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The Last decade saw significant growth in the digital terrestrial television (DTT) subscribers. The High peak-to-average power ratio (PAPR) of the transmitted signal is implied by the large fluctuations in its amplitude, which is a serious issue in the contemporary and next generation DTT broadcasting systems. These large fluctuations prevent feeding the high power amplifier (HPA) at an operating point near its non-linear (NL) saturation region, thereby lowering its power efficiency. For a better future, energy-efficient green communications is the need of hour. This paper focuses on digital predistortion (DPD), which is among the widely used techniques for HPA linearization in order to minimize the NL effects. DPD technique requires a feedback signal. This paper proposes a novel adding signal-based digital predistortion, where the output of the HPA is gradually linearized by adding a signal at the input in an iterative manner. By Taylor series approximation, the DPD problem can be posed as a convex one. We prove that a tighter upper bound exists for the convergence factor and instead of directly solving the convex problem, we pick a convergence factor value within its bounds that offers a good trade-off between convergence speed and residual error value. The simulation results show that the proposed technique can quickly linearize the HPA with good convergence. [ABSTRACT FROM AUTHOR]

Multiple-input-multiple-output (MIMO) systems are of interest for meeting the expected demand for higher data rates and lower delays in future wireless packet data systems. In such systems, it is optimal to simultaneously transmit to multiple users compared with a single user in a single-input-single-output system. In addition, multicarrier systems are of interest to combat frequency-selective fading that is experienced over the larger bandwidth that these future broadband systems will use. The use of dirty paper coding further complicates the matter, because the order in which the users are encoded will affect the rates that they can achieve. A well-designed cross-layer scheduling algorithm must take into account the multiple dimensions of this resource-allocation problem and other quality-of-service (QoS) parameters to fully exploit the communications channel. The scheduling problem is often expressed in terms of optimizing some utility function. Unfortunately, the search space for this optimization problem is extremely large, which prohibits an optimal exhaustive search. To this end, we investigate the use of genetic algorithms to reduce the complexity of the scheduling. This paper builds upon prior work that implements scheduling via genetic algorithms in the context of single-carrier systems using zero-forcing beamforming (ZFB). In this paper, we investigate how the genetic algorithm can be adapted to account for the effect of encoding order on the scheduling and how the scheduling can be extended to a multicarrier system. In particular, we investigate the maximum throughput and proportionally fair scheduling criteria. We demonstrate that the performance of the genetic algorithm is near optimal compared with an exhaustive search at a greatly reduced computational complexity. Furthermore, in the case of a multicarrier orthogonal frequency-division multiplexing (OFDM) system, an increase in capacity is shown relative to the single-carrier case. [ABSTRACT FROM AUTHOR]

The guest editors introduce the Top Picks and Honorable Mentions from the 2016 computer architecture conferences. [ABSTRACT FROM PUBLISHER]

This paper is concerned with an important matrix condition in compressed sensing known as the restricted isometry property (RIP). We demonstrate that testing whether a matrix satisfies RIP is \ssr NP-hard. As a consequence of our result, it is impossible to efficiently test for RIP provided \ssr P\ne\ssr NP. [ABSTRACT FROM PUBLISHER]

Visual surveillance for autonomous power facility inspection is considered to bethe prominent field of study in the power industry. This research field completely focuses on either object detection or image fusion which lacks the overall consideration. By considering this, a single end-to-end object detection method by incorporating the image fusion named Fast Detection Fusion Network (FDFNet) is proposed in this paper to output qualitative fused images with detection results. The parameters in the FDFnet are greatly reduced by sharing the feature extraction network between image fusion and object detection tasks, due to which a huge reduction in computational complexity is achieved. On this basis, the object detection algorithm performance on various types of power facility images is compared and analyzed. For experimentation purposes, an IR (infrared) and VIS (visible) image acquisition system has also been designed. In addition, a dataset named CVPower with different sets of images for power facility fusion detection is constructed for this research field. Experimental results demonstrate that the proposed method can achieve the mAP of not less than 70%, process 2 frames per second, and produce high qualitative fused images. [ABSTRACT FROM AUTHOR]

Many networked embedded sensing and control systems can be modeled as hybrid systems with interacting continuous and discrete dynamics. These systems present significant challenges for monitoring and diagnosis. Many existing model-based approaches focus on diagnostic reasoning assuming appropriate fault signatures have been generated. However, an important missing piece is the integration of model-based techniques with the acquisition and processing of sensor signals and the modeling of faults to support diagnostic reasoning. This paper addresses key modeling and computational problems at the interface between model-based diagnosis techniques and signature analysis to enable the efficient detection and isolation of incipient and abrupt faults in hybrid systems. A hybrid automata model that parameterizes abrupt and incipient faults is introduced. Based on this model, an approach for diagnoser design is presented. The paper also develops a novel mode estimation algorithm that uses model-based prediction to focus distributed processing signal algorithms. Finally. the paper describes a diagnostic system architecture that integrates the modeling, prediction, and diagnosis components. The implemented architecture is applied to fault diagnosis of a complex electro-mechanical machine, the Xerox DC265 printer, and the experimental results presented validate the approach. A number of design trade-otis that were made to support implementation of the algorithms for online applications are also described. [ABSTRACT FROM AUTHOR]

We consider a variable-length source coding problem subject to local decodability constraints. In particular, we investigate the blocklength scaling behavior attainable by encodings of $r$ -sparse binary sequences, under the constraint that any source bit can be correctly decoded upon probing at most $d$ codeword bits. We consider both adaptive and non-adaptive access models, and derive upper and lower bounds that often coincide up to constant factors. Such a characterization for the fixed-blocklength analog of our problem, known as the bit probe complexity of static membership, remains unknown despite considerable attention from researchers over the last few decades. We also show that locally decodable schemes for sparse sequences are able to decode 0s (frequent source symbols) of the source with far fewer probes on average than they can decode 1s (infrequent source symbols), thus rigorizing the notion that infrequent symbols require high probe complexity, even on average. Connections to the fixed-blocklength model and to communication complexity are also briefly discussed. [ABSTRACT FROM AUTHOR]

Chemical mechanical polishing/planarization (CMP) combines physical grinding and chemical reactions to planarize the wafer surface. The complex mechanism of CMP brings great challenges to the mechanism-based modeling process. The data-driven CMP modeling process is limited by insufficient datasets. At the same time, these two types of models generally have high computational complexity. In this paper, we introduce the group method of data handling (GMDH)-type polynomial network to build the CMP model to address the above challenges. We designed and manufactured the test chip using a 28nm process. The measurement data from the test chip shows that compared with the mechanism-based CMP model, the trained CMP model based on GMDH-type polynomial network has higher accuracy and lower computational complexity, with the average simulation speed being 115x faster. Experiments based on silicon data show that this modeling method has a small demand for data, and 20 randomly selected sets of data can meet the needs for modeling the current CMP process. [ABSTRACT FROM AUTHOR]

We consider practical schemes for binary dirty-paper channels and broadcast channels (BCs) with two receivers and varying channel quality. With the BC application in mind, this paper proposes a new design for binary dirty paper coding (DPC). By exploiting the concept of coset binning, the complexity of the system is greatly reduced when compared to the existing works. Some design challenges of the coset binning approach are identified and addressed. The proposed binary DPC system achieves similar performance to the state-of-the-art, superposition-coding-based system while demonstrating significant advantages in terms of complexity and flexibility of system design. For binary BCs, achieving the capacity generally requires the superposition of a normal channel code and a carefully designed channel code with non-uniform bit distribution. The non-uniform bit distribution is chosen according to the channel conditions. Therefore, to achieve the capacity for binary BCs with varying channel quality, it is necessary to use quantization codes of different rates, which significantly increases the implementation complexity. In this paper, we also propose a broadcast scheme that generalizes the concept of binary DPC, which we term soft DPC. By combining soft DPC with time sharing, we achieve a large percentage of the capacity for a wide range of channel quality with little complexity overhead. Our scheme uses only one fixed pair of codes for users 1 and 2, and a single quantization code, which possesses many practical advantages over traditional time sharing and superposition coding solutions and provides strictly better performance. [ABSTRACT FROM PUBLISHER]

Fast and accurate power-flow methods are of great importance, especially in near real-time optimal operation of power systems. This importance will be even more highlighted in the presence of more repetitions of power-flow calculations, which cause more computational complexities in optimization problems. As a solution, in this paper, a novel fast and accurate approach of linear power-flow formulation is proposed. Principles of the proposed approach are based on dividing power-flow calculations into base and variable parts. To this aim, at first, system modeling of base and variable parts are presented. For the base-part modeling, utilizing a nonlinear power-flow, an accurate base power-flow (BPF) is extracted. Afterwards, by linearizing the power system around the BPF, variable-part model which is the result of a linear fitting process, is obtained. Then, it is shown that the variable-part of the operating point is always a function of the obtained base-part and variable-part models. In this paper, by focusing on the stochastic application of the proposed approach, different uncertainties in a distribution system are considered. Finally, numerical results carried out in the Matlab environment, for a IEEE 34-bus standard distribution system and then a 1486-bus case study, verify the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. Previous channel predictors are based on theoretical channel models, which would be deviated from realistic channels. In this paper, we develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor using the realistic channels from the spatial channel model (SCM), which has been adopted in the 3GPP standard for years. First, we propose a low-complexity mobility estimator based on the spatial average using a large number of antennas in massive MIMO. The mobility estimate can be used to determine the complexity order of developed predictors. The VKF-based channel predictor developed in this paper exploits the autoregressive (AR) parameters estimated from the SCM channels based on the Yule-Walker equations. Then, the ML-based channel predictor using the linear minimum mean square error (LMMSE)-based noise pre-processed data is developed. Numerical results reveal that both channel predictors have substantial gain over the outdated channel in terms of the channel prediction accuracy and data rate. The ML-based predictor has larger overall computational complexity than the VKF-based predictor, but once trained, the operational complexity of ML-based predictor becomes smaller than that of VKF-based predictor. [ABSTRACT FROM AUTHOR]

In traditional spectrum sensing, binary hypothesis testing has been used to detect whether a frequency band is being occupied by the primary user or not. In this correspondence paper, we investigate $M$ -ary hypothesis testing for spectrum sensing to further identify the signal type of the primary user. Linear cooperation among spatially distributed cognitive radios is applied to combine the observation statistics and make a final decision. The problem of data fusion is formulated as minimizing the total probability of misclassification subject to the constraint on the probability of successful classification. To deal with the non-convex problem formulated, we develop two solutions in this paper. In the first solution, we transform the problem into an unconstrained one, and adopt a zooming-based search algorithm to iteratively update multiple continuous variables until convergence. This solution requires searching all possible combinations of variables. To reduce the computation complexity and achieve closed-form analytical expressions, in the second solution, we decompose the original problem into multiple subproblems, each with the objective of minimizing an individual probability of misclassification. These subproblems can lead to closed-form expressions, and the weights are chosen as the best ones that correspond to the minimum total probability of misclassification. The proposed solutions are examined numerically, and the results show that the decomposition-based solution can achieve performance comparable to the zooming-based one but with much less complexity. [ABSTRACT FROM AUTHOR]

This paper investigates the hybrid precoding design for millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems with finite-alphabet inputs. The mmWave MIMO system employs partially-connected hybrid precoding architecture with dynamic subarrays, where each radio frequency (RF) chain is connected to a dynamic subset of antennas. We consider the design of analog and digital precoders utilizing statistical and/or mixed channel state information (CSI), which involve solving an extremely difficult problem in theory: First, designing the optimal partition of antennas over RF chains is a combinatorial optimization problem, whose optimal solution requires an exhaustive search over all antenna partitioning solutions; Second, the average mutual information under mmWave MIMO channels lacks closed-form expression and involves prohibitive computational burden; and Third, the hybrid precoding problem with given partition of antennas is nonconvex with respect to the analog and digital precoders. To address these issues, this paper first presents a simple criterion and the corresponding low complexity algorithm to design the optimal partition of antennas using statistical CSI. Then, it derives the lower bound and its approximation for the average mutual information, in which the computational complexity is greatly reduced compared to calculating the average mutual information directly. In addition, it also shows that the lower bound with a constant shift offers a very accurate approximation to the average mutual information. This paper further proposes utilizing the lower bound approximation as a low-complexity and accurate alternative for developing a manifold-based gradient ascent algorithm to find near-optimal analog and digital precoders. Several numerical results are provided to show that our proposed algorithm outperforms the existing hybrid precoding algorithms. [ABSTRACT FROM AUTHOR]

The growing interest in concepts such as smart cities and smart mobility is giving more and more importance to place of interest (POI) information, which proves to be crucial in providing efficient and tailored location-based services (LBSs). Though plenty of solutions exist for recognizing indoor places, the literature lacks of approaches aimed at recognizing big outdoor places without the GPS employment. Even if GPS-based solutions assure great accuracy, they have a strong request in terms of energy necessary to achieve such result. As a consequence, if LBSs are thought on the move (e.g., mobile devices such as smartphones are used) energy consumption is a key constraint. This paper proposes a POI recognition algorithm called enhanced location recognition algorithm for automatic check-in applications (E-LRACI). It is an evolution of LRACI (Location Recognition Algorithm for automatic Check-In applications, originally reported in [I. Bisio, F. Lavagetto, M. Marchese, and A. Sciarrone, ''GPS/HPS-andwifi fingerprint-based location recognition for check-in applications over smartphones in cloud-based LBSS,'' IEEE Transactions on Multimedia, vol. 15, no. 4, pp. 858–869, Jun. 2013]) which aims at recognizing big outdoor places by only exploiting radio beacons emitted by WiFi access points. In terms of contributions, this paper first, proposes a novel fingerprint algorithm; second, solves the problem of big outdoor POI recognition without using GPS by leveraging the concept of spot; and third, compares the results obtained by E-LRACI and other reference works both in terms of recognition accuracy and computational complexity. The obtained numerical results, carried out on real data (acquired with Android-based smartphones), prove that E-LRACI provides the best results since it is able to guarantee the highest accuracy (95% versus, at most, 89%) at a lowest computational complexity with respect to the existing POI recognition algorithms. [ABSTRACT FROM AUTHOR]

The tridiagonal solver is an important kernel and is widely supported in mainstream numerical libraries. While parallel algorithms have been studied for many-core architectures, the performance of current algorithms and implementations is still hindered by input size sensitivity and cross-platform portability. In this paper, we propose a novel algorithm WM-pGE for the batched solution of diagonally dominant tridiagonal systems. The algorithm balances the key design objectives, including computation complexity, memory complexity, parallelism, and input size sensitivity, better than existing algorithms. Moreover, an elegant formulation is presented to show the implementation and cross-platform optimization without loss of efficiency and generality, by extracting the platform-dependent works into only four vector operators. The results from our batched tridiagonal experiments show that the proposed algorithm outperforms the prior work PCR-pThomas by 25% and 12% on NVIDIA Tesla V100 in single and double precision, respectively. On Intel KNL, our method achieves a 10% improvement in performance over PCR-pThomas in double precision. [ABSTRACT FROM AUTHOR]

Hardware non-idealities are among the main performance restrictions for upcoming wireless communication systems. Asymmetric hardware distortions (HWD) happen when the impairments of the I/Q branches are correlated or imbalanced, which in turn generate improper additive interference at the receiver side. When the interference is improper, as well as in other interference-limited scenarios, improper Gaussian signaling (IGS) has been shown to provide rate and/or power efficiency benefits. In this paper, we investigate the rate benefits of IGS in a two-user interference channel (IC) with additive asymmetric HWD when interference is treated as noise. We propose two iterative algorithms to optimize the parameters of the improper transmit signals. We first rewrite the rate region as an pseudo-signal-to-interference-plus-noise-ratio (PSINR) region and employ majorization minimization and fractional programming to find a suboptimal solution for the achievable user rates. Then, we propose a simplified algorithm based on a separate optimization of the powers and complementary variances of the users, which exhibits lower computational complexity. We show that IGS can improve the performance of the two-user IC with additive HWD. Our proposed algorithms outperform proper Gaussian signaling and competing IGS algorithms in the literature that do not consider asymmetric HWD. [ABSTRACT FROM AUTHOR]

The capacity of noisy channels with memory and finite input has been difficult to characterize explicitly in many cases. In this paper, we consider single-tap binary-input Gaussian channels with inter-symbol interference (ISI). The dual capacity method is used to obtain upper bounds using Markov test distributions. The bound, expressed as a single-letter optimization problem, is solved numerically. For higher memory, the notion of cycle basis is used to provide an exponential reduction in the computational complexity of the optimization problem. Bounds were obtained for the important case of the dicode channel, and these are better than the previously known upper bounds and are close to achievable rates over a wide range of SNRs. [ABSTRACT FROM AUTHOR]

Signed quadrature spatial modulation (SQSM) was recently introduced to enhance the throughput of spatial modulation (SM) in multiple-input multiple-output (MIMO) systems. This strategy involves independently transmitting the real and imaginary components of a symbol and its inverse from four independent antennas. In this paper, an extended SQSM (ESQSM) system is proposed. It combines $K$ SQSM constellations and transmits them from the same transmit-antenna set with the aim of realizing a $K$ -fold improvement in the spectral efficiency (SE). The proposed ESQSM employs the average power of the $K$ SQSM constellations as an additional dimension for transmission. Performance of the proposed ESQSM surpasses that of other state-of-art SM systems such as the QSM, DSM, IQSM SQSM, and EQSM by 13 dB, 11 dB, 10.55 dB, 10 dB, and 6.8 dB, respectively. In this paper, we also propose an efficient ESQSM with a multi-user (ESQSM-MU) system inspired by the ESQSM ability in increasing the SE. The proposed ESQSM-MU system offers a significant bit error rate (BER) improvement and a higher capacity (i.e., $5K$ -users) compared to the current non orthogonal multiple access-SM (NOMA-SM) and SM-assisted multi-antenna NOMA (SM-AMA-NOMA) systems. Moreover, an efficient low-complexity detector is introduced for the ESQSM signal detection, promising a 91.2% reduction in the computational complexity of ESQSM and ESQSM-MU systems relative to the maximum likelihood (ML) with a trivial loss in performance. [ABSTRACT FROM AUTHOR]

In this paper, we study a simplified affine motion model-based coding framework to overcome the limitation of a translational motion model and maintain low-computational complexity. The proposed framework mainly has three key contributions. First, we propose to reduce the number of affine motion parameters from 6 to 4. The proposed four-parameter affine motion model can not only handle most of the complex motions in natural videos, but also save the bits for two parameters. Second, to efficiently encode the affine motion parameters, we propose two motion prediction modes, i.e., an advanced affine motion vector prediction scheme combined with a gradient-based fast affine motion estimation algorithm and an affine model merge scheme, where the latter attempts to reuse the affine motion parameters (instead of the motion vectors) of neighboring blocks. Third, we propose two fast affine motion compensation algorithms. One is the one-step sub-pixel interpolation that reduces the computations of each interpolation. The other is the interpolation-precision-based adaptive block size motion compensation that performs motion compensation at the block level rather than the pixel level to reduce the number of interpolation. Our proposed techniques have been implemented based on the state-of-the-art high-efficiency video coding standard, and the experimental results show that the proposed techniques altogether achieve, on average, 11.1% and 19.3% bits saving for random access and low-delay configurations, respectively, on typical video sequences that have rich rotation or zooming motions. Meanwhile, the computational complexity increases of both the encoder and the decoder are within an acceptable range. [ABSTRACT FROM AUTHOR]

High efficiency video coding (HEVC) fractional interpolation algorithm has very high computational complexity. In this paper, two approximate HEVC fractional interpolation filters are proposed. They significantly reduce computational complexity of HEVC fractional interpolation with a negligible PSNR loss and bit rate increase. In addition, two approximate HEVC fractional interpolation hardware are proposed. They, in the worst case, can process 45 quad full HD (${3840\times 2160}$) fps. They consume up to 67.1% less energy than original HEVC fractional interpolation hardware. Therefore, they can be used in consumer electronics products that require a low energy HEVC encoder. [ABSTRACT FROM AUTHOR]

The latest video coding standard, high efficiency video coding (HEVC), aims to achieve better coding efficiency than the H.264/AVC standard. To improve the coding performance of HEVC, tools and structures are included that also increase the computational complexity of encoding, especially for the mode decisions in the coding unit (CU), which is a structural element in the HEVC. For each CU size, the HEVC encoder performs mode decision and motion estimation using all prediction unit (PU) types and incurs massive computational burdens. In this paper, we investigate the depth correlation between the current and the collocated CU to avoid irrelevant CU procedure and truncate some PU predictions as a result. We also investigate the relationship of the rate-distortion (RD) costs after the Merge/SKIP prediction. By analyzing different CU sizes and quantization parameters, we build a mathematical model to represent the relationship for the RD costs after the Merge/SKIP prediction with an adaptive termination of the CU procedure. In addition, the relationship of the RD costs after the Merge/SKIP prediction and 2N \boldsymbol \times 2\textN mode is also explored. Search range reduction is used to further speed up our algorithm. Experimental results show that our algorithm can reduce the coding time by up to 80%, and provide average coding time savings of 46%, 45%, and 52%, for low delay B, low delay P, and random access configurations, respectively. Moreover, the proposed algorithm is able to maintain coding performance similar to HM 11.0. The proposed scheme outperforms previous work in terms of both the coding speed and the RD performance. [ABSTRACT FROM PUBLISHER]

Spike sorting technologies support neuroscientists to access the neural activity with single-neuron or single-action-potential resolutions. However, conventional spike sorting technologies perform the feature extraction and the clustering separately after the spikes are well detected. It not only induces many redundant processes, but it also yields a lower accuracy and an unstable result especially when noises and/or overlapping spikes exist in the dataset. To address these issues, this paper proposes a unified optimization model integrating the feature extraction and the clustering for spike sorting. Unlike the widely used combination strategies, i.e., performing the principal component analysis (PCA) for spike feature extraction and the K-means (KM) for clustering in sequence, interestingly, this paper finds the solution of the proposed unified model by iteratively performing PCA and KM-like procedures. Subsequently, by embedding the K-means++ strategy in KM-like initializing and a comparison updating rule in the solving process, the proposed model can well handle the noises and overlapping interference as well as enjoy a high accuracy and a low computational complexity. Finally, an automatic spike sorting method is derived after taking the best of the clustering validity indices into the proposed model. The extensive numerical simulation results on both synthetic and real-world datasets confirm that our proposed method outperforms the related state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This paper presents a new objective quality metric that was adapted to the complex characteristics of immersive video (IV) which is prone to errors caused by processing and compression of multiple input views and virtual view synthesis. The proposed metric, IV-PSNR, contains two techniques that allow for the evaluation of quality loss for typical immersive video distortions: corresponding pixel shift and global component difference. The performed experiments compared the proposal with 31 state-of-the-art quality metrics, showing their performance in the assessment of quality in immersive video coding and processing, and in other applications, using commonly used image quality assessment databases– TID2013 and CVIQ. As presented, IV-PSNR outperforms other metrics in immersive video applications and still can be efficiently used in the evaluation of different images and videos. Moreover, basing the metric on the calculation of PSNR allowed the computational complexity to remain low. Publicly available, efficient implementation of IV-PSNR software was provided by the authors of this paper and is used by ISO/IEC MPEG for evaluation and research on the upcoming MPEG Immersive video (MIV) coding standard. [ABSTRACT FROM AUTHOR]

Non-Line-of-Sight (NLOS) imaging reconstructs occluded scenes based on indirect diffuse reflections. The computational complexity and memory consumption of existing NLOS reconstruction algorithms make them challenging to be implemented in real-time. This paper presents a fast and memory-efficient phasor field-diffraction-based NLOS reconstruction algorithm. In the proposed algorithm, the radial property of the Rayleigh Sommerfeld diffraction (RSD) kernels along with the linear property of Fourier transform are utilized to reconstruct the Fourier domain representations of RSD kernels using a set of kernel bases. Moreover, memory consumption is further reduced by sampling the kernel bases in a radius direction and constructing them during the run-time. According to the analysis, the memory efficiency can be improved by as much as $\mathbf {220}\times$ 220 × . Experimental results show that compared with the original RSD algorithm, the reconstruction time of the proposed algorithm is significantly reduced with little impact on the final imaging quality. [ABSTRACT FROM AUTHOR]

Driven by the outstanding performance of neural networks in the structured euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged at each layer of the neural network as a parameterization to capture detail at the node level with a reduced number of parameters and computational complexity. Following this rationale, this paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. By extrapolating this strategy to more iterations between neighboring nodes, the EdgeNet learns edge- and neighbor-dependent weights to capture local detail. This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs). In writing different GNN architectures with a common language, EdgeNets highlight specific architecture advantages and limitations, while providing guidelines to improve their capacity without compromising their local implementation. For instance, we show that GCNNs have a parameter sharing structure that induces permutation equivariance. This can be an advantage or a limitation, depending on the application. In cases where it is a limitation, we propose hybrid approaches and provide insights to develop several other solutions that promote parameter sharing without enforcing permutation equivariance. Another interesting conclusion is the unification of GCNNs and GATs —approaches that have been so far perceived as separate. In particular, we show that GATs are GCNNs on a graph that is learned from the features. This particularization opens the doors to develop alternative attention mechanisms for improving discriminatory power. [ABSTRACT FROM AUTHOR]

The depth video contains large smooth contents with sharp edges. Since the deep learning-based color video orientated intra prediction methods pay no attention to the characteristics of depth video, they are unsuitable for optimizing the coding efficiency of depth video. In this paper, a multiple resolution prediction method with deep up-sampling is proposed to promote the coding efficiency of depth video. To efficiently encode the depth blocks of different complexity, the depth block is selectively encoded at different resolutions, including $\times 1$ , $\times 1$ /2, and $\times 1$ /4 resolutions. If the block is encoded with a low-resolution (LR), the resolution of reconstructed LR depth block is recovered by an up-sampling network. To constrain the quality of both reconstructed high-resolution depth block and its synthesized view, a view synthesis distortion guidance mechanism is proposed for the up-sampling network. In addition, a distillation-based lightweight up-sampling network is proposed to reduce the computational complexity. Experimental results demonstrate that the proposed multiple resolution prediction method obtains an average of 10.84% BD-rate saving in comparison with 3D-HEVC. [ABSTRACT FROM AUTHOR]

This paper presents hardware-friendly algorithm for Gini-index (GI) cooperative-spectrum-sensing (CSS) algorithm for the data-fusion based cooperative cognitive-radio network. It simplifies the complex computations of sample-covariance-matrix (SCM) elements and test-statistics value of the conventional GI-based CSS algorithm. It delivers excellent detection performance under the realistic scenario of non-uniform dynamical noise and signal-power. Based on GI-based CSS algorithm, three different VLSI architectures are proposed for the cooperative spectrum sensor (CSR): CSR-VLAR1, CSR-VLAR2, and CSR-VLAR3. Here, CSR-VLAR1 is the first-time reported CSR-architecture for the conventional GI-based CSS algorithm. Subsequently, CSR-VLAR2 represents hardware-architecture of the proposed hardware-friendly GI-based CSS algorithm. Eventually, additional architectural optimization has been applied to CSR-VLAR2 that is transformed into the most hardware-efficient VLSI-architecture of CSR, referred as CSR-VLAR3, which is ASIC chip-fabricated in UMC 130 nm-CMOS technology node. Furthermore, both CSR-VLAR1 and CSR-VLAR2 are synthesized and post-layout simulated in the same technology node. Our ASIC-chip of CSR-VLAR3 occupies 0.35 mm2 of core-area and consumes 8.31 mW of total power at 88.8 MHz of maximum clock frequency, when the supply voltage is 1.2 V. Our CSR ASIC-chip has been functionally verified with the aid of real-world signals, using USRPs and FPGAs based test-setup of cooperative cognitive-radio network. Measured results of our design are compared with reported implementations where the proposed CSR is $4.52\times $ hardware-efficient and $2.8\times $ power-efficient than the state-of-the-art CSR-implementations. Thus, our work addresses the key challenge of designing hardware-efficient CSR that delivers excellent detection performance in the real-world scenario. [ABSTRACT FROM AUTHOR]

Although 5G networks have enabled mobile users to get a better experience, task scheduling remains challenging for massive Internet of Things (IoT) devices in remote areas. This paper investigates the task scheduling problem for delay-aware IoT applications in civil aircraft-augmented space-air-ground integrated networks (CAA-SAGIN), where the normalized sky access platforms (SAPs) can collect and forward the terrestrial tasks. Specifically, we first propose an access control scheme for a non-preemptive priority queuing system and a transmission control scheme with cross-layer optimization. Secondly, considering the uncertain distribution of the transmission numbers and generated data, we formulate a robust two-stage stochastic optimization problem of delay minimization. With the proposed robust task scheduling with risk aversion (RTS-RA) algorithm, the original problem can be decomposed into two subproblems, which can be further transformed into tractable semi-definite program (SDP) problems respectively. Simulation results show that the cross-layer optimization scheme can achieve a good tradeoff between delay and throughput. Also, the RTS-RA algorithm outperforms the exiting offloading schemes in terms of end-to-end delay, transmitted data, and energy consumption with lower computational complexity. [ABSTRACT FROM AUTHOR]

Symbol-level precoding (SLP), which converts the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol-error-rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol precoder design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop efficient design algorithms for typical max-min fairness and power minimization problems. In particular, after dividing all users into several groups, the precoders for each group are separately designed on a symbol-by-symbol basis by only utilizing the symbol information of the users in that group, in which the intra-group MUI is exploited using the concept of constructive interference (CI) and the inter-group MUI is also effectively suppressed. In order to further reduce the computational complexity, we utilize the Lagrangian dual, Karush-Kuhn-Tucker (KKT) conditions and the majorization-minimization (MM) method to transform the resulting problems into more tractable forms, and develop efficient algorithms for obtaining closed-form solutions to them. Extensive simulation results illustrate that the proposed G-SLP strategy and design algorithms dramatically reduce the computational complexity without causing significant performance loss compared with the traditional SLP schemes. [ABSTRACT FROM AUTHOR]

In this paper, we consider the joint base station (BS) association, power control, and beamforming problem for an uplink SISO/SIMO cellular network under the max-min fairness criterion. We first prove a strange discrepancy: a normalized fixed point (NFP) iterative algorithm has geometric convergence to global optima, but it only has pseudo-polynomial time complexity and thus whether the problem is NP-hard or not is an open question. In this paper, we resolve this discrepancy by proving that this problem is indeed polynomial-time solvable. Our proof is based on converting this mixed integer programming (MIP) problem to a series of auxiliary convex problems. Our results fill in a gap in the understanding of the computational complexity of BS association problem. Another implication of our result is that the uplink SIMO problem is easy, but either changing uplink to downlink or changing SIMO to MIMO will make the problem NP-hard. Empirically, the polynomial time algorithm converges much slower than the NFP algorithm, leaving open the question of whether a polynomial time algorithm that converges fast in practice exists for this problem. [ABSTRACT FROM AUTHOR]

This paper derives a maximum-likelihood decision rule to detect the signaling information conveyed by the bootstrap for an ATSC 3.0 system and proposes an iterative detection scheme to improve the detection performance. The proposed detection scheme iteratively averages the channel estimates for two consecutive bootstrap symbols by exploiting the previously detected symbol. The simulation results show that the proposed detection scheme can obtain a signal-to-noise ratio gain of approximately 2.0 dB at a frame error rate of $10^{-3}$ compared to the conventional detection scheme. Furthermore, this paper analyzes the computational complexity of the proposed detection scheme and presents the number of iterations sufficient to provide a good performance-complexity trade-off. [ABSTRACT FROM AUTHOR]

In this paper, two new decoding algorithms to decode Reed–Solomon codes during transmission over burst Rayleigh fading channels with additive white Gaussian noise (AWGN) are proposed. They only conduct error correction for coded symbols located in the pure AWGN region and conduct error and erasure correction for those symbols located in the burst fading region by treating those coded symbols that are very likely erroneous as erasures. The first algorithm does not need to know the fading locations in advance, while the second algorithm assumes that the fading locations are known. In addition, the performance of such two algorithms is studied when a pre-computed threshold is used to determine the erasures of the code. Simulation results show that our proposed algorithms not only significantly perform better than the classic Berlekamp–Messay algorithm with a comparable computational complexity but also achieve a better tradeoff between the performance and the computational complexity when compared with other existing algorithms. In particular, our algorithms exhibit excellent robustness for tested various code parameters and fading configurations. Furthermore, a more detailed mathematical analysis is also developed in this paper in order to estimate the performance of the new algorithms in the burst Rayleigh fading channels. We observe that the performance of the first algorithm can only be estimated relatively accurately when encountering burst deep-fading, whereas the performance prediction for the second algorithm is always in agreement with the simulation results for various fading cases. [ABSTRACT FROM AUTHOR]

Part I of this paper proposed a development process and a system platform for the development of autonomous cars based on a distributed system architecture. The proposed development methodology enabled the design and development of an autonomous car with benefits such as a reduction in computational complexity, fault-tolerant characteristics, and system modularity. In this paper (Part II), a case study of the proposed development methodology is addressed by showing the implementation process of an autonomous driving system. In order to describe the implementation process intuitively, core autonomous driving algorithms (localization, perception, planning, vehicle control, and system management) are briefly introduced and applied to the implementation of an autonomous driving system. We are able to examine the advantages of a distributed system architecture and the proposed development process by conducting a case study on the autonomous system implementation. The validity of the proposed methodology is proved through the autonomous car A1 that won the 2012 Autonomous Vehicle Competition in Korea with all missions completed. [ABSTRACT FROM PUBLISHER]

This paper presents a novel approach for computation of high-order axial modes (HOAMs) in a conventional and uptapered gyrotron resonator. A code has been developed which computes the eigenfrequency, Q-factor and axial field profile of HOAMs in the absence of electron beam in the resonator. Cyclic coordinate search method has been used for the optimization of complex eigenfrequencies, which obviates the computation of gradients of radiation boundary conditions and, hence, reduces computational complexity. HOAMs have been computed for resonators operating at 10, 132, 203, and 424 GHz, and the results obtained have been found to be in good quantitative agreement with the experimental and numerical data available in the published literature. Broadband continuous frequency tuning bandwidth found for subterahertz resonators also agrees well with the published results. HOAMs computed for 1-THz gyrotron resonator designed at CSIR-CEERI have also been presented. [ABSTRACT FROM AUTHOR]

Spatially coupled low-density parity-check (SC-LDPC) codes have emerged to possess capacity-approaching performance. The SC-LDPC codes can be decoded by a sliding window, therefore, the decoding latency and complexity of SC-LDPC codes are lower than those of underlying LDPC codes when the codeword length is very large. In this paper, the SC-LDPC decoder with the sliding window is employed in turbo equalization of magnetic recording systems. We examine the bit error rates (BERs) of the output of the sliding window during the iterative decoding, and then observe that the consecutive code blocks have approximately the same BERs. Herein, to reduce decoding complexity of SC-LDPC codes, the consecutive code blocks can be considered as the output of SC-LDPC codes. In addition, the non-uniform schedule update is adopted in the window decoding to avoid unnecessary updates within a window. The simulation results show that the proposed decoding algorithms applied in bit-patterned media magnetic recording systems can achieve a significant reduction in complexity compared to the traditional decoding without any loss in BER performance. [ABSTRACT FROM AUTHOR]

In-memory computing architecture is an emerging revolutionary computing paradigm that can break the von Neumann bottleneck. Computing methodology and circuit codesign using the CMOS compatible 1T1R resistive random access memory (RRAM) integration structure is presented in this paper. Functionally complete Boolean logic and arithmetic functions are experimentally demonstrated. With a single 40-nm CMOS process 1T1R unit, each of the 16 binary logics can be realized in two logic steps with an additional readout step for cascading, which shows functional reconfiguration and low computational complexity. Up to 107 cycles of NAND and XOR logic operations are performed to validate the correctness and reliability. Moreover, several fundamental adder circuits are designed and experimentally demonstrated in 1T1R devices as the proof of concept of the 1T1R computing architecture. The adders proposed in this paper include a ripple-carry adder and its optimized design and a carry-select adder, which all show promising advantages in nonvolatility, computation speed, and circuit area. This paper reports the most complex yet efficient RRAM-based 8-bit addition function experimentally so far and lays a solid foundation for constructing the future in-memory computing architecture. [ABSTRACT FROM AUTHOR]

This paper presents a new block-based method for the High Efficiency Video Coding (HEVC) intra coding. First, we have found through analysis and test that the prediction errors on some pixels in each prediction block (PB) that are neighboring to the reference pixels would be no bigger than the corresponding coding errors. Based on this observation, the pixels in each PB are divided into two parts: half pixels are coded via a novel padding technique together with a constrained quantization algorithm (leading to around 3 dB gain under the same bit rate), whereas the other half are reconstructed by linear interpolations along a prediction direction by utilizing the neighboring reference pixels and the first half coded pixels. In the final implementation, a competition mechanism is employed between this new method and the original HEVC intra coding in order to choose the best mode for each PB. Experimental results show that about 2% BD-rate reduction has been achieved both for luma and chroma with respect to the original HEVC intra coding, whereas the encoder complexity increases by 130%, but the decoding time remains nearly unchanged. [ABSTRACT FROM AUTHOR]

It was shown earlier that certain primary input values have a negative effect on the fault coverage of a functional test sequence when they appear repeatedly in the sequence. A gate-level sequential test generation procedure based on this observation computed a primary input cube c with preferred values, and generated random functional test sequences that conformed to c with a high probability 0.5 \leq p < 1 . This procedure selected values for p out of a set of possible values, and assigned the same value of p to all the primary inputs. Motivated by the low computational complexity of this procedure, this paper addresses the selection of p and the possibility of using different values of p for different primary inputs. The goal is to increase the fault coverage and reduce the number of functional test sequences. The procedure described in this paper adjusts a functional test sequence to a circuit by complementing values that conflict with c . The procedure requires fewer functional test sequences to reach or exceed the fault coverage of the earlier procedure for benchmark circuits. The procedure can be applied to any functional test sequence or set of functional test sequences. [ABSTRACT FROM AUTHOR]

How animals navigate in their cross-continental migration has puzzled scientists for centuries. To date, the mechanism behind this mysterious navigation remains unclear. In this paper, a hypothesis is developed and computer simulations have been performed to investigate long-distance animal navigation. Simulation results show that animals may be able to complete their long-distance navigation by observing a spatial angle included by the geomagnetic field line and a geographic direction, without necessarily knowing any prior landmark information. Our results not only match existing observation reports in detail on the long-distance migration routes of animals, but also are able to explain the conflicting evidence on the role of geomagnetic field in animal navigation. We think our results could help reveal the actual mechanism of animal navigation. This paper also provides a potential solution for global and local area navigation of mankind and machines assisted by artificial sensors. [ABSTRACT FROM AUTHOR]

The paper proposes a semi-implicit formulation of the differential-algebraic equations (DAEs) describing power system models for transient stability analysis. This formulation, if coupled to an implicit integration scheme, shows two relevant advantages with respect to the conventional explicit formulation: (i) reduce the computational burden; and (ii) increase the sparsity of the Jacobian matrix of the system. The proposed model also allows using null time constants and thus simplifies the computer implementation of the DAEs. The properties and the performance of the proposed semi-implicit formulation and the conventional explicit one are compared through a dynamic 21,177-bus model of the European transmission system. [ABSTRACT FROM PUBLISHER]

In an information-theoretic framework, we introduce a two-stage decoding scheme capable of achieving identification capacity to address search and memory complexities in large-scale identification systems. This two-stage decoding procedure is accomplished as follows. For a given query, at the first stage, a list of cluster indices is estimated. Then, at the second stage, refinement checks are performed for all the members of the clusters to produce a single index. The first result this paper presents is the achievable rate quadruple region that specifies necessary conditions that the two-stage decoding scheme should satisfy to be able to achieve the identification capacity. The rest of this paper is designated to investigate various achievable rate quadruples in which the proposed two-stage identification setup can reduce the search complexity with respect to conventional identification setups. [ABSTRACT FROM PUBLISHER]

This paper investigates optical orthogonal frequency division multiplexing (O-OFDM) systems with index modulation (IM) based on discrete Hartley transform (DHT) for visible light communications (VLC). An interesting trade-off between spectral efficiency (SE), energy efficiency, peak-to-average power ratio (PAPR) and bit error rate (BER) performance can be made by using the IM technique. And the DHT-based O-OFDM-IM systems can achieve higher SE compared with the DFT (discrete Fourier transform)-based counterparts. Employing DHT instead of DFT enables the removal of the Hermitian symmetry requirement, which contributes to transmitting more index bits and results in SE improvement. To acquire the same SE, the proposed systems can significantly lower the constellation order leading to better BER performance by contrast with the DFT-based counterparts. We employ DC (direct current) biasing and AC (Asymmetrically clipped) techniques to investigate the O-OFDM-IM systems in this paper. Simulation results indicate that SNR gains can be obtained by the DHT-based DCO/ACO-IM in comparison to the DFT-based counterparts. We also propose a novel reduced-complexity ML (maximum likelihood) detector specifically applicable to VLC. The new detector exhibits the same BER performance as the ML detector and performs better than the LLR (log-likelihood ratio) detector with reduced computational complexity. [ABSTRACT FROM AUTHOR]

Common spatial pattern (CSP) is a classic method commonly used in multichannel electroencephalogram (EEG) signal processing, which aims to extract effective features for binary classification by solving spatial filters that maximize the ratio of filtered dispersion between two classes. The aim of this paper is to improve the performance of the conventional CSP method, which will be badly influenced by noises. The recently proposed quantized minimum error entropy (QMEE) criterion is applied to structure a new objective function instead of the ${L} _{2}$ -norm in the conventional CSP. Quantization is utilized to reduce the computational complexity. The new objective function is optimized by a gradient-based iterative algorithm. The desirable performance of the QMEE-based CSP method, namely CSP-QMEE, is demonstrated with a toy example and two real EEG datasets, including Dataset IIb of the brain–computer interfaces (BCIs) Competition IV (three channels) and Dataset IIIa of the BCI Competition III (60 channels). The new method can achieve satisfactory performance compared to existing methods on all datasets. The promising results in this paper suggest that the CSP-QMEE may become a powerful tool for BCIs. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed to obtain full-diversity, rate-2 (rate of two complex symbols per channel use) space-time block codes (STBCs) that are full-rate for multiple input double output (MIDO) systems. Using this method, rate-2 STBCs for 4\,\times\,2, 6\,\times\,2, 8\,\times\,2, and 12\,\times\,2 systems are constructed and these STBCs are fast ML-decodable, have large coding gains, and STBC-schemes consisting of these STBCs have a non-vanishing determinant (NVD) so that they are DMT-optimal for their respective MIDO systems. It is also shown that the Srinath-Rajan code for the 4\,\times\,2 system, which has the lowest ML-decoding complexity among known rate-2 STBCs for the 4\,\times\,2 MIDO system with a large coding gain for 4-/16-QAM, has the same algebraic structure as the STBC constructed in this paper for the 4\,\times\,2 system. This also settles in positive a previous conjecture that the STBC-scheme that is based on the Srinath-Rajan code has the NVD property and hence is DMT-optimal for the 4\,\times\,2 system. [ABSTRACT FROM AUTHOR]

Single image dehazing has been a challenging problem because of its ill-posed nature. For this reason, numerous efforts have been made in the field of haze removal. This paper proposes a simple, fast, and powerful algorithm for haze removal. The medium transmission is derived as a function of the saturation of the scene radiance only, and the saturation of scene radiance is estimated using a simple stretching method. A different medium transmission can be estimated for each pixel because this method does not assume that transmission is constant in a small patch. Furthermore, this paper presents a color veil removing algorithm, which is useful for an image with fine or yellow dust, using the white balance technique. The proposed algorithm requires no training, prior, and refinement process. The simulation results show that the proposed dehazing scheme outperforms state-of-the-art dehazing approaches in terms of both computational complexity and dehazing efficiency. [ABSTRACT FROM AUTHOR]

Quadtree with nested multi-type tree (QTMT) partition structure is an efficient improvement in versatile video coding (VVC) over the quadtree (QT) structure in the advanced high-efficiency video coding (HEVC) standard. With the exception of the recursive QT partition structure, recursive multi-type tree partition is applied to each leaf node, which generates more flexible block sizes. Besides, intra prediction modes are extended from 35 to 67 so as to satisfy various texture patterns. These newly developed techniques achieve high coding efficiency but also result in very high computational complexity. To tackle this problem, we propose a fast intra-coding algorithm consisting of low-complexity coding tree units (CTU) structure decision and fast intra mode decision in this paper. The contributions of the proposed algorithm lie in the following aspects: 1) the new block size and coding mode distribution features are first explored for a reasonable fast coding scheme; 2) a novel fast QTMT partition decision framework is developed, which can determine the partition decision on both QT and multi-type tree with a novel cascade decision structure; and 3) fast intra mode decision with gradient descent search is introduced, while the best initial search point and search step are also investigated in this paper. The simulation results show that the complexity reduction of the proposed algorithm is up to 70% compared to VVC reference software (VTM), and averagely 63% encoding time saving is achieved with 1.93% BDBR increasing. Such results demonstrate that our method yields a superior performance in terms of computational complexity and compression quality compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]