The blind multichannel identification is useful in many applications. Although many approaches have been proposed to address this challenging problem, the adaptive filtering-based methods are attractive due to their computational efficiency and good convergence property. The multichannel normalized least mean-square (MCNLMS) algorithm is easy to implement, but it converges very slowly for a correlated input. The multichannel affine projection algorithm (MCAPA) is thus proposed to speed up the convergence. However, the convergence of the MCNLMS and MCAPA is still unsatisfactory in practice. In this paper, we propose a time-domain Kalman filtering approach to the blind multichannel identification problem. Specifically, the proposed adaptive Kalman filter is based on the cross relation method and also uses more past input vectors to explore the decorrelation property. Simulation results indicate that the proposed method outperforms the MCNLMS and MCAPA significantly in terms of the initial convergence and tracking capability.

This paper presents an interference suppression and signal restoration technique that can create the clean signals required by automotive frequency-modulated continuous wave radar systems. When a radar signal from another radar system interferes with own transmitted radar signal, the target detection performance is degraded. This is because the beat frequency corresponding to the target cannot be estimated owing to the increase in the noise floor. In this case, advanced weighted-envelope normalization or wavelet denoising can be used to mitigate the effect of the interference; however, these methods can also lead to the loss of the desired signal containing the range and velocity information of the target. Therefore, we propose a method based on an autoregressive model to restore a signal damaged by mutual interference. The method uses signals that are not influenced by the interference to restore the signal. In experiments conducted using two different automotive radar systems, our proposed method is demonstrated to effectively suppress the interference and restore the desired signal. As a result, the noise floor resulting from the mutual interference was lowered and the beat frequency corresponding to the desired target was accurately estimated.

A multi-carrier and blind shift-frequency jamming(MCBSFJ) against the pulsed compression radar with order-statistic (OS) constant false alarm rate (CFAR) detector is proposed. Firstly, according to the detection principle of the OS-CFAR detector, the design requirements for jamming signals are proposed. Then, some key parameters of the jamming are derived based on the characteristics of the OS-CFAR detector. As a result, multiple false targets around the real target with the quantity, amplitude and space distribution which can be controlled are produced. The simulation results show that the jamming method can reduce the detection probability of the target effectively.

This paper reports a 24GHz ISM band radar module for pedestrian detection in crosswalks. The radar module is composed of an RF transceiver board, a baseband board, and a microcontroller unit board. The radar signal is a sawtooth frequency-modulated continuous-wave signal with a center frequency of 24.15GHz, a bandwidth of 200MHz, a chirp length of 80µs, and a pulse repetition interval of 320µs. The radar module can detect a pedestrian on a crosswalk with a width of 4m and a length of 14m. The radar outputs the range, angle, and speed of the detected pedestrians every 50ms by radar signal processing and consumes 7.57W from 12V power supply. The size of the radar module is 110×70mm2.

This article presents a universal and versatile model of multiservice overflow systems based on Hayward's concept. The model can be used to analyze modern telecommunications and computer networks, mobile networks in particular. The advantage of the proposed approach lies in its ability to analyze overflow systems with elastic and adaptive traffic, systems with distributed resources and systems with non-full-availability in primary and secondary resources.

In this letter, a flexible and compatible with fine resolution radar frequency measurement receiver is designed. The receiver is implemented on the platform of Virtex-5 Field Programmable Grid Array (FPGA) from Xilinx. The Digital Down Conversion (DDC) without mixer based on polyphase filter has been successfully introduced in this receiver to obtain lower speed data flow and better resolution. This receiver can adapt to more modulation types and higher density of pulse flow, up to 200000 pulses per second. The measurement results indicate that the receiver is capable of detecting radar pulse signal of 0.2us to 2.5ms width with a major frequency root mean square error (RMSE) within 0.44MHz. Moreover, the wider pulse width and the higher decimation rate of DDC result in better performance. This frequency measurement receiver has been successfully used in a spaceborne radar system.

This paper presents a method combining array signal processing and adaptive noise cancellation to suppress unwanted ambient interferences in in situ measurement of radiated emissions of equipment. First, the signals received by the antenna array are processed to form a main data channel and an auxiliary data channel. The main channel contains the radiated emissions of the equipment under test and the attenuated ambient interferences. The auxiliary channel only contains the attenuated ambient interferences. Then, the adaptive noise cancellation technique is used to suppress the ambient interferences based on the correlation of the interferences in the main and auxiliary channels. The proposed method overcomes the problem that the ambient interferences in the two channels of the virtual chamber method are not correlated, and realizes the suppression of multi-source ambient noises in the use of fewer array elements. The results of simulation and experiment show that the proposed method can effectively extract radiated emissions of the equipment under test in complex electromagnetic environment. Finally, discussions on the effect of the beam width of the main channel and the generalization of the proposed method to three dimensionally distributed signals are addressed.

In this paper, we propose a quality-level selection method for adaptive video streaming with scalable video coding (SVC). The proposed method works on the client with the dynamic adaptive streaming over HTTP (DASH) with SVC. The proposed method consists of two components: introducing segment group and a buffer-aware layer selection algorithm. In general, quality of experience (QoE) performance degrades due to stalling (playback buffer underflow), low playback quality, frequent quality-level switching, and extreme-down quality switching. The proposed algorithm focuses on reducing the frequent quality-level switching, and extreme-down quality switching without increasing stalling and degrading playback quality. In the proposed method, a SVC-DASH client selects a layer every G segments, called a segment group to prevent frequent quality-level switching. In addition, the proposed method selects the quality of a layer based on a playback buffer in a layer selection algorithm for preventing extreme-down switching. We implement the proposed method on a real SVC-DASH system and evaluate its performance by subjective evaluations of multiple users. As a result, we confirm that the proposed algorithm can obtain better mean opinion score (MOS) value than a conventional SVC-DASH, and confirm that the proposed algorithm is effective to improve QoE performance in SVC-DASH.

A novel Nyquist Folding Receiver (NYFR) based passive localization algorithm with Sparse Bayesian Learning (SBL) is proposed to estimate the position of a spaceborne Synthetic Aperture Radar (SAR).Taking the geometry and kinematics of a satellite into consideration, this paper presents a surveillance geometry model, which formulates the localization problem into a sparse vector recovery problem. A NYFR technology is utilized to intercept the SAR signal. Then, a convergence algorithm with SBL is introduced to recover the sparse vector. Furthermore, simulation results demonstrate the availability and performance of our algorithm.

Language models are a key technology in various tasks, such as, speech recognition and machine translation. They are usually used on texts covering various domains and as a result domain adaptation has been a long ongoing challenge in language model research. With the rising popularity of neural network based language models, many methods have been proposed in recent years. These methods can be separated into two categories: model based and feature based adaptation methods. Feature based domain adaptation has compared to model based domain adaptation the advantage that it does not require domain labels in the corpus. Most existing feature based adaptation methods are based on bias adaptation. We propose a novel feature based domain adaptation technique using hidden layer factorisation. This method is fundamentally different from existing methods because we use the domain features to calculate a linear combination of linear layers. These linear layers can capture domain specific information and information common to different domains. In the experiments, we compare our proposed method with existing adaptation methods. The compared adaptation techniques are based on two different ideas, that is, bias based adaptation and gating of hidden units. All language models in our comparison use state-of-the-art long short-term memory based recurrent neural networks. We demonstrate the effectiveness of the proposed method with perplexity results for the well-known Penn Treebank and speech recognition results for a corpus of TED talks.

In this paper, we analyze the impact of channel estimation errors in an amplify-and-forward (AF)-based two-way relaying network (TWRN) where adaptive modulation (AM) is employed in individual relaying path. In particular, the performance degradation caused by channel estimation error is investigated over Nakagami-m fading channels. We first derive an end-to-end signal-to-noise ratio (SNR), a cumulative distribution function, and a probability density function in the presence of channel estimation error for the AF-based TWRN with adaptive modulation (TWRN-AM). By utilizing the derived SNR statistics, we present accurate expressions of the average spectral efficiency and bit error rates with an outage-constraint in which transmission does not take place during outage events of bidirectional communications. Based on our derived analytical results, an optimal power allocation scheme for TWRN-AM is proposed to improve the average spectral efficiency by minimizing system outages.

Radar-absorbent materials (RAM) with various characteristics, such as broadband, oblique-incidence, and polarization characteristics, have been developed according to applications in recent years. This paper presents the optimized design method of two flat layers RAM with both broadband and oblique-incidence characteristics for the required RAM performance. The oblique-incidence characteristics mean that the RAM is possible to absorb radio waves continuously up to the maximum incidence angle. The index of the wave-absorption amount is 20dB, corresponding to an absorption rate of 99%. Because determination of the electrical material constant of each layer is the most important task with respect to the received frequency and the incidence angle, we optimized the values by using Non-dominated sorting genetic algorithm-II (NSGA-II). Two types of flat-layer RAM composed of dielectric and magnetic materials were designed and their characteristics were evaluated. Consequently, it was confirmed that oblique-incidence characteristics were better for the RAM composed of dielectric materials. The dielectric RAM achieved an incidence angle of up to 60° with broadband characteristics and a relative bandwidth of 77.01% at the transverse-magnetic (TM) wave incidence. In addition, the magnetic RAM could lower the minimum frequency of the system more than the dielectric RAM. The minimum frequency of the magnetic RAM was 1.38GHz with a relative bandwidth of 174.18% at TM-wave incidence and an incidence angle of 45°. We confirmed that it is possible to design RAM with broadband characteristics and continuous oblique-incidence characteristics by using the proposed method.

Deep neural networks (DNNs) have achieved significant success in the field of automatic speech recognition. One main advantage of DNNs is automatic feature extraction without human intervention. However, adaptation under limited available data remains a major challenge for DNN-based systems because of their enormous free parameters. In this paper, we propose a filterbank-incorporated DNN that incorporates a filterbank layer that presents the filter shape/center frequency and a DNN-based acoustic model. The filterbank layer and the following networks of the proposed model are trained jointly by exploiting the advantages of the hierarchical feature extraction, while most systems use pre-defined mel-scale filterbank features as input acoustic features to DNNs. Filters in the filterbank layer are parameterized to represent speaker characteristics while minimizing a number of parameters. The optimization of one type of parameters corresponds to the Vocal Tract Length Normalization (VTLN), and another type corresponds to feature-space Maximum Linear Likelihood Regression (fMLLR) and feature-space Discriminative Linear Regression (fDLR). Since the filterbank layer consists of just a few parameters, it is advantageous in adaptation under limited available data. In the experiment, filterbank-incorporated DNNs showed effectiveness in speaker/gender adaptations under limited adaptation data. Experimental results on CSJ task demonstrate that the adaptation of proposed model showed 5.8% word error reduction ratio with 10 utterances against the un-adapted model.

This paper proposes a new design criterion of adaptively scaled belief (ASB) in Gaussian belief propagation (GaBP) for large multi-user multi-input multi-output (MU-MIMO) detection. In practical MU detection (MUD) scenarios, the most vital issue for improving the convergence property of GaBP iterative detection is how to deal with belief outliers in each iteration. Such outliers are caused by modeling errors due to the fact that the law of large number does not work well when it is difficult to satisfy the large system limit. One of the simplest ways to mitigate the harmful impact of outliers is belief scaling. A typical approach for determining the scaling parameter for the belief is to create a look-up table (LUT) based on the received signal-to-noise ratio (SNR) through computer simulations. However, the instantaneous SNR differs among beliefs because the MIMO channels in the MUD problem are random; hence, the creation of LUT is infeasible. To stabilize the dynamics of the random MIMO channels, we propose a new transmission block based criterion that adapts belief scaling to the instantaneous channel state. Finally, we verify the validity of ASB in terms of the suppression of the bit error rate (BER) floor.

In order to provide an alternative solution of human machine interfaces, this paper proposed to recognize 10 human hand gestures regularly used in the consumer electronics controlling scenarios based on a three-dimensional radar array. This radar array was composed of three low cost 24GHz K-band Doppler CW (Continuous Wave) miniature I/Q (In-phase and Quadrature) transceiver sensors perpendicularly mounted to each other. Temporal and spectral analysis was performed to extract magnitude and phase features from six channels of I/Q signals. Two classifiers were proposed to implement the recognition. Firstly, a decision tree classifier performed a fast responsive recognition by using the supervised thresholds. To improve the recognition robustness, this paper further studied the recognition using a two layer CNN (Convolutional Neural Network) classifier with the frequency spectra as the inputs. Finally, the paper demonstrated the experiments and analysed the performances of the radar array respectively. Results showed that the proposed system could reach a high recognition accurate rate higher than 92%.

Multi-static Primary Surveillance Radar (MSPSR) has recently attracted attention as a new surveillance technology for civil aviation. Using multiple receivers, Primary Surveillance Radar (PSR) detection performance can be improved by synthesizing the reflection characteristics which change due to the aircraft's position. In this paper, we report experimental results from our proposed optical-fiber-connected passive PSR system with transmit signal installed at the Sendai Airport in Japan. The signal-to noise ratio of experimental data is evaluated to verify moving target detection. In addition, we confirm the operation of the proposed system using a two-receiver setup, to resemble a conventional multi-static radar. Finally, after applying time correction, the delay of the reflected signal from a stationary target remains within the expected range.

360-degree video is an important component of the emerging Virtual Reality. In this paper, we propose a new adaptation method for tiling-based viewport adaptive streaming of 360-degree video. The proposed method is able to dynamically select the best tiling scheme given the network conditions and user status. Experiments show that our proposed method can improve the viewport quality by up to 2.3 dB compared to a conventional fixed tiling method.

A 19.1-to-20.4 GHz sigma-delta fractional-N frequency synthesizer with two-point modulation (TPM) for frequency modulated continuous wave (FMCW) radar applications is presented. The FMCW synthesizer proposes a digital and voltage controlled oscillator (D/VCO) with large continuous frequency tuning range and small digital controlled oscillator (DCO) gain variation to support TPM. By using TPM technique, it avoids the correlation between loop bandwidth and chirp slope, which is beneficial to fast chirp, phase noise and linearity. The start frequency, bandwidth and slope of the FMCW signal are all reconfigurable independently. The FMCW synthesizer achieves a measured phase noise of -93.32 dBc/Hz at 1MHz offset from a 19.25 GHz carrier and less than 10 µs locking time. The root-mean-square (RMS) frequency error is only 112 kHz with 94 kHz/µs chirp slope, and 761 kHz with a fast slope of 9.725 MHz/µs respectively. Implemented in 65 nm CMOS process, the synthesizer consumes 74.3 mW with output buffer.

Financial Technology (FinTech) is considered a taxonomy that describes a wide range of ICT (information and communications technology) associated with financial transactions and related operations. Improvement of service quality is the main issue addressed in this taxonomy, and there are a large number of emerging technologies including blockchain-based cryptocurrencies and smart contracts. Due to its innovative nature in accounting, blockchain can also be used in lots of other FinTech contexts where token models play an important role for financial engineering. This paper revisits some of the key concepts accumulated behind this trend, and shows a generalized understanding of the technology using an adapted stochastic process. With a focus on financial instruments using blockchain, research directions toward stable applications are identified with the help of a newly proposed stabilizer: interpretation function of token valuation. The idea of adapted stochastic process is essential for the stabilizer, too.

In this paper, we propose an online antenna-pulse selection method in space time adaptive processing, while maintaining considerable performance and low computational complexity. The proposed method considers the antenna-pulse selection and covariance matrix estimation at the same time by exploiting the structured clutter covariance matrix. Such prior knowledge can enhance the covariance matrix estimation accuracy and thus can provide a better objective function for antenna-pulse selection. Simulations also validate the effectiveness of the proposed method.