In the log-likelihood ratio (LLR) domain, the belief propagation (BP) decoding algorithm for polar codes incurs high computation complexity due to the computation of the hyperbolic functions in the node update rules. In this paper, we propose a linear approximation method based on the principle of equal spacing to simplify the hyperbolic functions in the BP decoding algorithm. Our method replaces the computation of hyperbolic functions with addition and multiplication operations in the node update rules. Simulation results show that the performance of the modified BP decoding algorithm is almost the same as the original BP decoding algorithm in the low Signal to Noise Ratio (SNR) region, and in the high SNR region the performance of our method is slightly worse. The modified BP decoding algorithm is only implemented with addition and multiplication operations, which greatly reduces computation complexity, and simplifies hardware implementation.

The combined linear frequency modulation continuous wave (LFMCW) and inverse synthetic aperture radar (ISAR) can be used for imaging long-distance targets because of its long-distance and high resolution imaging abilities. In this paper, we find and study the dechirp distortion phenomenon (DDP) for imaging long-distance targets by a dechirp-on-receive LFMCW radar. If the targets are very far from the radar, the maximum delay-time is not much smaller than a single sweep duration, and the dechirp distortion is triggered since the distance of the target is unknown in a LFMCW-ISAR system. DDP cannot be ignored in long-distance imaging because double images of a target appear in the frequency domain, which reduces resolution and degrades image quality. A novel LFMCW-ISAR signal model is established to analyze DDP and its negative effects on long-distance target imaging. Using the proportionately distributed energy of double images, the authors propose a method to correct dechirp distortion. In addition, the applicable scope of the proposed method is also discussed. Simulation results validate the theoretical analysis and the effectiveness of the proposed method.

This paper proposes a new algorithm for substring searching. Our algorithm is an improvement on the famous BM algorithm. When a mismatch happens while searching a substring (pattern), the BM algorithm will use two strategies to calculate shifting distances of the substring respectively and selects the larger one. In comparison, our algorithm uses each of the two strategies for their most suitable cases separately without a selection operation. Experimental results demonstrated that our algorithm is more efficient than the BM algorithm and the Quick Search algorithm, especially for binary strings and DNA strings.

Lossy communication networks may be one of the most challenging issues for Transmission Control Protocol (TCP), as random loss could be erroneously interpreted into congestion due to the original mechanism of TCP. Network coding (NC) promises significant improvement in such environment thanks to its ability to mix data across time and flows. Therefore, it has been proposed to combine with TCP called TCP-NC by MIT. In this paper, we dedicated to quantifying the R, a key parameter for redundant packets, and make it close to the loss rate as much as possible, which has not been considered in the previous research. All of these are done by the sender who is completely unconscious of the network situation. Simulation results by NS2 under both wired and wireless networks showed that our method retains all the advantages of TCP-NC, and meanwhile outperforms TCP-NC and the other TCP variants in time-varying lossy networks.

In this paper, resource-efficient multiple description coding (MDC) multicast is investigated in cognitive radio networks with the consideration of imperfect spectrum sensing and imperfect channel feedback. Our objective is to maximize the system goodput, which is defined as the total successfully received data rate of all multicast users, while guaranteeing the maximum transmit power budget and the maximum average received interference constraint. Owing to the uncertainty of the spectrum state and the non-closed-form expression of the objective function, it is difficult to solve the problem directly. To circumvent this problem, a pretreatment is performed, in which we first estimate the real spectrum state of primary users and then propose a Gaussian approximation for the probability density functions of transmission channel gains to simplify the computation of the objective function. Thereafter, a two-stage resource allocation algorithm is presented to accomplish the subcarrier assignment, the optimal transmit channel gain to interference plus noise ratio (T-CINR) setting, and the transmit power allocation separately. Simulation results show that the proposed scheme is able to offset more than 80% of the performance loss caused by imperfect channel feedback when the feedback error is not high, while keeping the average interference on primary users below the prescribed threshold.

The segmentation of Mycobacterium tuberculosis images forms the basis for the computer-aided diagnosis of tuberculosis. The segmented objects are often broken due to the low-contrast objects and the limits of segmentation method. This will result in decreasing the accuracy of segmentation and recognition. A simple and effective post-processing method is proposed to connect the broken objects. The broken objects in the segmented binary images are connected based on the information obtained from their skeletons. Experimental results demonstrate the effectiveness of our proposed method.

There are often low contrast Mycobacterium tuberculosis (MTB) objects in the MTB images. Based on improved histogram equalization (HE), a framework of contrast enhancement is proposed to increase the contrast of MTB images. Our proposed algorithm was compared with the traditional HE and the weighted thresholded HE. The experimental results demonstrate that our proposed algorithm has better performance in contrast enhancement, artifacts suppression, and brightness preserving for MTB images.

In this paper, invulnerability and attack strategies are discussed for the undirected unweighted urban road networks and the directed weighted taxi networks of Beijing. Firstly, five new attack strategies, i.e., Initial All Degree (IAD), Initial All Strength (IAS), Recalculated Closeness (RC), Recalculated All Degree (RAD) and Recalculated All Strength (RAS) and five traditional attack strategies, i.e., Initial Degree (ID), Initial Betweenness (IB), Initial Closeness (IC), Recalculated Degree (RD) and Recalculated Betweenness (RB) are adopted to provoke the nodes failure. Secondly, we assess the impacts of these attack strategies using two invulnerability metrics, i.e., S (the relative size of the giant component) and E (the average network efficiency) through simulation experiments by MATLAB. Furthermore, we obtain some conclusions on the basis of the simulation results. Firstly, we discover that IB is more efficient than others for the undirected unweighted 5th ring Beijing road network based on S, and IB is more efficient than others at the beginning while ID is more efficient than IB at last based on E, while IAD causes a greater damage than IAS for the directed weighted 5th ring Beijing taxi network no matter with metrics S or E. Secondly, we find that dynamic attacks are more efficient than their corresponding static attacks, and RB is more destructive than others in all attack graphs while RAD is more destructive than RAS in all attack graphs. Moreover, we propose some suggestions to advance the reliability of the networks according to the simulation results. Additionally, we notice that the damage between ID (RD) and IAD (RAD) is similar due to the large proportion of two-way roads, and we realize that global measures should be employed to estimate the best attack strategy on the basis of that we find the best attack strategy changes with the nodes failure.

Crosstalk delay within an on-chip bus can induce severe transmission performance penalties. The Bus-grouping Asynchronous Transmission (BAT) scheme is proposed to mitigate the performance degradation. Furthermore, considering the distinct spatial locality of transition distribution on some types of buses, we use the locality to optimize the BAT. In terms of the implementation, we propose the Differential Counter Cluster (DCC) synchronous mechanism to synchronize the data transmission, and the Delay Active Shielding (DAS) to protect some critical signals from crosstalk and optimize the routing area overhead. The BAT is scalable with the variation of bus width with little extra implementation complexity. The effectiveness of the BAT is evaluated by focusing on the on-chip buses of a superscalar microprocessor simulator using the SPEC CPU2000 benchmarks. When applied to a 64-bit on-chip instruction bus, the BAT scheme, compared with the conservative approach, Codec and Variable Cycle Transmission (DYN) approaches, improves performance by 55+%, 10+%, 30+%, respectively, at the expense of 13% routing area overhead.

This paper studies the energy-saving problem in cognitive multicast orthogonal frequency-division multiplexing (OFDM) systems, for which a time-frequency two-dimensional model is established to enable the system energy conservation through joint temporal and spectral adaptations. The formulated two-dimensional problem, minimizing the total power consumption whilst guaranteeing the minimal-rate requirement for each multicast session and constraining the maximal perceived interference in each timeslot for the active primary user, is categorized as mixed integer non-convex programming, whose optimal solution is intractable in general. However, based on the time-sharing property, an asymptotically optimal algorithm is proposed by jointly iterating spectrum element (SE) assignment and power allocation. Moreover, a suboptimal algorithm, which carries out SE assignment and power allocation sequentially, is presented as well to reduce the computation complexity. Simulation results show the proposed joint algorithm can achieve the near-optimal solution, and the proposed sequential algorithm approximates to the joint one very well with a gap of less than 3%. Compared with the existing slot-by-slot energy-saving algorithms, the total power consumption is considerably decreased due to the combined exploitation of time and frequency dimensions.

Hotspots occur frequently in 3D multi-core processors (3D-MCPs), and they may adversely impact both the reliability and lifetime of a system. We present a new thermally constrained task scheduler based on a thermal-pattern-aware voltage assignment (TPAVA) to reduce hotspots in and optimize the performance of 3D-MCPs. By analyzing temperature profiles of different voltage assignments, TPAVA pre-emptively assigns different initial operating-voltage levels to cores for reducing temperature increase in 3D-MCPs. The proposed task scheduler consists of an on-line allocation strategy and a new voltage-scaling strategy. In particular, the proposed on-line allocation strategy uses the temperature-variation rates of the cores and takes into two important thermal behaviors of 3D-MCPs that can effectively minimize occurrences of hotspots in both thermally homogeneous and heterogeneous 3D-MCPs. Furthermore, a new vertical-grouping voltage scaling (VGVS) strategy that considers thermal correlation in 3D-MCPs is used to handle thermal emergencies. Experimental results indicate that, when compared to a previous online thermally constrained task scheduler, the proposed task scheduler can reduce hotspot occurrences by approximately 66% (71%) and improve throughput by approximately 8% (2%) in thermally homogeneous (heterogeneous) 3D-MCPs. These results indicate that the proposed task scheduler is an effective technique for suppressing hotspot occurrences and optimizing throughput for 3D-MCPs subject to thermal constraints.

In this paper, we exploit MapReduce framework and other optimizations to improve the performance of hash join algorithms on multi-core CPUs, including No partition hash join and partition hash join. We first implement hash join algorithms with a shared-memory MapReduce model on multi-core CPUs, including partition phase, build phase, and probe phase. Then we design an improved cuckoo hash table for our hash join, which consists of a cuckoo hash table and a chained hash table. Based on our implementation, we also propose two optimizations, one for the usage of SIMD instructions, and the other for partition phase. Through experimental result and analysis, we finally find that the partition hash join often outperforms the No partition hash join, and our hash join algorithm is faster than previous work by an average of 30%.

Selected mapping (SLM) is a promising distortionless technique for controlling the high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) systems. In SLM, phase sequence selection plays an important role for efficient PAPR reduction. Although some phase sequence sets have been proposed in past studies, we show that an optimal selection is still desired for the phase sequences from the same set. Therefore, this letter develops a measureable phase sequence selection criterion to optimally select the phase sequences from both the same and different sets, so as to achieve near optimum PAPR reduction performance in SLM-OFDM systems.

Cognitive orthogonal frequency-division multiplexing (OFDM) systems are spectrum-efficient yet vulnerable to intercarrier interference (ICI), especially in high-mobility scenarios. In this paper, the energy efficiency optimization problem in high-mobility cognitive OFDM system is considered. The aim is to maximize the energy efficiency by adapting subcarrier bandwidth, power allocation and sensing duration in the presence of ICI, under the constraints of the total power budget of secondary networks, the probabilistic interference limits for the protection of primary networks, and the subcarrier spacing restriction for high-mobility OFDM systems. In order to tackle the intractable non-convex optimization problem induced by ICI, an ICI-aware power allocation algorithm is proposed, by referring to noncooperative game theory. Moreover, a near-optimal subcarrier bandwidth search algorithm based on golden section methods is also presented to maximize the system energy efficiency. Simulation results show that the proposed algorithms can achieve a considerable energy efficiency improvement by up to 133% compared to the traditional static subcarrier bandwidth and power allocation schemes.

This paper realized the automatic recognition of Mycobacterium tuberculosis in Ziehl-Neelsen stained images by the conventional light microscopy, which can be used in the computer-aided diagnosis of the tuberculosis. We proposed a novel recognition method based on active shape model. First, the candidate bacillus objects are segmented by a method of marker-based watershed transform. Next, a point distribution model of the object shape is proposed to label the landmarks on the object automatically. Then the active shape model is performed after aligning the training set with a weight matrix. The deformation regulation of the object shape is discovered and successfully applied in recognition without using geometric and other commonly used features. During this process, a width consistency constraint is combined with the shape parameter to improve the accuracy of the recognition. Experimental results demonstrate that the proposed method yields high accuracy in the images with different background colors. The recognition accuracy in object level and image level are 92.37% and 97.91% respectively.

In machine translation (MT) mediated human-to-human communication, it is not an easy task to select the languages and translation services to be used as the users have various language backgrounds and skills. Our previous work introduced the best-balanced machine translation mechanism (BBMT) to automatically select the languages and translation services so as to equalize the language barriers of participants and to guarantee their equal opportunities in joining conversations. To assign proper languages to be used, however, the mechanism needs information of the participants' language skills, typically participants' language test scores. Since it is important to keep test score confidential, as well as other sensitive information, this paper introduces agents, which exchange encrypted information, and secure computation to ensure that agents can select the languages and translation services without destroying privacy. Our contribution is to introduce a multi-agent system with secure computation that can protect the privacy of users in multilingual communication. To our best knowledge, it is the first attempt to introduce multi-agent systems and secure computing to this area. The key idea is to model interactions among agents who deal with user's sensitive data, and to distribute calculation tasks to three different types of agents, together with data encryption, so no agent is able to access or recover participants' score.

Mobility Robustness Optimization (MRO) is one of the most important goals in LTE-Advanced Self-Organizing Networks (SON). Seamless handover in femtocell network is urgent and challenging, which has not been paid enough attention. Handover decision parameters, such as Time-To-Trigger (TTT), Hysteresis, Cell Individual Offset (CIO), have great effect on mobility performance, which may lead to Radio Link Failures (RLFs) and Unnecessary Handover. This letter proposes a handover parameters optimization approach based on Ant Colony Algorithm in the femtocell networks. The simulation result shows that the proposed scheme has a better performance than the fixed parameters method.

Security and privacy of wireless sensor networks are key research issues recently. Most existing researches regarding wireless sensor networks security consider homogenous sensor networks. To achieve better security and performance, we adopt a heterogeneous wireless sensor network (HWSN) model that consists of physically different types of sensor nodes. This paper presents a secure message distribution scheme with configurable privacy for HWSNs, which takes advantage of powerful high-end sensor nodes. The scheme establishes a message distribution topology in an efficient and secure manner. The sensor node only need generate one signature for all the messages for all the users, which can greatly save the communication and computation cost of the sensor node. On the other hand, the user can only know the messages that let him know based on a pre-set policy, which can meet the requirement of the privacy. We show that the scheme has small bandwidth requirements and it is resilient against the node compromise attack.

In this letter, we propose a novel method for change detection in multitemporal optical satellite images. Unlike the tradition methods, the proposed method is able to detect changed region even from unregistered images. In order to obtain the change detection map from the unregistered images, we first compute the sum of the color difference (SCD) of a pixel to all pixels in an input image. Then we calculate the SCD of this pixel to all pixels in the other input image. Finally, we use the difference of the two SCDs to represent the change detection map. Experiments on the multitemporal images demonstrates the good performance of the proposed method on the unregistered images.