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Catalytic combustion provides a feasible technology to effectively avoid NOx formation due to the high temperature in the process of hydrogen combustion. Recently, the development of efficient and affordable catalysts has attracted extensive attention as the cost of noble metals remained high. In this study, a list of cordierite monolithic catalysts with low noble metal loading are prepared by introducing the appropriate amount of transition metals (Mn, Cu, Fe, Co, and Ce), and their catalytic performance are further investigated. On this basis, the study on catalytic stability and kinetic is carried out, and the synergistic interaction between the bimetals is revealed with comprehensive characterization methods. The results show that Cu could promote the reaction rate of the Pt-based catalyst which show the best catalytic performance with the Cu loading of 1 g/L. The ignition temperature T10 is 58.5 °C, and the burnout temperature T100 is 100 °C, with activation energy as low as 44.00 kJ/mol. The enhancement of the catalytic performance is attributed to the interaction between the bimetallic elements. On the one hand, the valence state of Cu and Pt is changed, and on the other hand the dispersion of Pt get improved, which contributed to the catalytic reaction rate.

In 2019, the world experienced the rapid outbreak of the Covid-19 pandemic creating an alarming situation worldwide. The virus targets the respiratory system causing pneumonia with other symptoms such as fatigue, dry cough, and fever which can be mistakenly diagnosed as pneumonia, lung cancer, or TB. Thus, the early diagnosis of COVID-19 is critical since the disease can provoke patients’ mortality. Chest X-ray (CXR) is commonly employed in healthcare sector where both quick and precise diagnosis can be supplied. Deep learning algorithms have proved extraordinary capabilities in terms of lung diseases detection and classification. They facilitate and expedite the diagnosis process and save time for the medical practitioners. In this paper, a deep learning (DL) architecture for multi-class classification of Pneumonia, Lung Cancer, tuberculosis (TB), Lung Opacity, and most recently COVID-19 is proposed. Tremendous CXR images of 3615 COVID-19, 6012 Lung opacity, 5870 Pneumonia, 20,000 lung cancer, 1400 tuberculosis, and 10,192 normal images were resized, normalized, and randomly split to fit the DL requirements. In terms of classification, we utilized a pre-trained model, VGG19 followed by three blocks of convolutional neural network (CNN) as a feature extraction and fully connected network at the classification stage. The experimental results revealed that our proposed VGG19 + CNN outperformed other existing work with 96.48 % accuracy, 93.75 % recall, 97.56 % precision, 95.62 % F1 score, and 99.82 % area under the curve (AUC). The proposed model delivered superior performance allowing healthcare practitioners to diagnose and treat patients more quickly and efficiently.

Real-time monitoring in smart grids requires efficient handling of massive amount of data. Fog cloud nodes can be strategically located within the smart grid to: pull readings from smart meters, implement local processing and control, and make all data available to the smart grid control center with minimum overall latency. Unlike existing studies in literature, we propose a novel Fog node allocation strategy that is tightly coupled with the power grid structure, and hence, accounts for the spatial distribution of data traffic sources (e.g., smart meters) within the power grid. Furthermore, the allocation strategy considers the diverse latency requirements of fixed scheduling and event driven data services within the power grid. The proposed allocation strategy first implements an unsupervised machine learning approach to determine initial number and locations of Fog nodes that can serve the data traffic with minimum overall latency. Then, a reinforcement-based mechanism is applied to minimize the required number of Fog nodes, and hence capital cost, through efficient mapping between Fog nodes and smart meters while still complying with the latency requirements. Our simulation studies demonstrate that a 50% reduction in required number of Fog nodes can be achieved while minimizing overall latency when the proposed allocation strategy is adopted.

The majority of cancer-related deaths globally are due to lung cancer, which also has the second-highest mortality rate. The segmentation of lung tumours, treatment evaluation, and tumour stage classification have become significantly more accessible with the advent of PET/CT scans. With the advent of PET/CT scans, it is possible to obtain both functioning and anatomic data during a single examination. However, integrating images from different modalities can indeed be time-consuming for medical professionals and remains a challenging task. This challenge arises from several factors, including differences in image acquisition techniques, image resolutions, and the inherent variations in the spectral and temporal data captured by different imaging modalities. Artificial Intelligence (AI) methodologies have shown potential in the automation of image integration and segmentation. To address these challenges, multimodal fusion approach-based U-Net architecture (early fusion, late fusion, dense fusion, hyper-dense fusion, and hyper-dense VGG16 U-Net) are proposed for lung tumour segmentation. Dice scores of 73% show that hyper-dense VGG16 U-Net is superior to the other four proposed models. The proposed method can potentially aid medical professionals in detecting lung cancer at an early stage.

Abstract Real‐time facial expression recognition is the basis for computers to understand human emotions and detect abnormalities in time. To effectively solve the problems of server overload and privacy information leakage, a real‐time facial expression recognition method based on iterative transfer learning and efficient attention network (EAN) for edge resource‐constrained scenes is proposed in this paper. Firstly, an EAN is designed with its parameter number and computation amount strictly limited by depth separable convolution and local channel attention mechanism. Then, the soft labels of facial expression data were obtained by EAN based on the idea of knowledge distillation, so as to provide more supervision information for the training process. Finally, an iterative transfer learning method of teacher‐student (T‐S) network was proposed; it refines the soft labels of the teacher network and further improves the recognition accuracy of the student network. The tests on the public datasets, FER2013 and RAF‐DB, show that this method can significantly reduce the model complexity and achieve high recognition accuracy. Compared with other advanced methods, the proposed method strikes a good balance between complexity and accuracy, and well meets the real‐time deployment requirements of facial expression recognition technology for edge resource‐constrained scenes.

In the near future, intelligent vehicles will be part of the Internet of Things (IoT) and will offer valuable services and opportunities that could revolutionise human life in smart cities. The Vehicular Ad-hoc Network (VANET) is the core structure of intelligent vehicles. It ensures the accuracy and security of communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) modes to enhance road safety and decrease traffic congestion. However, VANET is subject to security vulnerabilities such as denial-of-service (DoS), replay attacks and Sybil attacks that may undermine the security and privacy of the network. Such issues may lead to the transmission of incorrect information from a malicious node to other nodes in the network. In this paper, we present a biometrics blockchain (BBC) framework to secure data sharing among vehicles in VANET and to retain statuary data in a conventional and trusted system. In the proposed framework, we take advantage of biometric information to keep a record of the genuine identity of the message sender, thus preserving privacy. Therefore, the proposed BBC scheme establishes security and trust between vehicles in VANET alongside the capacity to trace identities whenever required. Simulations in OMNeT++, veins and SUMO were carried out to demonstrate the viability of the proposed framework using the urban mobility model. The performance of the framework is evaluated in terms of packet delivery rate, packet loss rate and computational cost. The results show that our novel model is superior to existing approaches.

In licensed shared access (LSA) systems, the protection of the incumbent in the shared spectrum may degrade the spectrum and energy efficiency of the licensee. In this paper, we examine the optimization of these two performance metrics in a LSA vertical sharing scenario between an airport incumbent, and a mobile network operator licensee. Considering a restriction zone of a pre-defined radius, we derive the probability of the incumbent's interference threshold and then formulate a power allocation scheme as a multi-objective optimization of both energy and spectrum efficiency. We then adopt the weighted sum method to convert this multi-objective optimization into a single objective optimization and convert that into a quasi concave optimization problem. The optimum power allocation is then obtained using fractional programming. We further investigate the impact of various critical operational parameters in conjunction with the two performance metrics. Simulation results indicate a significantly improved energy efficiency in the licensee network as well as the spectrum efficiency comparable to even when the LSA spectrum utilization is unrestricted by the incumbent's maximum interference threshold. Furthermore, we show that with careful selection of the licensee eNodeB coverage radius, transmit power, and users number per eNodeB coverage area, one can engineer the best possible trade-off between the spectrum and energy efficiency.

Due to the tremendous volume of data generated by urban surveillance systems, big data oriented low-complexity automatic background subtraction techniques are in great demand. In this paper, we propose a novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected. This method is both simple and robust with respect to changes in light conditions.

Cognitive radio (CR) enables unlicensed users to sense for and access underutilized licensed channels (or white spaces) owned by the licensed users in an opportunistic manner. Clustering segregates nodes in a network into logical groups called clusters. In CR networks (CRNs), larger cluster size improves network scalability thereby contributing to reduced routing overhead; however, it reduces cluster stability as the number of available common channels in a cluster reduces resulting in increased number of re-clusterings and clustering overhead. This paper presents our proposed first-of-its-kind cluster size adjustment scheme based on an artificial intelligence approach called reinforcement learning. The proposed scheme adapts the cluster size with the amount of white spaces as time goes by in order to improve network scalability and cluster stability in CRNs. Due to the lack of progress in the investigation of cluster size adjustment schemes in the literature, this paper also analyzes their attributes, and then presents such schemes investigated in various kinds of distributed wireless networks. Simulation results show that our proposed scheme improves network scalability by creating larger clusters, and improves cluster stability by reducing the number of re-clusterings (i.e., the number of cluster splits) and clustering overhead, while reducing interference between licensed and unlicensed users in CRNs.

Hybrid networks consisting of both millimeter wave (mmWave) and microwave $(\mu W)$ capabilities are strongly contested for next-generation cellular communications. A similar avenue of current research is device-to-device (D2D) communications, where users establish direct links with each other rather than using central base stations. However, a hybrid network, where D2D transmissions coexist, requires special attention in terms of efficient resource allocation. This paper investigates dynamic resource sharing between network entities in a downlink transmission scheme to maximize energy efficiency (EE) of the cellular users (CUs) served by either $(\mu W)$ macrocells or mmWave small cells while maintaining a minimum quality-of-service (QoS) for the D2D users. To address this problem, first, a self-adaptive power control mechanism for the D2D pairs is formulated, subject to an interference threshold for the CUs while satisfying their minimum QoS level. Subsequently, an EE optimization problem, which is aimed at maximizing the EE for both CUs and D2D pairs, has been solved. Simulation results demonstrate the effectiveness of our proposed algorithm, which studies the inherent tradeoffs between system EE, system sum rate, and outage probability for various QoS levels and varying densities of D2D pairs and CUs.

In this paper, we investigate the resource allocation problem for achieving spectral efficiency (SE) and energy efficiency (EE) tradeoff with users' minimum rate requirements in hybrid multi-carrier non-orthogonal multiple access (MC-NOMA) systems which incorporate both NOMA and orthogonal multiple access (OMA) modes into one unified framework. All the degrees of freedom involved in resource allocation, including the choice of multiple access (MA) modes, user clustering, subcarrier assignment, and power allocation, are jointly considered. We first formulate the SE-EE tradeoff as a multiobjective optimization (MOO) problem with minimum rate requirement constraints. Then, considering the non-convexity of the MOO problem, it is converted into a single-objective optimization (SOO) problem by utilizing weighted Tchebycheff method. Lagrangian dual decomposition and sequential convex programming are applied to solve the SOO problem. We propose a joint resource allocation algorithm which is applicable to the general case, where an arbitrary number of users can be multiplexed on the same subcarrier. Simulation results demonstrate that users' minimum rate requirements and channel conditions have great impact on the selection of MA modes. The proposed hybrid MC-NOMA mode significantly outperforms MC-NOMA and OMA in terms of SE-EE tradeoff, and the performance gain brought by four or more users sharing the same subcarrier is minimal. Meanwhile, the hybrid MC-NOMA also shows great potential to improve the tradeoff between fairness and system efficiency.

Non-orthogonal multiple access (NOMA) has recently received much attention as a candidate technique for the fifth generation networks. In this paper, considering both the direct and relay-aid paths, we investigate the performance of a downlink NOMA-based cooperative system and further analyze two different user ordering schemes. The outage probability, diversity gain, and ergodic rate are studied as three benchmarks to evaluate the system performance. For different user ordering schemes, the exact outage probabilities of users are first solved in the closed form. Then, the outage behavior in the high signal-to-noise ratio (SNR) region is discussed to obtain the diversity gain. In addition, the closed-form expression of ergodic rate for the strongest user and upper bounds for the rest users at high SNR is provided. Finally, numerical results verify the accuracy of our analysis and demonstrate that sorting users based on the relay-aided path can provide a larger ergodic sum rate in some cases. By contrast, sorting users based on the direct path can provide a better diversity gain, and the corresponding performance is less sensitive to relay's location.

This paper considers a line-of-sight underwater acoustic (UWA) sensor network consisting of M underwater sensor nodes randomly deployed according to uniform distribution within a vertical half-disc (the so-called trusted zone). The sensor nodes report their sensed data to a sink node on water surface on a shared UWA reporting channel in a time-division multiple-access fashion, while an active-yet-invisible adversary (so-called Eve) is present in the close vicinity who aims to inject malicious data into the system by impersonating some Alice node. To this end, this paper first considers an additive white Gaussian noise (AWGN) UWA channel, and proposes a novel, multiple-feature-based, two-step method at the sink node to thwart the potential impersonation attack by Eve. Specifically, the sink node exploits the noisy estimates of the distance, the angle of arrival, and the location of the transmit node as device fingerprints to carry out a number of binary hypothesis tests (for impersonation detection) as well as a number of maximum-likelihood (ML) hypothesis tests (for transmitter identification when no impersonation is detected). We provide closed-form expressions for the error probabilities (i.e., the performance) of most of the hypothesis tests. We then consider the case of a UWA with colored noise and frequency-dependent pathloss, and derive a ML distance estimator as well as the corresponding Cramer-Rao bound. We then invoke the proposed two-step, impersonation detection framework by utilizing distance as the sole feature. Finally, we provide detailed simulation results for both AWGN UWA channel and the UWA channel with colored noise. Simulation results verify that the proposed scheme is indeed effective for a UWA channel with the colored noise and frequency-dependent pathloss.

In this paper, we investigate the resource optimization problem for device-to-device (D2D) communications in the fifth-generation cellular networks, where multiple D2D links and cellular links share the same spectrum. A general framework is proposed to jointly optimize the energy efficiency (EE), spectral efficiency (SE), and queuing delay. Specifically, we formulate the problem as a stochastic optimization model aiming at maximizing the EE and SE concurrently under the network stability constraint, where subchannel allocation and power control are jointly optimized. Afterwards, with the help of Lyapunov techniques and weighted sum method, it is then transformed into a single-objective optimization problem, which is a mixed-integer and non-convex problem. Therefore, to solve this challenging subchannel allocation and power control problem with low computational complexity, we separate it into two levels of problems, and a two-stage iterative algorithm is proposed, which only requires polynomial computational complexity. Through theoretical analysis and numerical results, the effectiveness, convergence, and optimality of the proposed algorithm are validated.

In this paper, we present a novel power allocation scheme for multicarrier cognitive radio networks. The proposed scheme performs subchannel power allocation by incorporating primary users activity in adjacent cells. Therefore, we first define the aggregated subchannel activity index (ASAI) as an average indicator which characterizes the collective networkwide primary users’ communication activity level. The optimal transmit power allocation is then obtained with the objective of maximizing a total utility function at the secondary base station (SBS), subject to the maximum SBS transmit power, and collision probability constraint at the primary receivers. Utilizing ASAI, we further obtain an energy efficient power allocation for the secondary system. Optimal energy efficiency (EE) and spectral efficiency (SE) are contradicting objectives, and thus, there is a tradeoff between these two performance metrics. We also propose a design approach to handle this tradeoff as a function of the ASAI, which provides quantitative insights into efficient system design. In addition to a lower signaling overhead, the simulation results confirm that the proposed scheme achieves a significantly higher achievable rate. Simulation results further indicate that using ASAI enables obtaining an optimal operating point based on the tradeoff between EE and SE. The optimal operating point can be further adjusted by relaxing/restricting the sensing parameters depending on the system requirements.

The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorithm exhibits slightly faster convergence speed than its classical ANN counterpart, which was considered in our previous work. Finally, we conclude that our solution can accurately resolve the energy efficiency problem and that it can be extended to optimize other communications problems, such as the global optimal power control problem, with promising trainability and generalization ability.

Current resource allocation techniques in cellular networks are largely based on single-slope path loss model, which falls short in accurately capturing the effect of physical environment. The phenomenon of densification makes cell patterns more irregular; therefore, the multi-slope path loss model is more realistic to approximate the increased variations in the links and interferences. In this paper, we investigate the impacts of multi-slope path loss models, where different link distances are characterized by different path loss exponents. We propose a framework for joint user association, power and subcarrier allocation on the downlink of a heterogeneous network (HetNet). The proposed scheme is formulated as a weighted sum rate maximization problem, ensuring the users' quality-of-service requirements, namely users' minimum rate, and the base stations' (BSs) maximum transmission power. We then compare the performance of the proposed approach under different path loss models with demonstrate the effectiveness of dual-slope path loss model in comparison to the single-slope path loss model. Simulation results show that the dual-slope model leads to significant improvement in network's performance in comparison to the standard single-slope model by accurately approximating the path loss exponent dependence on the link distance. Moreover, it improves the user offloading from macrocell BS to small cells by connecting the users to nearby BSs with minimal attenuation. It has been shown that the path loss exponents significantly influence the user association lying across the critical radius in the case of the dual-slope path loss model.

The quality of experience (QoE) perceived by users is a critical performance measure for Web browsing. “Above-The-Fold” (ATF) time has been recently recognized and widely used as a direct measure of user-end QoE by a number of studies. To reduce the ATF time, the existing works mainly focus on reducing the delay of networking. However, we observe that the webpage structures and content orders can also significantly affect theWeb QoE. In this paper, we propose a novel optimization framework that reorders the webpage objects to minimize the user-end ATF time. Our core idea is to first identify the webpage objects that consume the ATF time but have no impact on the page experience and then change the positions of these objects to achieve the minimum ATF time. We implement this framework and evaluate its performance with popular websites. The results show that the ATF time is greatly reduced compared with the existing works, especially for complex webpages.

This work was supported in part by the China MOST Program of International S&T Cooperation under Grant 2016YFE0133000 and Grant 2016YFE0119000, in part by the National Natural Science Foundation of China (NSFC) under Grant 61461136004, in part by NSFC Major International Joint Research Project under Grant 61210002, in part by the Fundamental Research Funds for the Central Universities under Grant 2015XJGH011, in part by EU Horizon 2020 Program EXCITING project under Grant 723227, in part by EU FP7 CROWN project under Grant PIRSES-GA-2013-610524, and in part by the China International Joint Research Center of Green Communications and Networking under Grant 2015B01008.

In this paper, we investigate the resource allocation problem for multi-user full-duplex deviceto-device (D2D) underlay communication, considering both perfect channel state information (CSI) and statistical CSI scenarios. In perfect CSI scenario, the weighted sum-rate maximization problem under cellular users' minimum rate constraints is formulated as a mixed integer programming problem. To solve the challenging problem, we decouple it into two subproblems as power allocation and channel assignment. Then we proposed a power allocation algorithm based on difference of two convex functions programming and a channel assignment algorithm based on Kuhn-Munkres algorithm, respectively. In statistical CSI scenario, we formulate the resource allocation problem as an outage probability constrained weighted ergodic sumrate maximization problem. To solve the problem, the closed-form expressions of outage probability and weighted ergodic sum-rate are derived first. Then we decouple resource allocation problem into power allocation and channel assignment. An optimization solution that consists of a 2-D global searching and Kuhn-Munkres algorithm is then developed. Simulation results demonstrate that the proposed algorithms can improve the weighted sum-rate of full-duplex D2D communications significantly both in perfect CSI and statistical CSI scenarios and confirm the accuracy of our derived closed-form expressions.

This paper proposes a joint energy efficiency (EE) and spectrum efficiency (SE) tradeoff analysis as a multi-objective optimization problem (MOP) in the uplink of multi-user multi-carrier two-tier orthogonal frequency division multiplexing access heterogeneous networks subject to users' maximum transmission power and minimum rate constraints. The proposed MOP is modeled such that the network providers can dynamically tune the tradeoff parameters to switch between different communication scenarios with diverse design requirements. In order to find its Pareto optimal solution, the MOP is transformed, using a weighted sum method, into a single-objective optimization problem (SOP), which itself can further be transformed from a fractional form, by exploiting fractional programming, into a subtractive form. Since the formulated SOP is hard to solve due to the combinatorial channel allocation indicators, we reformulate the SOP into a better tractable problem by relaxing the combinatorial indicators using the idea of time-sharing. We then prove that this reformulated SOP is strictly quasi-concave with respect to the transmission power and the subcarrier allocation indicator. We then propose an iterative two-layer distributed framework to achieve an upper bound Pareto optimal solution of the original proposed MOP. The numerical simulations demonstrate the effectiveness of our proposed two-layer framework achieving an upper bound Pareto optimal solution, which is very close to an optimal solution, with fast convergence, lower and acceptable polynomial complexity, and balanced EE-SE tradeoff.

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS).

To understand the possibility of monitoring the crustal stress and tectonic activities via satellite remote sensing technology, an experimental study focused on the thermal infrared variation was performed for cyclic loaded rock in the outdoor condition with two types of strong interference background. The stress-induced infrared radiation was extracted using wavelet analysis. The results showed that due to the significant effect of the ambient temperature, the weak stress-induced infrared signal was indistinguishable from the original infrared radiation. However, after wavelet decomposition, the infrared radiation concurrent with the change in stress became clear, and the correlation coefficient with the stress increased significantly with the value of 0.91 after decomposition. Additionally, the amplitude of the extracted stress-induced infrared signal was close to the theoretical result, indicating that the wavelet analysis method can extract the weak infrared signals induced by cyclic loading in the background of strong interference to some degree. The results provide an experimental basis and ideas for monitoring crustal stress and tectonic activities using thermal infrared remote sensing.

Previous studies have shown that thermal infrared radiation (TIR) changes with stress for loaded rocks. TIR changes were mainly attributed to temperature change without considering the change in surface emissivity. And it remains unclear whether there was a change in emissivity during the rock loading process. Therefore, based on the spectral radiance observations in this paper, an experimental study involving the emissivity variation in the 8.0–13.0 μm range for elastic loaded quartz sandstone under outdoor conditions was conducted. The experiments yield the following results. First, a variation in the stress condition led to the emissivity change in addition to the temperature change. The spectral radiance change was the combined result of the temperature changes and emissivity changes. Second, the emissivity changes linearly with the stress change, and the amplitude is relatively large in the 8.0–10.0 μm range. The waveband features of emissivity variation are the main factor leading to the waveband features of stress-induced radiance change. Third, the explanations for the changes in temperature and emissivity during loading process are analyzed. And the significance and difficulty for further satellite remote sensing purpose is discussed. The experimental results provide an experimental foundation for crustal stress field monitoring.

High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

The landscape of human phosphorylation networks has not been systematically explored, representing vast, unchartered territories within cellular signaling networks. Although a large number of in vivo phosphorylated residues have been identified by mass spectrometry (MS)‐based approaches, assigning the upstream kinases to these residues requires biochemical analysis of kinase‐substrate relationships (KSRs). Here, we developed a new strategy, called CEASAR, based on functional protein microarrays and bioinformatics to experimentally identify substrates for 289 unique kinases, resulting in 3656 high‐quality KSRs. We then generated consensus phosphorylation motifs for each of the kinases and integrated this information, along with information about in vivo phosphorylation sites determined by MS, to construct a high‐resolution map of phosphorylation networks that connects 230 kinases to 2591 in vivo phosphorylation sites in 652 substrates. The value of this data set is demonstrated through the discovery of a new role for PKA downstream of Btk (Bruton's tyrosine kinase) during B‐cell receptor signaling. Overall, these studies provide global insights into kinase‐mediated signaling pathways and promise to advance our understanding of cellular signaling processes in humans.