Sparse code division multiple access (SCMA) is a non-orthogonal multiple access (NOMA) technology that can improve frequency band utilization and allow many users to share quite a few resource elements (REs). This paper uses the modulation of lattice theory to develop a systematic construction procedure for the design of SCMA codebooks under Gaussian channel environments that can achieve near-optimal designs, especially for cases that consider large-scale SCMA parameters. However, under the condition of large-scale SCMA parameters, the mother constellation (MC) points will overlap, which can be solved by the method of the partial dimensions transformation (PDT). More importantly, we consider the upper bounded error probability of the signal transmission in the AWGN channels, and design a codeword allocation method to reduce the inter symbol interference (ISI) on the same RE. Simulation results show that under different codebook sizes and different overload rates, using two different message passing algorithms (MPA) to verify, the codebook proposed in this paper has a bit error rate (BER) significantly better than the reference codebooks, moreover the convergence time does not exceed that of the reference codebooks.

The increasing amount of fake news is a growing problem that will progressively worsen in our interconnected world. Machine learning, particularly deep learning, is being used to detect misinformation; however, the models employed are essentially black boxes, and thus are uninterpretable. This paper presents an overview of explainable fake news detection models. Specifically, we first review the existing models, datasets, evaluation techniques, and visualization processes. Subsequently, possible improvements in this field are identified and discussed.

We consider a regulation problem for an uncertain chain of integrators with an unknown time-varying delay in the input. To deal with uncertain parameters and unknown delay, we propose an adaptive event-triggered controller with a dynamic gain. We show that the system is globally regulated and interexecution times are lower bounded. Moreover, we show that these lower bounds can be enlarged by adjusting a control parameter. An example is given for clear illustration.

This brief proposes a solar-cell-assisted wireless biosensing system that operates using a biofuel cell (BFC). To facilitate BFC area reduction for the use of this system in area-constrained continuous glucose monitoring contact lenses, an energy harvester combined with an on-chip solar cell is introduced as a dedicated power source for the transmitter. A dual-oscillator-based supply voltage monitor is employed to convert the BFC output into digital codes. From measurements of the test chip fabricated in 65-nm CMOS technology, the proposed system can achieve 99% BFC area reduction.

This paper investigates the charging control strategy design problem of a large-scale plug-in electric vehicle (PEV) group, where each PEV aims to find an optimal charging strategy to minimize its own cost function. It should be noted that the collective behavior of the group is coupled in the individual cost function, which complicates the design of decentralized charging strategies. To obtain the decentralized charging strategy, a mean-field game (MFG) formulation is proposed where a penalty on collective consensus is embedded and a class of mean-field coupled time-varying stochastic systems is targeted for solving the MFG which involves the charging model of PEVs as a special case. Then, an augmented system with dimension extension and the policy iteration algorithm are proposed to solve the mean-field game problem for the class of mean-field coupled time-varying stochastic systems. Moreover, analysis of the convergence of proposed approach has been studied. Last, simulation is conducted to illustrate the effectiveness of the proposed MFG-based charging control strategy and shows that the charging control strategy can achieve desired mean-field state and impact to the power grid can be buffered.

Loan default prediction has been a significant problem in the financial domain because overdue loans may incur significant losses. Machine learning methods have been introduced to solve this problem, but there are still many challenges including feature multicollinearity, imbalanced labels, and small data sample problems. To replicate the success of deep learning in many areas, an effective regularization technique named muddling label regularization is introduced in this letter, and an ensemble of feed-forward neural networks is proposed, which outperforms machine learning and deep learning baselines in a real-world dataset.

Currently, wireless power transmission technology is being developed for capsule endoscopes. By removing the battery, the capsule endoscope is miniaturized, the number of images that can be taken increases, and the risk of harmful substances leaking from the battery when it is damaged inside the body is avoided. Furthermore, diagnostic accuracy is improved by adjusting the directivity of radio waves according to the position of the capsule endoscope to improve efficiency and adjusting the number of images to be taken according to position by real-time position estimation. In this study, we report the result of position estimation in a high-definition numerical human body model and in an experiment on an electromagnetic phantom.

In this paper, an algorithm based on Taylor series expansion is proposed to calculate the logarithm (log2x) of IEEE754 binary32 accuracy floating-point number by a multi-domain partitioning method. The general mantissa (1≤x<2) is multiplied by 2, 4, 8, … (or equivalently left-shifted by 1, 2, 3, … bits), the regions of (2≤x<4), (4≤x<8), (8≤x<16),… are considered, and Taylor-series expansion is applied. In those regions, the slope of f(x)=log2 x with respect to x is gentle compared to the region of (1≤x<2), which reduces the required number of terms. We also consider the trade-offs among the numbers of additions, subtractions, and multiplications and Look-Up Table (LUT) size in hardware to select the best algorithm for the engineer's design and build the best hardware device.

Low-hit-zone frequency-hopping sequences (LHZ-FHSs) are frequency-hopping sequences with low Hamming correlation in a low-hit-zone (LHZ), which have important applications in quasi-synchronous communication systems. However, the strict quasi-synchronization may be hard to maintain at all times in practical FHMA networks, it is also necessary to minimize the Hamming correlation for time-shifts outside of the LHZ. The main objective of this letter is to propose a lower bound on the maximum correlation magnitude outside the low-hit-zone for LHZ-FHS sets. It turns out that the proposed bound is tight or almost tight in the sense that it can be achieved by some LHZ-FHS sets.

The shared last level cache (SLLC) in tile chip multiprocessors (TCMP) provides a low off-chip miss rate, but it causes a long on-chip access latency. In the two-level cache hierarchy, data replication stores replicas of L1 victims in the local LLC (L2 cache) to obtain a short local LLC access latency on the next accesses. Many data replication mechanisms have been proposed, but they do not consider both L1 victim reuse behaviors and LLC replica reception capability. They either produce many useless replicas or increase LLC pressure, which limits the improvement of system performance. In this paper, we propose a two-level cache aware adaptive data replication mechanism (TCDR), which controls replication based on both L1 victim reuse behaviors prediction and LLC replica reception capability monitoring. TCDR not only increases the accuracy of L1 replica selection, but also avoids the pressure of replication on LLC. The results show that TCDR improves the system performance with reasonable hardware overhead.

This paper proposes novel test techniques for high accuracy measurement of ADCs and a ramp generator on a CMOS image sensor (CIS) chip. The test circuit for the ADCs has a dual path and has an ability of multi-functional fine pattern generator that can define any input for each column to evaluate CIS specific characteristics electrically. The test circuit for the ramp generator can realize an on-chip current cell test and reject the current cell failure within 1LSB accuracy. We fabricated the test sensor using 55nm CIS process and measured the IP characteristics. Measured results show INL of 14.6LSB, crosstalk of 14.9LSB and column interference noise of 5.4LSB. These measured results agree with the designed values. By using this technique, we confirmed the accurate ADC measurement can be realized without being affected by the ambiguity of the optical input.

As known to us all, L-shaped co-prime array (LCA) is a recently introduced two-dimensional (2-D) sparse array structure, which is extended from linear co-prime array (CA). Such sparse array geometry can be used for 2-D parameters estimation with higher degrees-of-freedom (DOF). However, in the scenario where several narrowband transmissions spread over a wide spectrum, existing technique based on LCA with Nyquist sampling may encounter a bottleneck for both analog and digital processing. To alleviate the burden of high-rate Nyquist sampling, a method of joint wideband spectrum and direction-of-arrival (DOA) estimation with compressed sampling based on LCA, which is recognized as LCA-based modulated wideband converter (MWC), is presented in this work. First, the received signal along each antenna is mixed to basebands, low-pass filtered and down-sampled to get the compressed sampling data. Then by constructing the virtual received data of 2-D difference coarray, we estimate the wideband spectrum and DOA jointly using two recovery methods where the first is a joint ESPRIT method and the other is a joint CS method. Numerical simulations illustrate the validity of the proposed LCA based MWC system and show the superiority.

Intelligent transportation systems (ITSs) have been extensively studied in recent years to improve the safety and efficiency of transportation. The use of a radar system to enable the ITSs monitor the environment is robust to weather conditions and is less invasive to user privacy. Moreover, equipping the roadside units (RSUs) with radar modules has been deemed an economical and efficient option for ITS operators. However, because the detection and tracking parameters can significantly influence the radar system performance and the best parameters for different scenarios are different, the selection of appropriate parameters for the radar systems is critical. In this study, we investigated radar parameter selection and consequently proposes a parameter selection approach capable of automatically choosing the appropriate detection and tracking parameters for radar systems. The experimental results indicate that the proposed method realizes appropriate selection of parameters, thereby significantly improving the detection and tracking performance of radar systems.

Manufacturers are coping with increasing pressures in quality, cost and efficiency as more and more industries are moving from traditional setup to industry 4.0 based digitally transformed setup due to its numerous playbacks. Within the manufacturing domain organizational structures and processes are complex, therefore adopting industry 4.0 and finding an optimized re-engineered business process is difficult without using a systematic methodology. Authors have developed Business Process Re-engineering (BPR) and Business Process Optimization (BPO) methods but no consolidated methodology have been seen in the literature that is based on industry 4.0 and incorporates both the BPR and BPO. We have presented a consolidated and systematic re-engineering and optimization framework for a manufacturing industry setup. The proposed framework performs Evolutionary Multi-Objective Combinatorial Optimization using Multi-Objective Genetic Algorithm (MOGA). An example process from an aircraft manufacturing factory has been optimized and re-engineered with available set of technologies from industry 4.0 based on the criteria of lower cost, reduced processing time and reduced error rate. At the end to validate the proposed framework Business Process Model and Notation (BPMN) is used for simulations and perform comparison between AS-IS and TO-BE processes as it is widely used standard for business process specification. The proposed framework will be used in converting an industry from traditional setup to industry 4.0 resulting in cost reduction, increased performance and quality.

Optical phase conjugation (OPC) is an all-optical signal processing technique for mitigating fiber nonlinearity and is promising for building cost-efficient fiber networks with few optic-electric-optic conversions and long amplification spacing. In lumped amplified systems, OPC has a little nonlinearity mitigation efficiency for nonlinear distortion induced by cross-phase modulation (XPM) due to the asymmetry of power and chromatic dispersion (CD) maps during propagation in transmission fiber. In addition, the walk-off of XPM-induced noise becomes small due to the CD compensation effect of OPC, so the deterministic nonlinear distortion increases. Therefore, lumped amplified transmission systems with OPC are more sensitive to channel spacing than conventional systems. In this paper, we show the channel spacing dependence of NZ-DSF transmission using amplification repeater with OPC. Numerical simulations show comprehensive characteristics between channel spacing and CD in a 100-Gbps/λ WDM signal. An experimental verification using periodically poled LiNbO3-based OPC is also performed. These results suggest that channel spacing design is more important in OPC-assisted systems than in conventional dispersion-unmanaged systems.

This letter presents an innovative solution for real-time interaction during online classes. Synchronous sharing enables instructors to provide real-time feedback to students. This encourages students to stay focused and feel engaged during class. Consequently, students evaluated anonymously that this solution significantly enhanced their learning experience during real-time online classes.

This letter presents a new framework for synchronous remote online exams. This framework proposes new monitoring of notebooks in remote locations and limited messaging only enabled between students and their instructor during online exams. This framework was evaluated by students as highly effective in minimizing cheating during online exams.

In this study, we investigate outdoor propagation measurements performed in an industrial park environment at 28.3GHz band. The propagation characteristics were evaluated with the measurement result regarding the path loss characteristics. Ray tracing simulation was also studied and compared with the measurement data to evaluate the quantitative accuracy of ray tracing in millimeter-wave band wireless propagations. Ray tracing, whose accuracy was evaluated based on a comparison with the measurement results, can aid in the theoretical design of the coverage area and deterministic channel modeling.

A robust saliency detection approach for images with a complex background is proposed. The absorbing Markov chain integrating low-level, mid-level and high-level cues dynamically evolves by using the similarity between pixels to detect saliency objects. The experimental results show that the proposed algorithm has advantages in saliency detection, especially for images with a chaotic background or low contrast.

To accelerate deep learning (DL) processes on the supercomputer Fugaku, the authors have ported and optimized oneDNN for Fugaku's CPU, the Fujitsu A64FX. oneDNN is an open-source DL processing library developed by Intel for the x86_64 architecture. The A64FX CPU is based on the Armv8-A architecture. oneDNN dynamically creates the execution code for the computation kernels, which are implemented at the granularity of x86_64 instructions using Xbyak, the Just-In-Time (JIT) assembler for x86_64 architecture. To port oneDNN to A64FX, it must be rewritten into Armv8-A instructions using Xbyak_aarch64, the JIT assembler for the Armv8-A architecture. This is challenging because the number of steps to be rewritten exceeds several tens of thousands of lines. This study presents the Xbyak_translator_aarch64. Xbyak_translator_aarch64 is a binary translator that at runtime converts dynamically produced executable codes for the x86_64 architecture into executable codes for the Armv8-A architecture. Xbyak_translator_aarch64 eliminates the need to rewrite the source code for porting oneDNN to A64FX and allows us to port oneDNN to A64FX quickly.