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Recent years have witnessed a tremendous increasing of first-person videos captured by wearable devices. Such videos record information from different perspectives than the traditional third-person view, and thus show a wide range of potential usages. However, techniques for analyzing videos from different views can be fundamentally different, not to mention co-analyzing on both views to explore the shared information. In this paper, we take the challenge of cross-view video co-analysis and deliver a novel learning-based method. At the core of our method is the notion of "joint attention", indicating the shared attention regions that link the corresponding views, and eventually guide the shared representation learning across views. To this end, we propose a multi-branch deep network, which extracts cross-view joint attention and shared representation from static frames with spatial constraints, in a self-supervised and simultaneous manner. In addition, by incorporating the temporal transition model of the joint attention, we obtain spatial-temporal joint attention that can robustly capture the essential information extending through time. Our method outperforms the state-of-the-art on the standard cross-view video matching tasks on public datasets. Furthermore, we demonstrate how the learnt joint information can benefit various applications through a set of qualitative and quantitative experiments.

Detecting highly-overlapped objects in crowded scenes remains a challenging problem, especially for one-stage detector. In this paper, we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme, named YOLO-CS. Specifically, we give YOLOv4 the power to detect multiple objects in one cell. Center to our method is the carefully designed joint prediction scheme, which is executed through an assignment of bounding boxes and a joint loss. Equipped with the derived joint-object augmentation (DJA), refined regression loss (RL) and Score-NMS (SN), YOLO-CS achieves competitive detection performance on CrowdHuman and CityPersons benchmarks compared with state-of-the-art detectors at the cost of little time. Furthermore, on the widely used general benchmark COCO, YOLO-CS still has a good performance, indicating its robustness to various scenes.

The performance of smart vehicle (SV) applications like autonomous driving and in-vehicle augmented reality based traffic information system depends on the Field of View (FoV) and the timely processing of the SVs sensor data. Vehicular networking (VN) technology can enhance the performance of these applications by enabling a SV to access the sensing and processing capabilities of other neighbouring SVs. The processing and storage capacity of a SV is limited compared to cloud servers and the communication link between two SVs is unreliable due to their mobility and the nature of wireless channels. Hence, developing efficient processing and sensing schemes for SVs and VNs can help in optimizing the performance of SV applications. In this paper, we propose Contextual Bandits (CB), Markov decision process (MDP) and deep Q-network (DQN) based sensing and processing schemes for VNs. Simulation results show that the proposed schemes outperform the baseline schemes in a variety of scenarios.

Self-supervised learning has attracted plenty of recent research interest. However, most works for self-supervision in speech are typically unimodal and there has been limited work that studies the interaction between audio and visual modalities for cross-modal self-supervision. This work (1) investigates visual self-supervision via face reconstruction to guide the learning of audio representations; (2) proposes an audio-only self-supervision approach for speech representation learning; (3) shows that a multi-task combination of the proposed visual and audio self-supervision is beneficial for learning richer features that are more robust in noisy conditions; (4) shows that self-supervised pretraining can outperform fully supervised training and is especially useful to prevent overfitting on smaller sized datasets. We evaluate our learned audio representations for discrete emotion recognition, continuous affect recognition and automatic speech recognition. We outperform existing self-supervised methods for all tested downstream tasks. Our results demonstrate the potential of visual self-supervision for audio feature learning and suggest that joint visual and audio self-supervision leads to more informative audio representations for speech and emotion recognition., Accepted for publication in IEEE Transactions on Affective Computing; v3: Publication-ready version including additional experiments and discussion

Although state estimation using a bad data detector (BDD) is a key procedure employed in power systems, the detector is vulnerable to false data injection attacks (FDIAs). Substantial deep learning methods have been proposed to detect such attacks. However, deep neural networks are susceptible to adversarial attacks or adversarial examples, where slight changes in inputs may lead to sharp changes in the corresponding outputs in even well-trained networks. This article introduces the joint adversarial example and FDIAs (AFDIAs) to explore various attack scenarios for state estimation in power systems. Considering that perturbations added directly to measurements are likely to be detected by BDDs, our proposed method of adding perturbations to state variables can guarantee that the attack is stealthy to BDDs. Then, malicious data that are stealthy to both BDDs and deep learning-based detectors can be generated. Theoretical and experimental results show that our proposed state-perturbation-based AFDIA method (S-AFDIA) can carry out attacks stealthy to both conventional BDDs and deep learning-based detectors, while our proposed measurement-perturbation-based adversarial FDIA method (M-AFDIA) succeeds if only deep learning-based detectors are used. The comparative experiments show that our proposed methods provide better performance than state-of-the-art methods. Besides, the ultimate effect of attacks can also be optimized using the proposed joint attack methods.

Multiview subspace clustering (MVSC) leverages the complementary information among different views of multiview data and seeks a consensus subspace clustering result better than that using any individual view. Though proved effective in some cases, existing MVSC methods often obtain unsatisfactory results since they perform subspace analysis with raw features that are often of high dimensions and contain noises. To remedy this, we propose a self-guided deep multiview subspace clustering (SDMSC) model that performs joint deep feature embedding and subspace analysis. SDMSC comprehensively explores multiview data and strives to obtain a consensus data affinity relationship agreed by features from not only all views but also all intermediate embedding spaces. With more constraints being cast, the desirable data affinity relationship is supposed to be more reliably recovered. Besides, to secure effective deep feature embedding without label supervision, we propose to use the data affinity relationship obtained with raw features as the supervision signals to self-guide the embedding process. With this strategy, the risk that our deep clustering model being trapped in bad local minima is reduced, bringing us satisfactory clustering results in a higher possibility. The experiments on seven widely used datasets show the proposed method significantly outperforms the state-of-the-art clustering methods. Our code is available at https://github.com/kailigo/dmvsc.git.

Edge caching can significantly improve the quality of wireless video services by deploying cache servers at network edges. Recently, video conversion and recommendation have been introduced to improve the caching performance at the edges. Specifically, they work to produce lower quality versions of videos via video converting (for the former) or provide alternative similar videos when requested videos are not available by using video recommendation (for the latter). However, existing work in this aspect has utilized these two techniques separately, which largely limit their capabilities in providing improved video services. In this article, we study how to jointly utilize these two techniques in edge caching for improved caching performance. The objective is to maximally reduce the video delivery delay while satisfying users’ requirements. We first formulate the optimal video caching problem in this case and derive its NP-hardness. We, then, propose an effective transcoding- and recommending-based caching algorithm (TRBA). The TRBA works in a greedy manner to iteratively buffer the most valuable video versions, one video version each time, until no more video version or cache space is available. We define the value of a video version as the reduced delivery delay if this version were buffered divided by the extra cache space required for its buffering. The computational complexity of TRBA is deduced as $O(|V|^3|Q|^3)$ , where $|V|$ and $|Q|$ represent the total number of videos and the number of versions per video, respectively. Numerical results demonstrate that, compared with existing caching algorithms, TRBA can significantly improve the caching performance.

Medical image segmentation and registration are two tasks to analyze the anatomical structures in clinical research. Still, deep-learning solutions utilizing the connections between segmentation and registration remain underdiscovered. This article designs a joint learning framework named RSegNet that can realize concurrent deformable registration and segmentation by minimizing an integrated loss function, including three parts: diffeomorphic registration loss, segmentation similarity loss, and dual-consistency supervision loss. The probabilistic diffeomorphic registration branch could benefit from the auxiliary segmentations available from the segmentation branch to achieve anatomical consistency and better deformation regularity by dual-consistency supervision. Simultaneously, the segmentation performance could also be improved by data augmentation based on the registration with well-behaved diffeomorphic guarantees. Experiments on the human brain 3-D magnetic resonance images have been implemented to demonstrate the effectiveness of our approach. We trained and validated RSegNet with 1000 images and tested its performances on four public datasets, which shows that our method successfully yields concurrent improvements of both segmentation and registration compared with separately trained networks. Specifically, our method can increase the accuracy of segmentation and registration by 7.0% and 1.4%, respectively, in terms of Dice scores.

Wireless rechargeable sensor networks (WRSNs) have emerged as a potential solution to solve the challenge of prolonging battery-powered sensor networks’ lifetime. In a WRSN, a mobile charger moves around and charges the rechargeable sensors when stopping at charging spots. This paper newly considers a joint optimization of the charging location and the charging time to avoid node failure in WRSNs. We formulate the optimization and propose a solution for that. The proposal includes an algorithm to find a list of potential charging locations and their associated optimal charging time. Moreover, the Q-learning technique is adopted to determine the optimal next charging location among the candidates. We implement the algorithm and conduct experiments to compare it to the related works. The results show that the proposed algorithm outperforms the others in terms of network lifetime. Specifically, the highest performance gap of our proposal to the best algorithm among the others is 8.29 times.

Video highlight detection (VHD) has emerged as a pressing task due to the unprecedentedly increasing amount of video data, such as those from e-commerce live-broadcasting platforms. Many approaches focus on exploiting text data, in the form of video description or time-sync comments, to facilitate the VHD task. Despite the promising results, they have largely overlooked the noises inherent in the text data and have mostly relied on isolating the feature of text and video. In this paper, we introduce a novel model to handle VHD, termed Coevolution Network (CoEvo-Net), that allows us to account for joint learning of the language and video features explicitly via a coevolution paradigm, in which features from the two data modalities progressively refine each other. This is achieved by a dedicated CoEvo-Cell that takes language and video together as inputs, extracts cross-modality, and filters the undesired parts of the input, such as words in a sentence. Furthermore, we release a large-scale dataset of e-commerce for VHD, in which each video is coupled with a sentence for description, to benchmark the sentence-based VHD approaches. Extensive experiments on the released dataset demonstrate that CoEvo-Net achieves state-of-the-art performance. Our dataset and code will be made publicly available.

In multiple-input-multiple-output (MIMO) systems, the selection of receive and transmit antennas is not just effective in increasing system capacity, but also in reducing RF link costs and system complexity. The exhaustive algorithm, i.e. the joint transmit and receive antenna selection (JTRAS) with the best accuracy, can search all the subsets of both transmit and receive antennas in order to find the optimal solution. However, with the increase of the number of antennas, the computational complexity is too large and its applicability is limited. In this paper, the antennas are coded by fractional coding with the maximization of channel capacity as the basic criterion, and three intelligent algorithms, namely genetic algorithm, cat swarm algorithm and particle swarm algorithm, are applied for antenna selection. The simulation results demonstrate that all three algorithms can efficiently accomplish the antenna selection. In the end, we compare them in terms of speed, accuracy and complexity of the search in MIMO systems.

Video watermarking on the dual tree-complex wavelet (DT CWT) domain is shown to be effective to offer high robustness. Existing DT CWT video watermarking schemes tend to use all the coefficients on the high-pass sub-bands of the DT CWT domain for watermark embedding and detection, which lack of investigating the correlations among different sub-bands and fail to explore the stable coefficients for robust watermarking. In this paper, we propose a novel DT CWT video watermarking scheme by exploring the stable coefficients on joint sub-bands. We first extract a set of candidate coefficients by applying block singular value decomposition (SVD) on the DT CWT domain. Then, we simulate the watermark embedding by modifying the candidate coefficients on each sub-band, from which we identity two pairs of strongly correlated sub-bands termed as the joint sub-bands. The watermark is eventually embedded by modifying the candidate coefficients of the joint sub-bands on a level which is adaptively chosen according to the video resolution. During the watermark detection, we identify and extract a set of stable coefficients from the candidate coefficients of the joint sub-bands to verify the ownership of the video. Extensive experiments demonstrate the advantage of our propose scheme over the latest DT CWT based schemes, which also performs better than the existing non-DT CWT transformed domain video watermarking schemes.

With the large-scale access of all kinds of distributed generations (DGs), the operation mode of the distribution network is increasingly diverse and changeable. To monitor the operation of an active distribution network, an adaptive dynamic estimation method is proposed to address the new generation of power system. Considering the features of different types of operation scenario change of distribution network and DGs, the proposed method uses the state deviation index to identify the current operation mode before state estimation. In the adaptive estimation stage, two typical estimators are improved to cope with the typical operation mode and embedded in the interactive multiple model (IMM) algorithm framework. IMM uses the identification results of operation mode to give higher weight to the corresponding estimator and finally outputs the joint estimation results. The proposed estimation method is investigated in an improved IEEE 33-bus system and an actual distribution network in China, which results indicate the proposed method converges more quickly and maintains better accuracy while facing the complex distribution network.

Focused energy delivery (FED) is a technique that can precisely bring energy to the specific region, which arouses wide attention in precision electronic warfare (PREW). This paper first proposes a joint optimization model with respect to the locations of the array and the transmitted signals to improve the performance of FED. As the problem is nonconvex and NP-hard, particle swarm optimization (PSO) is adopted to solve the locations of the array, while designing the transmitted signals under a feasible array is considered as a unimodular quadratic program (UQP) subproblem to calculate the fitness criterion of PSO. In the PSO-UQP framework established, two methods are presented for the UQP subproblem, which are more efficient and more accurate respectively than previous works. Furthermore, a threshold value is set in the framework to determine which method to adopt to take full advantages of the methods above. Meanwhile, we obtain the maximum localization error that FED can tolerate, which is significant for implementing FED in practice. Simulation results are provided to demonstrate the effectiveness of the joint optimization algorithm, and the correctness of the maximum localization error derived.

In this paper, we investigate the grant-free communications with adaptive period for industrial Internet of Things, where only a fraction of devices are active at a time. To the best of our knowledge, this is the first work to exploit the non-continuous temporal correlation of the received signal for joint user activity detection (UAD), channel estimation and signal detection, while all the previous work requires continuous transmission. Two schemes are proposed toward this purpose, namely periodic block orthogonal matching pursuit (PBOMP) and periodic block sparse Bayesian learning (PBSBL), which outperform the previous schemes in terms of the success rate of UAD, bit error rate and accuracy in period estimation and channel estimation. The CramC)r-Rao lower bounds (CRLBs) of channel estimation by PBOMP and PBSBL are derived. It is shown that the two proposed approaches have close CRLBs and normalized mean square error at high SNR.

Automotive radar and vehicular communication are the two primary means of establishing an intelligent transportation system. However, both the systems are susceptible to interference. The inter-vehicular interference significantly affects the radar and communication performance, especially in the dense traffic scenarios. In this work, we have shown that lowering down radar range in dense traffic scenario provides twofold advantages; (a) it reduces radar-to-radar interference and, (b) it provides resources to support the vehicular communication. We propose a joint Radar-Communication (R-Comm) algorithm which enables connected vehicles to use a fraction of radar resources for vehicular communication based on the traffic density. Also, two different schemes have been proposed for R-Comm transmission in the sparse and dense traffic scenarios. Further, through simulation results, it is shown that R-Comm benefits both the radar and communication systems.

In underwater scenes, degraded underwater images caused by wavelength-dependent light absorption and scattering present huge challenges to vision tasks. Underwater image enhancement has attracted much attention due to the significance of vision-based applications in marine engineering and underwater robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, almost all existing approaches focus only on the enhancement of independent images. Considering that images photographed in the same underwater scene usually share similar degradation, related images can provide rich complementary information for each other’s enhancement. In this paper, we propose an Underwater Image Co-enhancement Network (UICoE-Net) based on an encoder-decoder Siamese architecture. For joint learning, we introduced correlation feature matching units into the multiple layers of our Siamese encoder-decoder structure in order to communicate the mutual correlation of the two branches. Extensive experiments using the Underwater Image Enhancement Benchmark (UIEB), Underwater Image Co-enhancement Dataset (UICoD) collected from an underwater video dataset with ground-truth reference and Stereo Quantitative Underwater Image Dataset (SQUID) dataset demonstrate the effectiveness of our method.

Facial expression recognition (FER) is a challenging task due to the large appearance variations and the lack of sufficient training data. Conventional deep approaches either learn a good representation through deep models or synthesize images automatically to enlarge the training set. In this paper, we perform both tasks jointly and propose an end-to-end deep model for simultaneous facial expression recognition and facial image synthesis. The proposed model is based on Generative Adversarial Network (GAN) and enjoys several merits. First, the facial image synthesis and facial expression recognition tasks can boost their performance for each other via the unified model. Second, paired images are not required in our facial image synthesis network, which makes the proposed model much more general and flexible. Meanwhile, the generated facial images largely expand the training set and ease the overfitting problem in our FER task. Third, different expressions are encoded in a disentangled manner in a latent space, which enables us to synthesize facial images with arbitrary expressions by exchanging certain parts of their latent identity features. Quantitative and qualitative evaluations on both controlled and in-the-wild FER benchmarks (Multi-PIE, MMI, and RAF-DB) demonstrate the effectiveness of our proposed method on both facial image synthesis and facial expression recognition task.

This article presents a reduced-complexity sequential integer carrier frequency offset (ICFO) and sidelink primary synchronization signal (S-PSS) detection scheme in the 5G new radio vehicle-to-everything system. To perform the ICFO detection without information about S-PSS, a two-dimensional detection problem of the ICFO and S-PSS is reduced to two one-dimensional subproblems. Such a design allows us to sequentially detect them and relieves the computational burden of the proposed joint detection scheme. We use both analytical and numerical examples to validate the usefulness of the proposed sequential detector. The simulation results show that the proposed method attains target detection performance with significantly reduced complexity at the cost of increased processing time, as compared to the conventional methods.

In this paper, we present a novel low area joint 2T spin orbit torque magnetic random access memory (SOT-MRAM) cell architecture. The proposed joint 2T cell achieves up to 15% of SOT-MRAM cell area reduction by sharing the diffusion regions of transistors between adjacent cells. In addition, the small bit-line capacitance of the proposed SOT-MRAM can lead to 27% read energy reduction in comparison to the conventional SOT-MRAM. When the proposed 1 MB SOT-MRAM is used as L2 cache of X86 processor, the gem5 simulation results show the average of 18% dynamic energy savings in various workloads of SPEC2006 benchmarks.

The tracking control problem for Flexible Joint Manipulator Control System (FJMCS) with unmeasurable states is addressed in this paper. Firstly, a High-Gain Observer (HGO) is constructed to estimate the unmeasurable states and the uncertainties. Then, a Dynamic Surface Control (DSC) scheme is developed by using the estimation of HGO. The newly proposed controller has two advantages over the existing methods: (A) a novel Spike Suppression Function (SSF) is developed to avoid the estimation spike problem in the existing HGO-based controllers. (B) Unlike the existing observer-based partial feedback control scheme that can only estimate the unmeasurable states, the proposed HGO can estimate both the unmeasurable states and uncertainties. The closed-loop system stability is proved by the Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed controller.

Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

In this study, the joint design of discrete phase transmit sequences and receive filters with the low peak sidelobe level and integrated sidelobe level in radar systems is considered. The problem is formulated as a minimisation of the peak (integrated) sidelobe level, forcing a unimodular discrete phase constraint at the design stage. To deal with the resulting non‐convex and in general the NP‐hard optimisation problem, an iterative method based on the alternate optimisation approach is proposed. Each iteration of the proposed method involves a non‐convex min–max problem to be solved. In addition, a sequential rank‐one constraint relaxation framework is proposed to initialise the procedure, each iteration of which requires the solution of a convex problem. Simulation results show that using the proposed framework for jointly finding transmit sequences and receive filters leads to a better peak sidelobe ratio and integrated sidelobe ratio for discrete phase sequences than the optimal mismatched filters design approach.

A joint message and state communication problem over a fading Gaussian channel with additive state is considered. The state process is assumed to be independent and identically distributed (i.i.d.) Gaussian, and known non-causally at the transmitter, while the instantaneous fading gain is assumed to be known at both the encoder and the decoder. The receiver not only has to decode the message from the transmitter, but also needs to estimate the state process to meet a prescribed distortion limit. A complete characterization of the optimal rate versus distortion performance is provided. Moreover, the converse proof presented here might be of independent interest, since it gives a simpler and more compact way of establishing the converse for settings with non-fading links studied in the literature – by virtue of relying only upon the differential entropy maximizing property of Gaussian random variables for a given variance.

Prevalent video codec implementations deployed in the field often solely support chrominance sub-sampled video data represented by the YCbCr 4:2:0 format. For certain applications like screen sharing, however, chroma sub-sampling leads to disturbing artifacts, especially for text or graphics with thin lines. It is desirable to reduce these artifacts while maintaining compatibility with all user devices. For this reason, an enhancement layer coding framework for chroma-format scalable coding with specific focus on screen content is proposed in this paper. Based on an analysis of screen content data characteristics, the enhancement layer codec is optimized specifically for this content class, is of low algorithmic complexity, and applicable with any image or video codec for base layer compression in a joint coding system. The system is intentionally not designed as a general purpose lossless YCbCr 4:4:4 coding scheme, but instead chooses to close a quality gap that prevalent video codecs do not address. Experimental analysis reveals an average BD-PSNR gain in the RGB domain of 1.0 dB comparing the proposed two-layer scalable coding approach to single layer compression using base layer coding only. In relation to simulcast coding, an average BDR RGB difference of 8.1% is observed.

In this paper, we investigate the power minimization problem in an Internet of Things (IoT) sensor network assisted by reconfigurable intelligent surface (RIS). Different from the existing literature, we consider both transmit and non-transmit energy consumption, which leads to a joint sensor selection, beamforming design and RIS phase configuration problem. Based on the cutting-the-edge majorization minimization (MM) and penalty duality decomposition (PDD) frameworks, we develop two solutions which can achieve nearly identical performance as that of exhaustive search method. Extensive numerical results verify the great benefit in power saving via sensor selection and the deployment of RIS device.

The inevitable voltage sag events cause losses to distributed generation (DG) and sensitive users. Reasonable planning for both of them helps to mitigate the impact of voltage sag. In this paper, a joint optimal planning method is proposed for DG and large sensitive user (LSU) considering the influence of voltage sag. Firstly, the new models of accelerated trip and chain trip for DG under voltage sag are established, to characterize the influence of DG tripping on other DG and sag characteristics during the sag event accurately. Secondly, a new voltage sag frequency assessment method is proposed, which considers models of accelerated trip and chain trip for DG, the tripping characteristics of distance protection (DP), to quantify the voltage sag severity accurately of DGs and LSUs in the gird which is with DP. Lastly, a joint optimal planning model is established considering the losses of DG and LSU under voltage sag, to support the planning of DG and LSU in different scenarios. The method is applied to IEEE RTS-30 for verification, proving that the method can give a direction of the most beneficial solution for the planner, to reduce the losses of voltage sag to both the power system and users.

In this paper, we study joint adaptive M-QAM modulation and power adaptation for a downlink two-user non-orthogonal multiple access (NOMA) network. Without sacrificing bit error rate (BER), joint adaptive transmission can fully utilize the time-varying nature of wireless channels, by allowing both power and rate to adapt to channel fading. Two adaptive power allocation strategies, namely, Scheme 1 and Scheme 2, each of which guarantees the minimum target rate for one user while supporting the highest possible rate for the other, are first proposed. Then, based on the two power schemes, the performance of joint adaptive transmission in terms of average spectral efficiency (SE) is studied for continuous-rate and discrete-rate modulation, while guaranteeing the minimum required rate and BER requirements. With the focus on practical discrete-rate M-QAM modulation, it is proved that for the strong user in Scheme 1 and the weak user in Scheme 2, their average SEs converge to the minimum target rates. In order to further increase the total transmission rate, we then propose a dynamic rate and power adaptation (DRPA) algorithm, aiming to increase the rate of one user without sacrificing the rate of the other. It is shown that at high SNRs, the DRPA algorithm allows the strong user in Scheme 1 and the weak user in Scheme 2 to continue to increase their transmission rates until reaching the highest modulation order that the system can support. Hence, the total transmission rate can be greatly increased at high SNRs due to the adoption of DRPA, by allowing both users in each scheme to reach the highest transmission rate in the system.

Grant-free (GF) access has been viewed as a promising solution to meet the low-latency requirement in massive access of short packets due to the cancellation of the procedure of requesting for the grant. While most GF schemes are realized in a synchronous way, a recently proposed packet-asynchronous GF (PA-GF) scheme can further reduce the overall delay. However, its performance was only validated under the ideal conditions, i.e., perfect active user detection (AUD) and channel estimation (CE). In this letter, in order to realize timely AUD and CE for PA-GF practically, a novel frame structure for multiplexing pilot sequences and data is proposed together with a sliding window detection algorithm. A modification of the orthogonal matching pursuit (OMP)—the prior-aided OMP (PA-OMP)— is also proposed. Numerical results show that the proposed algorithm achieves the low-latency advantage of PA-GF under imperfect AUD and CE conditions.

In this letter, a simultaneous wireless information and power transfer (SWIPT) enabled generalized frequency division multiplexing based non-orthogonal multiple access (GFDM-NOMA) cooperative communication model is proposed to guaranteed the transmission performance of the source while saving the power consumption of the relay. The GFDM subsymbols in two phases are both divided into two sets. In the first phase, the relay utilizes two different subsymbol sets to decode the source information and harvest the source energy, respectively. In the second phase, the relay uses the paired subsymbols to transmit the source information to the destination and the relay information to the relay’s receiver simultaneously by NOMA, and utilizes the remaining subsymbols for the transmissions between the relay and the relay’s receiver. The sum system rate is maximized through a joint optimization algorithm for subsymbol and power allocations. Simulation results show that the proposed scheme has better performance.

Objetivo: Comparar el estado nutricional, cronotipo y conducta alimentaria que presenta un sujeto que posee simultáneamente los polimorfismos rs3749474T y rs4864548A con res-pecto a grupos de sujetos que poseen solo uno de dichos polimorfismos. Métodos: La presencia de los polimorfismos se determinó mediante PCR. Se determinó IMC, riesgo cardiovascular y porcentaje de grasa según lo descrito por Durnin y Womersley y la ecuación de Siri. Se aplicó el cuestionario TFEQ-P19 adaptado a población chilena y el cuestionario Horne-Ostberg. Resultados: El sujeto con ambos polimorfismos presentó obesidad, riesgo cardiovascular y cronotipo trasnochador. Sus puntajes en las dimensiones de alimentación sin control y alimentación emocional fueron bajos. Su puntaje en cuanto a la restricción cognitiva fue el más alto. Conclusiones: La presencia del haplotipo TA (rs3749474T; rs4864548A) aumentaría la posibilidad de tener un cronotipo de tipo de tipo trasnochador, riesgo de obesidad y riesgo cardiovascular asociado a los centímetros de cintura.