In this letter, a novel mainlobe anti-jamming method via eigen-projection processing and covariance matrix reconstruction is proposed. The present work mainly focuses on two aspects: the first aspect is to obtain the eigenvector of the mainlobe interference accurately in order to form the eigen-projection matrix to suppress the mainlobe interference. The second aspect is to reconstruct the covariance matrix which is uesd to calculate the adaptive weight vector for forming an ideal beam pattern. Additionally, the self-null effect caused by the signal of interest and the sidelobe interferences elimination are also considered in the proposed method. Theoretical analysis and simulation results demonstrate that the proposed method can suppress the mainlobe interference effectively and achieve a superior performance.

This letter presents a Preferable Mad-y (PMad-y) turn model and Low-cost Adaptive and Fault-tolerant Routing (LAFR) method that use one and two virtual channels along the X and Y dimensions for 2D mesh Network-on-Chip (NoC). Applying PMad-y rules and using the link status of neighbor routers within 2-hops, LAFR can tolerate multiple faulty links and routers in more complicated faulty situations and impose the reliability of network without losing the performance of network. Simulation results show that LAFR achieves better saturation throughput (0.98% on average) than those of other fault-tolerant routing methods and maintains high reliability of more than 99.56% on average. For achieving 100% reliability of network, a Preferable LAFR (PLAFR) is proposed.

The problem of radar constant-modulus (CM) waveform design for the detection of multiple targets is considered in this paper. The CM constraint is imposed from the perspective of hardware realization and full utilization of the transmitter's power. Two types of CM waveforms — the arbitrary-phase waveform and the quadrature phase shift keying waveform — are obtained by maximizing the minimum of the signal-to-clutter-plus-noise ratios of the various targets. Numerical results show that the designed CM waveforms perform satisfactorily, even when compared with their counterparts without constraints on the peak-to-average ratio.

In this paper, we propose an efficient clutter suppression algorithm for automotive radar systems in iron-tunnel environments. In general, the clutters in iron tunnels makes it highly likely that automotive radar systems will fail to detect targets. In order to overcome this drawback, we first analyze the cepstral characteristic of the iron tunnel clutter to determine the periodic properties of the clutters in the frequency domain. Based on this observation, we suggest for removing the periodic components induced by the clutters in iron tunnels in the cepstral domain by using the cepstrum editing process. To verify the clutter suppression of the proposed method experimentally, we performed measurements by using 77GHz frequency modulated continuous waveform radar sensors for an adaptive cruise control (ACC) system. Experimental results show that the proposed method is effective to suppress the clutters in iron-tunnel environments in the sense that it improves the early target detection performance for ACC significantly.

This paper presents a method to accelerate target recognition processing in advanced driver assistance systems (ADAS). A histogram of oriented gradients (HOG) is an effective descriptor for object recognition in computer vision and image processing. The HOG is expected to replace conventional descriptors, e.g., template-matching, in ADAS. However, the HOG does not consider the occurrences of gradient orientation on objects when localized portions of an image, i.e., a region of interest (ROI), are not set precisely. The size and position of the ROI should be set precisely for each frame in an automotive environment where the target distance changes dynamically. We use radar to determine the size and position of the ROI in a HOG and propose a radar and camera sensor fusion algorithm. Experimental results are discussed.

Periodic interference frequently affects the measurement of small signals and causes problems in clinical diagnostics. Adaptive filters can be used as potential tools for cancelling such interference. However, when the interference has a frequency fluctuation, the ideal adaptive-filter coefficients for cancelling the interference also fluctuate. When the adaptation property of the algorithm is slow compared with the frequency fluctuation, the interference-cancelling performance is degraded. However, if the adaptation is too quick, the performance is degraded owing to the target signal. To overcome this problem, we propose an adaptive filter that suppresses the fluctuation of the ideal coefficients by utilizing a $rac{pi}{2}$ phase-delay device. This method assumes a frequency response that characterizes the transmission path from the interference source to the main input signal to be sufficiently smooth. In the numerical examples, the proposed method exhibits good performance in the presence of a frequency fluctuation when the forgetting factor is large. Moreover, we show that the proposed method reduces the calculation cost.

Driving safety related innovations received increasing interest from automotive industry. We performed an experiment to observe what situations are related to the secured feelings drivers feel when they drive, and found out that drivers need to have four to seven seconds to react possible collision when they operate onboard Human Machine Interface (HMI) devices and check display devices. We explored the distance of semantic space to see what factors of HMI interaction lead to the secured feeling in that time period, and extracted 32 types of factors that lead to the secured feelings. Furthermore, in the process of investigating the semantic space distance, the indicators relating to the secured feelings obtained in the prior studies were further determined to be ‘The layout of the operation device is the same as the driver's image' and ‘The driver can use the word he uses every day to give instructions’ in this time period.’, which were more concrete factors of the secured feelings.

Establishing drivers' trust in the automated driving system is critical to the success of automated vehicles. The focus of this paper is learning what drivers of automated vehicles need to feel confident during braking events. In this study, 10 participants drove a test vehicle and each experienced 24 different deceleration settings. Prior to each drive, it was indicated to each participant what the expected brake starting and stopping positions would be. During each drive, participants maintained a set speed, and then stopped the vehicle when they saw a signal to apply the brakes. After each drive, the participants were asked what their perceived safety level was during the deceleration setting they just experienced. The results revealed that ‘jerk’ movements have significant influence on drivers' perceived safety. For this study, we have named this jerk movement impression jerk (IJ). Using IJ, clearly divides the secure and anxious feelings of the drivers along with individual differences.

HTTP Adaptive Streaming (HAS) has become a popular solution for multimedia delivery nowadays. Because of throughput variations, video quality fluctuates during a streaming session. Therefore, a main challenge in HAS is how to evaluate the overall video quality of a session. In this paper, we explore the impacts of quality values and quality variations in HAS. We propose to use the histogram of segment quality values and the histogram of quality gradients in a session to model the overall video quality. Subjective test results show that the proposed model has very high prediction performance for different videos. Especially, the proposed model provides insights into the influence factors of the overall quality, thus leading to suggestions to improve the quality of streaming video.

Powerful jammers are able to disable consumer-grade global navigation satellite system (GNSS) receivers under normal operating conditions. Conventional anti-jamming techniques based on the time-domain are unable to effectively suppress wide-band interference, such as chirp-like jammer. This paper proposes a novel anti-jamming architecture, combining wavelet packet signal analysis with adaptive filtering theory to mitigate chirp interference. Exploiting the excellent time-frequency resolution of wavelet technologies makes it possible to generate a reference chirp signal, which is basically a “de-noised” jamming signal. The reference jamming signal then is fed into an adaptive predictor to function as a refined jamming signal such that it predicts a replica of the jammer from the received signal. The refined chirp signal is then subtracted from the received signal to realize the aim of anti-jamming. Simulation results demonstrate the effectiveness of the proposed method in combating chirp interference in Galileo receivers. We achieved jamming-to-signal power ratio (JSR) of 50dB with an acquisition probability exceeding 90%, which is superior to many anti-jamming techniques based on the time-domain, such as conventional adaptive notch filters. The proposed method was also implemented in an software-defined GPS receiver for further validation.

The study focuses on the adaptation problem for HTTP low-delay live streaming over mobile networks. In this context, the client's small buffer could be easily underflown due to throughput variations. To maintain seamless streaming, we present a probabilistic approach to adaptively decide the bitrate for each video segment by taking into account the instant buffer level. The experimental results show that the proposed method can significantly reduce buffer underflows while providing high video bitrates.

This paper proposes a dynamic spectrum controlled (DSTC) channel allocation algorithm to increase the total throughput of satellite communication (SATCOM) systems. To effectively use satellite resources such as the satellite's maximum transponder bandwidth and maximum transmission power and to handle the propagation gain variation at all earth stations, the DSTC algorithm uses two new transmission techniques: spectrum compression and spectrum division. The algorithm controls various transmission parameters, such as the spectrum compression ratio, number of spectrum divisions, combination of modulation method and FEC coding rate (MODCOD), transmission power, and spectrum bandwidth to ensure a constant transmission bit rate under variable propagation conditions. Simulation results show that the DSTC algorithm achieves up to 1.6 times higher throughput than a simple MODCOD-based algorithm.

The overdrive technique is widely used to eliminate motion blur in liquid-crystal displays (LCDs). However, this technique requires a large frame memory to store the previous frame. A reduction in the frame memory requires an image compression algorithm suitable for real-time data processing. In this paper, we present an algorithm based on multimode-color-conversion block truncation coding (MCC-BTC) to obtain a constant output bit rate and high overdrive performance. The MCC-BTC algorithm uses four compression methods, one of which is selected. The four compression modes either use the single-bitmap-generation method or the subsampling method for chrominance. As shown in the simulation results, the proposed algorithm improves the performance of both coding (up to 2.73dB) and overdrive (up to 2.61dB), and the visual quality is improved in comparison to other competing algorithms in literature.

It is crucial to provide Internet videos with the best possible content value (or quality) to users. To adapt to network fluctuations, existing solutions provide various client-based heuristics to change video versions without considering the actual quality. In this work, we present for the first time the use of a quality model in making adaptation decisions to improve the overall quality. The proposed method also estimates the buffer level in the near future to prevent the client from buffer underflows. Experiment results show that the proposed method is able to provide high and consistent video quality under strongly fluctuating bandwidths.

One of the major subjects for marine resources development and information processing is how to realize underwater short-range and large-capacity data transmissions. The acoustic wave is an effective carrier and has been used for underwater data transmissions because it has lower attenuation in seawater than the radio wave, and has average propagation distance of about 10km or more. However, along with the imaging of transmission data, the inherent low speed of the acoustic wave makes it cannot and become an ideal carrier for high-speed and large-capacity communications. On the other hand, visible-light wave with wavelength of 400nm-650nm is an ideal carrier, which has received much attention. Its attractive features are high transparency and low attenuation rate in underwater, easily control the propagation direction and range by the visibility, and high data rate and capacity, making it excellent for application in underwater wireless communications. However, visible-light waves in the seawater have the spectral attenuation characteristics due to different marine environment. Therefore, in this paper an underwater optical wireless communication method with adaptation seawater function is considered for seawater turbidity of the spatio-temporal change. Two crucial components in the underwater optical wireless communication system, the light wavelength and the modulation method are controlled using wavelength- and modulation-adaptation techniques, respectively. The effectiveness of the method of the adaptation wavelength is demonstrated in underwater optical image transmissions.

Case studies have reported that pedestrian detection methods using vehicle radar are not complete systems because each system has specific limitations at the cost of the calculating amounts, the system complexity or the range resolution. In this letter, we proposed a novel pedestrian detection method by template matching using Gabor filter bank, which was evaluated based on the data observed by 24GHz UWB radar.

We study a use of Gaussian kernels with a wide range of scales for nonlinear function estimation. The estimation task can then be split into two sub-tasks: (i) model selection and (ii) learning (parameter estimation) under the selected model. We propose a fully-adaptive and all-in-one scheme that jointly carries out the two sub-tasks based on the multikernel adaptive filtering framework. The task is cast as an asymptotic minimization problem of an instantaneous fidelity function penalized by two types of block l1-norm regularizers. Those regularizers enhance the sparsity of the solution in two different block structures, leading to efficient model selection and dictionary refinement. The adaptive generalized forward-backward splitting method is derived to deal with the asymptotic minimization problem. Numerical examples show that the scheme achieves the model selection and learning simultaneously, and demonstrate its striking advantages over the multiple kernel learning (MKL) method called SimpleMKL.

The diffraction by a thin material strip is analyzed for the H-polarized plane wave incidence using the Wiener-Hopf technique together with approximate boundary conditions. An asymptotic solution is obtained for the case where the thickness and the width of the strip are small and large compared with the wavelength, respectively. The scattered field is evaluated asymptotically based on the saddle point method and a far field expression is derived. Scattering characteristics are discussed in detail via numerical results of the radar cross section.

We propose a novel improved characteristic basis function method (IP-CBFM) for accurately analysing the radar cross section (RCS). This new IP-CBFM incorporates the effect of higher-order multiple scattering and has major influences in analyzing monostatic RCS (MRCS) of single incidence and bistatic RCS (BRCS) problems. We calculated the RCS of two scatterers and could confirm that the proposed IP-CBFM provided higher accuracy than the conventional method while significantly reducing the number of CBF.

This paper presents a prototype of a 3D imaging step-frequency radar system at 10-20GHz suitable for the nondestructive inspection of the walls of wooden houses. Using this prototype, it is possible to obtain data for 3D imaging with a single simple scan and make 3D volume images of braces — broken or not — in the walls of wooden houses using synthetic aperture radar processing. The system is a multistatic radar composed of a one-dimensional array antenna (32 transmitting and 32 receiving antennas, which are resistively loaded printed bowtie antennas) and is able to acquire frequency domain data for all the transmitting and receiving antenna pairs, i.e., 32×32=1024 pairs, in 33ms per position. On the basis of comparisons between two array antenna prototype designs, we investigated the optimal distance between a transmitting array and a receiving array to reduce the direct coupling effect. We produced a prototype multistatic radar system and used it to measure different types of wooden targets in two experiments. In the first experiment, we measured plywood bars behind a decorated gypsum board, simulating a broken wooden brace inside a house wall. In the second experiment, we measured a wooden brace made of Japanese cypress as a target inside a model of a typical (wooden) Japanese house wall. The results of both experiments demonstrate the imaging capability of the radar prototype for nondestructive inspection of the insides of wooden house walls.