Vision systems are widely adopted in industrial fields for monitoring and automation. As a typical example, industrial vision systems are extensively implemented in vibrator parts feeder to ensure orientations of parts for assembling are aligned and disqualified parts are eliminated. An efficient parts orientation recognition and counting method is thus critical to adopt. In this paper, an integrated method for fast parts counting and orientation recognition using industrial vision systems is proposed. Original 2D spatial image signal of parts is decomposed to 1D signal with its temporal variance, thus efficient recognition and counting is achievable, feeding speed of each parts is further leveraged to elaborate counting in an adaptive way. Experiments on parts of different types are conducted, the experimental results revealed that our proposed method is both more efficient and accurate compared to other relevant methods.

This paper addresses the problem of selecting a significant subset of candidate features to use for multiple linear regression. Bertsimas et al. [5] recently proposed the discrete first-order (DFO) algorithm to efficiently find near-optimal solutions to this problem. However, this algorithm is unable to escape from locally optimal solutions. To resolve this, we propose a stochastic discrete first-order (SDFO) algorithm for feature subset selection. In this algorithm, random perturbations are added to a sequence of candidate solutions as a means to escape from locally optimal solutions, which broadens the range of discoverable solutions. Moreover, we derive the optimal step size in the gradient-descent direction to accelerate convergence of the algorithm. We also make effective use of the L2-regularization term to improve the predictive performance of a resultant subset regression model. The simulation results demonstrate that our algorithm substantially outperforms the original DFO algorithm. Our algorithm was superior in predictive performance to lasso and forward stepwise selection as well.

This paper presents the feasibility of a capacitive coupler utilizing an electric double layer for wireless power transfer under seawater. Since seawater is an electrolyte solution, an electric double layer (EDL) is formed on the electrode surface of the coupler in direct current. If the EDL can be utilized in radio frequency, it is possible that high power transfer efficiency can be achieved under seawater because a high Q-factor can be obtained. To clarify this, the following steps need taking; First, measure the frequency characteristics of the complex permittivity in seawater and elucidate the behaviors of the EDL from the results. Second, clarify that EDL leads to an improvement in the Q-factor of seawater. It will be shown in this paper that capacitive coupling by EDL occurs using two kinds of the coupler models. Third, design a coupler with high efficiency as measured by the Q-factor and relative permittivity of EDL. Last, demonstrate that the designed coupler under seawater can achieve over 85% efficiency at a transfer distance of 5 mm and feasibility of the coupler with EDL.

To enhance the prediction accuracy of the number of faults, many studies proposed various prediction models. The model is built using a dataset collected in past projects, and the number of faults is predicted using the model and the data of the current project. Datasets sometimes have many data points where the dependent variable, i.e., the number of faults is zero. When a multiple linear regression model is made using the dataset, the model may not be built properly. To avoid the problem, the Tobit model is considered to be effective when predicting software faults. The model assumes that the range of a dependent variable is limited and the model is built based on the assumption. Similar to the Tobit model, the Poisson regression model assumes there are many data points whose value is zero on the dependent variable. Also, log-transformation is sometimes applied to enhance the accuracy of the model. Additionally, ensemble methods are effective to enhance prediction accuracy of the models. We evaluated the prediction accuracy of the methods separately, when the number of faults is zero and not zero. In the experiment, our proposed ensemble method showed the highest accuracy, and Pred25 was 21% when the number of faults was not zero, and it was 45% when the number was zero.

This paper proposes two variants of improved Compression by Substring Enumeration (CSE) with a finite alphabet. In previous studies on CSE, an encoder utilizes inequalities which evaluate the number of occurrences of a substring or a minimal forbidden word (MFW) to be encoded. The inequalities are derived from a contingency table including the number of occurrences of a substring or an MFW. Moreover, codeword length of a substring and an MFW grows with the difference between the upper and lower bounds deduced from the inequalities, however the lower bound is not tight. Therefore, we derive a new tight lower bound based on the contingency table and consequently propose a new CSE algorithm using the new inequality. We also propose a new encoding order of substrings and MFWs based on a sorted contingency table such that both its row and column marginal total are sorted in descending order instead of a lexicographical order used in previous studies. We then propose a new CSE algorithm which is the first proposed CSE algorithm using the new encoding order. Experimental results show that compression ratios of all files of the Calgary corpus in the proposed algorithms are better than those of a previous study on CSE with a finite alphabet. Moreover, compression ratios under the second proposed CSE get better than or equal to that under a well-known compressor for 11 files amongst 14 files in the corpus.

In this letter, an efficient hybrid direction-of-arrival (DOA) estimation scheme is devised for massive uniform rectangular array. In this scheme, the DOA estimator based on a two-dimensional (2D) discrete Fourier transform is first applied to acquire coarse initial DOA estimates for single data snapshot. Then, the fine DOA is accurately estimated through using the iterative search estimator within a very small region. Meanwhile, a Nyström-based method is utilized to correctly compute the required noise-subspace projection matrix, avoiding the direct computation of full-dimensional sample correlation matrix and its eigenvalue decomposition. Therefore, the proposed scheme not only can estimate DOA, but also save computational cost, especially in massive antenna arrays scenarios. Simulation results are included to demonstrate the effectiveness of the proposed hybrid estimate scheme.

This paper describes a novel approach to flexible control of speaker characteristics using tensor representation of multiple Gaussian mixture models (GMM). In voice conversion studies, realization of conversion from/to an arbitrary speaker's voice is one of the important objectives. For this purpose, eigenvoice conversion (EVC) based on an eigenvoice GMM (EV-GMM) was proposed. In the EVC, a speaker space is constructed based on GMM supervectors which are high-dimensional vectors derived by concatenating the mean vectors of each of the speaker GMMs. In the speaker space, each speaker is represented by a small number of weight parameters of eigen-supervectors. In this paper, we revisit construction of the speaker space by introducing the tensor factor analysis of training data set. In our approach, each speaker is represented as a matrix of which the row and the column respectively correspond to the dimension of the mean vector and the Gaussian component. The speaker space is derived by the tensor factor analysis of the set of the matrices. Our approach can solve an inherent problem of supervector representation, and it improves the performance of voice conversion. In addition, in this paper, effects of speaker adaptive training before factorization are also investigated. Experimental results of one-to-many voice conversion demonstrate the effectiveness of the proposed approach.

In this paper, we present the effectiveness of image compression based on a convolutional auto encoder (CAE) with region of interest (ROI) for quality control. We propose a method that adapts image quality for prioritized parts and non-prioritized parts for CAE-based compression. The proposed method uses annotation information for the distortion weights of the MS-SSIM-based loss function. We show experimental results using a road damage image dataset that is used to check damaged parts and an image dataset with segmentation data (ADE20K). The experimental results reveals that the proposed weighted loss function with CAE-based compression from F. Mentzer et al. learns some characteristics and preferred bit allocations of the prioritized parts by end-to-end training. In the case of using road damage image dataset, our method reduces bpp by 31% compared to the original method while meeting quality requirements that an average weighted MS-SSIM for the road damaged parts be larger than 0.97 and an average weighted MS-SSIM for the other parts be larger than 0.95.

The increase in computation cost and storage of convolutional neural networks (CNNs) severely hinders their applications on limited-resources devices in recent years. As a result, there is impending necessity to accelerate the networks by certain methods. In this paper, we propose a loss-driven method to prune redundant channels of CNNs. It identifies unimportant channels by using Taylor expansion technique regarding to scaling and shifting factors, and prunes those channels by fixed percentile threshold. By doing so, we obtain a compact network with less parameters and FLOPs consumption. In experimental section, we evaluate the proposed method in CIFAR datasets with several popular networks, including VGG-19, DenseNet-40 and ResNet-164, and experimental results demonstrate the proposed method is able to prune over 70% channels and parameters with no performance loss. Moreover, iterative pruning could be used to obtain more compact network.

We measured eye movements at gaze points while subjects performed calculation tasks and examined the relationship between the eye movements and fatigue and/or internal state of a subject by tasks. It was suggested that fatigue and/or internal state of a subject affected eye movements at gaze points and that we could measure them using eye movements at gaze points in real time.

The packet error rate (PER) performance of multi-hop STBC based cooperative and diversity relaying systems are studied. These systems consist of a source, a destination, and two relay stations in each hop. From in-lab experiments, it is confirmed that the cooperative relaying system has better PER performance than the diversity relaying system with highly correlated channels.

To improve the likability of speech, we propose a voice conversion algorithm by controlling the fundamental frequency (F0) and the spectral envelope and carry out a subjective evaluation. The subjects can manipulate these two speech parameters. From the result, the subjects preferred speech with a parameter related to higher brightness.

This work reports a new technique to suppress the undesirable multiple-triggering effect in the typical diode triggered silicon controlled rectifier (DTSCR), which is frequently used as an ESD protection element in the advanced CMOS technologies. The technique is featured by inserting additional N-Well areas under the N+ region of intrinsic SCR, which helps to improve the substrate resistance. As a consequence, the delay of intrinsic SCR is reduced as the required triggering current is largely decreased and multiple-triggering related higher trigger voltage is removed. The novel DTSCR structures can alter the stacked diodes to achieve the precise trigger voltage to meet different ESD protection requirements. All explored DTSCR structures are fabricated in a 65-nm CMOS process. Transmission-line-pulsing (TLP) and Very-Fast-Transmission-line-pulsing (VF-TLP) test systems are adopted to confirm the validity of this technique and the test results accord well with our analysis.

This paper proposes a successive interference cancellation (SIC) of independent component analysis (ICA) aided spatial division multiple access (SDMA) for Gaussian filtered frequency shift keying (GFSK) in Bluetooth low energy (BLE) systems. The typical SDMA scheme requires estimations of channel state information (CSI) using orthogonal pilot sequences. However, the orthogonal pilot is not embedded in the BLE packet. This fact motivates us to add ICA detector into BLE systems. In this paper, focusing on the covariance matrix of ICA outputs, SIC can be applied with Cholesky decomposition. Then, in order to address the phase ambiguity problems created by the ICA process, we propose a differential detection scheme based on the MAP algorithm. In practical scenarios, it is subject to carrier frequency offset (CFO) as well as symbol timing offset (STO) induced by the hardware impairments present in the BLE peripherals. The packet error rate (PER) performance is evaluated by computer simulations when BLE peripherals simultaneously communicate in the presence of CFO and STO.

As smartphones and IoT devices become widespread, probabilistic event streams, which are continuous analysis results of sensing data, have received a lot of attention. One of the applications of probabilistic event streams is monitoring of time series events based on regular expressions. That is, we describe a monitoring query such as “Has the tracked object moved from RoomA to RoomB in the past 30 minutes?” by using a regular expression, and then check whether corresponding events occur in a probabilistic event stream with a sliding window. Although we proposed the fundamental monitoring method of time series events in our previous work, three problems remain: 1) it is based on an unusual assumption about slide size of a sliding window, 2) the grammar of pattern queries did not include “negation”, and 3) it was not optimized for multiple monitoring queries. In this paper, we propose several techniques to solve the above problems. First, we remove the assumption about slide size, and propose adaptive slicing of sliding windows for efficient probability calculation. Second, we calculate the occurrence probability of a negation pattern by using an inverted DFA. Finally, we propose the merge of multiple DFAs based on disjunction to process multiple queries efficiently. Experimental results using real and synthetic datasets demonstrate effectiveness of our approach.

Depression is a major mental health problem in Thailand. The depression rates have been rapidly increasing. Over 1.17 million Thai people suffer from this mental illness. It is important that a reliable depression screening tool is made available so that depression could be early detected. Given Facebook is the most popular social network platform in Thailand, it could be a large-scale resource to develop a depression detection tool. This research employs techniques to develop a depression detection algorithm for the Thai language on Facebook where people use it as a tool for sharing opinions, feelings, and life events. To establish the reliable result, Thai Mental Health Questionnaire (TMHQ), a standardized psychological inventory that measures major mental health problems including depression. Depression scale of the TMHQ comprises of 20 items, is used as the baseline for concluding the result. Furthermore, this study also aims to do factor analysis and reduce the number of depression items. Data was collected from over 600 Facebook users. Descriptive statistics, Exploratory Factor Analysis, and Internal consistency were conducted. Results provide the optimized version of the TMHQ-depression that contain 9 items. The 9 items are categorized into four factors which are suicidal ideation, sleep problems, anhedonic, and guilty feelings. Internal consistency analysis shows that this short version of the TMHQ-depression has good to excellent reliability (Cronbach's alpha >.80). The findings suggest that this optimized TMHQ-depression questionnaire holds a good psychometric property and can be used for depression detection.

In this paper, we propose a salient region detection method with multi-feature fusion and edge constraint. First, an image feature extraction and fusion network based on dense connection structure and multi-channel convolution channel is designed. Then, a multi-scale atrous convolution block is applied to enlarge reception field. Finally, to increase accuracy, a combined loss function including classified loss and edge loss is built for multi-task training. Experimental results verify the effectiveness of the proposed method.

This paper presents a three-dimensional (3D) spatial localization algorithm by using multiple one-dimensional uniform linear arrays (ULA). We first discuss geometric features of the angle-of-arrival (AOA) measurements of the array and present the corresponding principle of spatial cone angle intersection positioning with an angular measurement model. Then, we propose a new positioning method with an analytic study on the geometric dilution of precision (GDOP) of target location in different cases. The results of simulation show that the estimation accuracy of this method can attain the Cramér-Rao Bound (CRB) under low measurement noise.

We propose image synthesizing using luminance adapted range compression and detail-preserved blending. Range compression is performed using the correlated visual gamma then image blending is performed by local adaptive mixing and selecting method. Simulations prove that the proposed method reproduces natural images without any increase in noise or color desaturation.

Most existing methods of effort estimations in software development are manual, labor-intensive and subjective, resulting in overestimation with bidding fail, and underestimation with money loss. This paper investigates effectiveness of sequence models on estimating development effort, in the form of man-months, from software project data. Four architectures; (1) Average word-vector with Multi-layer Perceptron (MLP), (2) Average word-vector with Support Vector Regression (SVR), (3) Gated Recurrent Unit (GRU) sequence model, and (4) Long short-term memory (LSTM) sequence model are compared in terms of man-months difference. The approach is evaluated using two datasets; ISEM (1,573 English software project descriptions) and ISBSG (9,100 software projects data), where the former is a raw text and the latter is a structured data table explained the characteristic of a software project. The LSTM sequence model achieves the lowest and the second lowest mean absolute errors, which are 0.705 and 14.077 man-months for ISEM and ISBSG datasets respectively. The MLP model achieves the lowest mean absolute errors which is 14.069 for ISBSG datasets.