In this paper, we present a new mathematical framework based on point process theory for a type of four-way handshake-based medium access control schemes which have so far only been studied by simulation. The theoretical model we present takes into account time-varying channel impairments, the interference inherent to large networks, different decoding requirements for each packet and the influence of the routing protocol. Moreover, in contrast with the majority of the literature, the influence of imperfect feedback is also considered in the analysis. Throughout the paper, we derive in closed forms the average link outage probability as well as the Average Spatial Density of Transport (ASDT) in a mobile multi-hop ad hoc network. All results are confirmed by comparison to simulated data and lead to two general conclusions. In the presence of fading uncorrelated between traffic and control handshakes, we observe the following. 1) Optimal contention is achieved by designing control packets that are decodable even in the presence of strong interference. 2) The impact of imperfect feedback on performance in an interference-limited mobile ad hoc network is not negligible.

The mobile cloud computing (MCC) paradigm is aimed at integrating mobile devices with cloud computing. In the client-server architecture of MCC, mobile devices offload tasks to the cloud to utilize the computation and storage resources of data centers. However, due to the rapid increase in the traffic demand and complexity of mobile applications, service providers have to continuously upgrade their infrastructures at great expense. At the same time, modern mobile devices have greater resources (communication, computation, and sensing), and these resources are not always fully utilized by device users. Therefore, mobile devices, from time to time, encounter other devices that could provide resources to them. Because the amount of such resources has increased with the number of mobile devices, researchers have begun to consider making use of these resources, located at the “edge” of mobile networks, to increase the scalability of future information networks. This has led to a cooperation based architecture of MCC. This paper reports the concept and design of an resource sharing mechanism that utilize resources in mobile devices through opportunistic contacts between them. Theoretical models and formal definitions of problems are presented. The efficiency of the proposed mechanism is validated through formal proofs and extensive simulation.

This paper proposes a method to reduce the playback suspension in a Video-on-Demand system based on the Peer-to-Peer technology (P2P VoD). Our main contribution is twofold. The first is the proposal of a hierarchical P2P architecture with the notion of dynamic swarms. Swarm is a group of peers to have similar playback position and those swarms are connected with an overlay so that requested pieces are forwarded from a swarm to another swarm in a bucket brigade manner, where the forward of pieces is regulated by the super-peer (SP) of each swarm. The second contribution is the proposal of a match making scheme between requests and uploaders. The simulation result indicates that the proposed scheme reduces the total waiting time of a randomized scheme by 24% and the load of the media server by 76%.

The increasing complexity of embedded applications and the prevalence of multiprocessor system-on-chip (MPSoC) introduce a great challenge for designers on how to achieve performance and programmability simultaneously in embedded systems. Automatic multithreaded code generation methods taking account of performance optimization techniques can be an effective solution. In this paper, we consider the issue of increasing processor utilization and reducing communication cost during multithreaded code generation from Simulink models to improve system performance. We propose a combination of three-layered multithreaded software with Integer Linear Programming (ILP) based design-time mapping and scheduling policies to get optimal performance. The hierarchical software with a thread layer increases processor usage, while the mapping and scheduling policies formulate a group of integer linear programming formulations to minimize communication cost as well as to maximize performance. Experimental results demonstrate the advantages of the proposed techniques on performance improvements.

In this paper, we propose a new structure for a compact matched filter bank for a mutually orthogonal zero-correlation zone (MO-ZCZ) sequence set consisting of ternary sequence pairs obtained by Hadamard and binary ZCZ sequence sets; this construction reduces the number of two-input adders and delay elements. The matched filter banks are implemented on a field-programmable gate array (FPGA) with 51,840 logic elements (LEs). The proposed matched filter bank for an MO-ZCZ sequence set of length 160 can be constructed by a circuit size that is about 8.6% that of a conventional matched filter bank.

This research deals with muffled speech as the evaluation target and introduces a criterion for evaluating the auditory impression in muffled speech. It focuses on the vocal tract area function (VTAF) to evaluate the auditory impression, and the criterion uses temporal differentiation of this function to track the temporal variation of the shape of the mouth. The experimental results indicate that the proposed criterion can be used to evaluate the auditory impression as well as the subjective impression.

This paper proposes an automatic error correction method for melanosome tracking. Melanosomes in intracellular images are currently tracked manually when investigating diseases, and an automatic tracking method is desirable. We detect all melanosome candidates by SIFT with 2 different parameters. Of course, the SIFT also detects non-melanosomes. Therefore, we use the 4-valued difference image (4-VDimage) to eliminate non-melanosome candidates. After tracking melanosome, we re-track the melanosome with low confidence again from t+1 to t. If the results from t to t+1 and from t+1 to t are different, we judge that initial tracking result is a failure, the melanosome is eliminated as a candidate and re-tracking is carried out. Experiments demonstrate that our method can correct the error and improves the accuracy.

In this paper, we present a computer vision-based human tracking system with multiple stereo cameras. Many widely used methods, such as KLT-tracker, update the trackers “frame-to-frame,” so that features extracted from one frame are utilized to update their current state. In contrast, we propose a novel optimization technique for the “multi-frame” approach that computes resultant trajectories directly from video sequences, in order to achieve high-level robustness against severe occlusion, which is known to be a challenging problem in computer vision. We developed a heuristic optimization technique to estimate human trajectories, instead of using dynamic programming (DP) or an iterative approach, which makes our method sufficiently computationally efficient to operate in realtime. Six video sequences where one to six people walk in a narrow laboratory space are processed using our system. The results confirm that our system is capable of tracking cluttered scenes in which severe occlusion occurs and people are frequently in close proximity to each other. Moreover, minimal information is required for tracking, instead of full camera images, which is communicated over the network. Hence, commonly used network devices are sufficient for constructing our tracking system.

Basic block vectorization consists in realizing instruction-level parallelism inside basic blocks in order to generate SIMD instructions and thus speedup data processing. It is however problematic, because the vectorized program may actually be slower than the original one. Therefore, it would be useful to predict beforehand whether or not vectorization will actually produce any speedup. This paper proposes to do so by expressing vectorization profitability as a classification problem, and by predicting it using a machine learning technique called support vector machine (SVM). It considers three compilers (icc, gcc and llvm), and a benchmark suite made of 151 loops, unrolled with factors ranging from 1 to 20. The paper further proposes a technique that combines the results of two SVMs to reach 99% of accuracy for all three compilers. Moreover, by correctly predicting unprofitable vectorizations, the technique presented in this paper provides speedups of up to 2.16 times, 2.47 times and 3.83 times for icc, gcc and LLVM, respectively (9%, 18% and 56% on average). It also lowers to less than 1% the probability of the compiler generating a slower program with vectorization turned on (from more than 25% for the compilers alone).

As interdomain routing protocol, BGP is fairly simple, and allows plenty of policies based on ISPs' preferences. However, recent studies show that BGP routes are often non-optimal in end-to-end performance, due to technological and economic reasons. To obtain improved end-to-end performance, overlay routing, which can change traffic routing in application layer, has gained attention. However, overlay routing often violates BGP routing policies and harms ISPs' interest. In order to take the advantage of overlay to improve the end-to-end performance, while overcoming the disadvantages, we propose a novel interdomain overlay structure, in which overlay nodes are operated by ISPs within an ISP alliance. The traffic between ISPs within the alliance could be routed by overlay routing, and the other traffic would still be routed by BGP. As economic structure plays very important role in interdomain routing, so we propose an effective and fair charging and pricing scheme within the ISP alliance in correspondence with the overlay routing structure. Finally, we give a simple pricing algorithm, with which ISPs can find the optimal prices in the practice. By mathematical analysis and numerical experiments, we show the correctness and convergence of the pricing algorithm.

The Hierarchical Memory Machine (HMM) is a theoretical parallel computing model that captures the essence of computing on CUDA-enabled GPUs. The approximate string matching (ASM) for two strings X and Y of length m and n is a task to find a substring of Y most similar to X. The main contribution of this paper is to show an optimal parallel algorithm for the approximate string matching on the HMM and implement it on GeForce GTX 580 GPU. Our algorithm runs in $O({nover w}+{mnover dw}+{nLover p}+{mnlover p})$ time units on the HMM with p threads, d streaming processors, memory band width w, global memory access latency L, and shared memory access latency l. We also show that the lower bound of the computing time is $Omega({nover w}+{mnover dw}+{nLover p}+{mnlover p})$ time units. Thus, our algorithm for the approximate string matching is time optimal. Further, we implemented our algorithm on GeForce GTX 580 GPU and evaluated the performance. The experimental results show that the ASM of two strings of 1024 and 4M (=222) characters can be done in 419.6ms, while the sequential algorithm can compute it in 27720ms. Thus, our implementation on the GPU attains a speedup factor of 66.1 over the single CPU implementation.

The joint power allocation (PA) issue is studied in multi-user three-cell systems under the degree-of-freedom (DoF) based transmission protocol. This protocol makes all the interferences received at each user orthogonal to the useful signal at the same user by Jafar's topological interference management through index coding, which is proved to offer full DoF. Under this protocol, we formulate the joint power allocations problem based on the objective of energy efficiency under the required quality-of-service constraint. Due to the highly complicated Lagrangian equation, the properties of Lambert function are widely exploited to solve the problem using a closed-form expression. It is discovered that the relationship among the optimal power coefficients are completely different from that of the well-known water-filling method. Simulations demonstrate the energy efficiency of the designed scheme.

This paper investigates the performance of an overloaded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with a repetition code. It has been demonstrated that diversity with block coding prevents the performance degradation induced by signal multiplexing. However, the computational complexity of a joint decoding scheme increases exponentially with the number of multiplexed signal streams. Thus, this paper proposes the use of a repetition code in the overloaded MIMO-OFDM system. In addition, QR decomposition with M-algorithm (QRM) maximum likelihood decoding (MLD) is applied to the decoding of the repetition code. QRM-MLD significantly reduces the amount of joint decoding complexity. In addition, virtual antennas are employed in order to increase the throughput that is reduced by the repetition code. It is shown that the proposed scheme reduces the complexity by about 1/48 for 6 signal streams with QPSK modulation while the BER degradation is less than 0.1dB at the BER of 10-3.

This paper presents an electrically small and circularly polarized antenna with an omnidirectional radiation pattern. The antenna consists of a horizontal loop element enclosed by two U-shaped elements and a vertical element from the feeding point. The radiation pattern of the circular polarization is omnidirectional and has a maximum gain of -2dBic in parallel to the ground plane at the 900MHz band. The antenna dimensions are 48 × 20 × 13.8mm (0.14λ × 0.06λ × 0.04λ) with ka =0.476 (i.e. < 0.5), where k is the wavenumber at the resonant frequency and a is the radius of a sphere surrounding the antenna. The dimension corresponds to the definition of an electrically small antenna. The omnidirectional circularly polarized pattern of a prototype antenna shows good agreement with that of the simulation. In addition, this paper introduces a mechanism that generates omnidirectional circular polarization from electrically small antennas.

This paper presents an optimal cooperative routing protocol (OCRP) aiming to improve the in-network cache utilization of the Content-Centric Networking (CCN). The objective of OCRP is to selectively aggregate the multiple flows of interest messages onto the same path in order to improve the cache utilization while mitigating the cache contention of the Content Stores (CSs) of CCN routers on the routing path. The proposed routing protocol consists of three processes: (1) Prefix Popularity Observation; (2) Prefix Group (Un)Subscription; and (3) Forwarding Information Base (FIB) Reconstruction. Prefix Popularity Observation observes the popularly cited prefixes to activate a prefix group (un)subscription function, which lets the Designated Router (DR) know which requester router wants to either join or leave a prefix group. Prefix Group (Un)Subscription lets the DR know which requester router is demanding to join or leave which prefix group. FIB Reconstruction reconstructs the FIB entries of the CCN routers involved in the newly computed optimal cooperative path of all prefix groups. The optimal routing path is obtained by binary linear optimization under a flow conservation constraint, cache contention mitigating constraint, and path length constraint. Two metrics of server load and round-trip hop distance are used to measure the performance of the proposed routing protocol. Simulation results from various network scenarios and various settings show advantages over the shortest path routing and our previously proposed cooperative routing schemes.

A method of round addition attack on substitution-permutation network (SPN) block ciphers using differential fault analysis (DFA) is presented. For the 128-bit advanced encryption standard (AES), we show that secret keys can be extracted using one correct ciphertext and two faulty ciphertexts. Furthermore, we evaluate the success rate of a round addition DFA attack, experimentally. The proposed method can also be applied to lightweight SPN block cipher such as KLEIN and LED.

This letter presents a new blind audio watermarking scheme using eigenvalue decomposition (EVD). Initially, the original audio is divided into frames and the samples of each frame are arranged into a square matrix. EVD is applied to each of these matrices. Watermark data is embedded into the largest eigenvalue of each diagonal matrix by quantization. Data embedding rate of the proposed scheme is 172.39bps. Simulation results confirm the imperceptibility of the proposed scheme and its higher robustness against various attacks compared to the state-of-the-art watermarking methods available in the literature.

Recently, design of sparse finite impulse response (FIR) digital filters has attracted much attention due to its ability to reduce the implementation cost. However, finding a filter with the fewest number of nonzero coefficients subject to prescribed frequency domain constraints is a rather difficult problem because of its non-convexity. In this paper, an algorithm based on binary particle swarm optimization (BPSO) is proposed, which successively thins the filter coefficients until no sparser solution can be obtained. The proposed algorithm is evaluated on a set of examples, and better results can be achieved than other existing algorithms.

In this letter, a cooperative scheme based on orthogonal frequency division multiplexing (OFDM) in vehicular communication system is proposed. In the conventional scheme, a destination exploits only one base station to communicate information. The proposed scheme can use an extra source from another base station through a relay, since the restriction of power in vehicle are less than cellular device. If a destination is distant from a base station, the performance is degraded. When a destination is distant from a base station, the proposed scheme employing space time block code (STBC) and cyclic delay diversity (CDD) has a higher bit error rate (BER) performance and throughput than the conventional scheme.

As the virtualization technology becomes the core ingredient for recent promising IT infrastructures such as utility computing and cloud computing, accurate analysis of the internal behaviors of virtual machines becomes more and more important. In this paper, we first propose a novel I/O fairness analysis tool for virtualization systems. It supports the following three features: fine-grained, multimodal and multidimensional. Then, using the tool, we observe various I/O behaviors in our experimental XEN-based virtualization system. Our observations disclose that 1) I/O fairness among virtual machines is broken frequently even though each virtual machine requests the same amount of I/Os, 2) the unfairness is caused by an intricate combination of factors including I/O scheduling, CPU scheduling and interactions between the I/O control domain and virtual machines, and 3) some mechanisms, especially the CFQ (Completely Fair Queuing) I/O scheduler that supports fairness reasonable well in a non-virtualization system, do not work well in a virtualization system due to the virtualization-unawareness. These observations drive us to design a new virtualization-aware I/O scheduler for enhancing I/O fairness. It gives scheduling opportunities to asynchronous I/Os in a controlled manner so that it can avoid the unfairness caused by the priority inversion between the low-priority asynchronous I/Os and high-priority synchronous I/Os. Real implementation based experimental results have shown that our proposal can enhance I/O fairness reducing the standard deviation of the finishing time among virtual machines from 4.5 to 1.2.