Automatic detection of immunoreactive areas in fluorescence microscopic images is becoming a key technique in the field of biology including neuroscience, although it is still challenging because of several reasons such as low signal-to-noise ratio and contrast variation within an image. In this study, we developed a new algorithm that exhaustively detects co-localized areas in multi-channel fluorescence images, where shapes of target objects may differ among channels. Different adaptive binarization thresholds for different local regions in different channels are introduced and the condition of each segment is assessed to recognize the target objects. The proposed method was applied to detect immunoreactive spots that labeled membrane receptors on dendritic spines of mouse cerebellar Purkinje cells. Our method achieved the best detection performance over five pre-existing methods.

This paper presents a K-band fully reconfigurable waveguide resonator filter with a new negative coupling structure. A pair of transmission zeros as well as the center frequency and bandwidth of the presented filter can be adjusted. The filter adopts the concept of a frequency-tunable coupling resonator in designing the coupling structure, which allows for controlling the coupling coefficient. All coupling values in the filter structure can be tuned by adjusting the resonant frequency of each frequency-tunable coupling resonator. This work also presents a design method in detail for the coupling resonator with a negative coupling coefficient. In addition, the approach for separating the resonant peak produced by the coupling resonator with a negative coupling value from the passband for the purpose of improving the stopband performance is described. For verifying the presented filter structure, a fourth-order waveguide filter has been fabricated and measured. The fabricated filter has the center frequency tuning range from 18.34GHz to 18.75GHz, the bandwidth tuning ratio of 1.94 : 1.

To provide a satellite communication system with high reliability for social infrastructure, building flexible beam adapting to change of communication traffic is necessary. Optical Beam Forming Network has the capability of broadband transmission and small light construction. However, in space environment, there are concerns that the reception efficiency is reduced by the relative phase error of receiving signal among antenna elements with temperature fluctuation. To prevent this, we control relative phase among received signals with optical phase locked loop. In this paper, we propose the active optical phased array system using multi dither heterodyning technique for receiving OBF, and present experimental results under temperature fluctuation. We evaluated the stability of relative phase among 3 elements for temperature fluctuation at multiplexer from -15 to 45, and checked the stability of PLL among 3 elements.

In this work, we propose a new statistical model for building robust dialog systems using neural networks to either retrieve or generate dialog response based on an existing data sources. In the retrieval task, we propose an approach that uses paraphrase identification during the retrieval process. This is done by employing recursive autoencoders and dynamic pooling to determine whether two sentences with arbitrary length have the same meaning. For both the generation and retrieval tasks, we propose a model using long short term memory (LSTM) neural networks that works by first using an LSTM encoder to read in the user's utterance into a continuous vector-space representation, then using an LSTM decoder to generate the most probable word sequence. An evaluation based on objective and subjective metrics shows that the new proposed approaches have the ability to deal with user inputs that are not well covered in the database compared to standard example-based dialog baselines.

Exploring a parsimonious model that is just enough to represent the temporal dependency of time serial signals such as audio or speech is a practical requirement for many signal processing applications. A well suited method for intuitively and efficiently representing magnitude spectra is to use convolutive non-negative matrix factorization (CNMF) to discover the temporal relationship among nearby frames. However, the model order selection problem in CNMF, i.e., the choice of the number of convolutive bases, has seldom been investigated ever. In this paper, we propose a novel Bayesian framework that can automatically learn the optimal model order through maximum a posteriori (MAP) estimation. The proposed method yields a parsimonious and low-rank approximation by removing the redundant bases iteratively. We conducted intuitive experiments to show that the proposed algorithm is very effective in automatically determining the correct model order.

In this paper, an iterative robust minimum-mean square error (MMSE) receiver for space-time block coding (STBC) is proposed to mitigate the performance degradations caused by channel state information (CSI) errors. The proposed scheme estimates an instantaneous covariance matrix of the effective noise, which includes additive white Gaussian noise and the effect of CSI errors. For this estimation, multiple solution candidate vectors are selected based on the distances between the MMSE estimate of the solution and the constellation points, and their a-posteriori probabilities are utilized to execute the estimation of the covariance matrix. To improve the estimation accuracy, the estimated covariance matrix is updated iteratively. Simulation results show that proposed robust receiver achieves substantial performance gains in terms of bit error rates as compared to conventional receiver schemes under CSI errors.

In a previous work [1], Wang et al. proposed a privacy-preserving outsourcing scheme for biometric identification in cloud computing, namely CloudBI. The author claimed that it can resist against various known attacks. However, there exist serious security flaws in their scheme, and it can be completely broken through a small number of constructed identification requests. In this letter, we modify the encryption scheme and propose an improved version of the privacy-preserving biometric identification design which can resist such attack and can provide a much higher level of security.

The dark channel prior (DCP)-based image dehazing method has been widely used for enhancing visibility of outdoor images. However, since the DCP-based method assumes that the minimum values within local patches of natural outdoor haze-free images are zero, underestimation of the transmission is inevitable when the assumption does not hold. In this letter, a novel iterative image dehazing algorithm is proposed to compensate for the underestimated transmission. Experimental results show that the proposed method can improve the dehazing performance by increasing the transmission estimation accuracy.

In satellite/terrestrial integrated mobile communication systems (STICSs), a user terminal directly connects both terrestrial and satellite base stations. STICS enables expansion of service areas and provides a robust communication service for large disasters. However, the cell radius of the satellite system is large (approximately 100km), and thus a capacity enhancement of the satellite subsystem for accommodating many users is needed. Therefore, in this paper, we propose an application of two methods — multiple-input multiple-output (MIMO) transmission using multi-satellites and non-orthogonal multiple access (NOMA) for STICS — to realize the performance improvement in terms of system capacity and user fairness. Through numerical simulations, we show that system capacity and user fairness are increased by the proposed scheme that applies the two methods.

Recently, many wireless sensor networks (WSNs) have employed mobile sensor nodes to collect a variety of data from mobile elements such as humans, animals and cars. In this letter, we propose an efficient mobile data aggregation scheme to improve the overall performance in gathering the data of the mobile nodes. We first propose a spatial mobile data aggregation scheme to aggregate the data of the mobile node spatially, which is then extended to a two-tier mobile data aggregation by supplementing a temporal mobile data aggregation scheme to aggregate the data of multiple mobile nodes temporally. Simulation results show that our scheme significantly reduces the energy consumption and gathering delay for data collection from mobile nodes in WSNs.

This paper proposes statistical analysis of phase-only correlation functions with phase-spectrum differences following wrapped distributions. We first assume phase-spectrum differences between two signals to be random variables following a linear distribution. Next, based on directional statistics, we convert the linear distribution into a wrapped distribution by wrapping the linear distribution around the circumference of the unit circle. Finally, we derive general expressions of the expectation and variance of the POC functions with phase-spectrum differences following wrapped distributions. We obtain exactly the same expressions between a linear distribution and its corresponding wrapped distribution.

Internal power losses in lumped-element impedance matching circuits are formulated by means of Q factors of the elements and port impedances to be matched. Assuming that Q factors are relatively high, the above mentioned loss is expressed by a simple formula containing only the tangents of the impedances. The formula is a powerful tool for such applications that put emphasis on power efficiency as wireless power transfer. As well as the formulation, we illustrate some design examples with the derived formula: design of the least lossy L-section circuit and two-stage low-pass ladder. The examples provide ready-to-use knowledge for low-loss matching design.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.

Along with remarkable advancement of radiocommunication services including satellite services, the radio-frequency spectrum and geostationary-satellite orbit are getting congested. WRC-15 was held in November 2015 to study and implement efficient use of those natural resources. There were a number of satellite-related agenda items associated with frequency allocation, new usages of satellite communications and satellite regulatory issues. This paper overviews the outcome from these agenda items of WRC-15 as well as the agenda items for the next WRC (i.e. the WRC-19).

This paper presents an innovative fabrication process for a planar circuits at millimeter-wave frequency. Screen printing technology provides low cost and high performance coplanar waveguides (CPW) lines in planar devices operated at millimeter-wave frequency up to 110GHz. Printed transmission lines provide low insertion losses of 0.30dB/mm at 110GHz and small return loss like as impedance standard lines. In the paper, Multiline Thru-Reflect-Line (TRL) calibration was also demonstrated by using the impedance standard substrates (ISS) fabricated by screen printing. Regarding calibration capability validation, verification devices were measured and compare the results to the result obtained by the TRL calibration using commercial ISS. The comparison results obtained by calibration of screen printing ISS are almost the same as results measured based on conventional ISS technology.

Automated biometrics identification using finger vein images has increasingly generated interest among researchers with emerging applications in human biometrics. The traditional feature-level fusion strategy is limited and expensive. To solve the problem, this paper investigates the possible use of infrared hybrid finger patterns on the back side of a finger, which includes both the information of finger vein and finger dorsal textures in original image, and a database using the proposed hybrid pattern is established. Accordingly, an Intersection enhanced Gabor based Direction Coding (IGDC) method is proposed. The Experiment achieves a recognition ratio of 98.4127% and an equal error rate of 0.00819 on our newly established database, which is fairly competitive.

The introduction of deep neural networks (DNNs) leads to a significant improvement of the automatic speech recognition (ASR) performance. However, the whole ASR system remains sophisticated due to the dependent on the hidden Markov model (HMM). Recently, a new end-to-end ASR framework, which utilizes recurrent neural networks (RNNs) to directly model context-independent targets with connectionist temporal classification (CTC) objective function, is proposed and achieves comparable results with the hybrid HMM/DNN system. In this paper, we investigate per-dimensional learning rate methods, ADAGRAD and ADADELTA included, to improve the recognition of the end-to-end system, based on the fact that the blank symbol used in CTC technique dominates the output and these methods give frequent features small learning rates. Experiment results show that more than 4% relative reduction of word error rate (WER) as well as 5% absolute improvement of label accuracy on the training set are achieved when using ADADELTA, and fewer epochs of training are needed.

Short texts usually encounter the problem of data sparseness, as they do not provide sufficient term co-occurrence information. In this paper, we show how to mitigate the problem in short text classification through word embeddings. We assume that a short text document is a specific sample of one distribution in a Gaussian-Bayesian framework. Furthermore, a fast clustering algorithm is utilized to expand and enrich the context of short text in embedding space. This approach is compared with those based on the classical bag-of-words approaches and neural network based methods. Experimental results validate the effectiveness of the proposed method.

Deep Neural Network (DNN) is a powerful machine learning model that has been successfully applied to a wide range of pattern classification tasks. Due to the great ability of the DNNs in learning complex mapping functions, it has been possible to train and deploy DNNs pretty much as a black box without the need to have an in-depth understanding of the inner workings of the model. However, this often leads to solutions and systems that achieve great performance, but offer very little in terms of how and why they work. This paper introduces Sensitivity-characterised Activity Neorogram (SCAN), a novel approach for understanding the inner workings of a DNN by analysing and visualising the sensitivity patterns of the neuron activities. SCAN constructs a low-dimensional visualisation space for the neurons so that the neuron activities can be visualised in a meaningful and interpretable way. The embedding of the neurons within this visualisation space can be used to compare the neurons, both within the same DNN and across different DNNs trained for the same task. This paper will present the observations from using SCAN to analyse DNN acoustic models for automatic speech recognition.

This paper aims to investigate the performance improvements made possible by combining various major language model (LM) technologies together and to reveal the interactions between LM technologies in spontaneous automatic speech recognition tasks. While it is clear that recent practical LMs have several problems, isolated use of major LM technologies does not appear to offer sufficient performance. In consideration of this fact, combining various LM technologies has been also examined. However, previous works only focused on modeling technologies with limited text resources, and did not consider other important technologies in practical language modeling, i.e., use of external text resources and unsupervised adaptation. This paper, therefore, employs not only manual transcriptions of target speech recognition tasks but also external text resources. In addition, unsupervised LM adaptation based on multi-pass decoding is also added to the combination. We divide LM technologies into three categories and employ key ones including recurrent neural network LMs or discriminative LMs. Our experiments show the effectiveness of combining various LM technologies in not only in-domain tasks, the subject of our previous work, but also out-of-domain tasks. Furthermore, we also reveal the relationships between the technologies in both tasks.