Intelligent traffic monitoring provides information support for autonomous driving, which is widely used in intelligent transportation systems (ITSs). A method for estimating vehicle moving target parameters based on millimeter-wave radars is proposed to solve the problem of low detection accuracy due to velocity ambiguity and Doppler-angle coupling in the process of traffic monitoring. First of all, a MIMO antenna array with overlapping elements is constructed by introducing them into the typical design of MIMO radar array antennas. The motion-induced phase errors are eliminated by the phase difference among the overlapping elements. Then, the position errors among them are corrected through an iterative method, and the angle of multiple targets is estimated. Finally, velocity disambiguation is performed by adopting the error-corrected phase difference among the overlapping elements. An accurate estimation of vehicle moving target angle and velocity is achieved. Through Monte Carlo simulation experiments, the angle error is 0.1° and the velocity error is 0.1m/s. The simulation results show that the method can be used to effectively solve the problems related to velocity ambiguity and Doppler-angle coupling, meanwhile the accuracy of velocity and angle estimation can be improved. An improved algorithm is tested on the vehicle datasets that are gathered in the forward direction of ordinary public scenes of a city. The experimental results further verify the feasibility of the method, which meets the real-time and accuracy requirements of ITSs on vehicle information monitoring.

This paper proposes the new series highly integrated intelligent power module (IPM), which is developed to provide a ultra-compact, high performance and reliable motor drive system. Details of the key design technologies of the IPM is given and practical application issues such as electrical characteristics, system operation performance and power dissipation are discussed. Layout placement and routing have been optimized in order to reduce and balance the parasitic impedances. By implementing an innovative direct bonding copper (DBC) ceramic substrate, which can effectively dissipate heat, the IPM delivers a fully integrated power stages including two three-phase inverters, power factor correction (PFC) and rectifier in an ultra-compact 75.5mm × 30mm package, offering up to a 17.3 percent smaller space than traditional motor drive scheme.

We propose Quality Enhancement via a Side bitstream Network (QESN) technique for lossy image compression. The proposed QESN utilizes the network architecture of deep image compression to produce a bitstream for enhancing the quality of conventional compression. We also present a loss function that directly optimizes the Bjontegaard delta bit rate (BD-BR) by using a differentiable model of a rate-distortion curve. Experimental results show that QESN improves the rate by 16.7% in the BD-BR compared to Better Portable Graphics.

A super-resolution algorithm is proposed to solve the problem of measuring the thin thickness of oil slick using compressed sensing theory. First, a mathematical model of a single pulse underwater ultrasonic echo is established. Then, the estimation model of the transmit time of flight (TOF) of ultrasonic echo within oil slick is given based on the sparsity of echo signals. At last, the super-resolution TOF value can be obtained by solving the sparse convex optimization problem. Simulations and experiments are conducted to validate the performance of the proposed method.

High-accuracy wideband signal transmission is essential for 5G and Beyond wireless communication systems. Memory nonlinearity in transmitters is a serious issue for the goal, because it deteriorates the quality of signal and lowers the system performance. This paper studies a post-reception nonlinear compensation (PRC) schemes consisting of frequency domain equalizers (FDEs) and a blind nonlinear compensator (BNLC). A frequency-domain memory nonlinearity modeling approach is employed, and several PRC configurations with FDEs and BNLC are evaluated through computer simulations. It is concluded that the proposed PRC schemes can effectively compensate memory nonlinearity in wideband transmitters via frequency-selective propagation channel. By implementing the PRC in a base station, uplink performance will be enhanced without any additional cost and power consumption in user terminals.

This paper proposes a layer-wise tunable retraining method for edge FeFET Computation-in-Memory (CiM) to compensate the accuracy degradation of neural network (NN) by FeFET device errors. The proposed retraining can tune the number of layers to be retrained to reduce inference accuracy degradation by errors that occur after retraining. Weights of the original NN model, accurately trained in cloud data center, are written into edge FeFET CiM. The written weights are changed by FeFET device errors in the field. By partially retraining the written NN model, the proposed method combines the error-affected layers of NN model with the retrained layers. The inference accuracy is thus recovered. After retraining, the retrained layers are re-written to CiM and affected by device errors again. In the evaluation, at first, the recovery capability of NN model by partial retraining is analyzed. Then the inference accuracy after re-writing is evaluated. Recovery capability is evaluated with non-volatile memory (NVM) typical errors: normal distribution, uniform shift, and bit-inversion. For all types of errors, more than 50% of the degraded percentage of inference accuracy is recovered by retraining only the final fully-connected (FC) layer of Resnet-32. To simulate FeFET Local-Multiply and Global-accumulate (LM-GA) CiM, recovery capability is also evaluated with FeFET errors modeled based on FeFET measurements. Retraining only FC layer achieves recovery rate of up to 53%, 66%, and 72% for FeFET write variation, read-disturb, and data-retention, respectively. In addition, just adding two more retraining layers improves recovery rate by 20-30%. In order to tune the number of retraining layers, inference accuracy after re-writing is evaluated by simulating the errors that occur after retraining. When NVM typical errors are injected, it is optimal to retrain FC layer and 3-6 convolution layers of Resnet-32. The optimal number of layers can be increased or decreased depending on the balance between the size of errors before retraining and errors after retraining.

In the massive machine-type communication scenario, aiming at the problems of active user detection and channel estimation in the grant-free non-orthogonal multiple access (NOMA) system, new sets of non-orthogonal spreading sequences are proposed by using the zero/low correlation zone sequence set with low correlation among multiple sets. The simulation results show that the resulting sequence set has low coherence, which presents reliable performance for channel estimation and active user detection based on compressed sensing. Compared with the traditional Zadoff-Chu (ZC) sequences, the new non-orthogonal spreading sequences have more flexible lengths, and lower peak-to-average power ratio (PAPR) and smaller alphabet size. Consequently, these sequences will effectively solve the problem of high PAPR of time domain signals and are more suitable for low-cost devices in massive machine-type communication.

To achieve the extreme high data rate and extreme coverage extension requirements of 6G wireless communication, new spectrum in sub-THz (100-300GHz) and non-terrestrial network (NTN) are two of the macro trends of 6G candidate technologies, respectively. However, non-linearity of power amplifiers (PA) is a critical challenge for both sub-THz and NTN. Therefore, high power efficiency (PE) or low peak to average power ratio (PAPR) waveform design becomes one of the most significant 6G research topics. Meanwhile, high spectral efficiency (SE) and low out-of-band emission (OOBE) are still important key performance indicators (KPIs) for 6G waveform design. Single-carrier waveform discrete Fourier transform spreading orthogonal frequency division multiplexing (DFT-s-OFDM) has achieved many research interests due to its high PE, and it has been supported in 5G New Radio (NR) when uplink coverage is limited. So DFT-s-OFDM can be regarded as a candidate waveform for 6G. Many enhancement schemes based on DFT-s-OFDM have been proposed, including null cyclic prefix (NCP)/unique word (UW), frequency-domain spectral shaping (FDSS), and time-domain compression and expansion (TD-CE), etc. However, there is no unified framework to be compatible with all the enhancement schemes. This paper firstly provides a general description of the 6G candidate waveforms based on DFT-s-OFDM enhancement. Secondly, the more flexible TD-CE supporting methods for unified non-orthogonal waveform (uNOW) are proposed and discussed. Thirdly, a unified waveform framework based on DFT-s-OFDM structure is proposed. By designing the pre-processing and post-processing modules before and after DFT in the unified waveform framework, the three technical methods (NCP/UW, FDSS, and TD-CE) can be integrated to improve three KPIs of DFT-s-OFDM simultaneously with high flexibility. Then the implementation complexity of the 6G candidate waveforms are analyzed and compared. Performance of different DFT-s-OFDM enhancement schemes is investigated by link level simulation, which reveals that uNOW can achieve the best PAPR performance among all the 6G candidate waveforms. When considering PA back-off, uNOW can achieve 124% throughput gain compared to traditional DFT-s-OFDM.

Conventional enzymatic biofuel cells (EBFCs) use glucose solution or glucose from human body. It is desirable to get glucose from a substance containing glucose because the glucose concentration can be kept at the optimum level. This work developed a biofuel cell that generates electricity from cellulose, which is the main components of plants, by using decomposing enzyme of cellulase. Cellulose nanofiber (CNF) was chosen for the ease of decomposability. It was confirmed by the cyclic voltammetry method that cellulase was effective against CNF. The maximum output of the optimized proposed method was 38.7 μW/cm2, which was 85% of the output by using the glucose solution at the optimized concentration.

The supercomputer, “Fugaku”, which ranked number one in multiple supercomputing lists, including the Top500 in June 2020, has various power control features, such as (1) an eco mode that utilizes only one of two floating-point pipelines while decreasing the power supply to the chip; (2) a boost mode that increases clock frequency; and (3) a core retention feature that turns unused cores to the low-power state. By orchestrating these power-performance features while considering the characteristics of running applications, we can potentially gain even better system-level energy efficiency. In this paper, we report on the performance and power consumption of Fugaku using SPEC HPC benchmarks. Consequently, we confirmed that it is possible to reduce the energy by about 17% while improving the performance by about 2% from the normal mode by combining boost mode and eco mode.

The demand for low-latency transmission of large-capacity video, such as 4K and 8K, is increasing for various applications such as live-broadcast program production, sports viewing, and medical care. In the broadcast industry, low-latency video transmission is required in remote production, an emerging workflow for outside broadcasting. For ideal remote production, long-distance transmission of uncompressed 8K60p video signals, ultra-low latency less than 16.7 ms, and PTP synchronization through network are required; however, no existing video-transmission system fully satisfy these requirements. We focused on optical transport technologies capable of long-distance and large-capacity communication, which were previously used only in telecommunication-carrier networks. To fully utilize optical transport technologies, we propose the first-ever video-transmission system architecture capable of sending and receiving uncompressed 8K video directly through large-capacity optical paths. A transmission timing control in seamless protection switching is also proposed to improve the tolerance to network impairment. As a means of implementation, we focused on whitebox transponder, an emerging type of optical transponder with a disaggregation configuration. The disaggregation configuration enables flexible configuration changes, additional implementations, and cost reduction by separating various functions of optical transponders and controlling them with a standardized interface. We implemented the ultra-low-latency video-transmission system utilizing whitebox transponder Galileo. We developed a hardware plug-in unit for video transmission (VideoPIU), and software to control the VideoPIU. In the video-transmission experiments with 120-km optical fiber, we confirmed that it was capable of transmitting uncompressed 8K60p video stably in 1.3 ms latency and highly accurate PTP synchronization through the optical network, which was required in the ideal remote production. In addition, the application to immersive sports viewing is also presented. Consequently, excellent potential to support the unprecedented applications is demonstrated.

Peg solitaire is a single-player board game. The goal of the game is to remove all but one peg from the game board. Peg solitaire on graphs is a peg solitaire played on arbitrary graphs. A graph is called solvable if there exists some vertex s such that it is possible to remove all but one peg starting with s as the initial hole. In this paper, we prove that it is NP-complete to decide if a graph is solvable or not.

Neuromorphic computing with a spiking neural network (SNN) is expected to provide a complement or alternative to deep learning in the future. The challenge is to develop optimal SNN models, algorithms, and engineering technologies for real use cases. As a potential use cases for neuromorphic computing, we have investigated a person monitoring and worker support with a video surveillance system, given its status as a proven deep neural network (DNN) use case. In the future, to increase the number of cameras in such a system, we will need a scalable approach that embeds only a few neuromorphic devices in a camera. Specifically, this will require a shallow SNN model that can be implemented in a few neuromorphic devices while providing a high recognition accuracy comparable to a DNN with the same configuration. A shallow SNN was built by converting ResNet, a proven DNN for image recognition, and a new configuration of the shallow SNN model was developed to improve its accuracy. The proposed shallow SNN model was evaluated with a few neuromorphic devices, and it achieved a recognition accuracy of more than 80% with about 1/130 less energy consumption than that of a GPU with the same configuration of DNN as that of SNN.

A Boolean network (BN) is well known as a discrete model for analysis and control of complex networks such as gene regulatory networks. Since complex networks are large-scale in general, it is important to consider model reduction. In this paper, we consider model reduction that the information on fixed points (singleton attractors) is preserved. In model reduction studied here, the interaction graph obtained from a given BN is utilized. In the existing method, the minimum feedback vertex set (FVS) of the interaction graph is focused on. The dimension of the state is reduced to the number of elements of the minimum FVS. In the proposed method, we focus on complement and absorption laws of Boolean functions in substitution operations of a Boolean function into other one. By simplifying Boolean functions, the dimension of the state may be further reduced. Through a numerical example, we present that by the proposed method, the dimension of the state can be reduced for BNs that the dimension of the state cannot be reduced by the existing method.

We have developed a Si-CMOS terahertz image sensor to address the paucity of low-cost terahertz detectors. Our imaging pixel directly connects to a VCO-based ADC and achieves pixel parallel ADC architecture for high-speed global shutter THz imaging. In this paper, we propose a digital calibration technique for offset and gain variation of each pixel using global shutter operation. The calibration technique gives reference signal to all pixels simultaneously and takes reference frames as a part of the high-speed image captures. Using this technique, we achieve offset/non-linear gain variation suppression of 85.7% compared to without correction.

To obtain a full picture of potential applications for propagation-delay based X channels, it is important to obtain all feasible schemes of cyclic interference alignment including the encoder, channel instance, and decoder. However, when the dimension goes larger, theoretical analysis about this issue will become tedious and even impossible. In this letter, we propose a computer-aided solution by searching the channel space and the scheduling space, which can find all feasible schemes in details. Examples are given for some typical X channels. Computational complexity is further analyzed.

Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient computing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algorithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%, and when the execution task volume is 600KBit, the total cost of system execution tasks can be reduced by 16.7% at most.

Automatic continuous tracking of objects involved in a construction project is required for such tasks as productivity assessment, unsafe behavior recognition, and progress monitoring. Many computer-vision-based tracking approaches have been investigated and successfully tested on construction sites; however, their practical applications are hindered by the tracking accuracy limited by the dynamic, complex nature of construction sites (i.e. clutter with background, occlusion, varying scale and pose). To achieve better tracking performance, a novel deep-learning-based tracking approach called the Multi-Domain Convolutional Neural Networks (MD-CNN) is proposed and investigated. The proposed approach consists of two key stages: 1) multi-domain representation of learning; and 2) online visual tracking. To evaluate the effectiveness and feasibility of this approach, it is applied to a metro project in Wuhan China, and the results demonstrate good tracking performance in construction scenarios with complex background. The average distance error and F-measure for the MDNet are 7.64 pixels and 67, respectively. The results demonstrate that the proposed approach can be used by site managers to monitor and track workers for hazard prevention in construction sites.

Pseudorandom sequences with large linear complexity can resist the linear attack. The trace representation plays an important role in analysis and design of pseudorandom sequences. In this letter, we present the construction of a family of new binary sequences derived from Euler quotients modulo pq, where pq is a product of two primes and p divides q-1. Firstly, the linear complexity of the sequences are investigated. It is proved that the sequences have larger linear complexity and can resist the attack of Berlekamp-Massey algorithm. Then, we give the trace representation of the proposed sequences by determining the corresponding defining pair. Moreover, we generalize the result to the Euler quotients modulo pmqn with m≤n. Results indicate that the generalized sequences still have high linear complexity. We also give the trace representation of the generalized sequences by determining the corresponding defining pair. The result will be helpful for the implementation and the pseudorandom properties analysis of the sequences.

A new zero-forcing block diagonalization (ZF-BD) scheme that enables both a more simplified ZF-BD and further increase in sum rate of MU-MIMO channels is proposed in this paper. The proposed scheme provides the improvement in BER performance for equivalent SU-MIMO channels. The proposed scheme consists of two components. First, a permuted channel matrix (PCM), which is given by moving the submatrix related to a target user to the bottom of a downlink MIMO channel matrix, is newly defined to obtain a precoding matrix for ZF-BD. Executing QR decomposition alone for a given PCM provides null space for the target user. Second, a partial MSQRD (PMSQRD) algorithm, which adopts MSQRD only for a target user to provide improvement in bit rate and BER performance for the user, is proposed. Some numerical simulations are performed, and the results show improvement in sum rate performance of the total system. In addition, appropriate bit allocation improves the bit error rate (BER) performance in each equivalent SU-MIMO channel. A successive interference cancellation is applied to achieve further improvement in BER performance of user terminals.