Deep Neural Network (DNN) is widely used for computer vision tasks, such as image classification, object detection, and segmentation. DNN accelerator on FPGA and especially Convolutional Neural Network (CNN) is a hot topic. More research and education should be conducted to boost this field. A starting point is required to make it easy for new entrants to join this field. We believe that FPGA-based Autonomous Driving (AD) motor cars are suitable for this because DNN accelerators can be used for image processing with low latency. In this paper, we propose an FPGA-based simple and open-source mini motor car system named RVCar with a RISC-V soft processor and a CNN accelerator. RVCar is suitable for the new entrants who want to learn the implementation of a CNN accelerator and the surrounding system. The motor car consists of Xilinx Nexys A7 board and simple parts. All modules except the CNN accelerator are implemented in Verilog HDL and SystemVerilog. The CNN accelerator is converted from a PyTorch model by our tool. The accelerator is written in C++, synthesizable by Vitis HLS, and an easy-to-customize baseline for the new entrants. FreeRTOS is used to implement AD algorithms and executed on the RISC-V soft processor. It helps the users to develop the AD algorithms efficiently. We conduct a case study of the simple AD task we define. Although the task is simple, it is difficult to achieve without image recognition. We confirm that RVCar can recognize objects and make correct decisions based on the results.

In the upcoming Internet of Things era, reducing energy consumption of embedded processors is highly desired. Minimum Energy Point Tracking (MEPT) is one of the most efficient methods to reduce both dynamic and static energy consumption of a processor. Previous works proposed a variety of MEPT methods over the past years. However, none of them incorporate their algorithms with practical real-time operating systems, although edge computing applications often require low energy task execution with guaranteeing real-time properties. The difficulty comes from the time complexity for identifying an MEP and changing voltages, which often prevents real-time task scheduling. The conventional Dynamic Voltage and Frequency Scaling (DVFS) only scales the supply voltage. On the other hand, MEPT needs to adjust the body bias voltage in addition. This additional tuning knob makes MEPT much more complicated. This paper proposes an approximate MEPT algorithm, which reduces the complexity of identifying an MEP down to that of DVFS. The key idea is to linearly approximate the relationship between the processor frequency, supply voltage, and body bias voltage. Thanks to the approximation, optimal voltages for a specified clock frequency can be derived immediately. We also propose a task scheduling algorithm, which adjusts processor performance to the workload and then provides a soft real-time capability to the system. The operating system stochastically adjusts the average response time of the processor to be equal to a specified deadline. MEPT will be performed as a general task, and its overhead is considered in the calculation of the frequency. The experiments using a fabricated test chip and on-chip sensors show that the proposed algorithm is a maximum of 16 times more energy-efficient than DVFS. Also, the energy loss induced by the approximation is only 3% at most, and the algorithm does not sacrifice the fundamental real-time properties.

We propose deep neural network (DNN)-based speech enhancement that reduces musical noise and achieves better auditory impressions. The musical noise is an artifact generated by nonlinear signal processing and negatively affects the auditory impressions. We aim to develop musical-noise-free speech enhancement methods that suppress the musical noise generation and produce perceptually-comfortable enhanced speech. DNN-based speech enhancement using a soft mask achieves high noise reduction but generates musical noise in non-speech regions. Therefore, first, we define kurtosis matching for DNN-based low-musical-noise speech enhancement. Kurtosis is the fourth-order moment and is known to correlate with the amount of musical noise. The kurtosis matching is a penalty term of the DNN training and works to reduce the amount of musical noise. We further extend this scheme to standardized-moment matching. The extended scheme involves using moments whose orders are higher than kurtosis and generalizes the conventional musical-noise-free method based on kurtosis matching. We formulate standardized-moment matching and explore how effectively the higher-order moments reduce the amount of musical noise. Experimental evaluation results 1) demonstrate that kurtosis matching can reduce musical noise without negatively affecting noise suppression and 2) newly reveal that the sixth-moment matching also achieves low-musical-noise speech enhancement as well as kurtosis matching.

In a speech enhancement system for impact noise, it is important for any impact noise activity to be detected. However, because impact noise occurs suddenly, it is not always easy to detect. We propose a method for impact noise activity detection based on the kurtosis of an instantaneous power spectrum. The continuous duration of a generalized impact noise is shorter than that of speech, and the power of such impact noise varies dramatically. Consequently, the distribution of the instantaneous power spectrum of impact noise is different from that of speech. The proposed detection takes advantage of kurtosis, which depends on the sharpness and skirt of the distribution. Simulation results show that the proposed noise activity detection improves the performance of the speech enhancement system.

This letter investigates the circularity of fractional Fourier transform (FRFT) coefficients containing noise only, and proves that all coefficients coming from white Gaussian noise are circular via the discrete FRFT. In order to use the spectrum kurtosis (SK) as a Gaussian test to check if linear frequency modulation (LFM) signals are present in a set of FRFT points, the effect of the noncircularity of Gaussian variables upon the SK of FRFT coefficients is studied. The SK of the α th-order FRFT coefficients for LFM signals embedded in a white Gaussian noise is also derived in this letter. Finally the signal detection algorithm based on FRFT and SK is proposed. The effectiveness and robustness of this algorithm are evaluated via simulations under lower SNR and weaker components.

In this letter, a new approach to segment depth-of-field (DoF) images is proposed. The methodology is based on a two-stage model of visual neuron. The first stage is a retinal filtering by means of luminance normalizing non-linearity. The second stage is a V1-like filtering using filters estimated by independent component analysis (ICA). Segmented image is generated by the response activity of the neuron measured in terms of kurtosis. Results demonstrate that the model can discriminate image parts in different levels of depth-of-field. Comparison with other methodologies and limitations of the proposed methodology are also presented.

In this paper, we introduce a generalized minimum mean-square error short-time spectral amplitude estimator with a new prior estimation of the speech probability density function based on moment-cumulant transformation. From the objective and subjective evaluation experiments, we show the improved noise reduction performance of the proposed method.

TCP's performance significantly degrades in multi-hop wireless networks because TCP's retransmission timeouts (RTOs) are frequently triggered regardless of congestion due to sudden delay and wireless transmission errors. Such RTOs non-related to congestions lead to TCP's unnecessary behaviors such as retransmitting all the outstanding packets which might be located in the bottleneck queue or reducing sharply its sending rate and increasing exponentially its back-off value even when the network is not congested. Since traditional TCP has no ability to identify if a RTO is triggered by congestion or not, it is unavoidable for TCP to underutilize available bandwidth by blindly reducing its sending rate for all the RTOs. In this paper, we propose an algorithm to detect the RTOs non-related to congestion in order to let TCP respond to the RTOs differently according to the cause. When a RTO is triggered, our algorithm estimates the queue usage in the network path during the go-back-N retransmissions, and decides if the RTO is triggered by congestion or not when the retransmissions end. If any RTO non-related to congestion is detected, our algorithm prevents TCP from increasing unnecessarily its back-off value as well as reducing needlessly its sending rate. Throughout the extensive simulation scenarios, we observed how frequently RTOs are triggered regardless of congestion, and evaluated our algorithm in terms of accuracy and goodput. The experiment results show that our algorithm has the highest accuracy among the previous works and the performance enhancement reaches up to 70% when our algorithm is applied to TCP.

In this paper, we propose a robust room inverse filtering algorithm for speech dereverberation based on a kurtosis maximization. The proposed algorithm utilizes a new normalized kurtosis function that nonlinearly maps the input kurtosis onto a finite range from zero to one, which results in a kurtosis warping. Due to the kurtosis warping, the proposed algorithm provides more stable convergence and, in turn, better performance than the conventional algorithm. Experimental results are presented to confirm the robustness of the proposed algorithm.

The interference imposed on conventional narrow-band systems by impulse radio UWB (IR-UWB) signals is examined by simulations. The Dirac delta function is employed to model the IR-UWB signal to reduce simulation costs. The simulation results show that the statistical characteristics of this interference deviate from Gaussian noise when the frequency band of the narrow-band system includes a half multiple of the data symbol rate of the IR-UWB system. In the case of pulse-position-modulation UWB signals and biorthogonal-coded bipolar-modulation UWB signals, the performance degradation of the narrow-band system depends on the number of pulse positions and the number of orthogonal codes, respectively.

This paper presents an RTOS-based methodology for design and validation of embedded systems. The heart of our methodology is the use of an RTOS simulation model from the very early stage of the system design. A case study with a JPEG decoder application is also presented in order to demonstrate the effectiveness of our methodology.

With the growing design complexity of contemporary embedded systems, real-time operating systems (RTOSs) have become one of important components of such complex embedded systems. This paper presents an RTOS-centric hardware/software cosimulator which we have developed for embedded system design. One of the most remarkable features in our cosimulator is that it has a complete simulation model of an RTOS which is widely used in industry, so that application tasks including RTOS service calls are natively executed on a host computer. Our cosimulator also features cosimulation with functional simulation models of hardware written in C/C++ and cosimulation with HDL simulators. A case study with a JPEG decoder application demonstrates the effectiveness of our cosimulator.

A new approach to build up a real-time multiprocessing system that is configuration flexible for evaluating space-time (ST) equalizers is described. The core of the system consists of fully programmable devices such as digital signal processors (DSPs), field-programmable gate arrays (FPGAs), and reduced instruction set computers (RISCs) with a real-time operating system (RTOS). The RTOS facilitates flexibility in the multi-processor configuration for the system conforming with ST processing algorithms. Timing jitter synchronization caused by use of the RTOS-embedded system is shown, and an adjustable frame format for a transmission system is described as a measure to avoid the jitter problem. Bit error rate (BER) performances measured in uncorrelated frequency-selective fading channels show that an ST equalizer provides a significantly lower BER than an array processor does.