The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. In this paper, we introduce the gaze estimation system of electrooculogram signals. Using this system, the electrooculogram signals can be recorded when the patients focused on each direct. All these recorded signals could be analyzed using math-method and the mathematical model will be set up. Gaze estimation can be recognized using electrooculogram signals follow these models.

The large amounts of freely available open source software over the Internet are fundamentally changing the traditional paradigms of software development. Efficient categorization of the massive projects for retrieving relevant software is of vital importance for Internet-based software development such as solution searching, best practices learning and so on. Many previous works have been conducted on software categorization by mining source code or byte code, but were verified on only relatively small collections of projects with coarse-grained categories or clusters. However, Internet-based software development requires finer-grained, more scalable and language-independent categorization approaches. In this paper, we propose a novel approach to hierarchically categorize software projects based on their online profiles. We design a SVM-based categorization framework and adopt a weighted combination strategy to aggregate different types of profile attributes from multiple repositories. Different basic classification algorithms and feature selection techniques are employed and compared. Extensive experiments are carried out on more than 21,000 projects across five repositories. The results show that our approach achieves significant improvements by using weighted combination. Compared to the previous work, our approach presents competitive results with more finer-grained and multi-layered category hierarchy with more than 120 categories. Unlike approaches that use source code or byte code, our approach is more effective for large-scale and language-independent software categorization. In addition, experiments suggest that hierarchical categorization combined with general keyword-based searching improves the retrieval efficiency and accuracy.

We proposed a method for estimating sound source positions in 3D space by integrating sound directions estimated by multiple microphone arrays and taking advantage of reflection information. Two types of sources with different directivity properties (human speech and loudspeaker speech) were evaluated for different positions and orientations. Experimental results showed the effectiveness of using reflection information, depending on the position and orientation of the sound sources relative to the array, walls, and the source type. The use of reflection information increased the source position detection rates by 10% on average and up to 60% for the best case.

Positive real approximation of sampled frequency data obtained from electromagnetic analysis or measurement is presented. The proposed two methods are based on the Fourier expansion method. The frequency data are approximated by the Laguerre series that becomes the Fourier series with an infinite interval at an imaginary axis of complex plane. The proposed methods do not require any passivity check algorithm. The first method approximates the real parts of sampled data by the piecewise linear matrix function. The second method uses discrete Fourier transform. It is here proven that the approximated matrix function is an interpolative function for the real parts of sampled data. The proposed methods are applied to the approximation of per unit length parameters of multi-conductor system. The capability of the proposed methods is demonstrated.

Sound field reproduction systems seek to realistically convey 3D spatial audio by re-creating the sound pressure inside a region enclosing the listener. High-order Ambisonics (HOA), a sound field reproduction technology, is notable for defining a scalable encoding format that characterizes the sound field in a system-independent way. Sound fields sampled with a particular microphone array and encoded into the HOA format can be reproduced using any sound presentation device, typically a loudspeaker array, by using a HOA decoder. The HOA encoding format is based on the spherical harmonic decomposition; this makes it easier to design a decoder for large arrays of loudspeakers uniformly distributed over all directions. In practice, it is seldom possible to cover all directions with loudspeakers placed at regular angular intervals. An irregular array, one where the angular separation between adjacent loudspeakers is not constant, does not perform as well as a regular one when reproducing HOA due to the uneven sampling of the spherical harmonics. This paper briefly introduces the techniques used in HOA and advances a new approach to design HOA decoders for irregular loudspeaker arrays. The main difference between conventional methods and our proposal is the use of a new error metric: the radial derivative of the reconstruction error. Minimizing this metric leads to a smooth reproduction, accurate over a larger region than that achieved by conventional HOA decoders. We evaluate our proposal using the computer simulation of two 115-channel loudspeaker arrays: a regular and an irregular one. We find that our proposal results in a larger listening region when used to decode HOA for reproduction using the irregular array. On the other hand, applying our method matches the high-quality reproduction that can be attained with the regular array and conventional HOA decoders.

For an integer q≥2, new sets of q-phase aperiodic complementary sequences (ACSs) are constructed by using known sets of q-phase ACSs and certain matrices. Employing the Kronecker product to two known sets of q-phase ACSs, some sets of q-phase aperiodic complementary sequences with a new length are obtained. For an even integer q, some sets of q-phase ACSs with new parameters are generated, and their equivalent matrix representations are also presented.

Activity recognition has recently been playing an important role in several research domains, especially within the healthcare system. It is important for physicians to know what their patients do in daily life. Nevertheless, existing research work has failed to adequately identify human activity because of the variety of human lifestyles. To address this shortcoming, we propose the high performance activity recognition framework by introducing a new user context and activity location in the activity log (AL2). In this paper, the user's context is comprised by context-aware infrastructure and human posture. We propose a context sensor network to collect information from the surrounding home environment. We also propose a range-based algorithm to classify human posture for combination with the traditional user's context. For recognition process, ontology-based activity recognition (OBAR) is developed. The ontology concept is the main approach that uses to define the semantic information and model human activity in OBAR. We also introduce a new activity log ontology, called AL2 for investigating activities that occur at the user's location at that time. Through experimental studies, the results reveal that the proposed context-aware activity recognition engine architecture can achieve an average accuracy of 96.60%.

Despite the increasing use of mobile computing, exploiting its full potential is difficult due to its inherent characteristics such as error-prone transmission channels, diverse node capabilities, frequent disconnections and mobility. Mobile Cloud Computing (MCC) is a paradigm that is aimed at overcoming previous problems through integrating mobile devices with cloud computing. Mobile devices, in the traditional client-server architecture of MCC, offload their tasks to the cloud to utilize the computation and storage resources of data centers. However, along with the development of hardware and software technologies in mobile devices, researchers have begun to take into consideration local resource sharing among mobile devices themselves. This is defined as the cooperation based architecture of MCC. Analogous to the conventional terminology, the resource platforms that are comprised of surrounding surrogate mobile devices are called local resource clouds. Some researchers have recently verified the feasibility and benefits of this strategy. However, existing work has neglected an important issue with this approach, i.e., how to construct local resource clouds in dynamic mobile wireless networks. This paper presents the concept and design of a local resource cloud that is both energy and time efficient. Along with theoretical models and formal definitions of problems, an efficient heuristic algorithm with low computational complexity is also presented. The results from simulations demonstrate the effectiveness of the proposed models and method.

In this paper, we consider multi-source multi-relay power allocation in cooperative wireless networks. A new intelligent optimization algorithm, multi-objective free search (MOFS), is proposed to efficiently allocate cooperative relay power to better support multiple sources transmission. The existence of Pareto optimal solutions is analyzed for the proposed multi-objective power allocation model when the objectives conflict with each other, and the MOFS algorithm is validated using several test functions and metrics taken from the standard literature on evolutionary multi-objective optimization. Simulation results show that the proposed scheme can effectively get the potential optimal solutions of multi-objective power allocation problem, and it can effectively optimize the tradeoff between network sum-rate and fairness in different applications by selection of the corresponding solution.

Energy harvesting technology was introduced into wireless sensor networks (WSNs) to solve the problem of the short lifetimes of sensor nodes. The technology gives sensor nodes the ability to convert environmental energy into electricity. Sufficient electrical energy can lengthen the lifetime and improve the quality of service of a WSN. This paper proposes a novel use of mutual information to evaluate data transmission behavior in the energy harvesting WSNs. Data at a sink for a node deteriorates over time until the next periodic transmission from the node is received. In this paper, we suggest an optimized intermittent transmission method for WSNs that harvest energy. Our method overcomes the problem of information deterioration without increasing energy cost. We show that by using spatial correlation between different sensor nodes, our proposed method can mitigate information deterioration significantly at the sink.

A directional array radiator is presented, the aim of which is to enhance the sound of the television in a particular direction and hence provide a volume boost to improve speech intelligibility for the hard of hearing. The sound radiated by the array in other directions is kept low, so as not to increase the reverberant level of sound in the listening room. The array uses 32 loudspeakers, each of which are in phase-shift enclosures to generate hypercardioid directivity, which reduces the radiation from the back of the array. The loudspeakers are arranged in 8 sets of 4 loudspeakers, each set being driven by the same signal and stacked vertically, to improve the directivity in this plane. This creates a 3D beamformer that only needs 8 digital filters to be made superdirective. The performance is assessed by means of simulations and measurements in anechoic and reverberant environments. The results show how the array obtains a high directivity in a reverberant environment.

Recently, video surveillance systems have been widely introduced in various places, and protecting the privacy of objects in the scene has been as important as ensuring security. Masking each moving object with a background subtraction method is an effective technique to protect its privacy. However, the background subtraction method is heavily affected by sunshine change, and a redundant masking by over-extraction is inevitable. Such superfluous masking disturbs the quality of video surveillance. In this paper, we propose a moving object masking method combining background subtraction and machine learning based on Real AdaBoost. This method can reduce the superfluous masking while maintaining the reliability of privacy protection. In the experiments, we demonstrate that the proposed method achieves about 78-94% accuracy for classifying superfluous masking regions and moving objects.

Researchers have developed several social-based routing protocols for delay tolerant networks (DTNs) over the past few years. Two main routing metrics to support social-based routing in DTNs are centrality and similarity metrics. These two metrics help packets decide how to travel through the network to achieve short delay or low drop rate. This study presents a new routing scheme called Community-Relevance based Opportunistic routing (CROP). CROP uses a different message forwarding approach in DTNs by combining community structure with a new centrality metric called community relevance. One fundamental change in this approach is that community relevance values do not represent the importance of communities themselves. Instead, they are computed for each community-community relationship individually, which means that the level of importance of one community depends on the packet's destination community. The study also compares CROP with other routing algorithms such as BubbleRap and SimBet. Simulation results show that CROP achieves an average delivery ratio improvement of at least 30% and can distribute packets more fairly within the network.

Sensing and reproduction of precise sound-space information is important to realize highly realistic audio communications. This study was conducted to realize high-precision sensors of 3D sound-space information for transmission to distant places and for preservation of sound data for the future. Proposed method comprises a compact and spherical object with numerous microphones. Each recorded signal from multiple microphones that are uniformly distributed on the sphere is simply weighted and summed to synthesize signals to be presented to a listener's left and right ears. The calculated signals are presented binaurally via ordinary binaural systems such as headphones. Moreover, the weight can be changed according to a human's 3D head movement. A human's 3D head movement is well known to be a crucially important factor to facilitate human spatial hearing. For accurate spatial hearing, 3D sound-space information is acquired as accurately reflecting the listener's head movement. We named the proposed method SENZI (Symmetrical object with ENchased ZIllion microphones). The results of computer simulations demonstrate that our proposed SENZI outperforms a conventional method (binaural Ambisonics). It can sense 3D sound-space with high precision over a wide frequency range.

Decimation and interpolation methods are utilized in image coding for low bit rate image coding. However, the decimation filter (prefilter) and the interpolation filter (postfilter) are irreversible with each other since the prefilter is a wide matrix (a matrix whose number of columns are larger than that of rows) and the postfilter is a tall one (a matrix whose number of rows are larger than that of columns). There will be some distortions in the reconstructed image even without any compression. The method of interpolation-dependent image downsampling (IDID) was used to tackle the problem of producing optimized downsampling images, which led to the optimized prefilter of a given postfilter. We propose integrating the IDID with time-domain lapped transforms (TDLTs) to improve image coding performance.

We design a full-order observer for discrete-time linear time-invariant systems with constant output delays. The observer design is based on the output delay model expressed by a two-dimensional state variable, with discrete-time and space independent variables. Employing a discrete-time state transformation, we construct an explicit strict Lyapunov function that enables us to prove the global exponential stability of the full-order observer error system with an explicit estimate of the exponential decay rate. The numerical example demonstrates the design of the full-order observer and illustrates the validity of the exponential stability.

The unique characteristic of Ternary ripple-carry addition enables us to optimize Ternary Full Adder for this specific application. Carbon nanotube field effect transistors are used in this paper to design new Ternary Half and Full Adders, which are essential components of Ternary ripple-carry adder. The novel designs take the sum of input variables as a single input signal, and generate outputs in a way which is far more efficient than the previously presented similar structures. The new ripple-carry adder operates rapidly, with high performance, and low-transistor-count.

In the optimization of decision diagrams, variable reordering approaches are often used to minimize the number of nodes. However, such approaches are less effective for analysis of multi-state systems given by monotone structure functions. Thus, in this paper, we propose algorithms to minimize the number of edges in an edge-valued multi-valued decision diagram (EVMDD) for fast analysis of multi-state systems. The proposed algorithms minimize the number of edges by grouping multi-valued variables into larger-valued variables. By grouping multi-valued variables, we can reduce the number of nodes as well. To show the effectiveness of the proposed algorithms, we compare the proposed algorithms with conventional optimization algorithms based on a variable reordering approach. Experimental results show that the proposed algorithms reduce the number of edges by up to 15% and the number of nodes by up to 47%, compared to the conventional ones. This results in a speed-up of the analysis of multi-state systems by about three times.

In this paper, we propose a novel method for road sign detection and recognition in complex scene real world images. Our algorithm consists of four basic steps. First, we employ a regional contrast based bottom-up visual saliency method to highlight the traffic sign regions, which usually have dominant color contrast against the background. Second, each type of traffic sign has special color distribution, which can be explored by top-down visual saliency to enhance the detection precision and to classify traffic signs into different categories. A bag-of-words (BoW) model and a color name descriptor are employed to compute the special-class distribution. Third, the candidate road sign blobs are extracted from the final saliency map, which are generated by combining the bottom-up and the top-down saliency maps. Last, the color and shape cues are fused in the BoW model to express blobs, and a support vector machine is employed to recognize road signs. Experiments on real world images show a high success rate and a low false hit rate and demonstrate that the proposed framework is applicable to prohibition, warning and obligation signs. Additionally, our method can be applied to achromatic signs without extra processing.

Visual quality evaluation is crucially important for various video and image processing systems. Traditionally, subjective image quality assessment (IQA) given by the judgments of people can be perfectly consistent with human visual system (HVS). However, subjective IQA metrics are cumbersome and easily affected by experimental environment. These problems further limits its applications of evaluating massive pictures. Therefore, objective IQA metrics are desired which can be incorporated into machines and automatically evaluate image quality. Effective objective IQA methods should predict accurate quality in accord with the subjective evaluation. Motivated by observations that HVS is highly adapted to extract irregularity information of textures in a scene, we introduce multifractal formalism into an image quality assessment scheme in this paper. Based on multifractal analysis, statistical complexity features of nature images are extracted robustly. Then a novel framework for image quality assessment is further proposed by quantifying the discrepancies between multifractal spectrums of images. A total of 982 images are used to validate the proposed algorithm, including five type of distortions: JPEG2000 compression, JPEG compression, white noise, Gaussian blur, and Fast Fading. Experimental results demonstrate that the proposed metric is highly effective for evaluating perceived image quality and it outperforms many state-of-the-art methods.