Automatic building extraction from high-resolution remote sensing imagery has various applications, such as urban planning and land use management. However, the existing methods for automatic building extraction focus on regional accuracy but not on boundary quality. As a result, these building extraction methods yield discontinuous results due to tree and shadow occlusions and complex building roof materials. In this article, BOMSC-Net, which can consider boundary optimization and multi-scale context awareness, is proposed to address these two problems. BOMSC-Net is an encoder-to-decoder network composed of a basic network, a multi-scale context-aware module (MSCAM), and a direction feature optimization module (DFOM). The basic network learns to predict building segmentation maps through attention gates (AGs) that connect low-level feature jumps to advanced semantics. MSCAM is used as a bridge, which uses graph reasoning blocks (GRBs) to solve intra-class discontinuities. Finally, the DFOM constructs a direction field (DF) and then uses direction information for boundary correction to achieve fine building segmentations. Experiments were conducted on the open Massachusetts building dataset, WHU aerial building dataset, and Inria aerial image labeling dataset. The quantitative and qualitative analyses demonstrate that the proposed BOMSC-Net can help building extraction overcome interruptions caused by shadows and occlusions, extract buildings with different scales and materials, and realize boundary optimization and complete segmentation of buildings.

As one of the most popular cloud services, data storage has attracted great attention in recent research efforts. Key-value (k-v) stores have emerged as a popular option for storing and querying billions of key-value pairs. So far, existing methods have been deterministic. Providing such accuracy, however, comes at the cost of memory and CPU time. In contrast, we present an approximate k-v storage for cloud-based systems that is more compact than existing methods. The tradeoff is that it may, theoretically, return errors. Its design is based on the probabilistic data structure called “bloom filter”, where we extend the classical bloom filter to support key-value operations. We call the resulting design as the kBF (key-value bloom filter). We further develop a distributed version of the kBF (d-kBF) for the unique requirements of cloud computing platforms, where multiple servers cooperate to handle a large volume of queries in a load-balancing manner. Finally, we apply the kBF to a practical problem of implementing a state machine to demonstrate how the kBF can be used as a building block for more complicated software infrastructures.

Next generation sequencing technologies generate normous amount of short reads, which poses a significant computational challenge for short read alignment. Furthermore, because of sequence polymorphisms in a population, repetitive sequences, and sequencing errors, there still exist difficulties in correctly aligning all reads. We propose a space-efficient compressed suffix array-based method for short read alignment (CS2A) whose space achieves the high-order empirical entropy of the input string. Unlike BWA that uses two bits to represent a nucleotide, suitable for constant-sized alphabets, our encoding scheme can be applied to the string with any alphabet set. In addition, we present approximate pattern matching on compressed suffix array (CSA) for short read alignment. Our CS2A supports both mismatch and gapped alignments for single-end and paired-end reads mapping, being capable of efficiently aligning short sequencing reads to genome sequences. The experimental results show that CS2A can compete with the popular aligners in memory usage and mapping accuracy. The source code is available online.

Anomaly detection becomes increasingly important in hyper-spectral image analysis, since it can now uncover many material substances which were previously unresolved by multi-spectral sensors. In this paper, we propose a Low-rank Tensor Decomposition based anomaly Detection (LTDD) algorithm for Hyperspectral Imagery. The HSI data cube is first modeled as a dense low-rank tensor plus a sparse tensor. Based on the obtained low-rank tensor, LTDD further decomposes the low-rank tensor using Tucker decomposition to extract the core tensor which is treated as the “support” of the anomaly spectral signatures. LTDD then adopts an unmixing approach to the reconstructed core tensor for anomaly detection. The experiments based on both simulated and real hyperspectral data sets verify the effectiveness of our algorithm.

In this paper, we study the compressed sensing (CS) image recovery problem. The traditional method divides the image into blocks and treats each block as an independent sub-CS recovery task. This often results in losing global structure of an image. In order to improve the CS recovery result, we propose a nonlocal (NL) estimation step after the initial CS recovery for denoising purpose. The NL estimation is based on the well-known NL means filtering that takes an advantage of self-similarity in images. We formulate the NL estimation as the low-rank matrix approximation problem, where the low-rank matrix is formed by the NL similarity patches. An efficient algorithm, nonlocal Douglas-Rachford (NLDR), based on Douglas-Rachford splitting is developed to solve this low-rank optimization problem constrained by the CS measurements. Experimental results demonstrate that the proposed NLDR algorithm achieves significant performance improvements over the state-of-the-art in CS image recovery.

In this demo, we present MoodMagician, a pervasive and unobtrusive mobile phone system for inferring human emotions through the recording, processing, and analysis of the real-time streaming Galvanic Skin Response (GSR) signal from human bodies. Being different from traditional multimodal emotion sensing systems which rely on data from multiple sensing sources and may hence interfere with people's daily life, our proposed system is able to detect various categories of human emotions using single GSR signal, which is captured by compact and wearable mobile sensing devices in an unobtrusive fashion. The proposed system has been evaluated by well-designed practical experiments to recognize human emotions. The recognition accuracy of each emotion can be up to 70% through the development of effective preprocessing algorithms and the extraction of representative features from the GSR signals.

We consider the approximate sparse recovery problem in multi-hop Wireless Sensor Networks (WSNs) using Compressed Sensing/Compressive Sampling (CS). The goal is to recover the n-dimensional data values by querying only m ≪ n sensors based on some linear projection of sensor readings. To solve this problem, a distributed compressive sparse sampling (DCSS) algorithm is proposed based on sparse binary CS measurement matrix. Each sensor first samples the environment independently, then the fusion center (FC), acting as a pseudo-sensor, samples the sensor network to select a subset of sensors (m out of n) that respond to the FC through shortest path for data recovery purpose. The sparse binary matrix is designed using the unbalanced expander graph which achieves the state-of-the-art performance for CS schemes. This binary matrix can be interpreted as a sensor selection matrix whose fairness is analyzed. Extensive experiments on both synthetic and real data sets show that by querying only the minimum amount of m sensors using the DCSS algorithm, the CS recovery accuracy outperforms existing sparse random matrices and can be as good as those using random dense measurement matrices but using much less number of sensors. We also show that the sparse binary measurement matrix works well on compressible data which has the closest recovery result to the known best k-term approximation. The recovery is robust against noisy measurements and does not require regular WSN deployments (e.g., grids). The sparsity and binary properties of the measurement matrix contribute, to a great extent, the reduction of the in-network communication cost as well as the computational burden.

Hyperspectral images consist of large number of spectral bands but many of which contain redundant information. Therefore, band selection has been a common practice to reduce the dimensionality of the data space for cutting down the computational cost and alleviating from the Hughes phenomenon. This paper presents a new technique for band selection where a sparse representation of the hyperspectral image data is pursued through an existing algorithm, K-SVD, that decomposes the image data into the multiplication of an overcomplete dictionary (or signature matrix) and the coefficient matrix. The coefficient matrix, that possesses the sparsity property, reveals how importantly each band contributes in forming the hyperspectral data. By calculating the histogram of the coefficient matrix, we select the top K bands that appear more frequently than others to serve the need for dimensionality reduction and at the same time preserving the physical meaning of the selected bands. We refer to the proposed band selection algorithm based on sparse representation as SpaBS. Through experimental evaluation, we first use synthetic data to validate the sparsity property of the coefficient matrix. We then apply SpaBS on real hyperspectral data and use classification accuracy as a metric to evaluate its performance. Compared to other unsupervised band selection algorithms like PCA and ICA, SpaBS presents higher classification accuracy with a stable performance.