Search

Block copolymer (BCP) is a promising precursor to design ordered mesoporous carbon (OMC) materials for proton exchange membrane fuel cells due to its accessibility and controllability. However, enhancing the performance of OMCs through the integration of heteroatoms during the self-assembly process remains a challenging endeavor. Herein, an ammonia-assisted pyrolysis method is used to deposit N atoms in self-assembled OMC (N-OMC). The resulting N-OMC exhibits a well-ordered mesoporous structure with the N content up to 6.43 at.%. Upon PtCo nanoparticles loading (PtCo/N-OMC), the PtCo/N-OMC demonstrates exceptional electrocatalytic activity, evidenced by a half-wave potential of 0.876 V RHE and an electrochemical surface area of 98.19 m2·g−1. The single-cell test confirms the superiority of the N-OMC structure, revealing a current density of 1.53 A·cm−2 at 0.6 V and a peak power density of 1.17 W·cm−2. Theoretical calculations affirm that the pyridinic N-doping reduces oxygen binding energy, thereby enhancing oxygen reduction reaction activity. The confinement effect of the mesopores avoids aggregation of nanoparticles, while doping-N-induced defects further bolster their electrochemical activities. This work highlights the synergistic effect of N-doping defects and ordered mesoporous architectures, illustrating the exceptional potential of the BCP-derived OMC as an ideal support for electrocatalysis applications. [Display omitted] • Synthesis of efficient block copolymer-derived 3D ordered mesoporous carbon support for Pt-based electrocatalyst with synchronized ammonia-assisted pyrolysis. • Coordination environment and stability of N atoms in the block copolymer-derived carbon. • Synergistic enhancement of catalytic performance by N doping and confinement effect of ordered mesoporous structures. [ABSTRACT FROM AUTHOR]

Objectives: To compare the diagnostic performance for the detection of clinically significant prostate cancer (csPCa) between bpMRI with only axial T2WI (simplified bpMRI) and standard-multiparametric MRI (mpMRI). Methods: A total of 569 patients who underwent mpMRI followed by biopsy or prostatectomy were enrolled in this retrospective study. According to PI-RADS v2.1, three radiologists (A, B, C) from three centers blinded to clinical variables were assigned scores on lesions with simplified bpMRI and then with mpMRI 2 weeks later. Diagnostic performance of simplified bpMRI was compared with mpMRI using histopathology as reference standard. Results: For all the three radiologists, the diagnostic sensitivity was significantly higher with mpMRI than with simplified bpMRI (P < 0.001 to P = 0.035); and although specificity was also higher with mpMRI than with simplified bpMRI for radiologist B and radiologist C, it was statistically significant only for radiologist B (P = 0.011, P = 0.359, respectively). On the contrary, for radiologist A, specificity was higher with simplified bpMRI than with mpMRI (P = 0.001). The area under the receiver operating characteristic curve (AUC) was significantly higher for mpMRI than for simplified bpMRI except for radiologist A (radiologist A: 0.903 vs 0.913, P = 0.1542; radiologist B: 0.861 vs 0.834 P = 0.0013; and radiologist C: 0.884 vs 0.848, P = 0.0003). Interobserver reliability of PI-RADS v2.1 showed good agreement for both simplified bpMRI (kappa = 0.665) and mpMRI (kappa = 0.739). Conclusion: Although the detection of csPCa with simplified bpMRI was comparatively lower than that with mpMRI, the diagnostic performance was still high in simplified bpMRI. Our data justify using mpMRI outperforms simplified bpMRI for prostate cancer screening and imply simplified bpMRI as a potential screening tool. [ABSTRACT FROM AUTHOR]

Although promising the formidable configuration, vigorous evolution, and satisfactory performance, software-defined networking (SDN) is in its initial period all the same. Some essential issues still remain not completely resolved, and the scalability of control plane is the most intractable one with the explosive increase of network traffic. To address this issue, many researchers have proposed multiple controllers to realize logically centralized control layer. Our previous research has proposed multi-controller load balancing approach called HybridFlow. In this paper, taking advantage of HybridFlow, we propose an M-N policy multiple-controller sleeping mode by switching off redundant controllers when the system is in the light traffic condition. We use queuing theory to model the operation procedure of controllers and formulate the energy consumption management issue as a 0-1 integer linear programming model. Through turning off the redundant controllers when the system is in the scenario of light traffic, the total energy consumption of the whole system can be cut down. Simulation results reveal that the proposed M-N policy multiple-controller sleeping mode achieves superior energy efficiency compared to no sleeping mode and N policy sleeping mode. However, it introduces tolerable time delay. [ABSTRACT FROM AUTHOR]

Objectives To investigate the efficiency of multiparametric MRI (mpMRI) based on Prostate Imaging Reporting and Data System (PI-RADS) Version 2 (v2) in detecting clinically significant prostate cancer (PCa) and to test the interobserver consistency. Methods Based on PI-RADS v2, two radiologists reviewed the images of 372 patients who underwent prostate biopsy and prebiopsy mpMRI. Results There's significant correlation between higher PI-RADS score and the presence of clinical significant PCa ( P < .001). PI-RADS score 3 was the best cutoff point with sensitivity and specificity over 80%. The diagnostic concordance was moderate (kappa = 0.478). Conclusions PI-RADS v2 demonstrated good accuracy in detecting clinically significant PCa, however the interobserver consistency needs to be improved. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the performance of interference alignment of device-to-device (D2D) uplink underlay cellular networks. By fully considering the impact of imperfect precoding vectors caused by limited feedback, the exact closed-form expressions of average throughput for cellular network and D2D network are derived in terms of transmit power and the number of feedback bits. The accuracy of the average throughput is verified by simulation results. Our analytic results provide great promises to practical system designs. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the diagnostic power of using vertebral hydroxyapatite concentration measurements in unenhanced and contrast-enhanced spectral CT for detecting and predicting the risk of osteoporosis-associated fractures. Methods: L1 of 210 patients (105 men, 105 women; mean age, 64 years, range, 19–103 years) who had undergone spectral CT examinations from January 1, 2018, to March 1, 2019, were retrospectively analyzed. Patient data for 3 years after spectral CT were retrieved from electronic medical record information systems to obtain the incidence of osteoporotic fractures. Baseline vertebral cancellous hydroxyapatite concentration from unenhanced and contrast-enhanced late-arterial-phase images was measured. The receiver operating characteristic curves were used to evaluate the diagnostic power for detecting and predicting the 3-year risk of osteoporosis-associated fractures using hydroxyapatite concentrations in both phases. Results: The hydroxyapatite concentrations in both phases had good diagnostic power to detect fractures at baseline. The sensitivity and specificity for predicting one or more osteoporosis-associated fractures within 3 years after spectral CT were 76.80% and 93.10%, respectively, using the cutoff of 74.79 mg/cm3 in vertebral hydroxyapatite concentration in the unenhanced CT phase, and 82.87% and 82.76%, respectively, using the cutoff of 84.65 mg/cm3 in the late-arterial phase. Furthermore, there was no significant difference in the diagnosis between unenhanced and enhanced CT-derived hydroxyapatite concentrations (p = 0.360). Conclusions: Both unenhanced and enhanced spectral CT-derived hydroxyapatite concentrations can accurately detect and predict future risk of osteoporosis-associated fractures. The hydroxyapatite concentration assessed in the late-arterial phase may have a similar diagnostic efficacy to that in the unenhanced phase. Key Points: • A cutoff of 74.79 mg/cm3 of vertebral hydroxyapatite concentration in the unenhanced CT scans had 76.80% sensitivity and 93.10% specificity to predict one or more osteoporosis-associated fractures within 3 years after spectral CT examinations. • A cutoff of 84.65 mg/cm3 of vertebral hydroxyapatite concentration in the late-arterial-enhanced CT scans had 82.87% sensitivity and 82.76% specificity to predict one or more osteoporosis-associated fractures within 3 years after spectral CT examinations. • The hydroxyapatite concentration assessed in the late-arterial phase may have a similar diagnostic efficacy to that in the unenhanced phase. [ABSTRACT FROM AUTHOR]

Cluster identification of ultra-wideband (UWB) propagations is of great significance to the parameter extraction and measurement analysis of channel modeling. In this paper, we address this challenging problem within a promising biological processing framework. Both the two large-scale characteristics of each multipath component, i.e., the decaying amplitude and the time of arrivals, are organically combined and fully explored in the suggested cluster identification algorithm. Each resolvable trajectory component is first projected onto a 2-D amplitude–time plane and further modeled as a virtual ant-agent, which can move around in this 2-D workspace with a preference to the high local-environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identifications can be realized by the biological ant colony clustering procedure. Owing to the population-based intelligence and the involved positive-feedback collaboration during the agents evolution, the suggested algorithm can efficiently identify the involved multiple clusters in a completely automatic manner. Experiments on UWB channels validate the proposed method. The practical parameter configuration is analyzed, and a group of numerical performance metrics is derived. As demonstrated by numerical investigations, multiple clusters involved in UWB channel impulse responses can be accurately extracted. [ABSTRACT FROM AUTHOR]

Most of the existing spectrum sensing schemes utilize only the statistical property of fading channels, which unfortunately fails to cope with the time-varying fading channel that has disastrous effects on sensing performance. As a consequence, such sensing schemes may not be applicable to distributed cognitive radio networks. In this paper, we develop a promising spectrum sensing algorithm for time-variant flat-fading (TVFF) channels. We first formulate a dynamic state-space model (DSM) to characterize the evolution behaviors of two hidden states, i.e., the primary user (PU) state and the fading gain, by utilizing a two-state Markov process and another finite-state Markov chain, respectively. The summed energy, which serves as the observation of DSM, is employed for the ease of implementation. Relying on a Bayesian statistical inference framework, the sequential importance sampling based particle filtering is then exploited to numerically and recursively estimate the involved posterior probability, and thus, the PU state and the fading gain are jointly estimated in time. The estimations of two states are soft-outputs, which are successively refined with a designed iterative approach. Simulation results demonstrate that the new scheme can significantly improve the sensing performance in TVFF channels, which, in turn, provides particular promise to realistic applications. [ABSTRACT FROM PUBLISHER]

This paper introduces IFUC, which is an Improved Fuzzy Unequal Clustering scheme for large scale wireless sensor networks (WSNs).It aims to balance the energy consumption and prolong the network lifetime. Our approach focuses on energy efficient clustering scheme and inter-cluster routing protocol. On the one hand, considering each node's local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine each node's chance of becoming cluster head and estimate the cluster head competence radius. On the other hand, we use Ant Colony Optimization (ACO) method to construct the energy-aware routing between cluster heads and base station. It reduces and balances the energy consumption of cluster heads and solves the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The validation experiment results have indicated that the proposed clustering scheme performs much better than many other methods such as LEACH, CHEF and EEUC. [ABSTRACT FROM AUTHOR]

Objectives: The aim of the study was to externally validate two AI models for the classification of prostate mpMRI sequences and segmentation of the prostate gland on T2WI. Materials and methods: MpMRI data from 719 patients were retrospectively collected from two hospitals, utilizing nine MR scanners from four different vendors, over the period from February 2018 to May 2022. Med3D deep learning pretrained architecture was used to perform image classification,UNet-3D was used to segment the prostate gland. The images were classified into one of nine image types by the mode. The segmentation model was validated using T2WI images. The accuracy of the segmentation was evaluated by measuring the DSC, VS,AHD.Finally,efficacy of the models was compared for different MR field strengths and sequences. Results: 20,551 image groups were obtained from 719 MR studies. The classification model accuracy is 99%, with a kappa of 0.932. The precision, recall, and F1 values for the nine image types had statistically significant differences, respectively (all P < 0.001). The accuracy for scanners 1.436 T, 1.5 T, and 3.0 T was 87%, 86%, and 98%, respectively (P < 0.001). For segmentation model, the median DSC was 0.942 to 0.955, the median VS was 0.974 to 0.982, and the median AHD was 5.55 to 6.49 mm,respectively.These values also had statistically significant differences for the three different magnetic field strengths (all P < 0.001). Conclusion: The AI models for mpMRI image classification and prostate segmentation demonstrated good performance during external validation, which could enhance efficiency in prostate volume measurement and cancer detection with mpMRI. Clinical relevance statement: These models can greatly improve the work efficiency in cancer detection, measurement of prostate volume and guided biopsies. Keypoints: 1st Key Point: Robust gland segmentation is critical for cancer detection, measurement of prostate volume and guided biopsies. 2nd Key Point: Artificial Intelligence models achieved excellent agreement with radiologist consensus for prostate sequence recognition and segmentation. 3rd Key Point: We recommend 3.0 T and T2WI-nan preferred over 1.5T and T2WI_Fs in the prostate scan. [ABSTRACT FROM AUTHOR]

Recently, deep trackers based on the siamese networking are enjoying increasing popularity in the tracking community. Generally, those trackers learn a high-level semantic embedding space for feature representation but lose low-level fine-grained details. Meanwhile, the learned high-level semantic features are not updated during online tracking, which results in tracking drift in presence of target appearance variation and similar distractors. In this paper, we present a novel end-to-end trainable Convolutional Neural Network (CNN) based on the siamese network for distractor-aware tracking. It enhances target appearance representation in both the offline training stage and online tracking stage. In the offline training stage, this network learns both the low-level fine-grained details and high-level coarse-grained semantics simultaneously in a multi-task learning framework. The low-level features with better resolution are complementary to semantic features and able to distinguish the foreground target from background distractors. In the online stage, the learned low-level features are fed into a correlation filter layer and updated in an interpolated manner to encode target appearance variation adaptively. The learned high-level features are fed into a cross-correlation layer without online update. Therefore, the proposed tracker benefits from both the adaptability of the fine-grained correlation filter and the generalization capability of the semantic embedding. Extensive experiments are conducted on the public OTB100 and UAV123 benchmark datasets. Our tracker achieves state-of-the-art performance while running with a real-time frame-rate. [ABSTRACT FROM AUTHOR]

We investigate the interference-aware spectrum reuse for heterogeneous small cell networks (SCNs), by specially considering dense-user deployment and stochastic-environment uncertainties. Most existing approaches, which are lack of evolving coordinations and rely on precise channel state information, tend to be inefficient in the context of dense SCNs with uncertainties. To improve the performance, by introducing a reliable metric of successful transmission probability to characterize the individual utility, we adopt a correlated equilibrium (CE)-based game to formulate spectrum reuse, and propose a distributed regret-matching learning algorithm to achieve the CE solutions. Eliminating the dependence on definite information and with general CE points consideration, our new scheme is feasible under the varying environment and can obtain more promising solutions than the state-of-art reinforcement learning methods by encouraging players to coordinate their strategies. Numerical simulations demonstrate the advantages of our proposed scheme. [ABSTRACT FROM AUTHOR]

Dynamic spectrum sharing is considered as one of the key features in the next-generation communications. In this correspondence, we investigate the dynamic tradeoff between the sensing performance and the achievable throughput, in the presence of time-varying fading (TVF) channels. We first establish a unified dynamic state-space model to characterize the involved dynamic behaviors. On this basis, a promising dynamic sensing schedule framework is proposed, whereby the sensing duration is adaptively adjusted based on the estimated real-time TVF channel. We formulate the sensing-throughput tradeoff problem mathematically. We then show that there exists the optimal sensing duration maximizing the throughput for the secondary user (SU), which will change dynamically with channel gains. Relying on our designed recursive sensing paradigm that is able to blindly acquire varying channel gains as well as the PU states, the sensing duration can be then adjusted in line with the evolving channel gains. Numerical simulations are provided to validate our dynamic sensing schedule algorithm, which can significantly improve the SU's throughput by reconfiguring the sensing duration according to dynamic channel conditions. [ABSTRACT FROM AUTHOR]

In this paper, the throughput maximization of millimeter-wave (mm-Wave) ultradense networks (UDN) using dynamic spectrum sharing (DSS) is considered. Most of the existing works only allow temporal-domain access and admit at most one user at each time slot, resulting in significant underutilization of spectrum resource, which will be less attractive to mm-wave UDN applications. A generalized temporal-spatial sharing scheme is proposed in this paper for UDN by exploiting the location information of incumbent devices, where multiple users are allowed to access each channel simultaneously via spatial separations. For distributed applications, the global information exchange among secondary users (SU) tends to be impractical, given the unaffordable signaling overhead and latency. Thus, a noncooperative game with fine-grained two-dimensional reuse is formulated, which leads to a more efficient access strategy. It is then proved to be an ordinary potential game (OPG), which guarantees the existence of the strategy Nash equilibrium (NE). Finally, an improved decentralized reinforcement learning algorithm is designed, with which SUs can learn from wireless environments and adapt toward an NE point, relying on the individual observation and the historical action reward (rather than the global information exchanging). The convergence efficiency of the new scheme is also rigorously proved. Numerical simulations are provided to validate the performances of the proposed schemes. [ABSTRACT FROM AUTHOR]

With the development of smart mobile devices and various mobile applications, content-oriented service has become the most popular service which occupies network resources and results in high traffic load. In order to improve quality of experience in radio access network and reduce the OpEx and CapEx of operators, wireless network virtualization and network slicing come into the vision and are deemed as promising solutions to radio access networks to provide tailored services. Therefore, network slicing and optimization based on content-oriented service become a challenging research direction. In this paper, network slicing and resource optimization on content-oriented application in cache-enabled hybrid radio access network based on complex network are investigated. A Cooperative Network Slicing Framework Based on Content in RAN (CNSC-RAN) is presented. Based on CNSC-RAN, procedures of content-oriented static network slicing and dynamic slicing are proposed. Content-oriented slicing is modeled and analyzed which includes slicing on content cache resources and communication resources. In order to obtain the optimized resources sliced for each content, the optimization problem is formulated to minimize the average system cost to get the contents required by users. The problem is solved by a heuristic algorithm called CCSOA (Content-Centric Slicing Optimization Algorithm) in a dynamic content-oriented network slicing procedure enabling UEs with self-evicting contents. The performance of CCSOA is evaluated by performance metrics including hit rate, average cache occupation, average system cost, and request traffic reduction to macro cell base station comparing with CEE and ProbCache. Simulation results reveal the effectiveness of CCSOA. [ABSTRACT FROM AUTHOR]

CsiNet, a deep neural network framework, utilizes an autoencoder to efficiently transmit downlink channel state information (CSI) in the feedback link, which reduces the cost of feedback, and significantly improves the quality of the reconstruction. However, the model with massive parameters incurs a lot of storage space and high computational complexity, which is impractical for low‐cost and low‐power edge devices. In this work, a lightweight CsiNet based SNIP (Single‐shot Network Pruning) is implemented, which prunes the model using the gradient information of the first training epoch, eliminating both pretraining and the complex pruning schedule. Numerical simulation results show that, under the same compression rates, the method can achieve a similar or even better reconstruction effect and more effectively reduce computational complexity, compared to traditional lightweight methods. [ABSTRACT FROM AUTHOR]

Accurate channel state information (CSI) is extremely crucial to realize high‐accurate hybrid precoding, in millimeter wave communication systems. In order to improve the CSI estimation performance, several traditional channel estimators have been developed by exploiting the sparse or low‐rank property, whilst they bring huge channel training overhead and computational complexity. In this work, based on jointly sparse and low‐rank property of massive multiple input multiple output (MIMO) channel, one non‐convex mmWave channel estimation problem is formulated and a novel scheme to acquire one accurate CSI estimation result with greatly reduced training overhead is proposed. Specifically, the non‐convex problem is reformulated as two simple sub‐problems, by exploiting the alternating direction method of multipliers (ADMM) technique. Based on the low‐rank characteristic, one fast gradient descent matrix completion algorithm is developed to accurately solve the first sub‐problem. On this basis, the compressed sensing (CS) technique to acquire the accurate CSI estimation matrix is further utilized. Numerical simulation validates that the method could achieve the much higher channel estimation accuracy, yet only incurs the lower overhead compared with the traditional scheme. [ABSTRACT FROM AUTHOR]

Introduction: Several researchers have focused on the role of skeletal muscle in metabolic problems in recent years. We aimed to evaluate influence of sleeve gastrectomy on skeletal muscular fat infiltration determined by magnetic resonance imaging in patients with metabolic syndrome (MetS). Methods: Sixty five MetS patients (male/female, 20/45; mean age, 35.5 years ± 6.6 [standard deviation]; age range, 22–59 years) enrolled in our study. Prior to and 1 year after sleeve gastrectomy, patients underwent routine measurement of skeletal muscular fat concentration (denoted by proton density fat fraction, PDFF) and chemical indexes. The associations of skeletal muscular fat concentration with other variables were determined using multiple linear regression analysis. Results: Difference between skeletal muscular PDFF at baseline (4.46 ± 2.01%) and PDFF 1-year after sleeve gastrectomy (3.00 ± 1.47%) was significant. Multivariable predictors of baseline skeletal muscular PDFF by descending order of standardized coefficient were fasting serum glucose (0.459; p = 0.001), age (0.395; p < 0.001), systolic pressure (0.319; p = 0.029), insulin (0.030; p = 0.026), white cell count (0.302; p = 0.007), diastolic pressure (−0.301; p = 0.046), and total alkaline phosphatase (−0.474; p < 0.001) all at baseline. Furthermore, multivariable predictors of change in PDFF were serum total cholesterol (3.510; p < 0.001), total alkaline phosphatase (0.535; p < 0.001), estrogen (0.457; p < 0.001), diastolic pressure (0.352; p < 0.001), systolic pressure (−0.409; p < 0.001), high-density lipoprotein cholesterol (−0.719; p < 0.001), insulin (−0.774; p < 0.001), C-reactive protein (−0.900; p < 0.001), triglyceride (−1.756; p < 0.001), and low-density lipoprotein cholesterol (−2.854; p < 0.001) all at baseline. Conclusion: Sleeve gastrectomy could alleviate myosteatosis in MetS patients during 1-year follow-up. The extent of remission on skeletal muscular fat infiltration after sleeve gastrectomy was influenced by baseline metabolic problems related to serum glucose, serum lipid, and blood pressure level. [ABSTRACT FROM AUTHOR]

Satellite networks can communicate with the outside world from anywhere in the world, and multihop networks are suitable for occasions in which infrastructure is lacking or for emergencies. Heterogeneous networks formed by satellite and multihop networks can further expand the communication range of wireless networks; this expansion is conducive to communication with the outside world in remote areas and in emergency situations. However, the formation of heterogeneous networks also brings new challenges to wireless network research. To improve the transmission performance of heterogeneous networks composed of satellite and multihop networks, this paper first introduces the heterogeneous network model of satellite and multihop networks, then analyzes the bandwidth delay products of heterogeneous networks and proposes an end-to-end transmission control algorithm for heterogeneous networks. The algorithm incorporates different congestion window settings in the slow start through a threshold and through the size of the receiver notification window by increasing the amount of data transmitted in the slow start to improve the throughput of the satellite link. The algorithm then differentiates packet losses in congestion avoidance through the sizes of unacknowledged data in the heterogeneous network, using different threshold settings for different unacknowledged data sizes. The simulation results show that the proposed algorithm has some advantages over the TCP Hybla, TCP Veno and TCP Reno schemes in terms of the throughput of the satellite link, the download response time of the multihop network and the queue delay of nodes. [ABSTRACT FROM AUTHOR]

The demand for content oriented service and compute-intensive service stimulates the shift of current cellular networks to deal with the explosive growth in mobile traffic. Information centric mobile caching network architectures have emerged in Information-Centric Networking as well as mobile cellular and ad-hoc networks deployed with caches. Caching optimization based on information centric mobile caching has become the key issue, and several significant research challenges remain to be addressed before its widespread adoption. In this paper, a brief survey on Information centric mobile caching network architecture and caching optimization is presented, including cache placement in different mobile wireless network architectures, the taxonomy of cache insertion and eviction policies, the modeling behavior of caching networks as well as caching optimization based on network centric and user centric metrics, and typical applications based on mobile caching. Finally, the research directions and open challenges are investigated. [ABSTRACT FROM AUTHOR]

As one of the key enabling technologies of the fifth generation wireless network (5G), software defined network (SDN) offers a logically centralized control model, flexible programmability, and a flow-based paradigm that is ideally suited for highly scalable wireless networks, from access to core part. Following this paradigm, a novel software-defined radio access network (SDRAN) architecture and the function modules have been proposed in this paper. In particular, the motivation, challenge, and deployment roadmap of SDRAN framework are discussed. The relationships between alternative solutions (Cloud RAN, network function virtualization) and complementary technologies (cognitive radio, self-organizing network, big data analysis) are analyzed in detail. Taking interference management of heterogeneous mobile network as the example use case, scheme design and preliminary system evaluations are given to show the benefit of SDRAN architecture. [ABSTRACT FROM AUTHOR]

In radar and sonar networks, heavy clutter and noise have generated strong impairments to information transmission and processing. Coding techniques have been widely used to cope with various channel noises and thereby to improve transmission performance. In the study, we investigate a novel cascaded coding scheme for systems operating in adverse electromagnetic environments, e.g., underwater acoustic communications (UAC), where the impulsive noise will be inevitable and the bit error ratio (BER) of receiver will be deteriorated greatly. Our cascaded encoding scheme employs the polar code as inner code while the Luby transform (LT) code as outer code. Inspired on a novel conception of channel polarization, the polar code uses a group of idea sub-channels to carry the useful information, while let other bad sub-channels bear no information. By cascading the outer LT code which accomplishes some parity check to the input of inner code, the performance of inner polar code is improved, as the decoding of polar code relies on a successive cancelation technique which is relatively sensitive to initial input. Error correcting performance of the new cascaded code is studied under impulsive noise characterized by a Middleton Class-A model. Simulations validate the proposed cascaded coding scheme. Compared with the popular low-density parity check (LDPC) code, the cascaded scheme can significantly improve the BER performance in the presence of impulsive interference, which also surpasses another cascaded code that is proved to be effective in impulsive noise channel, i.e., cascaded LDPC and LT coding scheme. [ABSTRACT FROM AUTHOR]

To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading mode of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge computing architecture of an intelligent factory, this paper puts forward five different computation offloading methods. From a global perspective, the energy consumption and delay of tasks offloading in local, edge, cloud center, local-edge collaboration, and local-edge-cloud collaboration are considered. In this paper, the algorithm based on the genetic algorithm and particle swarm optimization is used to design and obtain the decision task flow offloading strategy with the lowest energy consumption and delay. Simulation results show that the proposed algorithm can reduce the computation offloading energy consumption and delay of mobile devices. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is currently seen as one of the most promising future network technologies, which can realize the separation between control and data planes. Furthermore, the increasing complexity in future wireless networks (i.e., 5G, wireless sensor networks) renders the control and coordination of networks a challenging task. Future wireless networks need good separation of control and data planes and call for SDN method to handle the explosive increase of mobile data traffic. Relying on a single controller in future wireless networks imposes a potential scalability problem. To tackle this problem, the thought of using multiple controllers to manage the large wide-area wireless network has been proposed, where the load balance problem of multicontroller needs to be resolved. In this paper, we propose a multicontroller load balancing approach called HybridFlow in software-defined wireless networks, which adopts the method of distribution and centralization and designs a double threshold approach to evenly allocate the load. Simulation results reveal that the proposed approach can significantly relieve the working load on the super controller and reduces the load jitter of multicontroller load in a single cluster compared with the BalanceFlow method. [ABSTRACT FROM AUTHOR]

With the explosive increase of mobile data traffic, the energy efficiency issue in cellular networks is a growing concern. Recently, the advantages of in-network caching in Internet have been widely investigated, for example, speeding up content distribution and improving network resource utilization. In this paper, we analyze the energy-delay tradeoff problem in the context of single base station (BS), which has a cache capacity to buffer the contents through it. Although additional power is consumed by the cache, work load of BS and network delay will be improved, which makes a tradeoff between network power consumption and delay. Simulation results reveal that, by introducing the cache in a BS, the network power and delay can be obviously reduced in different network conditions compared to the scenario without a cache. In addition, we find that a large cache size does not always mean a less network cost because of the more cache power consumption. [ABSTRACT FROM AUTHOR]

Chlorpyrifos is one of the most extensively used insecticides in China. The distribution and residues of chlorpyrifos in a paddy environment were characterized under field and laboratory conditions. The half-lives of chlorpyrifos in the two conditions were 0.9–3.8 days (field) and 2.8–10.3 days (laboratory), respectively. The initial distribution of chlorpyrifos followed the increasing order of water < straw < soil, and soil was characterized as the major absorber. The ultimate residues in rice grain were below the maximum residue limit (MRL) with a harvest interval of 14 days. The chronic exposure for chlorpyrifos was rather low compared to the acceptable daily intake (ADI = 0.01 mg/kg bw) due to rice consumption. The chronic exposure risk from chlorpyrifos in rice grain was 5.90% and 1.30% ADI from field and laboratory results respectively. Concerning the acute dietary exposure, intake estimated for the highest chlorpyrifos level did not exceed the acute reference dose (ARfD = 0.1 mg/kg bw). The estimated short-term intakes (ESTIs) were 0.78% and 0.25% of the ARfD for chlorpyrifos. The results showed that the use of chlorpyrifos in rice paddies was fairly safe for consumption of rice grain by consumers. [ABSTRACT FROM AUTHOR]

The acquisitionof channel state information at transmitter (CSIT) is crucial to attain the potential benefits of the frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system. Traditionally, CSI estimation at receiver and feedback to transmitter are separately designed, failing to maximize the estimation accuracy while minimizing the complexity/overhead. In this work, we propose a novel computation and communication efficient CSIT acquisition scheme, by jointly designing downlink CSI estimation and uplink feedback, which is inspired by randomized matrix approximation techniques. In the process of CSI estimation, we represent a large channel matrix with three sub-matrices by exploiting its inherent low-rank characteristic, and then estimate these small matrices separately. Our new estimator significantly reduces the computation complexity in massive MIMO CSI acquisition, whilst attaining the high accuracy. The convergence as well as the error bound of our CSI estimator is derived theoretically. In the following CSI feedback process, only partial elements of sub-matrices are reported to transmitter without further compression, and finally the full CSI is recovered accurately. This novel CSI feedback scheme, relying on the estimated CSI with special structure, greatly reduces both the communication overhead. Numerical simulations are provided to valid our joint CSI estimation and feedback scheme, which would have great potential in massive MIMO communications. [ABSTRACT FROM AUTHOR]

Machine learning has provided great potential in intelligent signal processing, e.g., channel estimation and signal detection. But still, it is difficult to deploy deep neural network (DNN) on low-cost and low-power edge devices, e.g., in both training and inference phases. For one thing, the training of massive network parameters incurs a huge computational complexity and rarely becomes feasible; and for another, even a trained DNN involves still high computations and demands considerable storage size. In this work, we present a tiny machine learning (Tiny-ML) approach for hardware-efficient channel estimation and signal detection. The key innovation of our Tiny-ML is that we replace each large dense layer of DNN with three small cascading sub-layers; and therefore, the computation/storage of a large matrix is replaced with that of small ones. To enable the lightweight training of our Tiny-ML, a novel rank-restricted back-propagation algorithm is further designed. Numerical simulations demonstrate the advantages of our new method. Without sacrificing the estimation and detection accuracy, our Tiny-ML attains an acceleration of $\rm {\mathbf {2.5\sim\!3\times }}$ in model training and $\rm {\mathbf {\sim\!4.5\times }}$ in model inference, as well as a substantial storage reduction of $\rm {\mathbf {\sim\!4.5\times }}$ , when compared to classical fully-connected (FC) DNN. As such, our method paves the way for deploying tiny AI onto low-cost and low-power hardware devices, thereby exploiting the full potential of intelligent signal processing. [ABSTRACT FROM AUTHOR]

Background: Although European Society of Urogenital Radiology proposed the potential of multiparametric magnetic resonance imaging (MP-MRI) as a tool in the diagnostic pathway for prostate cancer (PCa) and published a unified scoring system named Prostate Imaging Reporting and Data System (PI-RADS version 1), these still need to be validated by real-life studies. Objective: To evaluate the role of MP-MRI in detection and prediction of PCa. Methods: Patients with clinical suspicion of PCa who underwent prebiopsy MP-MRI from 2002 to 2009 were recruited. MP-MRI results were retrospectively assigned as overall scores using PI-RADS by two radiologists. Patients were followed and the end point was the diagnosis of PCa. Receiver operating characteristics (ROC) curve was performed to test diagnostic efficacy of MP-MRI, under results of biopsy within three months. The cox proportional hazards model was used to identify independent variables for the detection of PCa. Results: Finally, 1113 of the 1806 enrolled patients were included for analysis. The median follow-up was 56.0 months (1–137 mo). For 582 patients biopsied within three months, area under the curve for the detection of PCa with MP-MRI was 0.88 (95% confidence interval [CI], 0.75–1.00) in group of baseline prostate specific antigen (PSA) 0.01–4.00 ng/ml (n = 31), 0.90 (95% CI, 0.84–0.95) in PSA 4.01–10.00 ng/ml (n = 142), and 0.91 (95% CI, 0.87–0.94) in PSA >10.00 ng/ml (n = 409), respectively. In the cox model adjusted for age and baseline PSA level, for the detection rate of PCa, compared with PI-RADS 1–2 (reference), the hazard ratio was 6.43 (95% CI, 4.29–9.65) for PI-RADS 3, 18.58 (95% CI, 13.36–25.84) for PI-RADS 4–5 (p < 0.001). Conclusions: Prebiopsy MP-MRI with PI-RADS is demonstrated as a valuable diagnostic and predictive tool for PCa. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) radar, enabled by millimeter-wave virtual MIMO techniques, provides great promises to the high-resolution automotive sensing and target detection in unmanned ground/aerial vehicles (UGA/UAV). As a long-established problem, however, existing subspace methods suffer from either high complexity or low accuracy. In this work, we propose two efficient methods, to accomplish fast subspace computation and accurate angle of arrival (AoA) acquisition. By leveraging randomized low-rank approximation, our fast multiple signal classification (MUSIC) methods, relying on random sampling and projection techniques, substantially accelerate the subspace estimation by orders of magnitude. Moreover, we establish the theoretical bounds of our proposed methods, which ensure the accuracy of the approximated pseudo-spectrum. As demonstrated, the pseudo-spectrum acquired by our fast-MUSIC would be highly precise; and the estimated AoA is almost as accurate as standard MUSIC. In contrast, our new methods are tremendously faster than standard MUSIC. Thus, our fast-MUSIC enables the high-resolution real-time environmental sensing with massive MIMO radars, which has great potential in the emerging unmanned systems. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) communications are vulnerable to smart attacks, where the attacker can change the attack mode (e.g., eavesdropping and jamming) via smart radio devices. To ensure secure transmissions against hybrid attacks, UAVs may transmit confidential information and misleading information alternately. In this paper, we consider a dynamic anti-hybrid attack framework with trajectory optimization, where both a UAV and an attacker attempt to find their optimal trajectories without knowing the information type and the attack mode of each other. Given the major challenge due to incomplete knowledge (i.e., each agent knows only its own information), we establish an adversarial game with partial-observation feature to formulate an optimization problem, and propose a counterfactual regret minimization learning scheme to achieve the correlated equilibrium for both the UAV and attacker. Simulation results validate the superiority of our scheme over a benchmark in UAV communication scenarios with partial information. [ABSTRACT FROM AUTHOR]

The measurement of an emitter's position using electronic support passive sensors is termed passive localization and plays an important part both in electronic support and electronic attack. The emitting target could be in terrestrial or underwater environment. In this paper, we propose a time difference of arrival (TDoA) algorithm for passive localization in underwater and terrestrial environment. In terrestrial environment, it is assumed that a Rician flat fading model should be used because there exists line of sight. In underwater environment, we apply a modified UWB Saleh-Valenzuela (S-V) model to characterize the underwater acoustic fading channel. We propose the TDoA finding algorithm via estimating the delay of two correlated channels, and compare it with the existing approach. Simulations were conducted for terrestrial and underwater environment, and simulation results show that our TDoA algorithm performs much better than the cross-correlation-based TDoA algorithm with a lower level of magnitude in terms of average TDoA error and root-mean-square error (RMSE). Compared to the TDoA performance in terrestrial environment, the TDoA performance in underwater environment is much worse. This is because the underwater channel has clusters and rays, which introduces memory and uncertainties. For the two scenarios in underwater environment, the performance in rich scattering underwater environment is worse than that in less scattering underwater environment, because the latter has less clusters and rays, which would cause less uncertainties in TDoA. [ABSTRACT FROM AUTHOR]

Benefiting from the global deployment and fast commercialization of cellular systems, cellular-enabled vehicle-to-everything (V2X) opens up a significant prospect in the foreseeable future, for supporting proliferating vehicular services. Reliable and efficient resource allocation is a prerequisite for enjoying excellent quality of vehicular communication experiences, which yet still remains a serious challenge due to the inherent vehicle mobility and network heterogeneity. In this work, taking the rapidly varying channel state information (CSI) into consideration, we investigate the joint time-frequency allocation problem for the heterogeneous cellular V2X network, where resource sharing based on hierarchical reuse among different V2X communication types are granted. To address the high dynamics of V2X environment, a mapped fuzzy space is introduced, in which the uncertain CSI is interpreted as fuzzy numbers. This could effectively avoid the deterioration of such dynamic environment to the stability and convergence of optimization mechanisms. On this basis, we construct a two-side many-to-many fuzzy matching game (MM-FMG) to formulate the optimization problem with the dynamic and uncertain information, and further propose a vehicle-resource dynamic matching algorithm to solve this MM-FMG problem. Our developed scheme can achieve both reliable and efficient resource assignment by excluding the precise CSI, which is hence more suitable to the concerned V2X scenarios. Finally, the superior performances of our designed algorithm are demonstrated via numerical simulations. [ABSTRACT FROM AUTHOR]

In emerging Internet-of-Nano-Thing (IoNT), information will be embedded and conveyed in the form of molecules through complex and diffusive medias. One main challenge lies in the long-tail nature of the channel response causing inter-symbol-interference (ISI), which deteriorates the detection performance. If the channel is unknown, existing coherent schemes (e.g., the state-of-the-art maximum a posteriori, MAP) have to pursue complex channel estimation and ISI mitigation techniques, which will result in either high computational complexity, or poor estimation accuracy that will hinder the detection performance. In this paper, we develop a novel high-dimensional non-coherent detection scheme for molecular signals. We achieve this in a higher-dimensional metric space by combining different non-coherent metrics that exploit the transient features of the signals. By deducing the theoretical bit error rate (BER) for any constructed high-dimensional non-coherent metric, we prove that, higher dimensionality always achieves a lower BER in the same sample space, at the expense of higher complexity on computing the multivariate posterior densities. The realization of this high-dimensional non-coherent scheme is resorting to the Parzen window technique based probabilistic neural network (Parzen-PNN), given its ability to approximate the multivariate posterior densities by taking the previous detection results into a channel-independent Gaussian Parzen window, thereby avoiding the complex channel estimations. The complexity of the posterior computation is shared by the parallel implementation of the Parzen-PNN. Numerical simulations demonstrate that our proposed scheme can gain 10dB in SNR given a fixed BER as $10^{-4}$ , in comparison with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Molecular communications rely on diffusive propagation to transport information, which is attractive for a variety of nano-scale applications. Due to the long-tail channel response, spatial-temporal coding of information may lead to severe inter-symbol interference (ISI). Classical linear signal processing in wireless communications is usually operating with high complexity and high signal-to-noise ratios, whereas signal processing in molecular communication system requires operating in opposite conditions. In this work, we propose a novel signal processing paradigm inspired by the biological principle, which enables low-complexity signal detection in extremely noisy environments. We first propose a non-linear filter inspired by stochastic resonance, which is found in a variety of biological systems, and it can significantly improve the output SNR by converting noise to useful signals. Then, we design a non-coherent detection method, one which exploits the generally transient trend of observed signals (i.e. quick-rising and slow-decaying) rather than hidden channel state information (CSI), thus excluding CSI estimation and involving only summations. Implementation issues are also discussed, including parameters configuration and adaptive threshold. Numerical results show that the proposed bio-inspired scheme can improve the performance remarkably over classical approaches. Even compared with the optimal linear methods, the required SNR of the proposed scheme can be reduced by 7 dB, which reaffirms why it can be used in noisy biological environments. As the first attempt to design bio-inspired molecular signal detectors, the proposed non-linear processing paradigm may provide the great promise to the emerging nano-machine applications. [ABSTRACT FROM AUTHOR]

An effective post etch treatment (PET) process was proposed to eliminate etch damage in the channel hole, of which the depth and the aspect ratio is beyond 3 μm and 30:1 respectively, prior to selective epitaxial growth (SEG) in three dimensions (3D) NAND flash memory. In this work, it is demonstrated that the damaged layer both at the channel hole bottom and sidewall induced by capacitively coupled plasma (CCP) was effectively eliminated using low energy plasma of CL2 + NF3/CH2F2 in PET, and then obtaining an excellent surface condition in channel hole and fabricating a void-free SEG epitaxial layer. [ABSTRACT FROM AUTHOR]

With the development of satellite networks, there is an emerging trend to integrate satellite networks with terrestrial networks, called satellite-terrestrial networks (STNs). The improvements of STNs need innovative information and communication technologies, such as networking, caching, and computing. In this paper, we propose a software-defined STN to manage and orchestrate networking, caching, and computing resources jointly. We formulate the joint resources allocation problem as a joint optimization problem, and use a deep Q-learning approach to solve it. Simulation results show the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

Highlights • Top select gate transistor (TSG) shows wider initial Vth distribution, and even worse after erase, in 3D NAND flash memory. • Grain boundary traps can induce a local potential barrier in offset region, which results in higher TSG initial Vth. • Random grain boundary position, leads to worse variation of TSG initial Vth. • The local potential barrier cannot be reduced by erase operation, leading to worse Vth distribution after erase. • Two methods are proposed to achieve tight distribution and improved erase uniformity. Abstract The electrical characteristics of top select gate transistor (TSG) has been investigated in vertical channel three dimensional NAND flash memory. TSG shows wider initial Vth distribution as compared with memory cells, and even worse after erase. By experimental analysis and TCAD simulation, a physical model based on grain boundary (GB) interface traps is proposed to explain the mechanism. Grain boundary traps in offset region between bit line contact and TSG can induce a higher local potential barrier in channel, which results in higher TSG initial Vth. Besides, random GB position within offset region, leads to worse variation of TSG initial Vth. Furthermore, the local potential barrier in offset region of TSG cannot be reduced by erase operation, leading to worse Vth distribution after erase. According to proposed model, two methods with optimization of offset doping energy and poly-Si GB trap passivation condition are proposed to achieve tight distribution and improved erase uniformity. [ABSTRACT FROM AUTHOR]

The persistent awareness of dynamic links quality is of critical importance to shared access systems, e.g., promoting sensing accuracy or even enabling seamless sharing, which remains yet as a challenging task. In this paper, we consider the shared access in heterogeneous small-cells, which are likely to be the main infrastructure components by utilizing the carrier aggregation (CA) scheme, potentially combining both licensed and unlicensed bands. We suggest a promising sensing scheme, which permits the simultaneous assessment of multiple links quality when detecting vacant spectrum, and hence is able to solve the long-standing hidden terminal problem. We incorporate an additional objective of acquiring dynamic link state information of multiple incumbent transmitters/receivers, and thereby, structure spectrum sensing as a mixed classification and estimation problem. The main challenge is that multiple channels may become similar with each other, and existing joint estimation schemes are unable to distinguish which incumbent is active and what its associated channel state is. To address this indistinguishable problem and alleviate the error propagation in recursive estimations, a novel alternating Bernoulli filtering algorithm is proposed. By fully exploiting the group transitional behavior of incumbents, a multiple-chains inference framework is developed. Numerical simulations validate our new scheme. Occupancy states of multiple incumbents can be identified accurately, by tracking their links quality and exploiting the underlying dynamics, which lays the foundation for more effective shared access. [ABSTRACT FROM AUTHOR]

Interactions between drugs and proteins occupy a central position during the process of drug discovery and development. Numerous methods have recently been developed for identifying drug–target interactions, but few have been devoted to finding interactions between post-translationally modified proteins and drugs. We presented a machine learning-based method for identifying associations between small molecules and binding-associated S-nitrosylated (SNO-) proteins. Namely, small molecules were encoded by molecular fingerprint, SNO-proteins were encoded by the information entropy-based method, and the random forest was used to train a classifier. Ten-fold and leave-one-out cross validations achieved, respectively, 0.7235 and 0.7490 of the area under a receiver operating characteristic curve. Computational analysis of similarity suggested that SNO-proteins associated with the same drug shared statistically significant similarity, and vice versa. This method and finding are useful to identify drug–SNO associations and further facilitate the discovery and development of SNO-associated drugs. [ABSTRACT FROM AUTHOR]

In recent years, the volume of data has grown exponentially. Big data analytic platforms for the smart grid have enormous potential since they play an essential role in the decision-making process and help to prevent disasters or instabilities in the grid. Vehicle to Grid (V2G) technology has become a buzzword in the industry. It is posited as a forthcoming technology to be grasped in a distant, ultimately green, and future. However, this view is now being challenged as V2G is being deployed at a large scale. The severe challenges of rapid population growth, ecological pollution, and energy deficiency have encouraged the design of smart grids and electric transport systems, especially electric vehicles containing central energy utilization systems in a smart city. This paper proposes a robust and distributed approach to balance supply and demand. This study have constructed a unique framework to manage the ambiguous performances of vehicles and supplied energy resources in a smart city. Furthermore, it analyze the influence of network topology by evaluating the strength of solid network motifs using predictive analysis for the whole system. [ABSTRACT FROM AUTHOR]

Control on the mesoporous nanostructures with uniform-sized and well-arranged mesopores can provide enhanced surface areas and mechanical properties, which are preferred for the development of energies, sensors, and electro-optical devices. Herein, Pt-containing ordered honeycomb (Pt/OHC) nanocomposites with regularly arranged mesopores were synthesized via self-assembly of polystyrene- block -poly(4-vinylpyridine) (PS- b -P4VP) block copolymers (BCPs), following mechanism study of morphological evolution, concept of structural control, potential in catalyst application, and exploration of various metals. Particularly, it was found that the self-assembly of BCP directed the ordered arrangement of the mesopores, whereas the P4VP blocks incorporated Pt and precisely positioned it on the pore walls and on the outer surface of the honeycomb matrix. Mono- to multi-layered Pt/OHCs could be generated by controlling the thicknesses of the self-assembled pristine templates, and the average diameters of the mesopores could be varied from 12 nm to 40 nm by tuning the PS:P4VP block mass ratios. The obtained Pt/OHC demonstrated promising application as electrocatalysts in hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR), for which an overpotential of 39 mV at current density of 10 mA/cm2 (η 10) and a high half wave potential (E 1/2) of 0.87 V (vs. RHE), respectively, were observed in acidic media. In addition, the strategy developed here could be expanded for other metals, such as Au and Ir, to be incorporated into the ordered honeycomb matrix, providing a robust method to synthesize metal-containing mesoporous nanomaterials. With this work, we aim to offer an insight of designing ordered mesoporous nanocomposites, a concept of control over the mesoporous nanostructures, and a potential for their application in electrocatalysts. [Display omitted] [ABSTRACT FROM AUTHOR]

The unrestrained mobility and dynamic spectrum sharing are considered as two key features of next-generation communications. In this paper, spectrum sensing in mobile scenarios is investigated, which faces still great challenges as both the mobile location of primary-user and fading channel will become time-variant. Such two uncertainties would arouse remarkable fluctuations in the strength of received signals, making most existing sensing schemes invalid. To cope with this exceptional difficulty, a novel paradigm, i.e., deep sensing, is designed, which estimates the time-dependent flat-fading gains and primary-user’s mobile positions jointly, at the same time of detecting its emission status. All three hidden states involved by the space-time doubly selective scenario are taken into accounts. A unified dynamic state-space model is established to characterize the dynamic behaviors of unknown states, in which the time-dependent flat fading is modeled as a stochastic discrete-state Markov chain. A Bayesian approach, premised on a formulation of random finite set, is suggested to recursively estimate primary user’s unknown states accompanying two others link uncertainties. In order to avoid the mis-tracking of the mobile positions, which is caused either by the incessant disappearance of primary-user or time-variant channels, an adaptive horizon expanding mechanism is also integrated. Numerical simulations validate the proposed scheme. [ABSTRACT FROM AUTHOR]

Molecular communications via diffusion (MCvD) represents a relatively new area of wireless data transfer with especially attractive characteristics for nanoscale applications. Due to the nature of diffusive propagation, one of the key challenges is to mitigate inter-symbol interference (ISI) that results from the long tail of channel response. Traditional coherent detectors rely on accurate channel estimations and incur a high computational complexity. Both of these constraints make coherent detection unrealistic for MCvD systems. In this paper, we propose a low-complexity and noncoherent signal detector, which exploits essentially the local convexity of the diffusive channel response. A threshold estimation mechanism is proposed to detect signals blindly, which can also adapt to channel variations. Compared to other noncoherent detectors, the proposed algorithm is capable of operating at high data rates and suppressing ISI from a large number of previous symbols. Numerical results demonstrate that not only is the ISI effectively suppressed, but the complexity is also reduced by only requiring summation operations. As a result, the proposed noncoherent scheme will provide the necessary potential to low-complexity molecular communications, especially for nanoscale applications with a limited computation and energy budget. [ABSTRACT FROM PUBLISHER]

Cognitive radio, as an important means of implementing spectrum-awareness and dynamic sharing, is of great significance to the future deployment of self-organizing networks. Given its practical application, cognitive radio technology may operate in various adverse self-organizing networks environments. For example, in wireless mobile communication, the multipath propagation with time-varying fading coefficients and unknown noise variance becomes inevitable. Unfortunately, most existing spectrum sensing methods could hardly acquire good performance in this adverse situation. To overcome this difficulty, in this paper we present a novel spectrum sensing algorithm in realistic cognitive radio applications. Firstly, we establish a dynamic state-space model which gives full consideration to the evolution characteristics of primary user's state and time-variant multipath flat fading channel, while the received signal processed by matched filtering is viewed as the observation. On this basis, spectrum sensing is realized by estimating the primary user's state, multipath channel impulse response and noise variance jointly and iteratively. This formulated problem is solved based on maximum a posteriori probability criterion and marginal particle filtering technology. Simulations demonstrate that the sensing performance achieved by proposed algorithm is satisfactory and at the same time, the channel response and noise variance are estimated accurately. [ABSTRACT FROM AUTHOR]

In this paper, spectrum sensing is investigated and a new detection framework, namely, deep sensing (DS), is proposed for more challenging scenarios of future dynamic spectrum sharing. In contrast to existing methods, the DS scheme is designed to proactively recover and exploit some other informative states associated with realistic cognitive links (e.g., fading gains), except detecting the occupancy of primary-band. A unified mathematical model, relying on the dynamic state-space approach, is formulated, in which the Bernoulli random finite set (RFS) is further exploited to theoretically characterize complex DS procedures. A Bernoulli filter algorithm is suggested to recursively estimate unknown PU states accompanying related link information, which is implemented by particle filtering based on numerical approximations. The proposed DS algorithm is applied to detect primary users under time-varying fading channel, which may increase the observation uncertainty and, therefore, deteriorate the sensing performance. With this new framework, the time-varying fading gain, modeled as a stochastic discrete-state Markov chain (DSMC), is estimated along with unknown PU states. Simulations demonstrate that, by exploiting the underlying dynamic fading property, the sensing performance will surpass other traditional schemes. The DS scheme may be conveniently generalized to other applications, which will promote sensing performance and provides a new paradigm for next-generation spectrum sharing. [ABSTRACT FROM PUBLISHER]

With more devices and emerging data-intensive services, mobile data traffic grows explosively, which makes the energy efficiency issue in current and future wireless networks be a growing concern. Recently, the advantages of bringing caching scheme in wireless networks have been widely investigated. Although additional power consumption is incurred by deploying caching equipments, the traffic loading balance of radio access network (RAN) and total network delay will be improved. In this paper, we study the energy efficiency problem in future wireless network. Particularly, we give an energy-delay tradeoff algorithm by using the sleeping control and power matching scheme. In the proposed algorithm, we consider a N-based sleeping control scheme, where the base station (BS) is in the sleep state whenever there is no arriving user, and works again when N users arrive. Moreover, the proposed algorithm is suitable for multi-BS scenario; to simplify the analysis, we mainly consider the single case. Simulation results shows that the proposed algorithm can obviously reduce the network power and delay in heterogeneous network conditions. Moreover, we find that a larger cache size does not always obtain a less network cost, because more cache power is consumed as cache size increases. [ABSTRACT FROM AUTHOR]

• Rheological parameters of backfill slurry added paraffin were tested and analyzed. • Flow pattern fits with different rheological model for different paraffin percentage. • Yield stress increases obviously with paraffin percentage especially exceeding 5%. • Viscosity increases significantly with the increase of backfill slurry concentration. • Shear stress increases with shear rate, paraffin percentage and slurry concentration. In phase-change heat storage backfill, pipeline transportation of slurry is one of the critical stages, using paraffin microcapsules as phase change material, the influence of paraffin percentage, slurry concentration and shear rate on rheological parameters was discussed and analyzed based on experimental test. Yield stress increases significantly especially paraffin percentage exceeding 5%, for slurry concentration of 68%, 70% and 72%, the increment of yield stress is 171.2 Pa, 353.3 Pa and 353.4 Pa respectively when paraffin percentage increases from 5% to 10%. Viscosity increases significantly with slurry concentration, the average increment is 0.054 Pa·s when slurry concentration increases from 68% to 72%. Shear stress increases with the increase of shear rate, paraffin percentage and slurry concentration. The average increment of shear stress is 32 Pa when shear rate increases from 50 s−1 to 70 s−1, shear stress increases significantly when paraffin percentage increases from 5% to 10%, especially reaching 10%, shear stress is larger, and the increment of shear stress caused by increasing concentration is also larger. So it is very important to control the dosage of heat storage material, adjust slurry proportion and determine pipeline parameters, which directly affect flow characteristics and flow resistance, in order to improving the transportation efficiency of backfill slurry. [ABSTRACT FROM AUTHOR]

With the development of global urbanization, the Internet of Things (IoT) and smart cities are becoming hot research topics. As an emerging model, edge computing can play an important role in smart cities because of its low latency and good performance. IoT devices can reduce time consumption with the help of a mobile edge computing (MEC) server. However, if too many IoT devices simultaneously choose to offload the computation tasks to the MEC server via the limited wireless channel, it may lead to the channel congestion, thus increasing time overhead. Facing a large number of IoT devices in smart cities, the centralized resource allocation algorithm needs a lot of signaling exchange, resulting in low efficiency. To solve the problem, this paper studies the joint policy of communication and computing of IoT devices in edge computing through game theory, and proposes distributed Q-learning algorithms with two learning policies. Simulation results show that the algorithm can converge quickly with a balanced solution. [ABSTRACT FROM AUTHOR]