Object tracking is one of the fundamental problems in computer vision. However, there is still a need to improve the overall capability in various tracking circumstances. In this letter, a patches-collaborative compressive tracking (PCCT) algorithm is presented. Experiments on various challenging benchmark sequences demonstrate that the proposed algorithm performs favorably against several state-of-the-art algorithms.

In this paper, a novel approach is proposed for stereo vision-based ground plane detection at superpixel-level, which is implemented by employing a Disparity Texture Map in a convolution neural network architecture. In particular, the Disparity Texture Map is calculated with a new Local Disparity Texture Descriptor (LDTD). The experimental results demonstrate our superior performance in KITTI dataset.

This paper introduces a random selection cooperation scheme that takes the Decode-and-Forward (DF) approach to solve the unfairness problem in selection cooperation. Compared to previous work which obtained fairness but introduced performance loss, the proposed scheme guarantees fairness without performance loss. Its essence is to randomly select from the relays that can ensure the successful communication between the source and the destination, rather than to select the best relay. Both a theoretical analysis and simulation results confirm that the proposed scheme could achieve fairness and introduce no performance loss. We also discuss the conditions under which the proposed scheme is practical to implement.

Effective indoor localization is the essential part of VR (Virtual Reality) and AR (Augmented Reality) technologies. Tracking the RGB-D camera becomes more popular since it can capture the relatively accurate color and depth information at the same time. With the recovered colorful point cloud, the traditional ICP (Iterative Closest Point) algorithm can be used to estimate the camera poses and reconstruct the scene. However, many works focus on improving ICP for processing the general scene and ignore the practical significance of effective initialization under the specific conditions, such as the indoor scene for VR or AR. In this work, a novel indoor prior based initialization method has been proposed to estimate the initial motion for ICP algorithm. We introduce the generation process of colorful point cloud at first, and then introduce the camera rotation initialization method for ICP in detail. A fast region growing based method is used to detect planes in an indoor frame. After we merge those small planes and pick up the two biggest unparallel ones in each frame, a novel rotation estimation method can be employed for the adjacent frames. We evaluate the effectiveness of our method by means of qualitative observation of reconstruction result because of the lack of the ground truth. Experimental results show that our method can not only fix the failure cases, but also can reduce the ICP iteration steps significantly.

In 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) systems, when HARQ (Hybrid Automatic Repeat request) retransmission is invoked, the data at the transmitter are retransmitted randomly or sequentially regardless of their relationship to the wrongly decoded data. Such practice is inefficient since precious transmission resources will be spent to retransmit data that may be of no use in error correction at the receiver. This paper proposes an incremental redundancy HARQ scheme based on Error Position Estimating Coding (ePec) and LDPC (Low Density Parity Check Code) channel coding, which is called ePec-LDPC HARQ. The proposal is able to feedback the wrongly decoded code blocks within a specific MAC (Media Access Control) PDU (Protocol Data Unit) from the receiver. The transmitter gets the feedback information and then performs targeted retransmission. That is, only the data related to the wrongly decoded code blocks are retransmitted, which can improve the retransmission efficiency and thus reduce the retransmission overload. An enhanced incremental redundancy LDPC coding approach, called EIR-LDPC, together with a physical layer framing method, is developed to implement ePec-LDPC HARQ. Performance evaluations show that ePec-LDPC HARQ reduces the overall transmission resources by 15% compared to a conventional LDPC HARQ scheme. Moreover, the average retransmission times of each MAC PDU and the transmission delay are also reduced considerably.

The Euclidean projection operation is the most complex and time-consuming of the alternating direction method of multipliers (ADMM) decoding algorithms, resulting in a large number of resources when deployed on hardware platforms. We propose a simplified line segment projection algorithm (SLSA) and present the hardware design and the quantization scheme of the SLSA. In simulation results, the proposed SLSA module has a better performance than the original algorithm with the same fixed bitwidths due to the centrosymmetric structure of SLSA. Furthermore, the proposed SLSA module with a simpler structure without hypercube projection can reduce time consuming by up to 72.2% and reduce hardware resource usage by more than 87% compared to other Euclidean projection modules in the experiments.

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.

Fetal electrocardiogram (FECG) extraction is of vital importance in biomedical signal processing. A promising approach is blind source extraction (BSE) emerging from the neural network fields, which is generally implemented in a semi-blind way. In this paper, we propose a robust extraction algorithm that can extract the clear FECG as the first extracted signal. The algorithm exploits the fact that the FECG signal's kurtosis value lies in a specific range, while the kurtosis values of other unwanted signals do not belong to this range. Moreover, the algorithm is very robust to outliers and its robustness is theoretically analyzed and is confirmed by simulation. In addition, the algorithm can work well in some adverse situations when the kurtosis values of some source signals are very close to each other. The above reasons mean that the algorithm is an appealing method which obtains an accurate and reliable FECG.

In this paper, we describe a post-processing technique having high extraction efficiency (ExE) for de-biasing and de-correlating a random bitstream generated by true random number generators (TRNGs). This research is based on the N-bit von Neumann (VN_N) post-processing method. It improves the ExE of the original von Neumann method close to the Shannon entropy bound by a large N value. However, as the N value increases, the mapping table complexity increases exponentially (2N), which makes VN_N unsuitable for low-power TRNGs. To overcome this problem, at the algorithm level, we propose a waiting strategy to achieve high ExE with a small N value. At the architectural level, a Hamming weight mapping-based hierarchical structure is used to reconstruct the large mapping table using smaller tables. The hierarchical structure also decreases the correlation factor in the raw bitstream. To develop a technique with high ExE and low cost, we designed and fabricated an 8-bit von Neumann with waiting strategy (VN_8W) in a 130-nm CMOS. The maximum ExE of VN_8W is 62.21%, which is 2.49 times larger than the ExE of the original von Neumann. NIST SP 800-22 randomness test results proved the de-biasing and de-correlation abilities of VN_8W. As compared with the state-of-the-art optimized 7-element iterated von Neumann, VN_8W achieved more than 20% energy reduction with higher ExE. At 0.45V and 1MHz, VN_8W achieved the minimum energy of 0.18pJ/bit, which was suitable for sub-pJ low energy TRNGs.

This paper proposes a novel blind multiuser detection scheme using CMA (Constant Modulus Algorithm) adaptive array. In the proposed scheme, the received signal is processed in two steps. In the primary step, only one user is captured by the CMA adaptive array, and at the same time, the other users' directions of arrival (DOA) are estimated. In the secondary step, initial weight vectors are set based on the estimated DOAs, and it processes with CMAs again to capture the other users in parallel. Thus, all the users are detected exactly and recovered separately. The Least-squares CMA is applied as an optimization algorithm to improve the performance of the proposed scheme, and the performances using the proposed scheme with linear arrays and circular arrays are discussed in detail. Simulation results are presented to verify the performance of the proposed scheme.

Accurate stereo matching remains a challenging problem in case of weakly-textured areas, discontinuities and occlusions. In this letter, a novel stereo matching method, consisting of leveraging feature ensemble network to compute matching cost, error detection network to predict outliers and priority-based occlusion disambiguation for refinement, is presented. Experiments on the Middlebury benchmark demonstrate that the proposed method yields competitive results against the state-of-the-art algorithms.

The unfairness problem among TCP connections has been proved to be very severe in the IEEE 802.11-based wireless ad hoc networks because the hidden station problem still exists and the binary exponential backoff algorithm always favors the latest successful station. In this paper, a novel protocol, neighbor-medium-aware MAC (NEMA-MAC), is proposed to improve the TCP fairness. By adding a medium (channel) state field in the head of the traditional IEEE 802.11 MAC frame, the NEMA-MAC protocol provides a communication mechanism to resolve the hidden station problem. In addition, when a collision occurs, the new backoff algorithm makes the senders cooperatively adjust the contention window according to their local and neighbors' channel usage indexes. The simulation results show that TCP sessions can acquire satisfying fairness and increase the throughput in the NEMA-MAC-based multihop ad hoc networks.

A broadband access network is required for supporting the increased Internet data traffic. One of the most cost-effective solutions is the Ethernet Passive Optical Networks (E-PONs) with the efficient bandwidth assignment function by which the upstream bandwidth can be shared among access users. To satisfy the services with heterogeneous QoS characteristics, it is very important to provide QoS guaranteed network access while utilize the bandwidth efficiently. In this paper, a dual DEB-GPS scheduler in E-PON is presented to provide delay-constraint and lossless QoS guarantee to QoS service and maximize the bandwidth to best-effort service. Simulation results show our scheme outperforms the conventional bandwidth allocation scheme in E-PON system.

Mixed-signal integrated circuit design and simulation highly rely on behavioral models of circuit blocks. Such models are used for the validation of design specification, optimization of system topology, and behavioral synthesis using a description language, etc. However, automatic behavioral model generation is still in its early stages; in most scenarios designers are responsible for creating behavioral models manually, which is time-consuming and error prone. In this paper an automatic behavioral model generation method for switched-capacitor (SC) integrator is proposed. This technique is based on symbolic circuit modeling with approximation, by which parametric behavioral integrator model can be generated. Such parametric models can be used in circuit design subject to severe process variational. It is demonstrated that the automatically generated integrator models can accurately capture process variation effects on arbitrarily selected circuit elements; furthermore, they can be applied to behavioral simulation of SC Sigma-Delta modulators (SDMs) with acceptable accuracy and speedup. The generated models are compared to a recently proposed manually generated behavioral integrator model in several simulation settings.

Using linear programming (LP) decoding based on alternating direction method of multipliers (ADMM) for low-density parity-check (LDPC) codes shows lower complexity than the original LP decoding. However, the development of the ADMM-LP decoding algorithm could still be limited by the computational complexity of Euclidean projections onto parity check polytope. In this paper, we proposed a bisection method iterative algorithm (BMIA) for projection onto parity check polytope avoiding sorting operation and the complexity is linear. In addition, the convergence of the proposed algorithm is more than three times as fast as the existing algorithm, which can even be 10 times in the case of high input dimension.

A novel Adaptive Resource-based Probabilistic Search algorithm (ARPS) for P2P networks is proposed in this paper. ARPS introduces probabilistic forwarding for query messages according to the popularity of the resource being searched. A mechanism is introduced to estimate the popularity and adjust the forwarding probability accordingly such that a tradeoff between search performance and cost can be made. Using computer simulations, we compare the performance of ARPS with several other search algorithms. It is shown that ARPS performs well under various P2P scenarios. ARPS guarantees a success rate above a certain level under all circumstances, and enjoys high and popularity-invariant search success rate. Furthermore, ARPS adapts well to the variation of popularity, resulting in high efficiency and flexibility.

The selection cooperation is a basic and attractive scheme of cooperative diversity in the multiple relays scenario. Most previous schemes of selection cooperation consist only one relay-stage in which one relay is selected to retransmit, and the signal from the selected relay is not utilized by other relays. In this paper, we introduce a two relay-stage selection cooperation scheme. The performance can be improved by letting all other relays to utilize the signal from the first selected relay to make another selection and retransmission in the second relay-stage. We derive the closed-form expression of the outage probability of the proposed scheme in the high SNR regime. Both theoretical and numerical results suggest that the proposed scheme can reduce the outage probability compared with the traditional scheme with only one relay-stage. Furthermore, we demonstrate that more than two relay-stage can not further reduce the outage probability. We also study the dependence of the proposed scheme on stage lengths and topology, and analyze the increased overhead.

This paper introduces an inverter-based true random number generator (I-TRNG). It uses a single CMOS inverter to amplify thermal noise multiple times. An adaptive calibration mechanism based on clock tuning provides robust operation across a wide range of supply voltage 0.5∼1.1V and temperature -40∼140°C. An 8-bit Von-Neumann post-processing circuit (VN8W) is implemented for maximum raw entropy extraction. In a 130nm CMOS technology, the I-TRNG entropy source only occupies 635μm2 and consumes 0.016pJ/raw-bit at 0.6V. The I-TRNG occupies 13406μm2, including the entropy source, adaptive calibration circuit, and post-processing circuit. The minimum energy consumption of the I-TRNG is 1.38pJ/bit at 0.5V, while passing all NIST 800-22 and 800-90B tests. Moreover, an equivalent 15-year life at 0.7V, 25°C is confirmed by an accelerated NBTI aging test.

The goal of Acoustic Scene Classification (ASC) is to simulate human analysis of the surrounding environment and make accurate decisions promptly. Extracting useful information from audio signals in real-world scenarios is challenging and can lead to suboptimal performance in acoustic scene classification, especially in environments with relatively homogeneous backgrounds. To address this problem, we model the sobering-up process of “drunkards” in real-life and the guiding behavior of normal people, and construct a high-precision lightweight model implementation methodology called the “drunkard methodology”. The core idea includes three parts: (1) designing a special feature transformation module based on the different mechanisms of information perception between drunkards and ordinary people, to simulate the process of gradually sobering up and the changes in feature perception ability; (2) studying a lightweight “drunken” model that matches the normal model's perception processing process. The model uses a multi-scale class residual block structure and can obtain finer feature representations by fusing information extracted at different scales; (3) introducing a guiding and fusion module of the conventional model to the “drunken” model to speed up the sobering-up process and achieve iterative optimization and accuracy improvement. Evaluation results on the official dataset of DCASE2022 Task1 demonstrate that our baseline system achieves 40.4% accuracy and 2.284 loss under the condition of 442.67K parameters and 19.40M MAC (multiply-accumulate operations). After adopting the “drunkard” mechanism, the accuracy is improved to 45.2%, and the loss is reduced by 0.634 under the condition of 551.89K parameters and 23.6M MAC.

The overall performance of P2P-based file sharing applications is becoming increasingly important. Based on the Adaptive Resource-based Probabilistic Search algorithm (ARPS), which was previously proposed by the authors, a novel probabilistic search algorithm with QoS guarantees is proposed in this letter. The algorithm relies on generating functions to satisfy the user's constraints and to exploit the power-law distribution in the node degree. Simulation results demonstrate that it performs well under various P2P scenarios. The proposed algorithm provides guarantees on the search performance perceived by the user while minimizing the search cost. Furthermore, it allows different QoS levels, resulting in greater flexibility and scalability.