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This study seeks to compare the quantity of information that is automatically transferred through the associations generated using a model-based systems engineering (MBSE) approach versus a traditional systems engineering approach to measure the benefits of MBSE in architecting a robotic space system. Both an MBSE approach and non-MBSE approach were applied to architecting an orbiting sample Capture and Orient Module (COM) system concept for a Capture, Containment, and Return System (CCRS) payload concept for Mars Sample Return (MSR). These approaches were applied in parallel to provide a side-by-side comparison of the approaches. The approaches were analyzed using design structure matrices (DSM) and evaluated based on the amount of information transferred between process tasks manually (e.g., elements physically typed into text boxes in a presentation slide) vs automatically (e.g., elements automatically filled out within a block in a model view due to explicitly defined element associations). A total of 4,819 information element transfers were traced in DSMs and used to quantitatively compare the two approaches. The non-MBSE approach required manual transfer for all 4,819 information elements. The MBSE approach required manual transfer for 4,189 information elements and automatic transfer for 630 information elements, providing a minor increase in the automation of information transfer relative to the non-MBSE approach. By incorporating the use of additional MBSE artifacts into the trade study and peer review tasks, manual transfer could potentially be reduced to 931 information elements, and automatic transfer increased to 3,888 information elements.

P300 signal is an endogenous event related potential component. It is mostly elicited from the frontal to parietal brain lobes. Electroencephalography is used for acquiring P300 signal from scalp. P300 signal is used for brain-computer interface systems. P300 based brain-computer interface systems are preferable since they have high overall performance. The most significant overall performance indicator is information transfer rate for P300 based brain-computer interface systems. P300 signal detection accuracy and P300 detection time are using for information transfer rate calculation. Hence, P300 signal classification accuracy is important for getting higher information transfer rate. In this study, it is aimed to investigate P300 detection model for higher classification accuracy. Thus, it is proposed 3-dimensional input convolutional neural network model for P300 detection. Moreover, the proposed model was applied with region based P300 speller which constituted audio and visual stimuli. In experiments, the participants were asked to spell desired words in two sessions which were offline and online session. Linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and the proposed method were compared in both online and offline sessions. It is reached highest average classification accuracy rate with the proposed method in both sessions. According to the online session result, average classification accuracy was 94.22% in 3-dimensional input convolutional neural network model. Furthermore, average information transfer rate was 5.53 bit/min in 3-dimensional input convolutional neural network model. We have also applied methods on BCI competition III-dataset II for 2 participants “A” and “B” for evaluating performance of algorithms. The proposed method had higher classification accuracy rate than linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and multi-classifier convolutional neural network which was used in other study on same dataset.

This article unveils the importance of statistical quality of service (QoS) for resource allocation in a two-hop network. Particularly, an access point (AP) serves multiple IoT devices for information transfer with the assistance of the energy harvesting (EH) relaying. To explore the maximum constant data arrival metric, we aim to maximize the effective capacity (EC) under specified QoS requirements. Also, the statistical QoS inspired resource allocation policies are investigated for half/full duplex (HD/FD) modes, respectively, to jointly optimize power allocation and power splitting (PS) ratio. To solve the formulated problem, we first derive the closed-form solution of the optimal power allocation at the AP and the PS ratio. To gain more insights, we further derive the boundary conditions of optimal power allocation and PS ratio. Finally, numerical results are demonstrated to validate the theoretical derivations, which highlights the proposed scheme in terms of EC performance in comparison to the benchmark scheme.

Reliable communication between the primary and the secondary side is a challenge for simultaneous wireless information and power transfer (SWIPT) system. Considering many factors, such as cost, device size, and transmission efficiency, the SWIPT system transferring multiple frequencies with a single inductive link is an effective scheme to integrate reverse data communication. However, those SWIPT systems change power transfer frequency or power carrier amplitude while transferring information, then information cannot be transmitted when the inverter does not work, which reduces the reliability of communication. To solve this problem, this paper proposes an idea of establishing an independent information transfer channel, the power and information transmits at different frequencies. The information transmitted in the reverse direction no longer flows through the primary side inverter. It is guided to an independent channel after being transmitted to the primary side through the common inductive link. Its equivalent circuit model is provided to analyze the transfer gain, power transfer performance, and the relationship between the information coupling circuit and information transfer characteristics. Finally, a SWIPT prototype is built to validate the proposed SWIPT system.

In this article, we investigate the performance of a piece-wise linear model of energy harvesting-based multiuser overlay spectrum sharing system. Herein, an energy-constrained secondary node acts as a cooperative relay to assist the information transmission between a primary transmitter and multiple primary receivers. In return for the cooperation, the secondary node enjoys access to the primary user’s spectrum for its own information transfer. Specifically, by employing a time-switching based receiver, the secondary node harvests energy from the received radio-frequency signal of primary transmitter during a dedicated energy harvesting phase. In the subsequent information transfer phase, the secondary node splits the harvested power to forward the primary data as well as its own information intended for another secondary user. We analyze the impact of decoding primary’s information at the secondary receiver on the performance of secondary network. Importantly, we propose an improved energy harvesting-based relaying scheme which makes an efficient use of available degrees of freedom and thereby enhances the performance of both primary and secondary networks significantly. For this analytical framework, we derive the expressions of outage probability for primary and secondary networks. Numerical and simulation results are obtained to extract various useful insights and to validate our theoretical developments.

Residual placenta is one of the common types of postpartum complications in clinical practice. Residual placenta is also the main and most common cause of late postpartum hemorrhage. This article proposes a spatial pyramid loop module, which solves the problem that the existing network structure cannot effectively extract the semantic information and category information in the image at the same time. The spatial pyramid structure is used to effectively extract the semantic information and category information. In addition, this article proposes to use cyclic convolutional network to realize the transfer function of information at different scales, and build it in the spatial pyramid structure to further strengthen the ability to extract semantic information and category information. This article proposes a feature fusion module to solve the impact of image classification network used in the base network in the existing network structure. The attention mechanism is used to achieve the effective fusion of high-dimensional features and low-dimensional features in the base network to reduce the influence of the base network, so as to better recover the recognition and prediction results. A semantic category loss function is proposed to supervise the categories of objects in images. This article builds it on the feature layer with the smallest scale, which not only increases the intermediate supervision to make the network fully converge, but also reduces the difficulty of extracting category information, and makes full use of the information transfer function of the cyclic convolutional network. This article introduces uncertainty information into the field of image segmentation to provide the accuracy of segmentation. For the purpose of uncertainty information, this article improves the network structure. At the same time for the image segmentation task, this article improves the Bayesian cross entropy loss function. The experiment verifies the necessity of improving the Bayesian crossover function in this article and the effectiveness of the conditional random field used in this article, and also verifies the effectiveness of the proposed semi-supervised learning method.

The paper presents for the first time an asymptotically stable distributed algorithm that provides nonuniform deployment of agents on a segment. It is shown that neither delays in information transfer between agents, nor changes in the network configuration - switches - affect the achievement of the goal using the proposed algorithm.

In this paper, we propose using a single protograph to design joint source-channel codes (JSCCs). We present a generalized algorithm, called protograph extrinsic information transfer for JSCC algorithm (PEXIT-JSCC algorithm), for analyzing the channel threshold of the proposed JSCC. Using the classic AR3A and AR4JA protographs, we construct AR3A-JSCC and AR4JA-JSCC codes. We analyze their decoding thresholds and compare their performance with JSCC systems using optimized double protograph low-density-party-check codes.

Smart surfaces represent a promising technology that allow to make the wireless environment a smart reconfigurable space playing an active role in the information transfer process. In this work, a reflectarray-based smart surface is investigated for next-generations communications, Device-to-Device systems and Internet of Things. The preliminary design of a smart surface is presented which is able to focus and redirect the impinging electromagnetic wave along a desired direction. The designed smart surface is extensively analyzed, under different environment conditions.

Today, people can see content on the Internet and share information and collaborate freely on the Internet. Like the example of Wikipedia, where the content can now be edited by anyone and the content with the development of technology, information transfer has become very practical to be done anywhere, anytime. When discussing communication and sharing information, social media are not separated. Social media has created a new way of communicating. Disinformation can be spread with the help of the actors involved. This research is to study how fake news spreads and its consequences, both in cyberspace and in the real world. Based on this knowledge, it is hoped to increase public awareness by responding to disinformation on social media. The expected outcome of this study is the discovery of a factor in the spread of disinformation. A study of 30 articles was conducted to find the aspects of the reach of disinformation. This research showed that various factors are divided into four categories: individual, situation, technology, and technical. From this category, the results show that the most used material for research is the individual factor. This factor is a factor of a person's ability to analyze information.

Since low-density parity-check (LDPC) codes have been studied and adopted in the DVB-S2 standard, many efforts have been made to improve their performance and optimize them up to the broadcasting standard. This paper proposes a family of protograph-based LDPC codes to provide a multi-rate coding for efficient DVB-S2 systems, considering low-complexity and excellent performance of the protograph codes. Moreover, we employ Bit Interleaved Code Modulation Iterative Decoding (BICM-ID) to enhance the performance of the ASK-modulated DVB systems. Both the protograph extrinsic information transfer (PEXIT) analysis and simulation results demonstrate the performance advantage of the proposed protograph-based LDPC codes over the conventional DVB-S2 codes for all the code rates. In particular, the proposed codes yield performance gains of more than 0.2 dB as compared to the conventional codes at high rates for 16-APSK modulations.

By a long-term experimental study of starling flocks, a phenomenon is observed that directional switch information propagation obeys a linear dispersion law with ignorable attenuation effect across the flock. However, conventional models like Vicsek model could not nicely explain this fast synchronization phenomenon, which naturally inspire us to propose a new model to describe directional switch information transfer for bird flocks. By extracting some factors influencing the collective motion, we establish a self-propelled model considering both moving directional change alignment and collision avoidance. Therein, collective moving direction switches are investigated with increasing proportion of leaders, which yields a phase transition of the collective moving direction. The present work sheds some light on the fact that a subtle value difference between different informed leaders might change the moving direction decision of the whole group.

The deployment of robot swarms that perform a task cooperatively is attracting more and more attention in these years. There is a big challenge for existing methods to transfer information without direct communication. In this work, inspired by the phenomenon of bee swarm convening recruits to the food resource with waggle dance, we present a behavior-based approach to transfer information, using the UAV’s trajectory pattern rather than direct communication. Two trajectory patterns are designed to transfer information. Furthermore, both of them are optimized subject to the constraint on UAVs. We show that both patterns are feasible through simulation, and the 8-type performs better than the b-type under given indexes. Our information transfer strategy can be used for search and rescue in extreme environments and other communication-denied scenarios to meet the transmission needs of target position information.

This paper studies the consensus problem for general linear networked systems subject to uniform quantization effect on directed topologies. A control framework consisting of a distributed control protocol and a dynamic event-triggering rule is developed to guarantee that the consensus error can exponentially converge to a bounded and adjustable set without bringing about Zeno phenomenon. Compared with traditional consensus problem requiring the continuous-time communication, the information transfer between adjacent agents in this paper only occurs at certain discrete instants at which specific events are triggered. Under the proposed dynamic event-based communication mechanism, the costs on the communication and computation can be further saved. Another distinct feature of this paper lies in that the designed control framework is applicable to directed strongly connected networks, which is more practical than existing results that are only suitable for undirected connected graphs. Finally, numerical simulation results are presented to confirm the validity of the theoretical results.

Due to the bypass of muscle activity or peripheral nerve control, brain-computer interface (BCI) technique has advantages in different fields such as medical field for the rehabilitation of paralyzed patients. Steady-state visual evoked potential (SSVEP) has been widely adopted in BCI applications. SSVEP based BCIs have the advantages of high information transfer rate, less or no need of training, and strong anti-interference, which could be used in the more natural asynchronous BCI application with control of the users rather than the operant system of the synchronous mode. In order to solve the problem of high false positive rate (FPR) in common asynchronous SSVEP BCI applications, this paper proposed an eye-tracking switch based BCI paradigm to reduce the FPR and to improve the performance of the asynchronous BCI system. In the proposed paradigm, the fixation point position instead of EEG signal is used to determine whether the system is in idle state. Experimental results showed that when eye-tracking switch was applied in the asynchronous SSVEP BCI, the FPR was reduced to less than 10% and the recognition accuracy (i.e., the true positive rate, TPR) can also be improved to a certain extent, which proved the applicability of the eye-tracking switch in asynchronous BCI applications.

Techniques designed to deliver information to users via the haptic channel often make use of multiple actuators spread over different body locations, sometimes employing multiple haptic modalities (e.g., vibration, skin stretch, temperature) in parallel. Instead, we sought to determine the information transfer capacity of a single vibrotactile actuator. To do so, we designed 90 multidimensional tactons using four parameters: the number of pulses, tempo, frequency, and amplitude, based on prior literature and results of pilot studies. We conducted a two-day experiment with 12 participants, which evaluated their accuracy in identifying each parameter and estimated the information transfer (IT) under continuously repeating and one-shot tacton presentation schemes. Participants achieved identification accuracy of individual parameters of approximately 70% for tempo, frequency, and amplitude, while their identification accuracy for number of pulses reached 90%. Although the overall tacton identification accuracy was only 34.12% and 38.66% for continuous and one-shot presentations, respectively, the high dimensionality of these tactons resulted in an estimated information transfer of 3.06 bits (for one-shot presentation), the highest IT achieved to date using only a single actuator. We provide a detailed analysis of these results, and drawing from the experiment, present guidelines for multidimensional tacton design.

This paper proposes an inter-vehicular communication model using Bluetooth for information transfer. V2vtechnology proposes a variety of solutions for passenger safety. According to research,1.35 million people die each year due to road crashes [1]. Present vehicle system uses radars, cameras to detect collisions and gives potential warnings to the drivers, leaving the decision to the driver. Our main motivation is to avoid crash collisions, reduce fatal accidents, traffic congestion. The proposed idea enhances the current systems by upgrading from alerting the drivers to communication between vehicles, helps the vehicle to take control over the situation and control its state. In this paper, the idea is demonstrated using two prototype models designed with an Ultrasonic sensor to detect nearby vehicles and objects, Bluetooth module which uses Bluetooth for real-time data transfer of mobility parameters such as speed, distance, etc. providing 360-degree awareness to the vehicle. Bluetooth can be replaced with any highly advanced wireless technologies according to requirements. Designed prototype models are tested under 3 common real-life scenarios such as slowdown, abrupt stop, overtaking. The average reaction brake time for a driver is 2.3 sec. Replacing the driver with the vehicle taking control over the situation when required helps us in reducing this reaction time which is a major cause of accidents, reduces traffic congestion.

During the development of complex customized products, task decomposition is highly subjective, and the coupling iteration between tasks is normally ignored, which might cause problems in practical process such as excessive iteration of design tasks, resource conflicts, and task delays. Therefore, this paper proposes a design task decomposition method based on the mapping rules of component nodes and design tasks, which establishes a design structure matrix based on task relevance. Through the K-means clustering algorithm, the design task is reorganized to make sure the information transfer between tasks considered, so as to satisfy the actual product development and to improve the accuracy and efficiency of design task decomposition. In the final part, the task decomposition process of the 28MN large aluminum extruding machine is demonstrated as an example to verify the feasibility of our method.

In this paper, an online fault diagnosis strategy based on time-series analysis is proposed, by combining the problem of inaccurate fault identification and fault classification during operation of the automatic control system in heat exchange station. First of all, this paper simulates common operating fault of heat exchange station, builds the fault database by selecting feature data, at the same time introduces Support Vector Data Description (SVDD) method to set the judgment threshold for the identification of unknown faults. Next, in order to solve the information transfer relationship between multiple operating conditions and various variables, transfer entropy and data sliding window technology are used to analyze the time series. Finally, introduces Long Short-Term Memory (LSTM) to classify of status and determine of fault type for different running data sequences. This method can identify known types of faults online, and can intelligently increase the abnormal detection of unknown faults verified by physical simulation platform, and it is helpful to guarantee the operation safety of the heat exchange station. This method can also be used as a reference for fault detection and dynamic and accurate classification in this kind of production process.

We consider a multi-way massive multi-input multi-output (mMIMO) relay via which several energy-constrained MIMO users exchange information by switching between wireless power generation and information transfer. The mMIMO relay and MIMO users experience spatially-correlated channels, which the current multi-way relaying literature ignores. We derive closed-form spectral efficiency (SE) expression for this system that is valid for a practical number of relay antennas. We design a max-min power control algorithm which ensures user fairness by jointly optimizing the charging time and transmit powers of the relay and users.

To enhance public safety, it is necessary to legitimately eavesdrop communication between suspicious users, such as terrorists and criminals. In this paper, we investigate legitimate eavesdropping in a wireless-powered suspicious communication network (WPSCN) with a wireless-powered suspicious transmitter and a suspicious receiver, who is legitimately eavesdropped by a legitimate monitor with the help from a wireless-powered friendly jammer. The “harvest-then-transmit” protocol is adopted by the suspicious communication to coordinate wireless energy transfer and information transfer. Both the suspicious receiver and the legitimate monitor can act as power sources for the suspicious transmitter and the jammer. The legitimate monitor can control the suspicious communication rate by broadcasting power signals, while the jammer can harvest energy and then interfere with the suspicious communication in collaboration with the legitimate monitor. Four different metrics are adopted to evaluate the eavesdropping performance based on different application requirements, and they are the successful eavesdropping probability, the average eavesdropping rate, the relative eavesdropping rate, and the eavesdropping energy efficiency. For each performance metric, we derive the optimal transmit power of the monitor for energy transfer and the optimal jamming transmit power of the jammer to maximize the performance metric under the peak transmit power constraint at the monitor and the energy causality constraint at the jammer. Some important insights are derived from these optimal power control policies. The simulations are conducted to verify the proposed optimal power control for legitimate eavesdropping in the WPSCN. It is shown that the proposed optimal power control policies greatly outperform other benchmark schemes.

The magnetic induction (MI) communication has been proved to be an effective method in the wireless underground sensor networks (WUSN). MI communication system based on quasi-cyclic low density parity check (QC-LDPC) codes is expected to improve the transmission performance in WUSN for its good performance and hardware friendliness. In this paper, we aim to construct an optimized QC-LDPC code with low encoding complexity for MI communication system. Firstly, we present a novel method, magnetic induction - protograph extrinsic information transfer (MI-PEXIT) algorithm, which can evaluate the performance of the QC-LDPC codes and predict the distance threshold for successfully transmission of the direct MI communication. Then, we combine the discrete particle swarm optimization (PSO) algorithm with the MI-PEXIT algorithm, and propose a novel scheme DPMIP method to optimize the QC-LDPC codes for MI communication system. Furthermore, two groups of QC-LDPC codes with low encoding complexity are constructed by the proposed DPMIP algorithm, which have different code parameters and different coil parameters. Simulation results validate the superiority of the proposed QC-LDPC codes for MI communication in WUSN, and the proposed codes also can achieve a good trade-off between the complexity and the performance.

At present, the modeling of an ultrasonic transducer at the lower ultrasound range (20 kHz) during data transmission is being defaced. Ultrasonic sensors are a key part of ultrasonic technology. The use of ultrasonic vibrations is one of the simplest and most effective ways to process an ultrasonic signal. The advantage of this method is that ultrasound is not audible to the human ear, so it does not have a negative mental effect on a person and by increasing the radiation power, a larger suppression area can be achieved. More recently, information-processing technologies in corporate communication systems and information transfer using ultrasonic vibrations are increasingly being used. This is because more advanced applications are emerging in the field of wireless (short and long range) communication and data transmission, and at the same time, new challenges arise that require these corporate systems to have high frequency, wide bandwidth and compact size. The use of simplified physical models does not allow obtaining the expected results in the process of structural analysis of the latter. In this regard, the need arose for the integration of models that regulate and take into account a wide variety of material properties and allow you to solve with the help of powerful software in computer modeling is the ANSYS software package.

Recently, Vehicular Ad hoc Networks (VANETs) is focused on by many researchers. VANET’s specific characteristics like high-speed, unstable communication link, network partitioning, and information transfer become a hard challenging problem. Access control depends on IEEE 802.11p and has been used for Vehicle to Everything (V2X) and Vehicle to Vehicle (V2V) communications. The high dynamics of VANET nodes which will cause frequent disconnection of links and even partitioning of networks causes the source vehicle is lost their message of data that disseminated to the destination region, and also may lead to the message redundancy, causing a broadcast storm. One of the suggested solutions for this in VANET is the clusters. This paper will focus on the dissemination of cluster-based data. The clustering algorithm is provided according to the highway road, the proposed algorithm seeks to increase the network stability by identifying the Cluster Head (CH) periodically. By using a suggested variable range of cluster head (R-of-CH) in this paper to be a measure of finding the CH for each new vehicle. The simulation results are performed by NetLogo, under different vehicle densities and different values of (R-of-CH). The results of the simulation show the proposed algorithm contributes to making the network more stable with a high delivery rate as the disseminated messages will reach all vehicles in the simulated area. It indicates that the coverage rate is also high and the lost message rate is very low.

Recent progress in technologies and medical science have given momentum to Brain-computer interfaces (BCIs) system in generalizing human-computer communication, specifically for the people struggling with neuro-muscular disabilities. Currently, BCI systems heavily rely upon efficient electroencephalograms (EEG) signal detection from the current available solutions due to their noninvasiveness. However, the existing BCI systems works efficiently for less number of user instructions. Therefore, in this study, Adaptive Correlation based Component Analysis (ACCA) method is proposed to establish a proper Brain-computer interfaces (BCIs) system between user and computer. Here, correlation analysis is performed to identify spatial probability for a particular frequency using Adaptive Correlation based Component Analysis (ACCA) method. Maximum correlation between EEG signal phases triggered by both single and multiple flickers are generated using ACCA method to efficiently extract SSVEP component. The performance of proposed ACCA method is analysed against traditional SSVEP extraction methods in terms of classification accuracy, time consumption and Information Transfer Rate (ITR) for different subjects.

The article describes the structure of the national ITS network and the formation of the structure of the certification system in the Russian Federation. To ensure the proper level of information and cybersecurity of intelligent transport systems and intelligent road infrastructure, including in matters of depersonalization of personal data of users, a system for issuing security certificates should be built. Ensuring the security of data transmission while ensuring the operation of ITS is the most important task when implementing new technologies and services. To ensure the control of the relevance of certificates of compliance with the level of ensuring the safety of equipment and technological processes in ITS, it is necessary to form a national structure for issuing safety certificates.

In this work, we consider the design of a space-division multiple access (SDMA) scheme where a set of energy harvesting users and a set of information decoding users receive energy and information, respectively, from a reconfigurable intelligent surface (RIS) assisted base station that replaces conventional repeaters. Based on precoding for SDMA and weight phases for the RIS used to model the BS signal, we propose optimization algorithms to optimize precoders at the BS and phase shifter coefficients of the IRS passive elements and obtain different trade-offs between information transfer and energy harvesting. The obtained results show that the use of a multiantenna base station and a RIS is very useful for simultaneous transmission of information and energy, permitting a reduction of the transmitted power and improving the performance in terms of the coverage area and harvested energy.

Brain-computer interface (BCI) is a technology that provides a direct communication channel between a user and the external environment using the user's brain activity. For the past decades, however, most BCI systems were tested with young people, even though the elderly are the primary target users of the BCI systems. Moreover, it has been frequently reported that a BCI system with the elderly showed significantly lower performance than that with young people. In the present study, to evaluate the feasibility of a steady-state visual evoked potential (SSVEP)-BCI-based home appliance control system, seventeen people over the age of 65 were recruited in an offline experiment in which their SSVEP responses were recorded while the visual stimuli were presented in an augmented reality (AR) environment via a see-through head-mounted display (HMD). The average classification accuracy of 94.1% and an information transfer rate (ITR) of 46.5 bits/min were achieved with respect to the window size of 3 s. Based on the results from the offline experiment, we tested the proposed online home appliance control system with a 65-year-aged female while the online experiment is still ongoing, increasing the number of the participants.

Inventory management is a part of the supply chain where inventory and quantities of stock are tracked in and out of the stockroom. Proper handling of inventory will leads to successful supply chain management in any organization. QR codes make this inventory management speedy. This fast information transfer will also reduce the number of errors in inventory records and also gives accurate results to make informed decisions during frequent reviews. But based on Quick Response (QR) code for inventory management will become a centralized database. Blockchain facilitates manufacturers to connect each party from distribution centers and retail partners, to suppliers and production sites — with an abiding record of each, single exchange that occurred. These put away records are available to everybody inside the P2P organize and gives decentralization. The degree of straightforwardness and permanency gave in blockchain are frequently useful for manufacturers to oversee item roots and traceability. Smart contracts, one of the features of blockchain, have built-in automation, which makes a lot of sense for transaction management. In this paper, we are using both the features of QR code and blockchain for transparent, distributed, and reliable inventory management.

The use of Remotely Piloted Vehicles (RPV), or Unmanned Aerial Vehicles (UAV), is constantly growing, thanks to their versatility, flexibility of use, low cost and capability of reuse, even in environments that are very dangerous or impossible for the human. UAVs are mostly used during various operation tasks (military, scientific or civil), as search and rescue, disaster assessment, urban traffic monitoring, war zone defense monitoring, 3D mapping, archaeological site prospection, delivery of small packages and others. Estimation of features around the flight path and surveillance of areas where there is a high number of vehicles are of extreme importance for typical UAV missions. This paper focuses on a system capable of monitoring crowded areas or impervious sites, identifying a safety area for appropriate vertical landing or takeoff maneuver of the UAV with a high level of accuracy and robustness, even without using GNSS-derived navigation information, and with on-board terrain hazard detection and avoidance (DAA) capabilities. We propose a support control system for vertiport airspace design that must precede practical operations in the framework of urban air mobility (UAM). The system is composed by a mechanically rotating real-time LiDAR (Light Detection And Ranging) sensor, used for perception and localization (ranging) of obstacles in a surveillance volume, linked to a Raspberry Pi 3 as SBC (Session Board Controller), and interfaced to a GCS (Ground Control Station) by wireless connection for data management and 3D information transfer.

In Wireless Sensor Networks (WSNs), the power utilization is high due to recognized data transmission as compared with processing of data carried out within the sensor hub. To save the energy data aggregation methodology generally used which conserves the energy by removing the redundant information transfer in highly dense sensor networks. In this paper, proposed convention improved energy efficient structure-free data aggregation and delivery (IESDAD) collects the redundant information at the transitional nodes. In the proposed convention, every transitional node calculates waiting time for data packets very carefully due to which data collection carries out proficiently along the routing path. The recognized information packets are sent legally at the comprehension point for information collection. The IESDAD convention, the bounce hub is chosen dependent on cost work for the information transmission and delivery without open preservation of a network configuration. It also uses three-dimensional and time-based correlation for minimizing the overall redundant data transmission in wireless sensor networks to maximize the network life-time. The presentation of the proposed convention is estimated by performing widespread simulations. The simulation outcome shows that it performs well in the existing structure-free conventions in accordance with average and maximum energy consumption, throughput, delay and average energy consumption in mobile MIMO & SISO.

Biological studies have found that the grid cells in the entorhinal cortex of the rat brain structure and the place cells in the hippocampus structure have a mutual input and output cyclic relationship in the process of information transmission, from entorhinal cortex-hippocampus information transfer loop. The traditional neural network model from grid cells to place cells is not enough to satisfy the information transmission relationships, ignores the information transfer from place cells to grid cells, to solve this problem, a neural network model based on grid cells in entorhinal cortex and place cells in hippocampus information transfer loop is proposed. The simulation results show that the model can simulate the firing characteristics of place cells and grid cells.

Consider a multi-user wireless information transfer system where a multiple-antenna access point (AP) aims to sell its downlink radio access to the end users in an auction premise so that the social welfare is maximized. To this end, the AP holds auctions in which each user bids for its desired minimum signal-to-interference-plus-noise power ratio. Based on the user’s channel state information, bids, and service demands, the AP seeks the optimal set of users for which information streams are to be allocated through beamforming. We formulate the optimization problem of finding the optimal allocation rule, apply brute-force search approach to find all the feasible allocation sets using uplink-downlink duality-based algorithm (UDD), and obtain the optimal allocation rule. To circumvent the time-greediness of conventional optimization methods such as semi-definite relaxation or UDD, we propose a deep neural network architecture, and use it to solve the optimization problem with a good accuracy. The training data is collected by solving offline plenty of network realizations via the application of the UDD algorithm.

The complex underwater environment leads to a small number of samples and a serious lack of target information. To solve the above problems, this paper proposes an underwater target detection method based on third party information transfer learning (TITL). Firstly, the method obtains the targets feature description from the knowledge base as third-party information. Using the third-party information and target information fusion, constitute a new target domain. Solved the problem of low detection accuracy caused by insufficient target data. Secondly, according to the uneven distribution of underwater image features, this method introduces the feature distribution difference adaptive principle into integrated migration learning. This method reduces the time of feature mapping and ensures the accuracy of migration learning. Experimental results show that the algorithm is effective in the Underwater Target dataset compared with existing algorithms.