This paper proposes a robust superpixel-based tracker via multiple-instance learning, which exploits the importance of instances and mid-level features captured by superpixels for object tracking. We first present a superpixels-based appearance model, which is able to compute the confidences of the object and background. Most importantly, we introduce the sample importance into multiple-instance learning (MIL) procedure to improve the performance of tracking. The importance for each instance in the positive bag is defined by accumulating the confidence of all the pixels within the corresponding instance. Furthermore, our tracker can help recover the object from the drifting scene using the appearance model based on superpixels when the drift occurs. We retain the first (k-1) frames' information during the updating process to alleviate drift to some extent. To evaluate the effectiveness of the proposed tracker, six video sequences of different challenging situations are tested. The comparison results demonstrate that the proposed tracker has more robust and accurate performance than six ones representing the state-of-the-art.

In PKC 2004, Choi et al. proposed an ID-based authenticated group key agreement (AGKA) protocol using bilinear pairings. Unfortunately, their protocol suffered from an impersonation attack and an insider colluding attack. In 2008, Choi et al. presented an improvement to resist insider attacks. In their modified protocol, they used an ID-based signature scheme on transcripts for binding them in a session to prevent replay of transcripts. In particular, they smartly used the batch verification technique to reduce the computational cost. In this paper, we first show that Choi et al.'s modified AGKA protocol still suffers from an insider colluding attack. Then, we prove that the batch verification of the adopted ID-based signature scheme in their modified protocol suffers from a forgery attack.

As a new generation voice service, Voice over LTE (VoLTE) has attracted worldwide attentions in both the academia and industry. Different from the traditional voice call based on circuit-switched (CS), VoLTE evolves into the packet-switched (PS) field, which has long been open to the public. Though designed rigorously, similar to VoIP services, VoLTE also suffers from SIP (Session Initiation Protocal) flooding attacks. Due to the high performance requirement, the SIP flooding attacks in VoLTE is more difficult to defend than that in traditional VoIP service. In this paper, enlightened by Counting Bloom Filter (CBF), we design a versatile CBF-like structure, PFilter, to detect the flooding anomalies. Compared with previous relevant works, our scheme gains advantages in many aspects including detection of low-rate flooding attack and stealthy flooding attack. Moreover, not only can our scheme detect the attacks with high accuracy, but also find out the attackers to ensure normal operation of VoLTE by eliminating their negative effects. Extensive experiments are performed to well evaluate the performance of the proposed scheme.

Multiple-shot person re-identification, which is valuable for application in visual surveillance, tackles the problem of building the correspondence between images of the same person from different cameras. It is challenging because of the large within-class variations due to the changeable body appearance and environment and the small between-class differences arising from the possibly similar body shape and clothes style. A novel method named “Bi-level Relative Information Analysis” is proposed in this paper for the issue by treating it as a set-based ranking problem. It creatively designs a relative dissimilarity using set-level neighborhood information, called “Set-level Common-Near-Neighbor Modeling”, complementary to the sample-level relative feature “Third-Party Collaborative Representation” which has recently been proven to be quite effective for multiple-shot person re-identification. Experiments implemented on several public benchmark datasets show significant improvements over state-of-the-art methods.

Unmanned aerial vehicle (UAV) communication has drawn rising interest recently with the distinctive gains brought by its inherent mobility. In this paper, we investigate the throughput maximization problem in UAV-enabled uplink communication, where multiple ground nodes communicate with a UAV while a group of ground jammers send jamming signals to jam the communications between UAV and the ground nodes. In contrast to the previous works that only considering UAV's transmit power allocation and two-dimension (2D) trajectory design, the ground nodes' transmit power allocation and scheduling along with the UAV's three-dimensional (3D) trajectory design are jointly optimized. The formulated throughput maximization problem is a mixed-integer non-convex programme that hard to be solved in general. Thus, we propose an iterative algorithm to make the problem trackable by applying the block coordinate descent and successive convex optimization techniques. Simulation results show that our proposed algorithm outperforms the benchmark methods that improving the throughput of the system significantly.

In this paper, we propose an efficient tracking method that is formulated as a multi-task reverse sparse representation problem. The proposed method learns the representation of all tasks jointly using a customized APG method within several iterations. In order to reduce the computational complexity, the proposed tracking algorithm starts from a feature selection scheme that chooses suitable number of features from the object and background in the dynamic environment. Based on the selected feature, multiple templates are constructed with a few candidates. The candidate that corresponds to the highest similarity to the object templates is considered as the final tracking result. In addition, we present a template update scheme to capture the appearance changes of the object. At the same time, we keep several earlier templates in the positive template set unchanged to alleviate the drifting problem. Both qualitative and quantitative evaluations demonstrate that the proposed tracking algorithm performs favorably against the state-of-the-art methods.

Re-identifying the same person in different images is a distinct challenge for visual surveillance systems. Building an accurate correspondence between highly variable images requires a suitable dissimilarity measure. To date, most existing measures have used adapted distance based on a learned metric. Unfortunately, real-world human image data, which tends to show large intra-class variations and small inter-class differences, continues to prevent these measures from achieving satisfactory re-identification performance. Recognizing neighboring distribution can provide additional useful information to help tackle the deviation of the to-be-measured samples, we propose a novel dissimilarity measure from the neighborhood-wise relative information perspective, which can deliver the effectiveness of those well-distributed samples to the badly-distributed samples to make intra-class dissimilarities smaller than inter-class dissimilarities, in a learned discriminative space. The effectiveness of this method is demonstrated by explanation and experimentation.

The application of Technology CAD simulations for development of IC processes in foundry is presented. Examples include device design, Flash cell design and optical proximity correction for SRAM cell. The challenges of using TCAD tools in the IC foundry is also discussed.

This paper proposes a new family of space-time block codes whose transmission rate is 1 symbol per channel use. The proposed space-time codes can achieve full transmit diversity with larger coding gain for the constellation carved from the scaled complex integer ring κZ[i]. It is confirmed that the performances of the proposed space-time codes are superior to the existing space-time block codes by our simulation results.