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Visible light communication (VLC) systems relying on commercial-off-the-shelf (COTS) devices have gathered momentum recently, due to the pervasive adoption of LED lighting and mobile devices. However, the achievable throughput by such practical systems is still several orders below those claimed by controlled experiments with specialized devices. In this paper, we engineer CoLight aiming to boost the data rate of the VLC system purely built upon COTS devices. CoLight adopts COTS LEDs as its transmitter, but it innovates in its simple yet delicate driver circuit wiring an array of LED chips in a combinatorial manner. Consequently, modulated signals can directly drive the on-off procedures of individual chip groups, so that the spatially synthesized light emissions exhibit a varying luminance following exactly the modulation symbols. To obtain a readily usable receiver, CoLight interfaces a COTS PD with a smartphone through the audio jack, and it also has an alternative MCU-driven circuit to emulate a future integration into the phone. The evaluations on CoLight are both promising and informative: they demonstrate a throughput up to 80 kbps at a distance of 2 m, while suggesting various potentials to further enhance the performance.

Neighbor discovery is a prerequisite procedure in communication among energy-limited Internet-of-Things (IoT) devices. Discovering neighbors should be achieved in an energy-efficient way. Communication between heterogeneous IoT devices is a common phenomenon for achieving extensive connections among IoT devices. Generally, existing asymmetric neighbor discovery methods are evolved from the symmetric methods and lack specialized designs for heterogeneous IoT devices which led to relatively poor energy-efficient performance. In this article, we use a generalized slot model using pure sending (PS) slot and pure listening (PL) slot, called PSPL, to convert existing neighbor discovery algorithms (symmetric and asymmetric) to highly energy-efficient asymmetric ones. The core idea of PSPL is that to achieve highly energy-efficient one-way discovery , PS slot and PL slot are only used in devices with smaller and larger energy budgets, respectively, and the length of PL slot is much larger than that of PS slot. In this way, the energy efficiency of asymmetric neighbor discovery can be substantially improved. After implementing one-way discovery , two-way discovery can be easily achieved with mutual assistance. Two examples of how to convert existing algorithms into highly efficient asymmetric ones are presented, the one is to convert Disco to Disco+PSPL and the other one is to convert Griassdi to Griassdi+PSPL. The theoretical analysis results indicate that with the same energy budget, Disco+PSPL and Griassdi+PSPL reduce the worst case latency bounds by up to 82.28% and 87.69%, respectively, compared with the best known asymmetric solutions. The simulation evaluation verifies the effectiveness of our designs.

Based on data of state-owned enterprises listed on the Shanghai and Shenzhen Stock Exchanges from 2003 to 2017, this study examines how mixed-ownership reform affects a company’s auditor choice fro...

The Internet of Things (IoT) has become more popular over the past decade. For the IoT to be successful, it is vital to track the location of these things (sensors or actuators). In this article, based on a new IoT underlying architecture, a narrowband-IoT (NB-IoT)-aided, bounding-based iterative and range-free method, named BBIL, is proposed to localize things. We make use of the location information of all anchor regular nodes that can help improve the localization accuracy as much as possible. Specifically, not only single-hop and multihop anchor things but also single-hop and multihop regular things are used for localization. In addition, the communication and computational loads of the local network are greatly decreased because the anchor things can directly access the Internet using the NB-IoT modules; hence, data can be sent to the Internet through a small number of hops in the local network and processed in the cloud/edge computing facilities in a centralized way. To balance the location accuracy and energy consumption of BBIL, we propose a theoretical model to obtain the optimal number of the anchor things. BBIL is evaluated and compared with the existing methods. The simulation results indicate that the average localization error of BBIL is less than 11.6%. Also, it performs well in anisotropic networks. In addition, we verified the validity of our method in real-world scenario.

A three-party authenticated key agreement (3PAKA) protocol allows two protocol entities to authenticate and agree on a common session key over common communication channels with the help of a trust server. Due to the better robustness and high computation efficiency, Chebyshev chaotic maps technology has been widely used in the authenticated key agreement protocol. In recent years, many researches of the 3PAKA protocol based on Chebyshev chaotic maps have been well-documented. However, cryptanalysis of previous schemes showcased that existing 3PAKA protocols commonly could not resist known session specific temporary attack and could not guarantee users anonymity. In addition, they assumed that the communication channels were absolutely secure during the registration process, which is impracticable in major applications. To overcome the existing weaknesses, we propose a security-enhanced protocol. Finally yet importantly, the security of our proposed scheme has been validated with Strand Space technology and ProVerif tool. Contrary to the existing schemes, the proposed protocol showcased improved security with lower computation and communication cost.

As an essential requirement of pervasive smart devices, free hand gestural input considered as necessary for user interactions has attracted lots of research attention for nearly decades. Nevertheless, existing proposals heavily rely on either expensive pre-deployed equipment or user on-body sensors, thus confine their application scenarios. In this paper, we propose a novel hand gesture recognition system which purely relies on ubiquitous ambient light and low-cost photodiodes. The proposed system does not need any modification to existing lighting infrastructure. While without complex signal pre-processing for modulated light, very low-cost photodiodes and processors can capture and process the light variations caused by hand gesture. To produce accurate hand gesture recognition, we design efficient algorithms based on recurrent neural network to process sensing data collected by a photodiode array. We implement a prototype consisting of an array of 8 photodiodes and extensive experiments demonstrate that the proposed solution can achieve a very high overall recognition accuracy of 99.31%.

Whereas the increasing popularity of both commercial light-emitting diode (LED) lighting and mobile devices certainly creates opportunity for real-life deployment of visible light communication (VLC) systems, reaching the high throughput promised by lab experiments still faces major obstacles. In particular, lacking the sophisticated hardware and software support under experimental conditions, real-life systems are challenged in many aspects, especially low signal-to-noise ratio, low operation frequency, uncontrollable LED nonlinearity, and illumination requirements. Nonetheless, deployments tapping commercial infrastructure are critical to gain market penetration for VLC-enabled wireless applications, so boosting the performance of these real-life systems becomes imperative. In this article, we consider the multiple LED chips and/or light sources nature of indoor commercial lighting infrastructure, and propose a spatial modulation that composes the cooperative light emissions (transmissions) from multiple LEDs. In addition to gaining a higher spectral efficiency, this scheme utilizes the multiple-LED nature of commercial lighting to overcome LED nonlinearity with a novel yet simplified hardware construction. We present two typical designs adopting this special modulation under different infrastructural constraints, and provide an introduction on its potential contribution to the standardization of VLC. Finally, we discuss potential extensions to further improve performance. Ministry of Education (MOE) This work was supported in part by National Natural Science Foundation of China under Grant 61902267 and Grant 61901065, in part by the Fundamental Research Funds for the Central Universities under Grant YJ201868, in part by the Sichuan Science and Technology Program under Grant 19ZDYF0045 and Grant 19CXTD0005, in part by the the AcRF Tier 2 Grant MOE2016-T2-2- 022, and in part by the DSAIR Center at NTU.

Neighbor discovery, meaning that a node receives other nodes’ radio frequency (RF) signals to be aware of their existence, is an indispensable procedure in Internet of Things (IoT)-oriented peer-to-peer (P2P) networks with energy-limited nodes. The main objective of neighbor discovery methods is to improve the energy efficiency, since node energy is usually very limited. As the neighborship maintaining time is very short in the mobile networks, neighbor discovery should be achieved in a very energy-efficient manner. The existing neighbor discovery methods only consider the received RF signals from other nodes (or neighbor table information derived from the received RF signals) as the basis of neighborship evaluation. The neighborship evaluation is inaccurate when the single source of neighbor information is employed. Inaccurate neighborship causes incorrect active slot scheduling and low energy efficiency of neighbor discovery. This paper proposes a generalized and probabilistic neighborship evaluation model to unify a variety of neighborship information in IoT-oriented P2P networks into neighborship probability values. Based on the model, we propose an energy-efficient neighbor discovery middle-ware algorithm by carefully replanning active slots of nodes according to the neighborship probability. The simulation evaluation results show that our proposed method decreases the average discovery delay by up to 11.46%, approximately, compared with other methods at the same energy budget.

Optical camera communication (OCC) systems, which utilize image sensors embedded in commercial-off-the-shelf devices to detect time and spatial variations in light intensity for enabling data communications, have stirred up researchers’ interest. Compared to a direct OCC system whose maximum data rate is strongly determined by the LED source size, a reflected OCC system can break that limitation since the camera captures the light rays reflecting off an observation plane (e.g., a wall) instead of those light rays directly emanated from the light source. However, the low signal-to-noise ratio caused by the non-uniform irradiance distribution produced by LED luminaire on the observation plane in current reflected OCC systems cannot be avoided, hence low complexity and accurate demodulation are hard to achieve. In this paper, we present a FreeOCC system, which employs a dedicatedly tailored freeform lens to precisely control the propagation of modulated light. A desired uniform rectangular illumination is produced on the observation plane by the freeform lens, yielding a uniform grayscale distribution within the received frame captured by the camera in the proposed FreeOCC system. Then, the received signal can be easily demodulated with high accuracy by a simple thresholding scheme. A prototype of the FreeOCC system demonstrates the high performance of the proposed system, and two pulse amplitude modulation schemes (4-order and 8-order) are performed. By using the freeform lens, the packet reception rate is increased by 35% and 32%, respectively; the bit error rate is decreased by 72% and 59%, respectively, at a transmission frequency of 5 kHz. The results clearly show that the FreeOCC system outperforms the common reflected OCC system.

Deemed as a practical approach to realize Visible Light Communication on commercial-off-the-shelf devices, the Optical Camera Communication (OCC) is attracting increasing attention, thanks to its readiness to be built purely upon ubiquitous LED illuminating infrastructure and handy smartphones. However, limited by the low sampling ability of the built-in camera on a smartphone, the performance of existing OCC systems is still far away from the requirements of practical applications. To this end, we further investigate the reception ability of the smartphone’s camera and propose an accumulative sampling scheme to improve the performance of the OCC system. Essentially, the proposed scheme can use all the grayscale information of the pixels projected by the LED transmitter, whereas the conventional ones normally use single row (or column) pixels for demodulating. By implementing the lightweight demodulation algorithm with accumulative sampling, we experimentally verify its effectiveness for supporting higher transmission frequency hence better performance in terms of data rate. Extensive evaluations have shown the BERs of the proposed method are over 87% and 96% lower than that provided by the baselines at a maximum transmission frequency of 5 kHz for the Samsung S8 and iPhone 8 Plus receivers, respectively.

Background An intracranial aneurysm is a cerebrovascular disorder that can result in various diseases. Clinically, diagnosis of an intracranial aneurysm utilizes digital subtraction angiography (DSA) modality as gold standard. The existing automatic computer-aided diagnosis (CAD) research studies with DSA modality were based on classical digital image processing (DIP) methods. However, the classical feature extraction methods were badly hampered by complex vascular distribution, and the sliding window methods were time-consuming during searching and feature extraction. Therefore, developing an accurate and efficient CAD method to detect intracranial aneurysms on DSA images is a meaningful task. Methods In this study, we proposed a two-stage convolutional neural network (CNN) architecture to automatically detect intracranial aneurysms on 2D-DSA images. In region localization stage (RLS), our detection system can locate a specific region to reduce the interference of the other regions. Then, in aneurysm detection stage (ADS), the detector could combine the information of frontal and lateral angiographic view to identify intracranial aneurysms, with a false-positive suppression algorithm. Results Our study was experimented on posterior communicating artery (PCoA) region of internal carotid artery (ICA). The data set contained 241 subjects for model training, and 40 prospectively collected subjects for testing. Compared with the classical DIP method which had an accuracy of 62.5% and an area under curve (AUC) of 0.69, the proposed architecture could achieve accuracy of 93.5% and the AUC of 0.942. In addition, the detection time cost of our method was about 0.569 s, which was one hundred times faster than the classical DIP method of 62.546 s. Conclusion The results illustrated that our proposed two-stage CNN-based architecture was more accurate and faster compared with the existing research studies of classical DIP methods. Overall, our study is a demonstration that it is feasible to assist physicians to detect intracranial aneurysm on DSA images using CNN.

Commercial-off-the-shelf (COTS) devices enabled visible light communication (VLC) for Internet of things (IoT) applications has attracted extensive attentions from both academic and industrial communities, thanks to the pervasive deployments of light emitting diode (LED) lighting infrastructure. However, due to the limitation of frequency response and non-linearity of the commercial illuminating LED light consisting of multiple LED chips, the achievable data rate is far less than that provided by the experimental VLC system with a single LED with specialized devices, e.g., lens. To this end, we propose a power-of-2 arrangement scheme for LED chips to generate spatial summing modulation with low control complexity, and demonstrate its availability in an orthogonal frequency division multiplexing (OFDM) VLC system purely built upon COTS devices. It significantly differs from a conventional OFDM VLC system relying on digital-to-analog converter (DAC) and analog signal chain, which is complex and confined by LED’s non-linearity, thanks to we design a novel digital-to-light converter (DLC) which can output 256 light intensities linearly and be directly controlled by the discrete digital signals generated by the OFDM modulator. An experimental demonstration with employing the QAM-OFDM modulation scheme successfully confirms the effectiveness of the proposed spatial summing VLC system, which can achieve low BERs of below the forward error correct (FEC) threshold of 3.8×10−3 for both QAM8 and QAM16 running transmission frequency of 300 kHz under a communication distance of 0.8 m. It demonstrates the promising potential for delivering a data rate at hundred kbps level with this novel spatial summing based OFDM VLC system, which is sufficient for many IoT applications.

As nodes' characteristics that they are self-governed and resource-limited, wireless sensor networks (WSNs) face potential threats due to various attacks, among which the most threatening attack is wormhole attack. Wormhole attack severely imperils WSNs and is difficult to be detected, for it causes incorrect routing by private tunnels and damages to WSNs in terms of data leakage, data dropping, and delayed delivery. However, the existing solutions are based on additional hardware, incur high communication overhead, or fail to give consideration to all types of wormholes. In this paper, we propose CREDND, a protocol for creating a Credible Neighbor Discovery against wormholes in WSN, which can detect not only external wormholes through the hop difference between the own exclusive neighbors but also internal wormholes through enabling the common neighbor nodes as witnesses to monitor whether the authentication packets are forwarded by malicious nodes. CREDND is a simple, localized protocol and needs no special hardware, localization, or synchronization, but it improves the ability of wormhole defense. The simulation results are provided, showing that CREDND outperforms in wormhole detection than other same types of solutions.

In this paper, we propose and demonstrate a secure and private non-orthogonal multiple access (NOMA) based visible light communication (VLC) system. Orthogonal frequency division multiplexing (OFDM) modulation is applied in the system and a two-level chaotic encryption scheme is further implemented, which can guarantee both the security of legitimate users against eavesdroppers and the privacy among all the legitimate users. An experimental demonstration with two legitimate users and one eavesdropper successfully verifies the feasibility of the proposed secure and private NOMA VLC system. To the best of our knowledge, it is the first time that simultaneous security and privacy improvement is considered for NOMA VLC systems. Published version

Thanks to eye tracking systems, movement data of eyes can be obtained when audiences gaze at advertisements. Eye tracking emerges as an effective means to obtain cognitive effects. Advanced techniques generally require customized equipment and systems. Meanwhile, acquisition of experimental data costs too much. It greatly limits the application scope of eye tracking systems. In this paper, our work consists of two parts. For the first part, we study the cognitive effect of ads through tracking the eyes of participants taking part in our experiments. In the other part, due to the limitation of the eye tracking, we propose a new eye tracking system based on edge computing to extend the application range. The experiment results show that products’ involvement and sexual appeal have an impact on the cognitive effect. Furthermore, by taking advantage of the new eye tracking system, we believe it can make psychological research easier.

Videos have become the new preference comparing with images in recent years. However, during the recording of videos, the cameras are inevitably occluded by some objects or persons that pass through the cameras, which would highly increase the workload of video editors for searching out such occlusions. In this paper, for releasing the burden of video editors, a frame-level video occlusion detection method is proposed, which is a fundamental component of automatic video editing. The proposed method enhances the extraction of spatial-temporal information based on C3D yet only using around half amount of parameters, with an occlusion correction algorithm for correcting the prediction results. In addition, a novel loss function is proposed to better extract the characterization of occlusion and improve the detection performance. For performance evaluation, this paper builds a new large scale dataset, containing 1,000 video segments from seven different real-world scenarios, which could be available at: https://junhua-liao.github.io/Occlusion-Detection/. All occlusions in video segments are annotated frame by frame with bounding-boxes so that the dataset could be utilized in both frame-level occlusion detection and precise occlusion location. The experimental results illustrate that the proposed method could achieve good performance on video occlusion detection compared with the state-of-the-art approaches. To the best of our knowledge, this is the first study which focuses on occlusion detection for automatic video editing.

In this Letter, we propose and demonstrate a practical optical-spatial-summing-based non-orthogonal multiple access (OSS-NOMA) technique for visible light communication (VLC) systems. This technique is innovative in adopting OSS in that the transmitter of OSS-NOMA VLC can be built upon commercial illuminating light emitting diodes (LEDs), free of LEDs’ harmful nonlinearity. Unlike conventional NOMA VLC using analog components such as digital-to-analog converters and bias-T in the transmitter side, OSS-NOMA exploits only digital control signals to drive a LED array in forming optical power superposition for NOMA signals. We demonstrate that by simply switching different amounts of LED chips on or off, the proposed OSS-NOMA transmitter can deliver a fine-grained power allocation ratio ranging from 0.01 to one for two users. The implemented OSS-NOMA VLC prototype leveraging commercial components can achieve low bit error rates of ≤ 3.1 × 10 − 3 for two users at a data rate of 800 kbps, confirming the promising potential of this novel OSS-NOMA VLC for Internet of Things (IoT) applications.

Free hand interaction with devices is a promising trend with the advent of Internet of Things (IoT). The unmodulated ambient light, which can be an exciting modality for interaction, is still deficient in research and practice when most of the efforts in the field of visible light sensing are put into solutions based on modulated light. In this paper, we propose a low-cost ambient light-based system which performs finger gesture recognition in real-time. The system relies on a recurrent neural network (RNN) architecture without complicated pre-processing algorithms for the gesture classification task. The results of experimental evaluation proves that the solution that we put forward achieves a rather high recognition accuracy with our proposed sensor layout across a certain group of users.

Limited biomarkers have been identified as prognostic predictors for stage III colon cancer. To combat this shortfall, we developed a computer-aided approach which combing convolutional neural network with machine classifier to predict the prognosis of stage III colon cancer from routinely haematoxylin and eosin (H&E) stained tissue slides. We trained the model by using 101 cancers from West China Hospital (WCH). The predictive effectivity of the model was validated by using 67 cancers from WCH and 47 cancers from The Cancer Genome Atlas Colon Adenocarcinoma database. The selected model (Gradient Boosting-Colon) provided a hazard ratio (HR) for high- vs. low-risk recurrence of 8.976 (95% confidence interval (CI), 2.824–28.528; P, 0.000), and 10.273 (95% CI, 2.177–48.472; P, 0.003) in the two test groups, from the multivariate Cox proportional hazards analysis. It gave a HR value of 10.687(95% CI, 2.908–39.272; P, 0.001) and 5.033 (95% CI,1.792–14.132; P, 0.002) for the poor vs. good prognosis groups. Gradient Boosting-Colon is an independent machine prognostic predictor which allows stratification of stage III colon cancer into high- and low-risk recurrence groups, and poor and good prognosis groups directly from the H&E tissue slides. Our findings could provide crucial information to aid treatment planning during stage III colon cancer.

As an emerging paradigm of intelligent manufacturing, Industrial Internet aims to transform the production modes and improve the production efficiency. Since the Industrial Internet refers to a lot of advanced information and communication technologies (ICT), it has attracted great interests to researchers from academia and industry. In order to give a more comprehensive understanding of the Industrial Internet, this article gives a detailed introduction from the perspective of emerging technologies involved in Industrial Internet. Firstly, the article investigated the current research outputs and analyzed the development trend. Then, the authors reviewed a common framework of Industrial Internet based on the architecture of internet of things (IoT), and introduced several emerging technologies that promote the development of Industrial Internet, including the concept, the applications, and their roles in the intelligent manufacturing. Finally, it presented some challenges and problems about these technologies in future works.

Automatic detection of intracranial aneurysm based on Digital Subtraction Angiography (DSA) images is a challenging task for the following reasons: 1) effectively leverage the temporal information of the DSA sequence; 2) effectively extract features by avoiding unnecessary interference in the raw DSA images of large resolution; 3) effectively distinguish the vascular overlap from intracranial aneurysm in DSA images. To better identify intracranial aneurysm from DSA images, this paper proposed an automatic detection framework with cascade networks. This framework is consisted of a region localization stage (RLS) and an intracranial aneurysm detection stage (IADS). The RLS stage can significantly reduce the interference from unrelated regions and determine the coarse effective region. The IADS stage fully employed the spatial and temporal features to accurately detect aneurysm from DSA sequence. This method was verified in the posterior communicating artery (PCoA) region of internal carotid artery (ICA). In clinical trials, the accuracy of the baseline method was 62.5% with area under curve (AUC) of 0.650, and the time cost of the detection was approximately 62.546s. However, the accuracy of this method was 85.5% with AUC of 0.918, and the time cost of detection was about 3.664s. The experimental results showed that the proposed method significantly improved the accuracy and speed of intracranial aneurysm automatic detection.

Visible Light Communication (VLC) systems relying on commercial-off-the-shelf (COTS) devices have gathered momentum recently, due to the pervasive adoption of LED lighting and mobile devices. However, the achievable throughput by such practical systems is still several orders below those claimed by controlled experiments with specialized devices. In this paper, we engineer SynLight aiming to significantly improve the data rate of a practical VLC system. SynLight adopts COTS LEDs as its transmitter, but it innovates in its simple yet delicate driver circuit wiring an array of LED chips in a combinatorial manner. Consequently, modulated signals can directly drive the on-off procedures of individual chip groups, so that the spatially synthesized light emissions exhibit a varying luminance following exactly the modulation symbols. To obtain a readily usable receiver, SynLight interfaces a COTS Photo-Diode with a smartphone through the audio jack. The evaluations on SynLight are both promising and informative: they demonstrate a throughput up to 60 kbps, more than 50× of that achieved by state-of-the-art systems, while suggesting various potentials to further enhance the performance.

Wireless sensor networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pairwise based, in which discovery is passively achieved when two nodes are prescheduled to wake up at the same time. In contrast, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Since proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pairwise one. Our testbed experiments using 40 sensor nodes and extensive simulations confirm the theoretical analysis, showing one order of magnitude reduction in discovery delay compared with legacy pairwise methods in dense, uniformly distributed sensor networks with at most 8.8 percent increase in energy consumption.

In wireless sensor networks (WSNs), due to the limited energy of nodes, discovering neighbors needs to be achieved in an energy-efficient way. This means that, at a given energy consumption, we need to reduce the worst-case neighbor discovery latency as far as possible. In this letter, we propose a novel deterministic neighbor discovery method, called Lightning. Lightning employs two kinds of active slots of different active lengths (we call A slot and C slot, respectively). The active time of C slot is much shorter than that of A slot in order to reduce the energy consumption. In addition, to maximize the energy efficiency, Lightning adopts one-way neighbor discovery method. This makes C slot have the smallest active length that is only enough to send one beaconing packet. Through proactively sending one beaconing packet after one-way discovery, Lightning also realizes bi-directional neighbor discovery. The theoretical analysis indicates that, with the same energy consumption, our method reduces the worst-case latency bound up to 37.9% compared with Searchlight-Trim, the best existing discovery algorithm. The simulation verifies the soundness and effectiveness of our design.

A safe charging algorithm in wireless rechargeable sensor network ensures the charging efficiency and the electromagnetic radiation below the threshold. Compared with the current charging algorithms, the safe charging algorithm is more complicated due to the radiation constraint and the mobility of the chargers. A safe charging algorithm based on multiple mobile chargers is proposed in this paper to charge the sensor nodes with mobile chargers, in order to ensure the premise of radiation safety, multiple mobile chargers can effectively complete the network charging task. Firstly, this algorithm narrows the possible location of the sensor nodes by utilizing the charging time and antenna waveform. Secondly, the performance of non-partition charging algorithm which algorithm allow chargers to charge different sensors sets in a different cycle is evaluated against the one of partition charging which does not allow for charging different ones. The moving distance of the charger node will be reduced by 18%. It not only improves the safety level which is inversely proportional to electromagnetic radiation but also expands the application scope of the wireless sensor nodes.

Purpose The type of pituitary adenoma (PA) cannot be clearly recognized with preoperative magnetic resonance imaging (MRI) but can be classified with immunohistochemical staining after surgery. In this study, a model to precisely immunohistochemically classify the PA subtypes by radiomic features based on preoperative MR images was developed. Methods Two hundred thirty-five pathologically diagnosed PAs, including t-box pituitary transcription factor (Tpit) family tumors (n = 55), pituitary transcription factor 1 (Pit-1) family tumors (n = 110), and steroidogenic factor 1 (SF-1) family tumors (n = 70), were retrospectively studied. T1-weighted, T2-weighted and contrast-enhanced T1-weighted images were obtained from all patients. Through imaging acquisition, feature extraction and radiomic data processing, 18 radiomic features were used to train support vector machine (SVM), k-nearest neighbors (KNN) and Naive Bayes (NBs) models. Ten-fold cross-validation was applied to evaluate the performance of these models. Results The SVM model showed high performance (balanced accuracy 0.89, AUC 0.9549) whereas the KNN (balanced accuracy 0.83, AUC 0.9266) and NBs (balanced accuracy 0.80, AUC 0.9324) models displayed low performance based on the T2-weighted images. The performance of the T2-weighted images was better than that of the other two MR sequences. Additionally, significant sensitivity (P = 0.031) and specificity (P = 0.012) differences were observed when classifying the PA subtypes by T2-weighted images. Conclusions The SVM model was superior to the KNN and NBs models and can potentially precisely immunohistochemically classify PA subtypes with an MR-based radiomic analysis. The developed model exhibited good performance using T2-weighted images and might offer potential guidance to neurosurgeons in clinical decision-making before surgery.

Conduct disorder (CD) is a psychiatric disorder in children and adolescence. To investigate changes in the power distribution in brain networks between CD and typically developing (TD) groups, resting-state functional magnetic resonance imaging (rsfMRI) data of thirty-six subjects were first recorded, and then the data were preprocessed using DPARSF and SPM8. Meanwhile, the power of the blood oxygenation level-dependent (BOLD) signals of ninety brain regions was acquired using the integral of the Welch power spectral density (PSD). Additionally, the powers of the brain regions that reached significance (p

As an indispensable part of Internet of Things (IoT), wireless sensor networks (WSNs) are more and more widely used with the rapid development of IoT. The neighbor discovery protocols are the premise of communication between nodes and networking in energy-limited self-organizing wireless networks, and play an important role in WSNs. Because the node energy is limited, neighbor discovery must operate in an energy-efficient manner, that is, under the condition of a given energy budget, the neighbor discovery performance should be as good as possible, such that the discovery latency would be as small as possible and the discovered neighbor percentage as large as possible. The indirect neighbor discovery mainly uses the information of the neighbors that have been found by a pairwise discovery method to more efficiently make a re-planning of the discovery wake-up schedules of the original pairwise neighbor discovery, thereby improving the discovery energy efficiency. The current indirect neighbor discovery methods are mainly divided into two categories: one involves removing the inefficient active slots in the original discovery wake-up schedules, and the other involves adding some efficient active slots. However, the two categories of methods have their own limitations. The former does not consider that this removal operation destroys the integrity of the original discovery wake-up schedules and hence the possibility of discovering new neighbors is reduced, which adversely affects the discovered neighbor percentage. For the latter category, there are still inefficient active slots that were not removed in the re-planned wake-up schedules. The motivation of this paper is to combine the advantages of these two types of indirect neighbor discovery methods, that is, to combine the addition of efficient active slots and the removal of inefficient active slots. To achieve this goal, this paper proposes, for the first time, the concept of virtual nodes in neighbor discovery to maximize the integrity of the original wake-up schedules and achieve the goals of adding efficient active slots and removing inefficient active slots. Specifically, a virtual node is a collaborative group that is formed by nodes within a small range. The nodes in a collaborative group share responsibility for the activating task of one member node, and the combination of these nodes&rsquo, wake-up schedules forms the full wake-up schedule of a node that only uses a pairwise method. In addition, this paper proposes a set of efficient group management mechanisms, and the key steps affecting energy efficiency are analyzed theoretically to obtain the energy-optimal parameters. The extended simulation experiments in multiple scenarios show that, compared with other methods, our neighbor discovery protocol based on virtual nodes (VN-NDP) has a significant improvement in average discovery delay and discovered neighbor percentage performance at a given energy budget. Compared with the typical indirect neighbor discovery algorithm EQS, a neighbor discovery with extended quorum system, our proposed VN-NDP method reduces the average discovery delay by up to 10 . 03 % and increases the discovered neighbor percentage by up to 18 . 35 % .

Node localisation for wireless sensor networks (WSNs) is applied in various fields, and is an indispensable core to promote the development of WSNs. Since the previous localisation algorithms did not fully utilise the anisotropy of nodes, according to the actual radiation model of the node's communication, a novel range-free localisation algorithm called range-free localisation based on the anisotropy of nodes (RLAN) is proposed. RLAN not only uses the information of multi-hop neighbours, but also considers fully the anisotropy of nodes in real networks, which influences the hop relationship and average hop distance, so as to improve the accuracy of node localisation. The simulation results demonstrate that RLAN has better localisation accuracy than other range-free node localisation algorithms. When the nodes are relatively uniformly deployed in the localisation area, the normalised localisation average error using RLAN can be

For range-free localisation in a wireless sensor network (WSN), the importance of indirect anchor neighbours of an unknown node has been underestimated in the past. In this reported work, for the unknown node, we show that its (n + 1)th-hop anchor neighbours are more useful than its nth-hop ones. By employing a theoretical model to analyse the possible localisation area (PLA) of the unknown node, it is found that the PLA and the hop count are negatively correlated, which indicates that (n + 1)th-hop anchor neighbours can provide more information to more accurately localise the unknown node. Meanwhile, the property of convergence of the theoretical model means that localisation accuracy cannot be unlimitedly improved with increasing hop counts. In the simulations, the presented theory is verified.

A novel distributed range-free localisation algorithm to accurately locate regular nodes in wireless sensor networks is presented. Existing methods reduce the possible area where a regular node would be located by using anchors (i.e. nodes with known locations) only. These methods are effective only when anchor density is high. In contrast, the work presented here reduces the possible area of a regular node iteratively, using both anchor node locations and the possible areas of its neighbouring and non-neighbouring ‘regular’ nodes from early iteration. This allows a significant improvement in the localisation accuracy. Simulations verify the advantage of the proposed algorithm and show that the localisation error can be controlled within 15% with off-the-shelf devices.

A novel distributed range-free localisation algorithm, named polygonal overlapped possible area (POPA), is proposed to accurately localise an unknown node. First, for an unknown node, to theoretically define its possible localisation area (PLA), virtual radiation regions of its n th-hop anchor neighbours are introduced. Then an effective area-splitting algorithm is employed to simplify and speed up the computation of the possible localisation area. Simulation shows localisation error can be controlled within 20% with off-the-shelf devices.

Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

This paper proposes a twice deployment node balance algorithm (TDNB) which guarantees successful detection of the moving target on road networks. Through dividing the deployment of the sensor nodes into two phases instead of deploying all the sensors at one time, TDNB has a better performance. In the former phase, some of the sensors are deployed on the road at random. In the latter phase, the rest of the sensors are deployed on demand to make the number on each path the same. Due to the equivalence of the node's number, the network will have a prolonged lifetime after inserting nodes into the segment according to this algorithm. TDNB extends the network's lifetime remarkably compared with the former algorithms. Furthermore, TDNB enables us to insert nodes to the segment required in the network instead of all the segments, which reduces the workload to a large extent. In short, without increasing the number of sensors in road network, TDNB has a better performance compared to VISA in terms of network lifetime, which meets the demand for persistent monitoring application.

For the purpose of combination of the basic and academic frontier knowledge by the application of the theory of constructivist learning in order to trace the rapid development of microelectronics, Academic frontier-based approach (AFA) is presented. The learner-centered instructional approach is valuable for promoting active learning by involving learners in learning academic frontier topics in an open-ended and collaborative environment. The design and implementation enrich the teaching modes and the content of the key curriculums in depth. It is effective in achieving positive and higher cognitive goals.

Wireless Sensor Networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pair-wise based, in which discovery is passively achieved when two nodes are pre-scheduled to wake-up at the same time. In contrast, for the first time, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Because proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of the nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pair-wise one. Our testbed experiments using 40 sensor nodes confirm our theoretical analysis, showing one order of magnitude reduction in discovery delay compared with traditional pair-wise methods with only 0.5%∼8.8% increase in energy consumption.

Based on spatiotemporal properties of neighborhood and mobile properties of nodes in the networks, we propose Selective Reference Mechanism to trade off between the delay and overhead of neighbor discovery in low-duty-cycle WSNs. Extensive simulation and test-bed experiment confirm our theoretical analysis, showing as much as 35.4% increase in discovery probability, 38.6% reduction in discovery delay and 27.7% reduction in total energy consumption.

In the field of reliable multicast, the design of most current router cache management protocols is limited by network environment with single multicast group. With such protocols, expected targets can not be achieved in Internet environment in which a router is shared by a plurality of multicast groups. ACP is a router cache management protocol suitable for the environment shared by multiple groups. In Internet in which there exists frequently emerged bursty data flow, however, ACP allocation algorithm has such drawbacks as slow convergence and instable allocation results when the number of multicast groups or group members changes. Based on the design and implementation of a dynamic cache allocation algorithm as well as time-out algorithm and packet discard policy, a novel Adaptive and Active Reliable Multicast Protocol (AARM for short) suitable for large-scale multicast network was proposed. Simulation results show that AARM will achieve significant enhancement of performance for multicast network in terms of delayed restoration, bandwidth consumption and network throughput, etc. compared with ACP.

Because of the complexity of the hospital environment, there exist a lot of medical information systems for cancer collaboration projects from different vendors with incompatible structures. In order to establish an enterprise hospital information system for cancer collaboration projects, the integration among these heterogeneous systems must be considered. Complete integration should cover three aspects: data integration, function integration and workflow integration. However most of the previous design of architecture did not accomplish such a complete integration. This article offers an architecture design of the hospital information system for cancer collaboration projects based on the concept of digital neural network system in hospital. It covers all three aspects of integration, and eventually achieves the target of one virtual data center with enterprise viewer for users of different roles. We present hospital information system for cancer collaboration Projects (HISCCP), a modular data and process management system designed to provide the infrastructure and environment for a collaborative cancer research project. This system can be further extended to other collaboration projects to achieve a complete solution to research and clinical problems.

An effective coupling coefficient is introduced for gain-coupled distributed feedback lasers with absorptive grating. When radiation and other partial wave coupling effects are considered, the effective coupling coefficient will change significantly. In some cases, it will become real, although both loss and index coupling are presented.