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Many applications such as smart metering and location based services pose strong privacy requirements but achieving privacy protection at the client side is a non-trial problem as payment for the services must be computed by the server at the end of each billing period. In this paper, we propose a privacy preservation and billing scheme termed PPDIR based on delayed information release. PPDIR relies on a novel group signature mechanism and the asymmetric Rabin cryptosystem to protect the privacy of the clients and their requests, to achieve accountability and non-repudiation, and to shift the computational complexity to the server side. It adopts a secret token for anonymity and the token is updated for each client at the beginning of each billing period and securely released only to the server at the end of the billing period. Such a strategy can prevent the server from linking a client’s requests made at different billing periods. It also prevents any adversary from linking any request to any client. Note that the server is able to figure out all requests made by a client within a billing period after receiving the delayed token, which is unavoidable for billing purpose. We prove the security properties of the group signature scheme, and analyze the security strength of PPDIR. Our study indicates that PPDIR can achieve privacy-preservation, confidentiality, non-repudiation, accountability, and other security objectives. We also evaluate the performance of our scheme in terms of communication and computational overheads.

Recently, there is a lot of progress in designing effective convolutional schemes on irregular graph structures. These graph convolutional networks mostly solve the semi-supervised learning problem on the graph where the number of labeled nodes typically is very small and the number of unlabeled nodes is very large. In the standard GCN, only labeled nodes will enter the loss function; unlabeled data will not get into the loss function. The model will be unstable due to the unconstrained nature of unlabeled nodes. This will lead to overfitting and negatively affect the generalization ability of the model. To solve this problem, we propose to regularize the graph convolutional networks using data augmentation. We provide the details of the regularization GCN and SGC model and design rich data augmentation schemes including individual features perturbation, individual edge addition, and combination of them. To verify the performance of our method, we conduct experiments on standard benchmarks and explore the intensity effect of different data augmentation strategies. The experimental results on the node classification tasks demonstrate that our method can outperform baseline methods.

We a practical multiunit heterogeneous spectrum market in which each buyer may request multiple channels with different bid prices at different geographical regions and each channel is associated with a reserve price indicating the desired revenue of the seller. The degree-of-freedom brought by multiunit trading and (reserve and bid) price diversity in such a market can be exploited to break the truthfulness of the two most popular schemes adopted by secondary spectrum auctions, namely Myerson’s Optimal Mechanism (MOM) and Vickrey-Clarke-Groves (VCG), via bidder self-collusion. In this paper, we conduct a thorough analysis on the root causes of untruthfulness in MOM and VCG, and prove the fundamental theories addressing when MOM and VCG are truthful and when their truthfulness is broken by bid rigging. Particularly, we demonstrate how self-collusion is exploited in MOM and VCG to improve the untruthful bidders’ utility. The critical findings provide a guidance for us to design a Self-collusion Resistant Auction (SIRI) in multiunit heterogeneous spectrum markets with reserve prices. The economic properties of SIRI are proved via rigorous theoretical analysis.

Estimation of third-order statistics relies on the availability of a huge amount of data records, which can pose severe challenges on the data collecting hardware in terms of considerable storage costs, overwhelming energy consumption, and unaffordably high sampling rate especially when dealing with high-dimensional data such as wideband signals. To overcome these challenges, this paper focuses on the reconstruction of the third-order cumulants under the compressive sensing framework. Specifically, this paper derives a transformed linear system that directly connects the cross-cumulants of compressive measurements to the desired third-order statistics. We provide sufficient conditions for lossless third-order statistics reconstruction via solving simple least-squares, along with the strongest achievable compression ratio. To reduce the computational burden, we also propose an approach to recover diagonal cumulant slices directly from compressive measurements, which is useful when the cumulant slices are sufficient for the inference task at hand. All the proposed techniques are tested via extensive simulations. The developed joint sampling and reconstruction approach to third-order statistics estimation is able to reduce the required sampling rates significantly by exploiting the cumulant structure resulting from signal stationarity, even in the absence of any sparsity constraints on the signal or cumulants.

The Kalman filter has successful applications in many fields. The applicability of the standard Kalman filter critically hinges on the accurate prior knowledge of all the system model parameters. However, in practical applications, it can be difficult or unrealistic to obtain these parameters, in which case it is a common practice to employ some pre-determined alternatives for the unknown model parameters. This letter investigates the case of unknown initial state priors by assessing how their pre-determined alternatives affect the performance of the Kalman filter. The ranking of three types of mean squared errors is established. It is found that the definiteness of the initial state prior deviation is critical, which determine the ranking of the three types of mean squared errors. Such results provide guideline on the choice of the pre-determined initial state prior. Numerical examples are provided to validate the results.

In the area of large-scale graph data representation and semi-supervised learning, deep graph-based convolutional neural networks have been widely applied. However, typical graph convolutional network (GCN) aggregates information of neighbor nodes based on binary neighborhood similarity (adjacency matrix). It treats all neighbor nodes of one node equally, which does not suppress the influence of dissimilar neighbor nodes. In this paper, we investigate GCN based on similarity matrix instead of adjacency matrix of graph nodes. Gaussian heat kernel similarity in Euclidean space is first adopted, which is named EGCN. Then biologically inspired manifold similarity is trained in reproducing kernel Hilbert space (RKHS), based on which a manifold GCN (named MGCN) is proposed for graph data representation and semi-supervised learning with four different kernel types. The proposed method is evaluated with extensive experiments on four benchmark document citation network datasets. The objective function of manifold similarity learning converges very quickly on different datasets using various kernel functions. Compared with state-of-the-art methods, our method is very competitive in terms of graph node recognition accuracy. In particular, the recognition rates of MGCN (Gaussian kernel) and MGCN (Polynomial Kernel) outperform that of typical GCN about 3.8% on Cora dataset, 3.5% on Citeseer dataset, 1.3% on Pubmed dataset and 4% on Cora_ML dataset, respectively. Although the proposed MGCN is relatively simple and easy to implement, it can discover local manifold structure by manifold similarity learning and suppress the influence of dissimilar neighbor nodes, which shows the effectiveness of the proposed MGCN.

With the proliferation of versatile devices and data-consuming services, the quest for spectrum efficiency has led to the merging of three disruptive technologies: millimeter-wave (mmWave) communications, massive MIMO (maMIMO), and non-orthogonal multiple access (NOMA). Emerging wireless networks move toward ultra-dense deployment of massive devices with diverse service demands, which call for efficient spectrum sharing even on mmWave bands. This article studies some key techniques that account for the unique angular selectivity of mmWave maMIMO channels and thus enable re-engineering the spectrum sharing paradigm of NOMA. An overview is provided on research challenges and opportunities related to spectrum and energy efficiency of spectrum-shared NOMA-mmWave systems with maMIMO, with focus on high-performance and low-complexity channel sensing, optimal sensing resource allocation, security and privacy provisioning, and learning- aided real-time system optimization.

N-butanol is a valuable renewable fuel that has been extensively studied. In this paper, the effect of n-butanol-gasoline blends on combustion and emissions performance of direct injection gasoline engine under urban conditions (throttle opening is 10% and 35%) is studied by using GT-Power simulation software. By comparing the several fuels, we find a fuel that is more suitable for the engine to operate in urban conditions. This is of great significance for reducing China's dependence on oil. The simulation results show that adding n-butanol into gasoline can significantly reduce the peak in-cylinder temperature (Tmax), improve the brake thermal efficiency (BTE) of the engine. It is advantageous for the engine to operate under urban conditions. By comparison, it is found that Bu40 is the most suitable fuel. When the throttle opening is 10%, high engine speed operation will reduce brake power (BP), brake torque (BT) and BTE, and increase CO emissions. In terms of emissions, n-butanol gasoline blends can reduce CO and NOx emissions, but HC emissions increase. Finally, the results show that n-butanol-gasoline blends can be used in SI direct injection gasoline engine instead of pure gasoline.

As Internet-of-Things (IoT) networks provide efficient ways to transfer data, they are used widely in data sensing applications. These applications can further include wireless sensor networks. One of the critical problems in sensor-equipped IoT networks is to design energy efficient data aggregation algorithms that address the issues of maximum value and distinct set query. In this paper, we propose an algorithm based on uniform sampling and Bernoulli sampling to address these issues. We have provided logical proofs to show that the proposed algorithms return accurate results with a given probability. Simulation results show that these algorithms have high performance compared with a simple distributed algorithm in terms of energy consumption.

Quasiperiodic radio frequency interference (RFI), such as those generated by telecommunication and active radar systems, is commonly encountered in radio astronomy observations. Such RFI-contaminated signals contain hidden periodicities due to cyclic features involved in their formation (e.g., carrier frequencies, periodic keying of the amplitude, and baud rates). RFI signal characterization and its subsequent excision based on the well-known cyclic spectrum analysis have been previously demonstrated; however, the high complexity of the algorithm and the computational cost of its implementation have limited its utility in radio astronomy, rendering less sophisticated solutions. To overcome this challenge, we present a novel method for RFI detection and mitigation based on efficient estimation of the cyclic spectrum by compressive statistical sensing (CSS) of sub-Nyquist data. CSS performs second-order statistical estimation such as cyclic spectrum using a reduced number of input samples, thereby enabling accelerated performance. To validate the feasibility of the proposed method, we conduct experiments with simulated data and assess the detection and mitigation results under different parameter settings, for example, interference-to-noise ratio, additional RFI sources, frequency resolution, and input data size. We demonstrate the real performance of the method by analyzing radio astronomy data (∼1.3 GHz) acquired with the L-wide band receiver at the Arecibo Observatory, which is typically corrupted by active air surveillance radars located nearby. Our CSS-based solution enables robust and efficient detection of the RFI frequency bands present in the L-band data, and subsequent excision by blanking is also demonstrated.

Historically, the price of WTI crude oil futures has long been higher than that of Brent by $2/barrel, but the spread between 2011 and 2015 was reversed and expanded to $24/barrel. In order to analyze the difference between two crude oil price variables with the same trend and phase separation using one method of analysis, this paper constructs a dynamic comparative analysis framework using the method of time-point decomposition of fluctuation factors to determine the different reasons and amplitudes for monthly fluctuations in the two price systems in the sample interval. The study found that the sensitive response of Brent futures price indicators to the world’s crude oil supply resulting from the depletion of oil in the North Sea oil field prompted it to rise in 2011–2015. For the WTI price system, due to the increase in the US shale oil production after 2008 and the restrictions in domestic pipeline transportation capacity, the increase in the Cushing crude oil inventory caused downward pressure on the WTI price. With the lifting of the US crude oil export ban in December 2015, arbitrage space disappeared, and the spread between the two gradually narrowed.

The papers in this special issue focus on array signal processing for angular models in massive MIMO communications. In recent years, there has been tremendous research progress in massive MIMO from communications theory viewpoint. An emerging trend is to explore the angular models of the propagation channels and design the massive system with array signal processing techniques. Particularly, fundamental concepts in array signal processing (e.g., direction of arrival/departure - DOA/DOD), can be conceptualized and exploited.

Social networks have gained tremendous popularity recently. Millions of people use social network apps to share precious moments with friends and family. Users are often asked to provide personal information such as name, gender, and address when using social networks. However, as the social network data are collected, analyzed, and re-published at a large scale, personal information might be misused by unauthorized third parties and even attackers. Therefore, extensive research has been carried out to protect the data from privacy violations in social networks. The most popular technique is graph perturbation, which modifies the local topological structure of a social network user (a vertex) via various randomization techniques before the social graph data is published. Nevertheless, graph anonymization may affect the usability of the data as random noises are introduced, decreasing user experience. Therefore, a trade-off between privacy protection and data usability must be sought. In this paper, we employ various graph and application utility metrics to investigate this trade-off. More specifically, we conduct an empirical study by implementing five state-of-the-art anonymization algorithms to analyze the graph and application utilities on a Facebook and a Twitter dataset. Our results indicate that most anonymization algorithms can partially or conditionally preserve the graph and application utilities and any single anonymization algorithm may not always perform well on different datasets. Finally, drawing on the reviewed graph anonymization techniques, we provide a brief overview on future research directions and challenges involved therein.

Health-related Internet of Things (IoT) devices are becoming more popular in recent years. On the one hand, users can access information of their health conditions more conveniently; on the other hand, they are exposed to new security risks. In this paper, we presented, to the best of our knowledge, the first in-depth security analysis on home-use electroencephalography (EEG) IoT devices. Our key contributions are twofold. First, we reverse-engineered the home-use EEG system framework via which we identified the design and implementation flaws. By exploiting these flaws, we developed two sets of novel easy-to-exploit PoC attacks, which consist of four remote attacks and one proximate attack. In a remote attack, an attacker can steal a user’s brain wave data through a carefully crafted program while in the proximate attack, the attacker can steal a victim’s brain wave data over-the-air without accessing the victim’s device on any sense when he is close to the victim. As a result, all the 156 brain–computer interface (BCI) apps in the NeuroSky App store are vulnerable to the proximate attack. We also discovered that all the 31 free apps in the NeuroSky App store are vulnerable to at least one remote attack. Second, we proposed a novel deep learning model of a joint recurrent convolutional neural network (RCNN) to infer a user’s activities based on the reduced-featured EEG data stolen from the home-use EEG IoT devices, and our evaluation over the real-world EEG data indicates that the inference accuracy of the proposed RCNN is can reach 70.55%.

Recent trends show that the popularity of online social networks (OSNs) has been increasing rapidly. From daily communication sites to online communities, an average person’s daily life has become dependent on these online networks. Hence, it has become evident that protection should be provided to these networks from unwanted intruders. In this paper, we consider the data privacy on OSNs at the network level rather than the user level. This network-level privacy helps us to prevent information leakage to third-party users, such as advertisers. We propose a novel scheme that combines the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We use an enhanced equi-cardinal clustering (ECC) as a way to achieve $k$ -anonymity. We further improve $k$ -anonymity with $l$ -diversity. Our proposed enhanced ECC ensures that there are at least “ $k$ ” users in any given network as well as the attributes in each cluster has at least $l$ -distinct values. We further provide proofs on how the proposed ECC ensures $k$ -anonymity and the maximum information loss. We consider a weighted directed social network graph as an input to our method to consider the existing complexities in a social network. With the help of two real-world data sets, we evaluate this method in terms of privacy and efficiency.

In this paper, we design an uplink/downlink channel estimation method for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems and investigate the impact of beam squint effect that accompanies large array configuration. Specifically, we adopt the off-grid sparse Bayesian learning (SBL) that directly works on the continuous angle-delay parameter domain and avoids the grid mismatch problem. Hence, the proposed method achieves good channel estimation accuracy and handles the wideband direction of arrival (DOA) estimation problem for mmWave massive MIMO communications, where beam squint effect was previously ignored by many existing literatures. The Cramer-Rao bound for unknown parameters is derived to make the proposed study complete. More importantly, a much simplified downlink channel estimation scheme is designed with the aid of angle-delay reciprocity , which significantly reduces training and feedback overhead. The simulation results are provided to demonstrate the superior performance of the proposed method over existing ones.

Cooperative jamming is deemed as a promising physical layer-based approach to secure wireless transmissions in the presence of eavesdroppers. In this paper, we investigate cooperative jamming in a two-tier 5G heterogeneous network (HetNet), where the macrobase stations (MBSs) at the macrocell tier are equipped with large-scale antenna arrays to provide space diversity and the local base stations (LBSs) at the local cell tier adopt non-orthogonal multiple access (NOMA) to accommodate dense local users (LUs). In the presence of imperfect channel state information, we propose three robust secrecy transmission algorithms that can be applied to various scenarios with different security requirements. The first algorithm employs robust beamforming (RBA) that aims to optimize the secrecy rate of a marcouser (MU) in a macrocell. The second algorithm provides robust power allocation (RPA) that can optimize the secrecy rate of an LU in a local cell. The third algorithm tackles a robust joint optimization (RJO) problem across tiers that seek the maximum secrecy sum rate of a target MU and a target LU robustly. We employ convex optimization techniques to find feasible solutions to these highly non-convex problems. The numerical results demonstrate that the proposed algorithms are highly effective in improving the secrecy performance of a two-tier HetNet.

Compressive sensing (CS) has been widely used in the Internet of Things (IoT) to achieve efficient information collection. However, existing works have mainly focused on utilizing CS to lower the sampling rate or reduce the number of transmissions, without explicitly accounting for the heterogeneity of energy consumption in IoT environments. In this paper, we propose a CS-based prejudiced random sensing strategy (PRSS) that explicitly considers the heterogeneous energy consumption of IoT sensor nodes at different locations, in order to accurately attain a desired tradeoff between the overall energy consumption and the sensing accuracy. Specifically, each sensor node participates in sensing via distributed random access based on an assigned sensing probability, which is determined by its energy consumption in sending the sensed data, data collision rate and its contribution to recovery accuracy. We employ the statistical restricted isometry property as a practical indicator of the recovery accuracy and derive a sufficiently good recovery error bound based on it. Accordingly, we devise a novel convex optimization framework to find the most energy-efficient sensing probability assignment strategy with accuracy guarantee. We evaluate the PRSS using real-world sea surface temperature data traces. Comparative simulations corroborate that the PRSS can significantly reduce energy consumption and prolong network lifetime without sacrificing sensing accuracy.

With the development of Internet of Things (IoT), heterogeneous sensory data appears everywhere in our lives. Unlike traditional sensory data, heterogeneous sensory data often involves variety modalities of data in one set, so that it is called as the multi-modal sensory data in this paper. The appearance of such data making it possible to monitor more complicated objects and improve monitoring accuracy. However, due to lack of integration model for multi-modal sensory data, most of the existing sensory data management algorithms only consider single modal sensory data, resulting in insufficient utilization of sensory data. Thus, we propose a model for integrating the heterogeneous sensory data generated in a IoT system based on Hidden Markov Process in the paper. The distributed algorithm for constructing such a model is then presented. The integration model can be applied to many applications, while we take the cooperative event detection as an example for illustration. The extensive theoretical analysis and experimental results show that all the proposed algorithms are efficient and effective .

This paper aims at developing low-complexity solutions for super-resolution two-dimensional (2D) harmonic retrieval via covariance reconstruction. Given the collected sample covariance, a novel gridless compressed sensing approach is designed based on the atomic norm minimization (ANM) technique. The key is to perform a redundancy reduction (RR) transformation that effectively reduces the large problem size at hand, without loss of useful frequency information. For uncorrelated sources, the transformed 2D covariance matrices in the RR domain retain a salient structure, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. Accordingly, the decoupled ANM (DANM) framework can be applied for super-resolution 2D frequency estimation, at low computational complexity on the same order of the 1D case. An analysis of the complexity reduction of the proposed RR-D-ANM compared with benchmark methods is provided as well, which is verified by our simulation results.

Estimation of popular third-order statistics hinges on the availability of a huge amount of data records, which brings severe challenges on the data collecting hardware in terms of considerable storage costs, overwhelming energy consumption and unaffordably high sampling rate especially when dealing with wideband signals. To overcome these challenges, we propose a systematic approach to estimate the third-order statistics of wide-sense stationary signals under the compressive sampling framework. Without any sparsity constraints on the signal or cumulants, we provide the sufficient conditions for lossless third-order statistics recovery via solving simple least-squares, and analyze the strongest achievable compression ratio in closed-form.

This paper concerns wideband direction of arrival (DoA) estimation with sparse linear arrays (SLAs). We rely on the assumption that the power spectrum of the wideband sources is the same up to a scaling factor, which could in theory allow us to resolve not only more sources than the number of antennas but also more sources than the number of degrees of freedom (DoF) of the difference co-array of the SLA. We resort to the Jacobi-Anger approximation to transform the coarray response matrices of all frequency bins into a single virtual uniform linear array (ULA) response matrix. Based on the obtained model, two super-resolution DoA estimation approaches based on atomic norm minimization (ANM) are proposed, one with and one without prior knowledge of the power spectrum. Simulation results show that our proposed methods outperform the state of the art and are indeed capable of resolving more sources than the number of DoF of the difference co-array.

Higher-order statistics (HOS) estimation hinges on the availability of a huge amount of data records, which causes exceedingly high sampling rates and overwhelming energy consumption for the sampling devices, especially when dealing with wideband signals. To overcome these challenges, this paper develops a novel compressive cumulant slice sensing (CCSS) method that aims to efficiently reconstructing the 1D diagonal slice of higher-order cumulants under the compressive sensing framework. It first parsimoniously collects data with a properly designed sampler based on the minimal sparse ruler principle, and then accurately reconstructs the 1D diagonal cumulant slice from those compressive measurements. The reconstructed HOS can be useful in many signal processing tasks, which is illustrated through the task of line spectrum estimation with resilience to colored Gaussian noise at reduced sampling rates.

To date, instance segmentation is dominated by twostage methods, as pioneered by Mask R-CNN. In contrast, one-stage alternatives cannot compete with Mask R-CNN in mask AP, mainly due to the difficulty of compactly representing masks, making the design of one-stage methods very challenging. In this work, we propose a simple singleshot instance segmentation framework, termed mask encoding based instance segmentation (MEInst). Instead of predicting the two-dimensional mask directly, MEInst distills it into a compact and fixed-dimensional representation vector, which allows the instance segmentation task to be incorporated into one-stage bounding-box detectors and results in a simple yet efficient instance segmentation framework. The proposed one-stage MEInst achieves 36.4% in mask AP with single-model (ResNeXt-101-FPN backbone) and single-scale testing on the MS-COCO benchmark. We show that the much simpler and flexible one-stage instance segmentation method, can also achieve competitive performance. This framework can be easily adapted for other instance-level recognition tasks. Code is available at: https://git.io/AdelaiDet, Accepted to Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020

Recently Convolutional Neural Networks (CNNs) are widely applied into novel intelligent applications and systems. However, the CNN computation performance is significantly hindered by its computation flow, which computes the model structure sequentially by layers with massive convolution operations. Such a layer-wise sequential computation flow can cause certain performance issues, such as resource under-utilization, huge memory overhead, etc. To solve these problems, we propose a novel CNN structural decoupling method, which could decouple CNN models into "critical paths" and eliminate the inter-layer data dependency. Based on this method, we redefine the CNN computation flow into parallel and cascade computing paradigms, which can significantly enhance the CNN computation performance with both multi-core and single-core CPU processors. Experiments show that, our DC-CNN framework could reduce 24% to 33% latency on multi-core CPUs for CIFAR and ImageNet. On small-capacity mobile platforms, cascade computing could reduce the latency by average 24% on ImageNet and 42% on CIFAR10. Meanwhile, the memory reduction could also reach average 21% and 64%, respectively.

A hybrid algorithm of extended Kalman filtering and recursive least-square estimator (EKF-RLSE) is developed to solve the nonlinear inverse heat conduction-radiation problems in participating medium. By measuring the time-resolved temperature signals from a thermocouple installed on the other boundary of the participating medium, the proposed EKF-RLSE algorithm can realize real-time retrieval of the time-varying heat flux on the medium surface. The influence factors of the sampling interval, measurement and process covariance, forgetting factor, and medium thickness on the reconstruction accuracy and stability of the EKF-RLSE are investigated by numerical experiments. All the estimated results show that the present algorithm is robust and accurate in real-time estimating transient heat flux. If the nonlinear coupled conduction and radiation heat transfer problems is regard as the linear problems, the unknown time-dependent heat flux predicted by the KF-RLSE are also illustrated in this work.

This letter presents a fast algorithm named iterative Vandermonde decomposition and shrinkage-thresholding (IVDST), which offers a low-complexity solution to atomic norm minimization (ANM) in off-grid compressed sensing for line spectral estimation from few measurements. It implements the ANM principle via the accelerated proximal gradient (APG) technique, without invoking computationally expensive semidefinite programming (SDP). To approximate the proximal operator in each APG iteration, Vandermonde decomposition is applied to utilize the Toeplitz structure inherent in the line spectral model, and the low-rank property of the Toeplitz-structured matrix is enforced via a simple shrinkage-thresholding operation. The IVDST algorithm effectively reduces the order of computational complexity compared to SDP-based solutions. It also offers an explicit way to bridge the ANM principle with classic super-resolution line spectral estimation algorithms, such as MUSIC.

For communications over large-scale antennas, noncoherent differential detection is an attractive option for avoiding expensive channel estimation, but it cannot maximally collect the performance benefits as the antenna number increases. For a desired performance-complexity tradeoff, this letter develops multiple symbol differential detection (MSDD) with simple implementation for single-input multiple-output (SIMO) systems, which jointly detects a block of symbols within the channel coherence time. The generalized likelihood ratio test criterion is adopted that maximizes the likelihood function over both the information symbols and the unknown channels. Simple formulas of the MSDD-SIMO detector are derived, whose performance is competitive to training-based coherent detection.

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.

3D point cloud semantic and instance segmentation are crucial and fundamental for 3D scene understanding. Due to the complex structure, point sets are distributed off-balance and diversely, appearing as both category and pattern imbalance. It has been proved that deep networks can easily forget the non-dominant cases during training, which influences the model generalization and leads to unsatisfactory performance. Although re-weighting on instances may reduce the influence, it is hard to find a balance between the dominant and the non-dominant cases. To tackle the above issue, we propose a memory-augmented network that learns and memorizes the representative prototypes that encode both geometry and semantic information. The prototypes are shared by diverse 3D points and recorded in a universal memory module. During training, the memory slots are dynamically associated with both dominant and non-dominant cases, alleviating the forgetting issue. In testing, the distorted observations and rare cases can thus be augmented by retrieving the stored prototypes, leading to better generalization. Experiments on the benchmarks, i.e., S3DIS and ScanNetV2, show the superiority of our method on both effectiveness and efficiency, which substantially improves the accuracy not only on the entire dataset but also on non-dominant classes and samples.

We present a private information retrieval (PIR) scheme that allows a user to retrieve a single message from an arbitrary number of databases by colluding with other users while hiding the desired message index. This scheme is of particular significance when there is only one accessible database -- a special case that turns out to be more challenging for PIR in the multi-database case. The upper bound for privacy-preserving capacity for these scenarios is $C=(1+\frac{1}{S}+\cdots+\frac{1}{S^{K-1}})^{-1}$, where $K$ is the number of messages and $S$ represents the quantity of information sources such as $S=N+U-1$ for $U$ users and $N$ databases. We show that the proposed information retrieval scheme attains the capacity bound even when only one database is present, which differs from most existing works that hinge on the access to multiple databases in order to hide user privacy. Unlike the multi-database case, this scheme capitalizes on the inability for a database to cross-reference queries made by multiple users due to computational complexity., Comment: $\copyright$ 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

This paper develops efficient super-resolution spatial channel covariance estimation techniques for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems under the hybrid precoding constraint. Two structure-based optimization techniques including low-rank structured covariance reconstruction and dynamic atomic norm minimization are proposed to accurately estimate the channel covariance matrix. For computational efficiency, a fast iterative algorithm is developed via the alternating direction method of multipliers. The extension of this work to the higher-dimensional cases is also discussed. Simulation results verify the effectiveness of the proposed methods in hybrid mmWave massive MIMO systems.

This paper aims at accurate channel estimation for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems under practical limitations, including an arbitrary array geometry and a hybrid hardware structure. Taking on an angle-based approach, this work adopts a generalized array manifold separation approach via the Jacobi-Anger approximation, which transforms a non-ideal, non-uniform array manifold into a virtual array domain with a desired uniform geometric structure to facilitate super-resolution angle estimation and channel acquisition. Accordingly, structure-based optimization techniques are developed to estimate the channel parameters within a short sensing time. In particular, the difference in time-variation of path angles and path gains is capitalized to design a two-step scheme that can quickly sense fading channels. Theoretical results are provided on the fundamental limits of the proposed technique in terms of sample efficiency. Simulations testify the effectiveness of the proposed approaches.

In this paper, we develop a quantized and communication-censored alternating direction method of multipliers (ADMM) to solve a dynamic optimization problem defined over a decentralized network. At every time, the network nodes collaboratively minimize the summation of their local objective functions, which are time-varying. The proposed algorithm, termed as DQC-ADMM, tracks the time-varying optimal solution through local iterative computation and communication. Unlike traditional approaches that require the nodes to transmit the exact local iterates to their neighbors at every time, we quantize the transmitted local iterates and introduce a communication-censoring mechanism so as to reduce the communication cost spent in the optimization process. Although the quantized and censored communications cause inaccuracy, we rigorously prove that the proposed algorithm is able to track the time-varying optimal solution with a bounded error under mild conditions. We also demonstrate the tracking performance and communication savings of the proposed algorithm through numerical experiments.

Recent progress has been made on developing a unified framework for joint text detection and recognition in natural images, but existing joint models were mostly built on two-stage framework by involving ROI pooling, which can degrade the performance on recognition task. In this work, we propose convolutional character networks, referred as CharNet, which is an one-stage model that can process two tasks simultaneously in one pass. CharNet directly outputs bounding boxes of words and characters, with corresponding character labels. We utilize character as basic element, allowing us to overcome the main difficulty of existing approaches that attempted to optimize text detection jointly with a RNN-based recognition branch. In addition, we develop an iterative character detection approach able to transform the ability of character detection learned from synthetic data to real-world images. These technical improvements result in a simple, compact, yet powerful one-stage model that works reliably on multi-orientation and curved text. We evaluate CharNet on three standard benchmarks, where it consistently outperforms the state-of-the-art approaches [25, 24] by a large margin, e.g., with improvements of 65.33%->71.08% (with generic lexicon) on ICDAR 2015, and 54.0%->69.23% on Total-Text, on end-to-end text recognition. Code is available at: https://github.com/MalongTech/research-charnet., To appear in ICCV 2019

This paper develops efficient channel estimation techniques for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems under practical hardware limitations, including an arbitrary array geometry and a hybrid hardware structure. Taking on an angle-based approach, this work adopts a generalized array manifold separation approach via Jacobi-Anger approximation, which transforms a non-ideal, non-uniform array manifold into a virtual array domain with a desired uniform geometric structure to facilitate super-resolution angle estimation and channel acquisition. Accordingly, structure-based optimization techniques are developed to effectively estimate both the channel covariance and the instantaneous channel state information (CSI) within a short sensing time. In particular, the difference in time-variation of channel path angles and path gains is capitalized to design a two-step CSI estimation scheme that can quickly sense fading channels. Theoretical results are provided on the fundamental limits of the proposed technique in terms of sample efficiency. For computational efficiency, a fast iterative algorithm is developed via the alternating direction method of multipliers. Other related issues such as spurious-peak cancellation in nonuniform linear arrays and extensions to higher-dimensional cases are also discussed. Simulations testify the effectiveness of the proposed approaches in hybrid mmWave massive MIMO systems with arbitrary arrays., 13 pages, 10 figures

Recent semantic segmentation methods exploit encoder-decoder architectures to produce the desired pixel-wise segmentation prediction. The last layer of the decoders is typically a bilinear upsampling procedure to recover the final pixel-wise prediction. We empirically show that this oversimple and data-independent bilinear upsampling may lead to sub-optimal results. In this work, we propose a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs. The main advantage of the new upsampling layer lies in that with a relatively lower-resolution feature map such as $\frac{1}{16}$ or $\frac{1}{32}$ of the input size, we can achieve even better segmentation accuracy, significantly reducing computation complexity. This is made possible by 1) the new upsampling layer's much improved reconstruction capability; and more importantly 2) the DUpsampling based decoder's flexibility in leveraging almost arbitrary combinations of the CNN encoders' features. Experiments demonstrate that our proposed decoder outperforms the state-of-the-art decoder, with only $\sim$20\% of computation. Finally, without any post-processing, the framework equipped with our proposed decoder achieves new state-of-the-art performance on two datasets: 88.1\% mIOU on PASCAL VOC with 30\% computation of the previously best model; and 52.5\% mIOU on PASCAL Context., Accepted to Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2019. Content may change prior to final publication

Orthogonal Matching Pursuit is an iterative greedy algorithm used to find a sparse approximation for high-dimensional signals. The algorithm is most popularly used in Compressive Sensing, which allows for the reconstruction of sparse signals at rates lower than the Shannon-Nyquist frequency, which has traditionally been used in a number of applications such as MRI and computer vision and is increasingly finding its way into Big Data and data center analytics. OMP traditionally suffers from being computationally intensive and time-consuming, this is particularly a problem in the area of Big Data where the demand for computational resources continues to grow. In this paper, the data-level parallelization of OMP through blocking is examined. Traditionally blocking has been used to ac- celerate the performance of OMP reconstruction for big data image analytics. However, as we show in this work, blocking, particularly in the form of vectorizing, introduces significant error in terms of PSNR and SSIM index in the reconstruction quality. In response, we deploy the concept of stitching to recover the lost accuracy. We further examine the influence of the level of blocking and amount of stitching (overlap between each block) with regard to recon- struction time and reconstructed image quality. While stitching boosts up the image reconstruction accuracy significantly, the ob- ject detection count results show anywhere from 11.84% to 140.54% improvement, depending on the cases being compared, it introduces significant overhead with regard to reconstruction time. To address the overhead, we deploy hardware accelerated base solutions. Given the emergence of hardware accelerators in data centers and for big data analytics in form of FPGAs, our solution effectively utilizes this resource to enhance the performance overhead of stitching by 25%. We show the minimum block size required for an FPGA speed-up.

This paper proposes a communication- and computation-efficient algorithm to solve a convex consensus optimization problem defined over a decentralized network. A remarkable existing algorithm to solve this problem is the alternating direction method of multipliers (ADMM), in which at every iteration every node updates its local variable through combining neighboring variables and solving an optimization subproblem. The proposed algorithm, called as communication-censored linearized ADMM (COLA), leverages a linearization technique to reduce the iteration-wise computation cost of ADMM and uses a communication-censoring strategy to alleviate the communication cost. To be specific, COLA introduces successive linearization approximations to the local cost functions such that the resultant computation is first-order and light-weight. Since the linearization technique slows down the convergence speed, COLA further adopts the communication-censoring strategy to avoid transmissions of less informative messages. A node is allowed to transmit only if the distance between the current local variable and its previously transmitted one is larger than a censoring threshold. We establish convergence as well as sublinear and linear rates of convergence of COLA, and demonstrate its satisfactory communication-computation tradeoff with numerical experiments.

In this paper, we propose a weighted alternating direction method of multipliers (ADMM) to solve the consensus optimization problem over a decentralized network. In the proposed algorithm, every node holds its local objective function, exchanges its current iterate with a subset of neighbors, carries on local computation, and eventually reaches an optimal and consensual solution that minimizes the summation of the local objective functions. Compared with the conventional ADMM that is popular in decentralized network optimization, the weighted ADMM is able to reduce the communication cost spent in the optimization process through tuning the weight matrices, which assign beliefs on the neighboring iterates. We first prove convergence and establish linear convergence rate of the weighted ADMM. Second, we maximize the derived convergence speed and obtain the best weight matrices on a given topology. Third, observing that exchanging information with all the neighbors is expensive, we maximize the convergence speed while limit the number of communication arcs. This strategy finds a subset of arcs within the underlying topology to fulfill the optimization task while leads to a favorable tradeoff between the number of iterations and the communication cost per iteration. Numerical experiments demonstrate advantages of the weighted ADMM over its conventional counterpart in expediting the convergence speed and reducing the communication cost.

Mobile opportunistic networks make use of node mobility to provide occasional contact opportunities for mobile devices to deliver data. In this paper, we investigate the inherent properties of data disseminations in mobile opportunistic networks by answering the question regarding how far and how fast a packet can be disseminated in mobile opportunistic networks . This problem has been overlooked by recent research, but it is very important because it reveals the potential for mobile opportunistic networks to support emerging applications such as mobile commerce and emergency services that may involve time and location sensitive information dissemination. Our investigations are taken from the perspectives of small and large scales. From the perspective of small scale, the probability distributions of the minimum time needed by the data to spread to a given region, namely, $\mathcal{T}$ , are deduced for both the one-copy case and the multiple-copy case. From the perspective of large scale, by using the large deviation theory, the probability distributions of $\mathcal{T}$ are deduced for both the one-copy case and the multiple-copy case when the destination region is far enough from the data origin.

Currently, alternative energy sources such as hydrogen and alcohol are increasingly used in IC engines. There is a gap in the literature of both experimental and numerical studies considering hydrogen addition in a high compression ratio engine (using methanol/hydrogen, ethanol/hydrogen and methane/hydrogen blends). Therefore, in this study, the combustion and emission performance analysis of methanol, ethanol and methane with different hydrogen volume ratios were carried out. The results showed that as the hydrogen addition, the ignition delay and the combustion duration were extended. Under the same ratio of hydrogenation, the ignition delay and combustion duration of methane/hydrogen are longer than methanol/hydrogen and ethanol/hydrogen blends. As the hydrogen ratio increases, the HC and NOx emissions of the methanol/hydrogen, ethanol/hydrogen and methane/hydrogen are increased, and the CO and CO2 emissions are decreased. Under the same ratio of hydrogenation, the engine power and BSFC of methanol/hydrogen blend is better than ethanol/hydrogen and methane/hydrogen blends, the HC and NOx emissions of the methanol/hydrogen are higher than the ethanol/hydrogen and methane/hydrogen blends, The CO emissions of methanol/hydrogen and methane/hydrogen blends are lower than the ethanol/hydrogen blends.

In this paper, we propose a communication- and computation-efficient algorithm to solve a convex consensus optimization problem defined over a decentralized network. A remarkable existing algorithm to solve this problem is the alternating direction method of multipliers (ADMM), in which at every iteration every node updates its local variable through combining neighboring variables and solving an optimization subproblem. The proposed algorithm, called as co mmunication-censored l inearized A DMM (COLA), leverages a linearization technique to reduce the iteration-wise computation cost of ADMM and uses a communication-censoring strategy to alleviate the communication cost. To be specific, COLA introduces successive linearization approximations to the local cost functions such that the resultant computation is first-order and light-weight. Since the linearization technique slows down the convergence speed, COLA further adopts the communication-censoring strategy to avoid transmissions of less informative messages. A node is allowed to transmit only if the distance between the current local variable and its previously transmitted one is larger than a censoring threshold. COLA is proven to be convergent when the local cost functions have Lipschitz continuous gradients and the censoring threshold is summable. When the local cost functions are further strongly convex, we establish the linear (sublinear) convergence rate of COLA, given that the censoring threshold linearly (sublinearly) decays to 0. Numerical experiments corroborate with the theoretical findings and demonstrate the satisfactory communication-computation tradeoff of COLA.

This paper proposes a class of distributed event-triggered algorithms that solve the average consensus problem in multi-agent systems. By designing events such that a specifically chosen Lyapunov function is monotonically decreasing, event-triggered algorithms succeed in reducing communications among agents while still ensuring that the entire system converges to the desired state. However, depending on the chosen Lyapunov function the transient behaviors can be very different. Moreover, performance requirements also vary from application to application. Consequently, we are instead interested in considering a class of Lyapunov functions such that each Lyapunov function produces a different event-triggered coordination algorithm to solve the multi-agent average consensus problem. The proposed class of algorithms all guarantee exponential convergence of the resulting system and exclusion of Zeno behaviors. This allows us to easily implement different algorithms that all guarantee correctness to meet varying performance needs. We show that our findings can be applied to the practical clock synchronization problem in wireless sensor networks (WSNs) and further corroborate their effectiveness with simulation results., Comment: 23 pages, 11 figures

We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at:Code is available at: https://tinyurl.com/FCOSv1, Comment: Accepted to Proc. Int. Conf. Computer Vision 2019. 13 pages. Code is available at: https://github.com/tianzhi0549/FCOS/

Both accuracy and efficiency are of significant importance to the task of semantic segmentation. Existing deep FCNs suffer from heavy computations due to a series of high-resolution feature maps for preserving the detailed knowledge in dense estimation. Although reducing the feature map resolution (i.e., applying a large overall stride) via subsampling operations (e.g., pooling and convolution striding) can instantly increase the efficiency, it dramatically decreases the estimation accuracy. To tackle this dilemma, we propose a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride. To handle the inconsistency between the features of the student and teacher network, we optimize the feature similarity in a transferred latent domain formulated by utilizing a pre-trained autoencoder. Moreover, an affinity distillation module is proposed to capture the long-range dependency by calculating the non-local interactions across the whole image. To validate the effectiveness of our proposed method, extensive experiments have been conducted on three popular benchmarks: Pascal VOC, Cityscapes and Pascal Context. Built upon a highly competitive baseline, our proposed method can improve the performance of a student network by 2.5\% (mIOU boosts from 70.2 to 72.7 on the cityscapes test set) and can train a better compact model with only 8\% float operations (FLOPS) of a model that achieves comparable performances., Comment: Accepted to IEEE Conf. Computer Vision and Pattern Recognition, 2019

As clean fuel, methanol is considered to be one of the most promising alternative to conventional fuels for internal combustion engines. In this paper, a four-cylinder ignited methanol engine simulation model was built by using a one-dimensional simulation software. The methanol engine was converted from a diesel engine with a compression ratio of 17.5, and the methanol injector was installed in the inlet. Setting the engine speed as 2400 r/min, the effects of the initial flame kernel radius and EGR rate on the performance, combustion and emissions of methanol engine were studied when the throttle opening was 10%, 30%, 50%, 70% and 90%, respectively. The results showed that at any throttle opening, with the increase of EGR rate, the performance of the engine decreased, the in-cylinder combustion deteriorated, the NOx emission decreased significantly, but the HC and CO emission increased. At any throttle opening, the engine performance decreased slightly with the increase of the initial flame kernel radius, and the combustion was optimized. In terms of emission, when the throttle opening was between 10% and 70%, the NOx and CO emission decreased with the increase of the initial flame kernel radius. When the throttle opening was 90%, the NOx and CO emission increased with the increase of the initial flame kernel radius.

This paper presents a novel low-rank matrix recovery approach that jointly performs measurement collection and matrix estimation to improve the overall sample efficiency under unknown rank information. It builds on a key observation that the minimum number of measurements needed for matrix rank estimation can be much less than that for matrix recovery. Such a gap in measurement requirements is first delineated in closes form through empirical quantification. Then, capitalizing on this quantified gap on measurements, a two-step procedure is developed for adaptive measurement collection. The actual rank of the matrix is estimated in the first step, which informs the number of measurements to be collected in the second step for low-rank matrix recovery. Simulations corroborate that our approach can considerably reduce the total number of required measurements for matrix recovery in practice. The improvement in sample efficiency is particularly pronounced for large-scale applications where low-rank matrix estimation is of great relevance.

In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data. We demonstrate the performance of this algorithm by analyzing radio astronomy data acquired from the Arecibo Observatory (AO)’s L-Wide band receiver (~1.3 GHz), which is typically corrupted by active radars for commercial applications located near AO facilities. Our CSS-based solution enables robust and efficient detection of the RFI frequency bands present in the data, which is measured by receiver operating characteristic (ROC) curves. As a result, it allows fast and computationally efficient identification of RFI-free frequency regions in wideband radio astronomy observations.