Search

The huge amount of data produced in the 6G networks not only brings new challenges to the reliability and efficiency of mobile devices but also drives rapid development of new storage techniques. With the benefits of fast access speed and high reliability, NAND flash memory has become a promising storage solution for the 6G networks. In this paper, we investigate a protograph-coded bit-interleaved coded modulation with iterative detection and decoding (BICM-ID) utilizing irregular mapping (IM) in the NAND flash-memory systems. First, we propose an enhanced protograph-based extrinsic information transfer (EPEXIT) algorithm to facilitate the analysis of protograph codes in the IM-BICM-ID systems. With the use of EPEXIT algorithm, a simple design method is conceived for the construction of a family of high-rate protograph codes, called irregular-mapped accumulate-repeat-accumulate (IMARA) codes, which possess excellent decoding thresholds and linear-minimum-distance-growth property. Furthermore, motivated by the voltage-region iterative gain characteristics of IM-BICM-ID systems, a novel read-voltage optimization scheme is developed to acquire accurate read-voltage levels, thus minimizing the decoding thresholds (in dB) of protograph codes. Analyses and simulations indicate that the proposed IMARA-aided IM-BICM-ID scheme and read-voltage optimization scheme remarkably improve the convergence and decoding performance of flash-memory systems. Thus, the proposed protograph-coded IM-BICM-ID can be viewed as a reliable and efficient storage solution for the new-generation mobile networks, such as Internet of Vehicles.

In a real-world setting, object instances from new classes can be continuously encountered by object detectors. When existing object detectors are applied to such scenarios, their performance on old classes deteriorates significantly. A few efforts have been reported to address this limitation, all of which apply variants of knowledge distillation to avoid catastrophic forgetting. We note that although distillation helps to retain previous learning, it obstructs fast adaptability to new tasks, which is a critical requirement for incremental learning. In this pursuit, we propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared. This ensures a seamless information transfer via a meta-learned gradient preconditioning that minimizes forgetting and maximizes knowledge transfer. In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection. We evaluate our approach on a variety of incremental learning settings defined on PASCAL-VOC and MS COCO datasets, where our approach performs favourably well against state-of-the-art methods., Comment: Published in IEEE Transactions on Pattern Analysis & Machine Intelligence, Nov 2021. Code is available in https://github.com/JosephKJ/iOD

Evolutionary multitasking (EMT) with the ability to tackle multiple different tasks has attracted more and more attention. The transferred knowledge among tasks can simultaneously improve the solving efficiency of all optimization problems in the evolutionary process. However, if the way of knowledge transfer is inappropriate, the negative knowledge transfer will make a bad effect on the performance of EMT algorithms. It is worth studying that how to promote the positive knowledge transfer across tasks. In this paper, a multiobjective multitasking algorithm named EMT-DAVT is introduced. The proposed algorithm contains two components, namely subspace distribution alignment (DA) strategy and decision variable transfer (VT) mechanism. In DA strategy, a learned mapping matrix is utilized to align the distributions in the subspaces and reduces the divergence between subpopulations belonging to different tasks. Then, VT mechanism is implemented which can further promote the positive information transfer. The two proposed strategies interact with each other to improve the knowledge transfer. Finally, the search engine is designed to balance exploration and exploitation. The experimental results on the multiobjective multitasking test suites demonstrate that EMT-DAVT outperforms other classical multiobjective multitasking evolutionary algorithms.

This article focuses on individualized degradation modeling and prognostics for a heterogeneous group, where each individual unit shows a distinct degradation process. Existing degradation models usually treat each unit separately and do not fully utilize the distinct characteristics of each individual. In this study, we propose a generic framework to handle the heterogeneity across units by effectively leveraging the intrinsic covariate information, which is closely related to the unit's degradation process. Specifically, we employ a multivariate Gaussian process (MGP) to nonparametrically establish the relation between the covariate information and degradation process. Through modeling the unit similarities based on the covariates, efficient information transfer among units is enabled for better degradation modeling and prognostics, as the collected degradation signals from one unit can be shared with the entire heterogeneous group. A theoretical justification for the proposed model is also investigated. Simulation studies are presented to evaluate the parameter estimation accuracy and the sensitivity of the proposed method. A case study on the Alzheimer's disease (AD) neuroimaging initiative data set is further conducted, which demonstrates the advantage of the proposed method over existing benchmark approaches.

Multiobjective multifactorial optimization (MO-MFO), rooted in a multitasking environment, is an emerging paradigm wherein multiple distinct multiobjective optimization problems are solved together. This article proposes an evolutionary multitasking algorithm with learning task relationships (LTR) for MO-MFO. In the proposed algorithm, a procedure of LTR is well designed. The decision space of each task is treated as a manifold, and all decision spaces of different tasks are jointly modeled as a joint manifold. Then, through solving a generalized eigenvalue decomposition problem, the joint manifold is projected to a latent space while keeping the necessary features for all tasks and the topology of each manifold. Finally, the task relationships are represented as the joint mapping matrix, which is composed of multiple mapping functions, and they are utilized for information transfer across different decision spaces during the evolutionary process. In the empirical experiments, the performance of the proposed algorithm is verified and compared with several state-of-the-art solvers for MO-MFO on three suites of MO-MFO test problems. Empirical results demonstrate that the proposed algorithm surpasses other competitors on most test instances, and can well tackle complicated MO-MFO problems which involve distinct optimization tasks with heterogeneous decision spaces.

This paper investigates an intelligent reflecting surface (IRS) aided wireless powered communication network (WPCN) with a focus on multicast transmissions in a game-theoretic way, where a transmitter first harvests energy from a power station (PS) and then transmits the information to multiple Internet of Things (IoT) devices in a multicast form. An IRS is deployed to assist the wireless energy transfer (WET) and wireless information transfer (WIT) processes. Considering that the PS and the transmitter belong to different service providers, we propose two schemes based on Stackelberg game, namely IRS-aided transmitter dominant energy trading (IRS-TDET) with the transmitter as the leader and IRS-aided PS dominant energy trading (IRS-PDET) with the PS as the leader. To solve the non-convex optimization problem of the leader-level game in the IRS-TDET scheme, we first derive the closed-form optimal phase shifts in the WET stage, and then use the semidefinite relaxation approach to solve the max-min problem about the phase shifts of the WIT stage. Finally, a low-complexity alternating optimization algorithm is developed to solve the simplified non-convex optimization problem with regard to the energy price and WET time. Numerical results show that the performance of proposed IRS-TDET and IRS-PDET is superior to that of the corresponding non-IRS aided schemes, and the deployment of IRS can effectively improve the utilities of both the PS and the transmitter. In addition, it is preferred to deploy the IRS near the transmitter in WPCN, and the transmitter can be deployed near the PS or the IoT devices to obtain higher utility.

This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

Brain computer interface (BCI) P300 speller can help severely disabled patients communicate and control with external machines or robots, so that the classification methods of P300 Electroencephalogram (EEG) signal play an important role in the development of BCI system and technology. In this paper, a novel support vector neural network (SVNN) is proposed and developed to obtain more accurate and effective EEG classification results. It is the first time to combine linear variational inequality (LVI) based primal-dual neural network with convex quadratic programming problem based on support vector machine to solve the classification problem. It has been proved that the support vector neural network globally converges to the optimal solution of convex optimization problem and iterates the parameters in the form of matrix, which means that the method has global convergence and parallelism. The proposed SVNN method is used to solve the classification problem of P300 EEG signals. Experimental results on data set IIb from BCI competition II and data set II from BCI competition III show that the accuracy of the proposed SVNN method is 100% and 98%, respectively. Compared with most of the state-of-theart algorithms, SVNN has the highest recognition accuracy and information transfer rate.

Existing multifactorial particle swarm optimization algorithms treat all particles equally with a consistent inter-task exemplar selection and generation strategy. This may lead to poor performance when the algorithm searches partial optimal areas belonging to different tasks at the later stage. In pedagogy, teachers teach students in different levels distinctively under their cognitive and learning abilities. Inspired by this idea, in this work, we devise a novel level-based inter-task learning strategy upon a dynamic local topology of inter-task particles. The proposed method separates particles into several levels and assigns particles to different levels with distinct inter-task learning methods. Specifically, we propose a level-based inter-task learning strategy to transfer sharing information among the cross-task neighborhood. By assigning the particles with diverse search preferences, the algorithm is able to explore the search space by using the cross-task knowledge, while reserving an ability to refine the search area. In addition, to address the issue of inter-task neighbor selection, we reform dynamically the local topology structure across the inter-task particles by methodical sampling, evaluating and selecting processes. Experimental results on the benchmark problems demonstrate that the proposed method enables the efficient cross-domain information transfer via the level-based inter-task learning.

Reflection modulation based on reconfigurable intelligent surface (RIS) is considered to be a promising information transfer mechanism without requiring any additional radio frequency chains. However, existing reflection modulation schemes consider manipulating the ON/OFF states of RIS elements, which suffers from power loss. In this paper, we propose a new scheme, called RIS-aided quadrature reflection modulation (RIS-QRM), for harvesting the reflection power based on an RIS-aided downlink multiple-input single-output (MISO) wireless system. To this end, RIS-QRM partitions RIS elements into two subsets for reflecting impinging signals into two orthogonal directions as well as passive beamforming and encodes its local data onto the element partition options. A closed-form expression for the unconditional pairwise error probability of RIS-QRM in Rician fading is derived assuming the maximum-likelihood detection. Moreover, we propose a low-complexity detection method with compromised performance for decoupling the joint search of the constellation symbol and the RIS element partition. Computer simulation results corroborate the effectiveness of RIS-QRM and validate the analytical results. It is shown that RIS-QRM is able to improve the error performance of the additional bits delivered by the RIS without deteriorating that of the bits carried on the constellation symbol, as compared to ON/OFF-based schemes.

In recent years, many efficient evolutionary multitasking (EMT) algorithms have been proposed to solve multi-objective multi-task optimization problems. However, EMT algorithms often face negative transfer problems. In this paper, a novel multi-objective evolution strategy, called CT-EMT-MOES, is proposed based on a cultural transmission theory for solving multi-objective multitask optimization problems. First, two evolutionary operators inspired by cultural transmission theory are proposed. The elite-guided variation strategy can transfer the information from the current Pareto front to all individuals and guide the population to quickly converge. The horizontal cultural transmission strategy can efficiently transfer information from the source task to the target task. Second, to solve the negative transfer problem, an adaptive information transfer strategy is proposed to adaptively adjust the probability of an information transfer. Third, the proposed algorithm can gain a Pareto front with good convergence and diversity by utilizing a smaller population size and fewer computing resources. As a result, the proposed algorithm can effectively utilize the implicit similarity and complementarity between simultaneous optimized tasks to improve the overall convergence efficiency and reduce a negative transfer. Finally, comprehensive experimental results show that the proposed algorithm can achieve a better performance compared with previous state-of-the-art multi-objective EMT algorithms.

The article questions if human speech communication (SC) involves a transfer of information. The information functioning in speech communication is dwelled upon in the information and systemic activity approaches. The informational approach adequately explains only the direct method of information transfer, while the systemic activity approach is relevant for the sign-mediated speech communication typical for human interaction. The more heuristic thesis is that the perception of the chain of linguistic sign bodies produced in the intersubjective space only starts the construction of the perceived speech message content by the recipient. The completeness of the constructed speech message content depends entirely on the recipient, who has the optimal common consciousness with the speaker. The purpose of speech messages is not the actual construction of the content by the recipient, but the development of the message personal meaning. In human speech communication, the communicants do not transmit information, but use verbal signs bodies to actualize images of consciousness which are developed within a single ethnic culture and therefore are common for them. The incentive for the common consciousness development by the communicants is their participation in joint activities that ensure their earthly existence.

Molecular communication often involves coexisting links where certain links may have priority over others. In this work, we consider a system in three-dimensional (3-D) space with two coexisting communication links, each between a point transmitter and fully-absorbing spherical receiver (FAR), where the one link (termed primary) has priority over the second link (termed secondary). The system implements the underlay cognitive-communication strategy for the co-existence of both links, which use the same type of molecules for information transfer. Mutual influence of FARs existing in the same communication medium results in competition for capturing the information-carrying molecules. In this work, first, we derive an approximate hitting probability equation for a diffusion-limited molecular communication system with two spherical FARs of different sizes considering the effect of molecular degradation. The derived equation is then used for the performance analysis of primary and secondary links in a cognitive molecular communication scenario. We show that the simple transmit control strategy at the secondary transmitter can improve the performance of the overall system. We study the influence of molecular degradation and decision threshold on the system performance. We also show that the systems parameters need to be carefully set to improve the performance of the system.

A magnetic recording read channel has numerous parameters that must be carefully tuned for its best performance; these include not only the equalizer coefficients, but also any parameters inside the soft-output detector, some of which may be pattern-dependent, including signal levels, predictor coefficients, and residual noise variances. Conventional tuning strategies based on a minimum-mean-squared error criterion are not optimal in terms of frame-error rate and ultimately areal density. Here, we propose a strategy for optimizing the parameters with the aim of minimizing the frame-error rate after error-control decoding. The proposed strategy exploits the close connection between the frame-error rate and the gap between the two curves in an extrinsic information transfer (EXIT) chart. A stochastic gradient algorithm applied to a cost function that quantifies this gap leads to our proposed adaptive minimum-frame-error rate (AMFER) algorithm for adapting the equalizer and detector parameters. Numerical results based on a quasi-micromagnetic simulated channel show that the AMFER parameters can reduce the frame-error rate by more than two orders of magnitude, leading to a 7% gain in areal density over conventional minimum mean-square-error (MMSE) parameters.

Recent years have witnessed the successful development of health-related Internet-of-Things (IoT) devices, i.e., electroencephalographic (EEG), paving a ground-breaking way for better understanding the functionals in our brains. Despite massive research on EEG, there lacks an effective way to interpret complex EEG signals due to the shortage of informative EEG data, the challenge in capturing sophisticated connectivity patterns of EEG signals, and unfavorable results of the inherent noise associated with data collection. In this article, novel heterogeneous-domain deep learning is proposed to address these issues. Especially, we first propose a new scheme to extract the multilevel latent features using hybrid networks and provide two pathways for reconstructing the EEG signals. As a core part of the proposed method, the scheme considers the complex dependencies among adjacent EEG channels, spatiotemporal connectivity, and signal denoising. In addition, a novel consistency regularization method is proposed to enhance information sharing among the multilevel latent feature obtained from the labeled and unlabeled EEG samples, which is beneficial for both the information transfer and accelerating the training. Finally, we provide comprehensive case studies on the Lomonosov Moscow State University EEG data set, demonstrating that the proposed methods achieve superior performance than all the competing ones over a wide range of experimental settings.

Visible thermal person reidentification (VT Re-ID) in the 6G-enabled Visual Internet of Things (VIoT) is essential for implementing 24-h surveillance in wide-area space. The existing methods mainly focus on reducing the visual difference between visible and thermal visual streams (V-streams). However, they cannot fully exploit the information of visible and thermal V-streams, which leads to suboptimal representations. In this article, we propose a novel deep network termed the mutual learning convolutional neural network (MLCNN) to transfer useful information between visible and thermal V-streams for VT Re-ID in 6G-enabled VIoT. The proposed MLCNN consists of the feature generation module and the mutual learning module. The feature generation module aims to map the input samples into a more discriminative feature space in order to decrease the intramodality difference. The mutual learning module employs the identification loss to supervise the predictive identity accuracy and utilizes the Kullback–Leibler divergence to constitute the mutual learning loss for useful information transfer. Extensive experiments on two popular visible thermal data sets (SYSU-MM01 and RegDB) prove the effectiveness of the proposed MLCNN.

This paper designs and analyzes the bit-interleaved coded modulation with iterative demapping and decoding (BICM-ID) framework under color-shift-keying (CSK) modulation to achieve spectrally efficient transmissions in visible light communication (VLC) systems. Specifically, we investigate the protograph low-density parity-check (LDPC) codes, bit-to-symbol mapping schemes, and constellation labeling rules in higher-order CSK-aided VLC systems. To fully explore the BICM-ID benefit, we propose an irregular CSK mapping, in which an $M$ -ary standardized constellation (called “Standard constellation” ) and a new $M$ -ary constellation (called “Target constellation” ) are separately applied to modulate two different coded sub-blocks in a codeword to optimize the error performance of the CSK-aided VLC systems. Furthermore, using a modified protograph extrinsic information transfer (PEXIT) algorithm, we construct two types of protograph LDPC codes (i.e., unpunctured and punctured protograph LDPC codes) that outperform the conventional protograph LDPC codes. Both analytical and simulation results show that the proposed irregular-CSK-mapped BICM-ID employing the new protograph LDPC codes and Target constellation is capable of achieving outstanding performance, and is a promising transmission solution for high-speed VLC applications.

Biology and electronics are both expert at receiving, analyzing, and responding to information, yet they use entirely different information processing paradigms. Biology processes information using networks that are intrinsically molecular while electronics process information through circuits that control the flow of electrons. There is great interest in coupling the molecular logic of biology with the electronic logic of technology, and we suggest that redox (reduction-oxidation) is a uniquely suited modality for interfacing biology with electronics. Specifically, redox is a native biological modality and is accessible to electronics through electrodes. We summarize recent advances in mediated electrochemistry to direct information transfer into biological systems intentionally altering function, exposing it for more advanced interpretation, which can dramatically expand the biotechnological toolbox.

Data communication and computer networks have enormously grown in every aspect of businesses. Computer networks are being used to offer instantaneous access to information in online libraries around the world. The popularity and importance of data communication has produced a strong demand in all sectors job for people with more computer networking expertise. Companies need workers to plan, use and manage the database system aspects of security. The security policy must apply data stored in a computer system as well as information transfer a network. This paper aimed to define computer data backup policies of the Incremental backup by using Unison synchronization as a file-synchronization tool and load balancing file synchronization management (LFSM) for traffic management. The policy is to be able to perform a full backup only at first as a one time from obtaining a copy of the data. The easiest aspect of value to assess is replacement for restoring the data from changes only and processing the correct information. As a result, the new synchronization technique was able to improve the performance of data backup and computer security system.

Information security, reliability of data transfer are today an important component of the globalization of information technology. Therefore, the proposed work is devoted to highlighting the results of the design and development of a hacking-resistant algorithm to ensure the integrity of information transfer via digital technology and computer engineering. To solve such problems, cryptographic hashing functions are used. In particular, elements of deterministic Chaos were introduced into the developed cyclic hashing algorithm. The investigation analyzes in detail the strengths and weaknesses of known hashing algorithms. They are shown to have disadvantages. The main ones are a large number of matches (Hamming (x, y) and the presence of a weak avalanche effect, which lead to a significant decrease in the reliability of the algorithm for hacking. The designed hashing algorithm uses an iterative Merkley-Damgard structure, augmented by the input message to a length multiple of 512bits. Processing in blocks of 128-bit uses cellular automata with mixed rules of 30, 105 and 90, 150 and takes into account the dependence of the generation of the initial vector on the incoming message. This allows half of the 10,000 pairs of arbitrary messages to have an inverse Hamming distance of 0 to 2. The proposed algorithm is four times slower than the well-known family of "secure hash algorithms." However, computation speed is not a critical requirement for a hash function. Decreasing the sensitivity to the avalanche effect allows the generation time to be approximately halved. Optimization of the algorithm, as well as its testing was carried out using new technologies of the Java programming language (version 15). Suggestions and recommendations for improving this approach to data hashing are given also

Developments in information and related technologies have led to a wider use of the Internet of things (IoT). By integrating both virtual and physical worlds, IoT creates an integrated communication framework of interrelated things and operating systems. With the advent of IoT systems based on digital remote care, transferring medical data is becoming a daily routine. Healthcare is one of the most popular IoT applications and tries to monitor patients’ vital signs during the day for weeks and to eliminate the need for hospitalization. In a healthcare system, many sensors are installed to collect the patient’s information, including environmental monitoring sensors and vital and unstructured message sensors in order to reduce the patients’ expenses. The IoT network contains flexible sensors in dynamically changing environments where sensors collect environmental information and send it to nursing stations for healthcare applications. Due to the wireless nature of IoT networks, secure data transmission in the healthcare context is very important. Data collected from sensors embedded in healthcare devices may be lost for various reasons along the transmission path. Therefore, establishing a secure communication path in IoT networks in the context of healthcare is of great importance. In this paper, in order to provide a reliable data transfer protocol in the context of healthcare, a reliable routing using multiobjective genetic algorithm (RRMOGA) method is presented. The contribution of this paper can be summarized in two steps: (i) using a multiobjective optimization approach to find near-optimal paths and (ii) using reliable agents in the network to find backup paths. The simulation outcomes reveal that the proposed approach, based on the use of the multiobjective optimization approach, tries to find optimal paths for information transfer that improve the main parameters of the network. Also, the use of secure agents leads to a secure information transfer in the network in the context of healthcare. Experimental results show that the proposed method has achieved reliability and data delivery rates, 99% and 99.9%, respectively. The proposed method has improved network lifetime, delivery rate, and delay by 14%, 2%, and 5.6%, respectively.

Bit-interleaved coded modulation with iterative decoding (BICM-ID) is a key technology enabling the communication systems to realize reliable and efficient data transmission. In this work, we present a study on the adaptive irregular-mapping (AIM)-aided BICM-ID systems with the use of protograph low-density parity-check (PLDPC) codes. To be specific, we first propose a three-step design strategy to generate a type of adaptive constellations, which can be combined with their corresponding initial constellations to formulate an AIM scheme for improving the BICM-ID performance. Then, we put forward a theoretical-analysis tool, called AIM-based protograph extrinsic information transfer (PEXIT) algorithm , to estimate the convergence performance of PLDPC codes in the AIM-aided BICM-ID systems. By making use of such an algorithm, we further develop a code-design method to construct a new type of PLDPC codes, called enhanced PLDPC codes , tailored for the AIM-aided BICM-ID systems. Both convergence analysis and simulations indicate that the proposed AIM-aided BICM-ID systems perform better than the existing counterparts, and their performance can be significantly improved by using the enhanced PLDPC codes. Consequently, the proposed designs have great potential to meet the high-reliability, high-throughput, and strong link-adaptation requirements of future wireless-communication scenarios, such as green Internet of Things (IoT).

In this mymargin letter, we investigate a backbone-assisted wireless powered communication network (WPCN) under the protocol of “harvest-then-transmit” protocol, where user nodes (UNs) harvest energy from the sinks in the downlink (DL), then they use the energy to transmit signals to the sinks by a non-orthogonal multiple access in the uplink (UL). We consider joint successive interference cancellation and backbone-assisted sink cooperation to reduce the impact of co-channel interference on the throughput performance. We maximize the system throughput by jointly optimizing user scheduling and the time allocation for DL energy harvesting and UL transmission. For solving the maximum throughput problem efficiently, we propose a column generation method based on a greedy algorithm. Numerical results show that our proposed energy and information transfer design is superior to other reference methods in WPCNs.

The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how detrimental ambiguous spike patterns, those that are neither clearly bursts nor isolated spikes, are for neuronal information transfer. We addressed this question using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on dynamical properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.

In this paper, the backward-compatible coding schemes are explored to achieve the near-capacity performances in dynamic LDPC-coded Gaussian multiple access channels (GMAC). Specifically, some user-load-compatible (ULC) masked edges in a raptor-like protograph are masked with the variation of user load $u$ . To distinguish the edges in a protograph, a multi-user protograph-based extrinsic information transfer (MU-PEXIT) chart is developed. Based on this chart, an iterative edge selection algorithm combined with a theoretical simplification is proposed to facilitate the selection of independent masked edge sets (MESs) for varying $u$ . To further decrease the complexity of the independent masking scheme, a nested version is presented, where the MESs for varying $u$ are nested and derived from the same mother MES. Meanwhile, nested selection algorithms are provided to select the nested MESs. Extensive simulation results demonstrate that the masked 5G-NR LDPC codes can maintain near-capacity performances regardless of the variation of $u$ . Besides, these ULC masked codes achieve comparable performances with the state-of-the-art LDPC codes which are specially re-designed in a static GMAC.

Wireless power transfer technologies such as simultaneous wireless information and power transfer (SWIPT) have shown significant potential to revolutionise the design of future wireless communication systems. When the only energy source is from the wireless signals that are mainly intended for information communications, the sustainability and outage performance of SWIPT systems become critical factors in theoretical evaluation and practical applications. This paper firstly models the energy harvesting and energy consumption of the power splitting protocol based SWIPT systems to investigate the general sustainability condition. We further model the power and information transfer outage probabilities using Markov Chains, which are unique for SWIPT systems since they both could cause communication outage. We further demonstrate how to apply the closed-form expression of the outage to optimise the key parameter of splitting ratio for SWIPT systems. Hardware and numerical experiments demonstrate the validity of the proposed model and outage analysis, and confirm the effectiveness of the solution to calculate the optimal splitting ratios under different signal and channel conditions.

Wireless communication technology is rapidly progressing due to its high quality and high speed of information transfer from one location to another. It is necessary to ensure the safety of wireless sensor networks with this progress. One of the main security concerns in WSNs is eavesdropping, an intrusion that collects information from other devices across the network. Eavesdropping attacks are insidious and it is hard to realise that they happen. When connected with a network, users can feed sensitive information inadvertently, such as passwords, account numbers, browsing, email content, etc. Security improvement in wireless network communication is therefore required.

Purpose The main concept analysis (MCA; Kong, 2009) quantifies the effectiveness and efficiency of information transfer during verbal discourse by means of four sets of sequential pictorial stimuli. This test was originally developed for a Cantonese-speaking population. The main goals of this study were (a) to translate and adapt the MCA to Dutch; (b) to establish normative data for healthy native Dutch-speaking adults; (c) to assess the effect of age, education level, and gender on MCA outcome; and (d) to establish inter- and intrarater reliability of the Dutch MCA. Method Language samples were collected from 60 healthy native Dutch speakers, equally recruited in different age (20–39 years, 40–59 years, 60–79 years) and education (middle and highly educated) categories through administration of the MCA. Utterances produced by at least 75% of the participants were included as a main concept in the Dutch MCA. Subsequently, age-specific normative data were established for each of the MCA parameters. Finally, an ICC was calculated in order to verify inter- and intrarater reliability of the Dutch MCA. Results The translated MCA consisted of 19 main concepts. Age-specific normative data were obtained. Both age and education level had a significant effect on MCA outcome. Information transfer in elderly was both less effectively and efficiently compared to young- and middle-aged adults. In addition, highly educated participants transferred information less efficiently compared to middle educated participants. Based on inter- and intrarater reliability measures, the Dutch MCA proved to be a reliable measuring instrument. Conclusions The MCA was translated to Dutch, and age-specific normative data were established for a healthy, Dutch-speaking population. The Dutch MCA is a reliable tool for eliciting and quantifying discourse production. Validation of the test for people with aphasia is necessary in order for the test to be useful in a clinical practice.

Motivation The emergence of single-cell RNA sequencing (scRNA-seq) has led to an explosion in novel methods to study biological variation among individual cells, and to classify cells into functional and biologically meaningful categories. Results Here, we present a new cell type projection tool, Hierarchical Random Forest for Information Transfer (HieRFIT), based on hierarchical random forests. HieRFIT uses a priori information about cell type relationships to improve classification accuracy, taking as input a hierarchical tree structure representing the class relationships, along with the reference data. We use an ensemble approach combining multiple random forest models, organized in a hierarchical decision tree structure. We show that our hierarchical classification approach improves accuracy and reduces incorrect predictions especially for inter-dataset tasks which reflect real-life applications. We use a scoring scheme that adjusts probability distributions for candidate class labels and resolves uncertainties while avoiding the assignment of cells to incorrect types by labeling cells at internal nodes of the hierarchy when necessary. Availability and implementation HieRFIT is implemented as an R package, and it is available at (https://github.com/yasinkaymaz/HieRFIT/releases/tag/v1.0.0). Supplementary information Supplementary data are available at Bioinformatics online.

This letter explores shortening technology for achieving near Gaussian multiple access channel (GMAC) capacity in low-density parity-check coded multi-user systems. To optimize the shortening patterns, a hybrid extrinsic information transfer (H-EXIT) tool, which integrates EXIT and protograph-based EXIT, is firstly developed. Based on this analysis, H-EXIT priority (HEP) algorithm is proposed to facilitate the optimization of shortening patterns. It can be observed that columns with larger degrees are prior to be selected, which differs from those for point-to-point scenarios. Inspired by this finding, we further propose largest-column-degree priority (LCDP) algorithm, which narrows the selection space to lower the complexity while maintains a comparable performance. Extensive simulation results demonstrate the superiority of proposed shortening schemes from two aspects: 1) Proposed shortening can bring nonnegligible gain over unshortening with consistent sum rate; 2) HEP and LCDP algorithms outperform benchmark algorithms.

This paper studies an intelligent reflecting surface (IRS) aided wireless powered sensor network (WPSN). Specifically, a power station (PS) provides wireless energy to multiple internet of thing (IoT) devices which supports them to deliver their own messages to an access point (AP). Moreover, we deploy an IRS to enhance the performance of the WPSN by intelligently adjusting the phase shift of each reflecting element. To evaluate the performance of the IRS assisted WPSN, we maximize its sum throughput to jointly optimize the phase shift matrices and the transmission time allocations. Due to the non-convexity of the formulated optimization problem, we first derive the optimal phase shifts of the wireless information transfer (WIT) in closed-form. Consequently, a semi-definite programming (SDP) relaxed approach is considered to jointly design the phase shift matrix of the wireless energy transfer (WET) and the transmission time allocations. In addition, we propose a low complexity scheme to gain insights and reduce the computational complexity incurred by the SDP relaxed scheme. Specifically, the optimal solutions of the phase shifts and the transmission time allocation are derived in closed-form by the Majorization-Minimization (MM) algorithm, the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. Finally, numerical results are presented to validate the proposed schemes and confirm the beneficial role of the IRS in comparison to the benchmark schemes, where the proposed IRS assisted scheme achieves almost 100% higher sum throughput, in comparison to the counterpart without IRS.

Content-sharing is one of the major applications of vehicular networks. To fully utilize the spectrum and the connection time, rate-compatible codes are required when sharing content. In this paper, we present a simple and flexible method to construct low-complexity rate-compatible codes for content sharing. We first present a novel construction framework for rate-compatible codes via recursive block Markov superposition transmission (rBMST). In the proposed construction, the shared content is partitioned into equal-length data chunks and transmitted directly, while their replicas are taken as the inputs of a given number of parallel systematic encoders to generate parity-check chunks. These parity-check chunks are then transmitted in parallel in a recursive block Markov superposition transmission manner. The proposed construction is flexible in the sense that codes with arbitrary rates can be obtained by adjusting the number of parallel rBMST encoders and the number of randomly punctured bits. We show that the simplest construction, using repetition to generate the parity-check chunks, leads to high-performance and low-complexity rate-compatible rBMST (RC-rBMST) codes. Specifically, the extrinsic information transfer (EXIT) chart analysis shows that asymptotic thresholds of the repetition-based RC-rBMST (RB-RC-rBMST) codes are within 0.25 dB of the channel capacities for a wide range of coding rates. Numerical results are presented to confirm the advantages of the RB-RC-rBMST codes in performance and complexity. Particularly, the RB-RC-rBMST codes perform as well as BMST-R codes but with much lower computational complexities.

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.

Aim. The presented study aims to examine approaches to organizing the educational process in the system of general education in accordance with the basic principles of lean philosophy, better known in Russia as “lean manufacturing philosophy”, which has proven to be an effective tool for building business processes in industrial enterprises.Tasks. The authors determine the value created by applying the principles of lean education in the general education system, describing the value creation flow and its customer; examine the classification of losses in the value stream and identify losses in the educational process based on national standards for lean manufacturing and quality management systems; apply the described system to the educational process of Gorchakov MGIMO Lyceum.Methods. This study uses empirical (observation, measurement) and theoretical (abstraction, formalization, and modeling) research methods, as well as methods of grouping, comparative, structural, and functional analysis.Results. The study substantiates the authors’ hypothesis that the analysis of the educational process through the definition of value for the consumer/customer and the identification of different types of losses transforms the value creation flow into the “lean education” flow, which perfectly meets the needs of stakeholders. The maps of the current and future state of the value creation flow of the educational process of Gorchakov MGIMO Lyceum are modeled. A comparative classification of the types of losses in the manufacturing and educational processes is created. The results of testing the hypothesis in the educational process of the specified lyceum are presented.Conclusions. Application of lean philosophy (the concept of lean manufacturing) can improve various activities, including education. The study provides evidence for this by defining and constructing the value creation flow in accordance with the requests of all stakeholders in the educational process and also by identifying losses in the methods and processes of information transfer and further evaluation of the knowledge and skills of students.

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).

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 paper presents and analyses two distinct Building Information Models (BIM) developed for the same heritage building, the Chalet of the Countess of Edla, located in Sintra, Portugal. This building, incorporated in 1995 in the UNESCO’s World Heritage List within the Cultural Landscape of Sintra, was severely damaged by fire in 1999, and two distinct BIM-based solutions were developed afterwards, in 2009 and 2020. The instances followed two approaches, the first being a rehabilitation-oriented digital representation, while the second is a centralized resource for building maintenance. In this study the distinct features and characteristics of both models are discussed, highlighting the importance of information transfer from the model set up during the reconstruction to the later, which was surveyed to document the “as-built” construction. An example of this is the complementarity between the two models, which allowed the incorporation of building features, including non-visible constructive elements, from the first model into the management-oriented BIM.