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Depression is a mental disorder of high prevalence, leading to a negative effect on individuals, their families, society and the economy. In recent years, the problem of automatic detection of depression from the speech signal has gained more interest. In this paper, a new multiple classifier system for depression recognition was developed and tested. The novel aspect of this methodology is the combination...
Depression detection using speech signal is becoming an attractive topic because it is fast, convenient and non-invasive. Many researches aimed at improving depression classification performance. This study investigated application of ensemble learners in depression detection and compared three speaking styles (interview, reading and picture description) in ensembles. A speech dataset collecting from...
Detecting depression via speech is an attractive topic in recent years. Significant correlation was found between speech pause time and depressive severity. In the present study, 92 depressed patients and 92 age-, gender- and education level-matched control participants were examined to investigate three temporal characteristics of speech: recording time (RT), phonation time (PT) and speech pause...
Location-aware devices will create new services and applications in emerging fields such as autonomous driving, smart cities, and the Internet of Things. Many existing localization systems rely on anchors such as satellites at known positions which broadcast radio signals. However, such signals may be blocked by obstacles, corrupted by multipath propagation, or provide insufficient localization accuracy...
Vehicles to Pedestrians (V2P) communication always plays a critical role in Intelligent Transportation System (ITS). Recent researches show that Wi-Fi module in smartphones could be used to improve the traffic safety by broadcasting pedestrian position data. However, these Wi-Fi-based schemes could not achieve two-way communication with low latency, which would seriously affect the user experience...
This paper studies the linear quadratic Gaussian (LQG) control problem for wireless networked control systems in which control inputs are randomly dropped without the packet acknowledgment The packet acknowledgment is based on a signal scheme between the actuator and the estimator, which make the estimator know whether control packets are dropped or not. For such systems, the calculation of the optimal...
It is widely accepted that traditional word embedding models, which rely on distributional semantics hypothesis, are relatively limited for contrast meaning problem. Distributional semantics hypothesis indicates that words lying in similar contexts have similar representations in vector space. Nevertheless, synonyms and antonyms often locate in similar contexts, which means they appear close to each...
Accurate detection of breast cancer region is essential for treatment. X-ray computed tomography (CT) is an effective diagnostic method of breast cancer besides MRI and ultrasound. In this paper, a semi-automated breast cancer segmentation method was proposed to CT images. First, maximum region searching was used to find the rough boundary of the lesion. Then, a modified Histogram Equalization with...
We present a multi-view convolutional neural networks (MV-CNN) for lung nodule segmentation. The MV-CNN specialized in capturing a diverse set of nodule-sensitive features from axial, coronal and sagittal views in CT images simultaneously. The proposed network architecture consists of three CNN branches, where each branch includes seven stacked layers and takes multi-scale nodule patches as input...
In this paper, we present an approach to recognize the Machine-printed Mongolian characters by CNN (convolutional neural network). Firstly, a training set of traditional Mongolian characters is collected in advance. There are 85 categories in all and each category is trained and recognized by the CNN. And then, we set a CNN with seven layers. There are three convolution layers, two subsampling layers,...
Wireless network localization (WNL) is a paradigm proposed recently for providing reliable location services. The localization performance is highly dependent on the placement of nodes in the network. In this paper, we study the node placement problem for localization networks. We first propose a mathematical formulation of the node placement problem. Such a formulation takes into account the uncertainty...
Deep Convolutional Neural Network (CNN) based methods have shown outstanding performance in a wide range of applications. Nowadays neural networks become deeper, leading to demand of substantial computation and memory resources. Customized hardware is one option which maintains high performance in lower energy consume than general CPUs or GPUs. While hardware designing, we need to address the problem...
System and software quality evaluation is an important method of quality assurance, and its standardization provides quality requirements and evaluation supported by quality measurement. ISO/IEC organizations have also published a series of standards on system and software quality requirements and evaluation, which is significant for assuring the quality of system and software. This paper introduces...
The prediction of stock price movements is considered as a challenging task for financial time series analysis. The difficulty of predicting the trends lies in the dynamic temporality and noise in the stock data. The Echo State Network (ESN) is a popular time series prediction model that considers the temporality of the stock time series, but ESN often falls into the dilemma of over-fitting due to...
Various problems caused by stress seriously affect individuals' physical and mental well-being and have been receiving an increasing attention in modern lives. Since traditional stress assessment methods are lack of objectivity, affective sensing technologies have been studied for years. As detecting stress in speech has the advantages of non-invasive, portable, fast, and less expensive, many explorations...
With the development of Ultra-High-Definition video, the power consumed by accessing reference frames in the external DRAM has become the bottleneck for the portable video encoding system design. To reduce the dynamic power of DRAM, a lossy frame memory recompression algorithm is proposed. The compression algorithm is composed of a content-aware adaptive quantization, a multi-mode directional prediction,...
Convolution Neural Network (CNN) is a state of the art machine learning algorithm. For CNN accelerator implementations, fixed-point and floating-point are two typical numeric representations. Because of the effects of rounding, reducing the word length would save the hardware and the power overheads while sacrificing the computation accuracy. The inherent robustness of neural network makes it possible...
Motion Estimation (ME), which is composed of integer motion estimation (IME) and fractional motion estimation (FME), is the most computational intensive module in HEVC encoding procedure. In this paper, a new fast fractional pixel motion search method, that is based on a six-parameter two-dimension error surface model, is proposed. In our proposal, by solving the over-determined equations, nine integer-pixel...
High efficiency video coding (HEVC) is the latest international video compression standard that achieves double compression efficiency than the previous standard H.264/AVC. To increase the compression accuracy, HEVC employs the coding unit (CU) ranging from 8 × 8 to 64 × 64. However, the encoding complexity of HEVC increase a lot since the manifold partition sizes. A lot of works are focused on reducing...
The fault-tolerance control in scale-free wireless sensor networks against cascading failure is analysed in this paper. Based on the node load redistribution and constant capacity of the scale-free wireless sensor networks, a new load distribution model is proposed under a single random node failure. By the probability generating function method, the relationships between the load and the largest...
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