solely on the human-designed attributes. However, few works study whether the human-designed attributes are discriminative enough for recognition task. To address this problem, we propose a couple semantic dictionaries (CSD) learning approach to exploit the latent visual attributes and align the visual-semantic spaces at the same time. Specifically, the learned visual attributes are elegantly incorporated into the semantic representation of image feature and then consolidate the discriminative visual cues for object recognition. In addition, existing ZSL methods suffer from the domain shift issue due to the source domain and target domain have completely separated label spaces. We further employ the visual-semantic alignment and latent visual attributes jointly from source domain to regularise the learning of target domain, which ensures the expansibility of information transfer across domains. We formulate this as an optimization problem on a unified objective and propose an iterative solver. Extensive experiments on two challenging benchmark datasets demonstrate that our proposed approach outperforms several state-of-the-art ZSL methods.

The use of a formation consisting of adaptive autonomous mobile agents allows solving a wide range of tasks that are often beyond the capabilities of individual agents. A multi-agent formation is a complex high-order dynamic system, so analyzing the stability of such a system is a complex task. At present, the problem of estimating the stability of a formation formed by substantially nonlinear high-order agents with variable dynamic parameters is not sufficiently considered. This task is particularly important for a formation that is affected by complex unstable environmental conditions, in particular for the formation of unmanned aerial vehicles (UAVs). A method for analyzing the stability of formations of nonlinear agents with different types and orders of transfer function has been developed for studying information exchange in UAV networks. The new approach is based on the use of the Popov frequency criterion and the piecewise linear approximation of the hodograph. A computational experiment was performed to analyze the stability of a formation with transfer functions of various types and orders from the 1st to the 10th. The conducted studies revealed a significant difference in the calculated boundary coefficients of formation stability in the linear and nonlinear modes, which confirms the need to analyze the nonlinear stability under the influence of strong destabilizing influences on the formation.

In this paper, we present a novel framework of a nanomechanical communication (NMC) system where nanomechanical (NM) systems can communicate using mechanical movements to encode and transfer information between a transmitter (TX) nanomachine and a receiver (RX) nanomachine. First, some unique features of the envisioned NMC system have been presented in contrast to traditional communications systems. It is demonstrated that the encoding of distinct repeatable movements or motions is the key to information transfer in an NMC system. Using a state transition framework, we identify rest states (RSs) and motional degrees of freedom (MDoF) as the two essential concepts that determine the transmission rate of an NMC system in the spatial domain in two different modes, namely, synchronous and asynchronous transmissions . While the synchronous transmission is found to achieve much higher transmission rate compared to the asynchronous transmission in the noise-free case, the asynchronous mode is less restrictive and may have wider applicability. A stochastic model is considered to incorporate the influence of thermal noise in the system. A closed-form expression of the symbol error rate (SER) and its bounds have been derived for a particular symbol of a synchronous sliding motion NMC system with MDoF = 2. Numerical results show a promising SER performance in support of developing an NMC system model for nanoscale communications networks.

Intelligent transportation is an indispensable part of the smart city and the primary development direction of the future transportation systems. Vehicle detection and recognition, which is one of the most important aspects of intelligent transportation, plays a very important role in various areas of our daily life; one such important area is criminal investigation. In the fine-grained vehicle type detection and recognition, several difficult issues such as problems in data acquisition and tagging, dramatic variance in the data of different vehicle types, and challenges in identifying vehicles of the same brand with highly similar appearances remain unsolved. For the problems of data acquisition and tagging, this paper presents a strategy for automatic data acquisition and tagging based on object detection that can label the vehicle images efficiently while rapidly acquiring all types of fine-grained models. Considering the problem of data imbalance in the training process, this paper proposes a Faster-RCNN based data equalizing strategy (Faster-BRCNN), thereby improving the performance of object detection. In view of the severe information attenuation caused by the feature information transfer obstruct between layers in the traditional deep learning network, the lack of mutual dependency of these features, and the inability of the network to focus on the important region and characteristics, we propose an intensive dense attention network (DA-Net). Through its intensive connection and attention unit, we enhance the model's detection ability. The proposed method achieves mAP of 94.5% and 95.8% in the Stanford Cars and FZU Cars datasets, respectively, thereby verifying its effectiveness.

Text is an important form of information transfer. In a P2P (Peer-to-Peer) loan market with severe information asymmetry, loan descriptive information voluntarily written by a borrower may generate significant influence on investors’ decision-making. As an important language feature, readability determines whether the audience can accurately identify textual contents. To discuss the influence of readability of loan description on borrowing behaviors, the data of a Chinese P2P lending platform Renrendai during 2013–2017 were used in this study. Readability index of loan description was constructed through the textual analysis method, followed by an empirical verification of the influences of readability of loan description on loan success rate and loan cost. Results indicate that the readability of loan description in China’s P2P loan market can generate incremental information, and the readability of loan description has evident nonlinear relations with loan success rate and loan interest rate. Moreover, the readability of loan description presents a “reverse U-shaped” relation with loan success rate and a “U-shaped” relation with loan interest rate. Demographic information (gender and educational background) of borrowers will not influence investors’ feedback behaviors toward the readability of loan description. Conclusions provide empirical evidence for standardizing loan descriptive information on P2P lending platforms.

A new concept of simultaneous wireless information and power transfer (SWIPT) is proposed, based on multiplexing a continuous-phase modulated (CPM) information transfer signal and a multitone power transfer (PT) signal. The tones of the PT signal have frequencies falling into low-power sub-bands and/or spectral null frequencies of the CPM signal. Therefore, the interference of the PT signal to the CPM signal is minimized and, simultaneously, the CPM and PT signals can be separated by the corresponding complementary filters in the receiver. Numerical evaluations show that the proposed PT signal design practically entails a negligible loss of CPM detection performance.

In order to provide information privacy for the primary network and support the secondary quality-of-service (QoS) requirement, we design a cooperative primary preserving strategy which simultaneously transfers information and power in cognitive radio networks. In this strategy, the secondary network provides information security for the primary information transmission in three phases and obtains some licensed spectrum for the secondary information transmission. Specifically, in the first phase, a fraction of the primary transmitter’s power is allocated for the primary privacy transmission, and the remaining power is utilized to beamform the energy signal for energy harvesting. After receiving the privacy signal, two secondary users cooperatively forward the privacy information under jamming protection. In the last phase, the secondary user occupies the licensed spectrum for the secondary information transmission. We first analyze the transmission reliability and secrecy performances of the primary network, and then the primary transmit power and the time slot are optimally allocated such that the secondary transmission rate is maximized under the requirements of the primary information reliability and security. Additionally, we also investigate the primary power and time slot allocation problem for the asymptotic scenario. Numerical results are presented to show the performance improvement of the proposed scheme about the secondary average rate under the primary performance requirements.

In the paper shown there is presented an approach to the solution of the problem of the effectiveness parameter assessment in telecommunication systems of operational and command communication, systems of warning speakerphone and audio-exchange. There are considered the matters of the dependence investigation of the acoustic speech signal/noise ratio to the assurance of the required syllabic legibility for the possibility to increase the function effectiveness of telecommunication systems and information exchange operated under complex noise situation. There is shown the dependence of formant legibility upon the meaning of average geometric frequencies in each i-th band of a frequency spectrum of acoustic speech signals. The degree of the impact upon syllabic legibility of the acoustic speech signal/noise ratio is shown. In the paper it is shown that for obtaining speech information with syllabic legibility higher than 93% required for complete perception by a subscriber it is necessary to ensure the acoustic signal/noise ratio at the level no less than 20 dB. The problems in the probability density approximation of acoustic signals with the use of generalized polynomials on function basis systems are presented.

The main purpose of image Co-saliency detection is to detect objects with similar properties or prospects in a group of images. In this paper, we propose a novel Converged Complementary Co-saliency detection framework in an end-to-end manner for Co-saliency detection. Specifically, considering the consistency and the difference between the features of the intra-image and the inter-image, our network contains two parts [forward information transfer module and complementary information enhancement module (CIEM)]. Each part is a double-branch network with short connections to avoid incomplete information transmission, including single-saliency detection branch (SSDB) and Co-saliency detection branch (CSDB). In FITM, the SSDB and CSDB extract the corresponding features, respectively. In order to produce more accurate results, we feed the SSDB and CSDB into the holistic attention and perform further feature extraction. In CIEM, we extract the useful information of the non-salient area. At the end of the network, we fuse the FITM and CIEM to generate the final Co-saliency map. Furthermore, we rearranged a new data set based on the existing public data set. Compared with other Co-saliency methods, the experimental results show that our proposed model achieves state-of-the-art performance.

Large intelligent surface (LIS) has emerged as a promising new solution to improve the energy and spectrum efficiency of wireless networks. A LIS, composed of a large number of low-cost nearly-passive reconfigurable reflecting elements, enhances wireless communications by reflecting impinging electromagnetic waves. In this letter, we propose a novel passive beamforming and information transfer (PBIT) technique by adopting spatial modulation on the index of the LIS elements, in which the LIS simultaneously enhances the primary communication (by passive beamforming) and sends its private data to the receiver (by spatial modulation). We develop a passive beamforming method to improve the average receive signal-to-noise ratio (SNR). We also establish a two-step approach at the receiver to retrieve the information from both the transmitter and the LIS. Numerical results show that the proposed PBIT system, especially with the optimized passive beamforming, significantly outperforms the system without LIS enhancement. Furthermore, a tradeoff between the passive-beamforming gain and the information rate of the LIS has been demonstrated.

Molecular communications has recently emerged as a new form of information transfer that uses chemical signals as information carriers. Alongside their novel applications in communications theory, chemical signals may also be utilized for various other applications, such as abnormality detection, direction finding, and entity localization. Among localization tasks, current literature mainly focuses on locating active entities that emanate chemicals, whereas the localization of a silent entity (e.g., an eavesdropper) is rarely considered. Exploiting the fact that different positions of a silent entity yields different received signals at the sensing device, this letter introduces a machine learning-based approach to detect the presence of a silent entity and localize it. Overall, the study shows that such a localization task is also achievable in cases where a clear analytical formula characterizing the received signal is not available, and provides a framework for further research on silent entity localization approaches.

Acquisition of sensor data in wireless sensor networks with compressive sensing reduces the cost involved with sensing and communication. The challenge behind this work in underwater environment is exploring the fact of transfer capability of sensor nodes and underwater channel. The robustness of compressive sensing scheme in underwater environment is further augmented by recovery and transfer path. Two algorithms have been proposed. The first which uses compressive sensing at source and reconstruct using orthogonal matching pursuit at sink named as Underwater Wireless Information Transfer with Compressive sensing. Second algorithm exploits bandwidth estimation exploiting the cross traffic at intermediate forwarders for each source sensors to its associated sink named as: Underwater Wireless Information Transfer with Compressive sensing Bandwidth Measurements. Performance metrics of both protocols are interpreted through simulations.

When testing agricultural machinery in order to determine its functional indicators, the ability to wirelessly transmit data between sensors, measuring and information systems are important. (Researchpurpose) To develop methods and create wireless digital devices for determining the functional indicators of agricultural tractors and machines with the ability to wirelessly transmit data to a remote control point in real time. (Materials and methods) The authors assumed that it was possible to determine the slipping of driving wheels using an inertial navigation system. It was found that in order to calculate real-time indicators obtained using wireless technologies, it was necessary to determine the characteristics of the input signals of discrete sensors on the side of the measuring system. (Results anddiscussions) The authors substantiated a method for determining the period of incoming signals of discrete sensors with an accuracy of 0.001 seconds for wireless information transmission. They proposed the design of a slipping sensor for an energy vehicle driving wheels, the main element of which is an inertial wheel position sensor. They developed a discrete signal input module and an inertial slipping sensor with the possibility of wireless data transmission based on a radio system with a carrier frequency of 433 megahertz. During field tests, it was found that the accuracy of determining slippage using the inertial wireless sensor IP-291 does not exceed 1 percent; the range of stable radio communication from the tested object to the test control center reaches 1000 meters; the current indicators obtained through digital radio communication did not differ from the indicators obtained in the tractor cab. (Conclusions) The authors worked out an effective system for wireless information transfer with the ability to calculate the performance of the tested equipment in real time.