The 3D measurement is widely required in modern industries. In this letter, a method based on the RGBD saliency detection with depth range adjusting (RGBD-DRA) is proposed for 3D measurement. By using superpixels and prior maps, RGBD saliency detection is utilized to detect and measure the target object automatically Meanwhile, the proposed depth range adjusting is processing while measuring to prompt the measuring accuracy further. The experimental results demonstrate the proposed method automatic and accurate, with 3 mm and 3.77% maximum deviation value and rate, respectively.

This paper presents the bit error rate (BER) performance of human body communication (HBC) receivers in interference-rich environments. The BER performance was measured while applying an interference signal to the HBC receiver to consider the effect of receiver performance on BER performance. During the measurement, a signal attenuator was used to mimic the signal loss of the human body channel, which improved the repeatability of the measurement results. The measurement results showed that HBC is robust against the interference when frequency selective digital transmission (FSDT) is used as a modulation scheme. The BER performance in this paper can be effectively used to evaluate a communication performance of HBC.

This paper introduces e-spill, an eager spill mechanism, which dynamically finds the optimal spill-threshold by monitoring the GC time at runtime and thereby prevent expensive GC overhead. Our e-spill adopts a slow-start model to gradually increase the spill-threshold until it reaches the optimal point without substantial GCs. We prototype e-spill as an extension to Spark and evaluate it using six workloads on three different parallel platforms. Our evaluations show that e-spill improves performance by up to 3.80× and saves the cost of cluster operation on Amazon EC2 cloud by up to 51% over the baseline system following Spark Tuning Guidelines.

Nowadays the computing technology is going through a major paradigm shift. Local processing platforms are being replaced by physically out of reach yet more powerful and scalable environments such as the cloud computing platforms. Previously, we introduced the OJIT system as a novel approach for obfuscating remotely executed programs, making them difficult for adversaries to reverse-engineer. The system exploited the JIT compilation technology to randomly and dynamically transform the code, making it constantly changing, thereby complicating the execution state. This work aims to propose the new design iOJIT, as an enhanced approach that patches the old systems shortcomings, and potentially provides more effective obfuscation. Here, we present an analytic study of the obfuscation techniques on the generated code and the cost of applying such transformations in terms of execution time and performance overhead. Based upon this profiling study, we implemented a new algorithm to choose which obfuscation techniques would be better chosen for “efficient” obfuscation according to our metrics, i.e., less prone to security attacks. Another goal was to study the system performance with different applications. Therefore, we applied our system on a cloud platform running different standard benchmarks from SPEC suite.

Many applications, such as image signal processing, has an inherent tolerance for insignificant inaccuracies. Multiplication is a key arithmetic function for many applications. Approximate multipliers are considered an efficient technique to trade off energy relative to performance and accuracy for the error-tolerant applications. Here, we design and analyze four approximate multipliers that demonstrate lower power consumption and shorter critical path delay than the conventional multiplier. They employ an approximate tree compressor that halves the height of the partial product tree and generates a vector to compensate accuracy. Compared with the conventional Wallace tree multiplier, one of the evaluated 8-bit approximate multipliers reduces power consumption and critical path delay by 36.9% and 38.9%, respectively. With a 0.25% normalized mean error distance, the silicon area required to implement the multiplier is reduced by 50.3%. Our multipliers outperform the previously proposed approximate multipliers relative to power consumption, critical path delay, and design area. Results from two image processing applications also demonstrate that the qualities of the images processed by our multipliers are sufficiently accurate for such error-tolerant applications.

In the context of resource isolation for network slicing, this paper introduces two resource allocation methods especially for the radio access network (RAN) part. Both methods can be implemented by slight modification of the ordinary packet scheduling algorithm such as the proportional fairness algorithm, and guarantee resource isolation by limiting the maximum number of resource blocks (RBs) allocated to each slice. Moreover, since both methods flexibly allocate RBs to the entire system bandwidth, there are cases in which the throughput performance is improved compared to when the system bandwidth is divided in a static manner, especially in a frequency selective channel environment. Numerical results show the superiority of these methods to dividing simply the system bandwidth in a static manner, and show the difference between the features of the methods in terms of the throughput performance of each slice.

Let G=(V,E) be an unweighted simple graph. A distance-d independent set is a subset I ⊆ V such that dist(u, v) ≥ d for any two vertices u, v in I, where dist(u, v) is the distance between u and v. Then, Maximum Distance-d Independent Set problem requires to compute the size of a distance-d independent set with the maximum number of vertices. Even for a fixed integer d ≥ 3, this problem is NP-hard. In this paper, we design an exact exponential algorithm that calculates the size of a maximum distance-3 independent set in O(1.4143n) time.

This letter proposes a representation learning framework of convolutional neural networks (Convnets) that aims to rectify and improve the feature representations learned by existing transformation-invariant methods. The existing methods usually encode feature representations invariant to a wide range of spatial transformations by augmenting input images or transforming intermediate layers. Unfortunately, simply transforming the intermediate feature maps may lead to unpredictable representations that are ineffective in describing the transformed features of the inputs. The reason is that the operations of convolution and geometric transformation are not exchangeable in most cases and so exchanging the two operations will yield the transformation error. The error may potentially harm the performance of the classification networks. Motivated by the fractal statistics of natural images, this letter proposes a rectifying transformation operator to minimize the error. The proposed method is differentiable and can be inserted into the convolutional architecture without making any modification to the optimization algorithm. We show that the rectified feature representations result in better classification performance on two benchmarks.

We propose a recursive algorithm to reduce the computational complexity of the r-order nonlinearity of n-variable Boolean functions. Applying the algorithm and using the sufficient and necessary condition put forward by [1] to cut the vast majority of useless search branches, we show that the covering radius of the Reed-Muller Code R(3, 7) in R(5, 7) is 20.

Quasi cyclic LDPC (QC-LDPC) codes consisting of circulant permutation matrices (CPM-QC-LDPC) are one of the most attractive types of LDPC codes due to their many advantages. In this paper, we mainly do some research on CPM-QC-LDPC codes. We first propose a two-stage decoding scheme mainly based on parity check matrix transform (MT), which can efficiently improve the bit error rate performance. To optimize the tradeoff between hardware implementation complexity and decoding performance, an improved method that combines our proposed MT scheme with the existing CPM-RID decoding scheme is presented. An experiment shows that both schemes can improve the bit error rate (BER) performance. Finally, we show that the MT decoding mechanism can be applied to other types of LDPC codes. We apply the MT scheme to random LDPC codes and show that it can efficiently lower the error floor.

As the data size of Web-related multi-label classification problems continues to increase, the label space has also grown extremely large. For example, the number of labels appearing in Web page tagging and E-commerce recommendation tasks reaches hundreds of thousands or even millions. In this paper, we propose a graph partitioning tree (GPT), which is a novel approach for extreme multi-label learning. At an internal node of the tree, the GPT learns a linear separator to partition a feature space, considering approximate k-nearest neighbor graph of the label vectors. We also developed a simple sequential optimization procedure for learning the linear binary classifiers. Extensive experiments on large-scale real-world data sets showed that our method achieves better prediction accuracy than state-of-the-art tree-based methods, while maintaining fast prediction.

Collocation is a ubiquitous phenomenon in languages and accurate collocation recognition and extraction is of great significance to many natural language processing tasks. Collocations can be differentiated from simple bigram collocations to collocation frames (referring to distant multi-gram collocations). So far little focus is put on collocation frames. Oriented to translation and parsing, this study aims to recognize and extract the longest possible collocation frames from given sentences. We first extract bigram collocations with distributional semantics based method by introducing collocation patterns and integrating some state-of-the-art association measures. Based on bigram collocations extracted by the proposed method, we get the longest collocation frames according to recursive nature and linguistic rules of collocations. Compared with the baseline systems, the proposed method performs significantly better in bigram collocation extraction both in precision and recall. And in extracting collocation frames, the proposed method performs even better with the precision similar to its bigram collocation extraction results.

Instance features based deep learning methods prompt the performances of high speed object tracking systems by directly comparing target with its template during training and tracking. However, from the perspective of human vision system, prior knowledge of target also plays key role during the process of tracking. To integrate both semantic knowledge and instance features, we propose a convolutional network based object tracking framework to simultaneously output bounding boxes based on different prior knowledge as well as confidences of corresponding Assumptions. Experimental results show that our proposed approach retains both higher accuracy and efficiency than other leading methods on tracking tasks covering most daily objects.

This paper studies the performance of the quantitative RF power variation in Radio-over-Fiber beam forming system utilizing a phased array-antenna integrating photo-diodes in downlink network for next generation millimeter wave band radio access. Firstly, we described details of fabrication of an integrated photonic array-antenna (IPA), where a 60GHz patch antenna 4×2 array and high-speed photo-diodes were integrated into a substrate. We evaluated RF transmission efficiency as an IPA system for Radio-over-Fiber (RoF)-based mobile front hall architecture with remote antenna beam forming capability. We clarified the characteristics of discrete and integrated devices such as an intensity modulator (IM), an optical fiber and the IPA and calculated RF power radiated from the IPA taking account of the measured data of the devices. Based on the experimental results on RF tone signal transmission by utilizing the IPA, attainable transmission distance of wireless communication by improvement and optimization of the used devices was discussed. We deduced that the antenna could output sufficient power when we consider that the cell size of the future mobile communication systems would be around 100 meters or smaller.

In this paper, we consider the partial gathering problem of mobile agents in arbitrary networks. The partial gathering problem is a generalization of the (well-investigated) total gathering problem, which requires that all the agents meet at the same node. The partial gathering problem requires, for a given positive integer g, that each agent should move to a node and terminate so that at least g agents should meet at each of the nodes they terminate at. The requirement for the partial gathering problem is no stronger than that for the total gathering problem, and thus, we clarify the difference on the move complexity between them. First, we show that agents require Ω(gn+m) total moves to solve the partial gathering problem, where n is the number of nodes and m is the number of communication links. Next, we propose a deterministic algorithm to solve the partial gathering problem in O(gn+m) total moves, which is asymptotically optimal in terms of total moves. Note that, it is known that agents require Ω(kn+m) total moves to solve the total gathering problem in arbitrary networks, where k is the number of agents. Thus, our result shows that the partial gathering problem is solvable with strictly fewer total moves compared to the total gathering problem in arbitrary networks.

Since camera networks for surveillance are becoming extremely dense, finding the most informative and desirable views from different cameras are of increasing importance. In this paper, we propose a camera selection method to achieve the goal of providing the clearest visibility possible and selecting the cameras which exactly capture targets for the far-field surveillance. We design a benefit function that takes into account image visibility and the degree of target matching between different cameras. Here, visibility is defined using the entropy of intensity histogram distribution, and the target correspondence is based on activity features rather than photometric features. The proposed solution is tested in both artificial and real environments. A performance evaluation shows that our target correspondence method well suits far-field surveillance, and our proposed selection method is more effective at identifying the cameras that exactly capture the surveillance target than existing methods.

We study the problem of transforming one (vertex) c-coloring of a graph into another one by changing only one vertex color assignment at a time, while at all times maintaining a c-coloring, where c denotes the number of colors. This decision problem is known to be PSPACE-complete even for bipartite graphs and any fixed constant c ≥ 4. In this paper, we study the problem from the viewpoint of graph classes. We first show that the problem remains PSPACE-complete for chordal graphs even if c is a fixed constant. We then demonstrate that, even when c is a part of input, the problem is solvable in polynomial time for several graph classes, such as k-trees with any integer k ≥ 1, split graphs, and trivially perfect graphs.