In this paper, for the purpose of clarifying the desired ITS information and communication systems considering both safety and social feasibility to prevention overengineering, using a microscopic traffic flow simulator, we discuss the required information acquisition rate of three types of safety driving support systems, that is, the sensor type and the communication type, the sensor and communication fusion type. Performances are evaluated from the viewpoint of preventing overengineering performance using the “TsRm evaluation method” that considers a vehicle approaching within the range of R meters within T seconds as the vehicle with a high possibility of collision, and that evaluates only those vehicles. The results show that regarding the communication radius and the sensing range, overengineering performance may be estimated when all vehicles in the evaluation area are used for evaluations without considering each vehicle's location, velocity and acceleration as in conventional evaluations. In addition, it is clarified that the sensor and communication fusion type system is advantageous by effectively complementing the defects of the sensor type systems and the communication type systems.

Face sketch synthesis refers to transform facial photos into sketches. Recent research on face sketch synthesis has achieved great success due to the development of Generative Adversarial Networks (GAN). However, these generative methods prone to neglect detailed information and thus lose some individual specific features, such as glasses and headdresses. In this paper, we propose a novel method called Feature Learning Generative Adversarial Network (FL-GAN) to synthesize detail-preserving high-quality sketches. Precisely, the proposed FL-GAN consists of one Feature Learning (FL) module and one Adversarial Learning (AL) module. The FL module aims to learn the detailed information of the image in a latent space, and guide the AL module to synthesize detail-preserving sketch. The AL Module aims to learn the structure and texture of sketch and improve the quality of synthetic sketch by adversarial learning strategy. Quantitative and qualitative comparisons with seven state-of-the-art methods such as the LLE, the MRF, the MWF, the RSLCR, the RL, the FCN and the GAN on four facial sketch datasets demonstrate the superiority of this method.

Sketch face recognition refers to matching photos with sketches, which has effectively been used in various applications ranging from law enforcement agencies to digital entertainment. However, due to the large modality gap between photos and sketches, sketch face recognition remains a challenging task at present. To reduce the domain gap between the sketches and photos, this paper proposes a cascaded transformation generation network for cross-modality image generation and sketch face recognition simultaneously. The proposed cascaded transformation generation network is composed of a generation module, a cascaded feature transformation module, and a classifier module. The generation module aims to generate a high quality cross-modality image, the cascaded feature transformation module extracts high-level semantic features for generation and recognition simultaneously, the classifier module is used to complete sketch face recognition. The proposed transformation generation network is trained in an end-to-end manner, it strengthens the recognition accuracy by the generated images. The recognition performance is verified on the UoM-SGFSv2, e-PRIP, and CUFSF datasets; experimental results show that the proposed method is better than other state-of-the-art methods.

Observations of gravity waves based on High Frequency Surface Wave Radar can make contributions to a better understanding of the energy transfer process between the ocean and the ionosphere. In this paper, through processing the observed data of the ionospheric clutter from HFSWR during the period of the Typhoon Rumbia with short-time Fourier transform method, HFSWR was proven to have the capability of gravity wave detection.

We consider an asymptotic stabilization problem for a chain of integrators by using an event-triggered controller. The times required between event-triggered executions and controller updates are uncertain, time-varying, and not necessarily small. We show that the considered system can be asymptotically stabilized by an event-triggered gain-scaling controller. Also, we show that the interexecution times are lower bounded and their lower bounds can be manipulated by a gain-scaling factor. Some future extensions are also discussed. An example is given for illustration.

Linear programming (LP) decoding based on the alternating direction method of multipliers (ADMM) has proved to be effective for low-density parity-check (LDPC) codes. However, for high-density parity-check (HDPC) codes, the ADMM-LP decoder encounters two problems, namely a high-density check matrix in HDPC codes and a great number of pseudocodewords in HDPC codes' fundamental polytope. The former problem makes the check polytope projection extremely complex, and the latter one leads to poor frame error rates (FER) performance. To address these issues, we introduce the even vertex algorithm (EVA) into the ADMM-LP decoding algorithm for HDPC codes, named as HDPC-EVA. HDPC-EVA can reduce the complexity of the projection process and improve the FER performance. We further enhance the proposed decoder by the automorphism groups of codes, creating diversity in the parity-check matrix. The simulation results show that the proposed decoder is capable of cutting down the average decoding time for each iteration by 30%-60%, as well as achieving near maximum likelihood (ML) performance on some BCH codes.

One of the main problems in list decoding is to determine the tradeoff between the list decoding radius and the rate of the codes w.r.t. a given metric. In this paper, we first describe a “stronger-weaker” relationship between two distinct metrics of the same code, then we show that the list decodability of the stronger metric can be deduced from the weaker metric directly. In particular, when we focus on matrix codes, we can obtain list decodability of matrix code w.r.t. the cover metric from the Hamming metric and the rank metric. Moreover, we deduce a Johnson-like bound of the list decoding radius for cover metric codes, which improved the result of [20]. In addition, the condition for a metric that whether the list decoding radius w.r.t. this metric and the rate are bounded by the Singleton bound is presented.

Binary sequence pairs with optimal periodic correlation have important applications in many fields of communication systems. In this letter, four new families of binary sequence pairs are presented based on the generalized cyclotomy over Z_5q, where q ≠ 5 is an odd prime. All these binary sequence pairs have optimal three-level correlation values {-1, 3}.

Pedestrian detection is an essential but challenging task in computer vision, especially in crowded scenes due to heavy intra-class occlusion. In human visual system, head information can be used to locate pedestrian in a crowd because it is more stable and less likely to be occluded. Inspired by this clue, we propose a dual-task detector which detects head and human body simultaneously. Concretely, we estimate human body candidates from head regions with statistical head-body ratio. A head-body alignment map is proposed to perform relational learning between human bodies and heads based on their inherent correlation. We leverage the head information as a strict detection criterion to suppress common false positives of pedestrian detection via a novel pull-push loss. We validate the effectiveness of the proposed method on the CrowdHuman and CityPersons benchmarks. Experimental results demonstrate that the proposed method achieves impressive performance in detecting heavy-occluded pedestrians with little additional computation cost.

An autoencoder is trained to generate the background from the surveillance image by setting the training label as the shuffled input, instead of the input itself in a traditional autoencoder. Then the multi-scale features are extracted by a sparse autoencoder from the surveillance image and the corresponding background to detect foreground.