Abstract The human inner perception is the core technology for human‐robot interactions. Emotion recognition has been established as a representative study for recognising the internal state of human beings in addition to research regarding intention recognition. In the study of emotion recognition using EEG signals, emotion evaluation is categorised into two methods: discrete emotion and continuous emotion. This study proposes an ant colony optimisation‐bidirectional LSTM network model. Unlike other LSTM network models, this model improves performance by applying more weight values that are valid for emotion recognition in the current LSTM cell state using past and future biosignal information and combining ACO to find the optimal combination of emotion recognition features among many features. Furthermore, it extracts valid features using peripheral nervous system signals PPG, GSR, and EOG as well as the central nervous system signals EEG, simultaneously. The authors reinforce feature performance by adding brain lateralisation for emotion recognition. Emotion label data were recorded by performing annotation labelling, in which the values are between −100 and 100 in the arousal‐valence domain. The experimental result shows that the ACO‐bidirectional LSTM model using brain lateralisation in the MAHNOB‐HCI, DEAP, MERTI‐Apps database yields the best valence performance, RMSE of 0.0442, 0.0523, and 0.0568, respectively.

The integration of blockchain with edge computing technology has emerged as a popular topic in recent years. Edge computing offers many advantages to IoT data computing by reducing the bandwidth in the cloud, whereas blockchain offers a promising solution to the data integrity issues of dynamic edge nodes. There is considerable technological mismatch, however, when employing a blockchain protocol for edge computing. Standard edge nodes usually deal with big data and require constant data exchange over time, whereas traditional blockchains prevent the storage of big data in a distributed network. This study presents a novel blockchain architecture called Recordchain, which is fully adapted to the edge computing environment. The innovative feature of Recordchain is its ability to locate actual data separately while providing security, scalability, and performance, thus addressing the storage utilization issues related to big data. The efficiency and performance of the proposed architecture is further demonstrated experimentally.

Summary This paper proposes a high‐performance graphics processing unit (GPU)‐based parity computing scheduler, which we call GPU‐redundant array of inexpensive disks (RAID), to reduce the encoding and decoding time for storage applications. The proposed GPU‐RAID differs from existing RAID in that it performs additional pairwise‐parallel XOR operations between data code words in each data stripe by applying divide‐and‐conquer approach using extra reserved space and it also increases parallelism by processing multiple strips in parallel using multiple GPU threads. And so the proposed GPU‐RAID pipelines data blocks into solid‐state disks and parity blocks into hard disk drives at the target server. The proposed algorithm decreases the span complexity of the parity computation schedule to O(log2nw) where n is the number of disks and w is the number of code words in a block, and it can be applied to various types of erasure codes. Experimental results show that the proposed storage application (SA1) improves average encoding performance by 63%, and 41%, and average decoding performance by 58%, and 38%, compared with traditional storage applications GPUStore (SA3) and Gibraltar RAID(SA2), respectively. Copyright © 2016 John Wiley & Sons, Ltd.

This letter presents lower limb human motion detection using a surface electromyogram (sEMG) with a top and slope (TAS) feature extraction algorithm. Lower limb human motion detection using sEMG signal is generally divided into gait subphase detection, locomotion mode recognition, and mode change detection. Existing feature extraction algorithms using sEMG signal have several innate problems in recognizing lower limb human motion detection. With respect to time-domain features, their values may be analogous because two different gait subphases and locomotion mode may have similar muscle activity pattern. Therefore, it is not easy to select the proper feature set of sEMG signals. The TAS feature extraction algorithm reflects better timing characteristics of sEMG signals than the existing time-domain feature extraction algorithm. Therefore, it can provide high accuracy in lower limb human motion detection. Experimental results show that the average detection accuracy values of the proposed method in terms of the gait subphase detection, locomotion mode recognition, and mode change detection are increased by 8%, 5%, and 4% better than those of feature values of the Willison amplitude, respectively.

Rotation invariant texture descriptor plays an important role in texture-based object classification. However the classification accuracy may decrease due to the inconsistent performance of texture descriptor with respect to various rotated angles. In this paper we propose a consistent rotation invariant texture descriptor named Sorted Neighborhood Differences (SND). SND is derived from the integration of sorted neigh- borhood and binary patterns. Experimental results show that overall texture classification accuracy of SND with respect to different rotations using OUTEX TC 0010 texture database is 91.81% whereas those of LBPriu and LBP-HF are 86.42% and 88.28%, respectively. The texture and coin classification accuracies of SND are also consistent in various rotation angles and illumination levels.

This paper proposes an interest point detector based on wavelet transform as well as a descriptor based on image variation and log-polar coordinate. Taking advantage of the wavelet properties, the proposed method detects a small number of interest points that are distinctive and robust to the illumination changes, scale changes and affine transform. A new descriptor based on the image variation and log-polar coordinate is proposed to represent the image local shape feature without edge detection. Since the proposed descriptor groups the image variation into various levels and separates the image local region into grids based on log-polar coordinate, it overcomes the problem of textured scenes or ill-defined edge images. Experimental results show that the proposed method achieves better matching accuracy and faster matching speed than those of the SIFT, PCA-SIFT and GLOH with less interest points.

The learning-enhanced relevance feedback has been one of the most active research areas in content-based image retrieval in recent years. However, few methods using the relevance feedback are currently available to process relatively complex queries on large image databases. In the case of complex image queries, the feature space and the distance function of the user's perception are usually different from those of the system. This difference leads to the representation of a query with multiple clusters (i.e., regions) in the feature space. Therefore, it is necessary to handle disjunctive queries in the feature space.In this paper, we propose a new content-based image retrieval method using adaptive classification and cluster-merging to find multiple clusters of a complex image query. When the measures of a retrieval method are invariant under linear transformations, the method can achieve the same retrieval quality regardless of the shapes of clusters of a query. Our method achieves the same high retrieval quality regardless of the shapes of clusters of a query since it uses such measures. Extensive experiments show that the result of our method converges to the user's true information need fast, and the retrieval quality of our method is about 22% in recall and 20% in precision better than that of the query expansion approach, and about 34% in recall and about 33% in precision better than that of the query point movement approach, in MARS.

Time series data, which are a series of one dimensional real numbers, have been studied in various database applications. We extend the traditional similarity search methods on time series data to support a multidimensional data sequence, such as a video stream. We investigate the problem of retrieving similar multidimensional data sequences from a large database. To prune irrelevant sequences in a database, we introduce correct and efficient similarity functions. Both data sequences and query sequences are partitioned into subsequences, and each of them is represented by a Minimum Bounding Rectangle (MBR). The query processing is based upon these MBRs, instead of scanning data elements of entire sequences. Our method is designed: (1) to select candidate sequences in a database, and (2) to find the subsequences of a selected sequence, each of which falls under the given threshold. The latter is of special importance in the case of retrieving subsequences from large and complex sequences such as video. By using it, we do not need to browse the whole of the selected video stream, but just browse the sub-streams to find a scene we want. We have performed an extensive experiment on synthetic, as well as real data sequences (a collection of TV news, dramas, and documentary videos) to evaluate our proposed method. The experiment demonstrates that 73-94 percent of irrelevant sequences are pruned using the proposed method, resulting in 16-28 times faster response time compared with that of the sequential search.

The database query optimizer requires the estimation of the query selectivity to find the most efficient access plan. For queries referencing multiple attributes from the same relation, we need a multi-dimensional selectivity estimation technique when the attributes are dependent each other because the selectivity is determined by the joint data distribution of the attributes. Additionally, for multimedia databases, there are intrinsic requirements for the multi-dimensional selectivity estimation because feature vectors are stored in multi-dimensional indexing trees. In the 1-dimensional case, a histogram is practically the most preferable. In the multi-dimensional case, however, a histogram is not adequate because of high storage overhead and high error rates.