Considering the growth of the IoT network, there is a demand for a decentralized solution. Incorporating the blockchain technology will eliminate the challenges faced in centralized solutions, such as i) high infrastructure, ii) maintenance cost, iii) lack of transparency, iv) privacy, and v) data tampering. Blockchain-based IoT network allows businesses to access and share the IoT data within their organization without a central authority. Data in the blockchain are stored as blocks, which should be validated and added to the chain, for this consensus mechanism plays a significant role. However, existing methods are not designed for IoT applications and lack features like i) decentralization, ii) scalability, iii) throughput, iv) faster convergence, and v) network overhead. Moreover, current blockchain frameworks failed to support resource-constrained IoT applications. In this paper, we proposed a new consensus method (WoG) and a lightweight blockchain framework (iLEDGER), mainly for resource-constrained IoT applications in a permissioned environment. The proposed work is tested in an application that tracks the assets using IoT devices (Raspberry Pi 4 and RFID). Furthermore, the proposed consensus method is analyzed against benign failures, and performance parameters such as CPU usage, memory usage, throughput, transaction execution time, and block generation time are compared with state-of-the-art methods.

In this paper, the notion of 2-tuples distribution matrices of the rotation symmetric orbits is proposed, by using the properties of the 2-tuples distribution matrix, a new characterization of 2-resilient rotation symmetric Boolean functions is demonstrated. Based on the new characterization of 2-resilient rotation symmetric Boolean functions, constructions of 2-resilient rotation symmetric Boolean functions (RSBFs) are further studied, and new 2-resilient rotation symmetric Boolean functions with prime variables are constructed.

Technology for sixth-generation (6G) mobile communication system is now being widely studied. A sub-Terahertz band is expected to play a great role in 6G to enable extremely high data-rate transmission. This paper has two goals. (1) Introduction of 6G concept and propagation characteristics of sub-Terahertz-band radio waves. (2) Performance evaluation of intelligent reflecting surfaces (IRSs) based on beamforming in a sub-Terahertz band for smart radio environments (SREs). We briefly review research on SREs with reconfigurable intelligent surfaces (RISs), and describe requirements and key features of 6G with a sub-Terahertz band. After that, we explain propagation characteristics of sub-Terahertz band radio waves. Important feature is that the number of multipath components is small in a sub-Terahertz band in indoor office environments. This leads to an IRS control method based on beamforming because the number of radio waves out of the optimum beam is very small and power that is not used for transmission from the IRS to user equipment (UE) is little in the environments. We use beams generated by a Butler matrix or a DFT matrix. In simulations, we compare the received power at a UE with that of the upper bound value. Simulation results show that the proposed method reveals good performance in the sense that the received power is not so lower than the upper bound value.

A 2-D beam scanning array antenna fed by a compact 16-way 2-D beamforming network (BFN) designed in Broadside Coupled Stripline (BCS) is addressed. The proposed 16-way 2-D BFN is formed by interconnecting two groups of 4x4 Butler Matrix (BM). Each group is composed of four compact 4x4 BMs. The critical point of the design is to propose a simple and compact 4x4 BM without crossover in BCS to achieve a better transmission coefficient of the 16-way 2-D BFN with reduced size of merely 0.8λ0×0.8λ0×0.04λ0. Moreover, the complexity of the interface connection between the 2-D BFN and the 4x4 patch array antenna is reduced by using probe feeding. The 16-way 2-D BFN is able to produce the phase shift of ±45°, and ±135° in x- and y- directions. The 2-D BFN is easily integrated under the 4x4 patch array to form a 2-D phased array capable of switching 16 beams in both elevation and azimuth directions. The area of the proposed 2-D beam scanning array antenna module has been significantly reduced to 2λ0×2λ0×0.04λ0. A prototype operating in the frequency range of 4-6GHz is fabricated and measured to validate the concept. The measurement results agree well with the simulations.

We developed a self-controllable voltage level (SVL) circuit and applied this circuit to a single-power-supply, six-transistor complementary metal-oxide-semiconductor static random-access memory (SRAM) to not only improve both write and read performances but also to achieve low standby power and data retention (holding) capability. The SVL circuit comprises only three MOSFETs (i.e., pull-up, pull-down and bypass MOSFETs). The SVL circuit is able to adaptively generate both optimal memory cell voltages and word line voltages depending on which mode of operation (i.e., write, read or hold operation) was used. The write margin (VWM) and read margin (VRM) of the developed (dvlp) SRAM at a supply voltage (VDD) of 1V were 0.470 and 0.1923V, respectively. These values were 1.309 and 2.093 times VWM and VRM of the conventional (conv) SRAM, respectively. At a large threshold voltage (Vt) variability (=+6σ), the minimum power supply voltage (VMin) for the write operation of the conv SRAM was 0.37V, whereas it decreased to 0.22V for the dvlp SRAM. VMin for the read operation of the conv SRAM was 1.05V when the Vt variability (=-6σ) was large, but the dvlp SRAM lowered it to 0.41V. These results show that the SVL circuit expands the operating voltage range for both write and read operations to lower voltages. The dvlp SRAM reduces the standby power consumption (PST) while retaining data. The measured PST of the 2k-bit, 90-nm dvlp SRAM was only 0.957µW at VDD=1.0V, which was 9.46% of PST of the conv SRAM (10.12µW). The Si area overhead of the SVL circuits was only 1.383% of the dvlp SRAM.

A novel displacement sensor was proposed based on a frequency delta-sigma modulator (FDSM) employing a microwave oscillator. To demonstrate basic operation, we fabricated a stylus surface profiler using a cylindrical cavity resonator, where one end of the cavity is replaced by a thin metal diaphragm with a stylus probe tip. Good surface profile was successfully obtained with this device. A 10 nm depth trench was clearly observed together with a 10 µm trench in a single scan without gain control. This result clearly demonstrates an extremely wide dynamic range of the FDSM displacement sensors.

In recent years, the market size for internet advertising has been increasing with the expansion of the Internet. Among the internet advertising technologies, affiliate services, which are a performance-based service, use cookies to track and measure the performance of affiliates. However, for the purpose of safeguarding personal information, cookies tend to be regulated, which leads to concerns over whether normal tracking by cookies works as intended. Therefore, in this study, the recent problems from the perspectives of affiliates, affiliate service providers, and advertisers are extracted, and a framework of cookie-independent measuring engagement method using access logs is proposed and open issues are discussed for future affiliate services.

We present a reconfigurable deep learning pedestrian detection system for surveillance systems that detect people with shadows in different lighting and heavily occluded conditions. This work proposes a region-based CNN, combined with CMOS and thermal cameras to obtain human features even under poor lighting conditions. The main advantage of a reconfigurable system with respect to processor-based systems is its high performance and parallelism when processing large amount of data such as video frames. We discuss the details of hardware implementation in the proposed real-time pedestrian detection algorithm on a Zynq FPGA. Simulation results show that the proposed integrated approach of R-CNN architecture with cameras provides better performance in terms of accuracy, precision, and F1-score. The performance of Zynq FPGA was compared to other works, which showed that the proposed architecture is a good trade-off in terms of quality, accuracy, speed, and resource utilization.

Sidon space is an important tool for constructing cyclic subspace codes. In this letter, we construct some Sidon spaces by using primitive elements and the roots of some irreducible polynomials over finite fields. Let q be a prime power, k, m, n be three positive integers and $ ho= lceil rac{m}{2k} ceil-1$, $ heta= lceil rac{n}{2m} ceil-1$. Based on these Sidon spaces and the union of some Sidon spaces, new cyclic subspace codes with size $rac{3(q^{n}-1)}{q-1}$ and $rac{ heta ho q^{k}(q^{n}-1)}{q-1}$ are obtained. The size of these codes is lager compared to the known constructions from [14] and [10].

The decoders, which improve the error-correction performance by finding and correcting the error bits caused by channel noise, are a hotspot for polar codes. In this paper, we present a threshold based D-SCFlip (TD-SCFlip) decoder with two improvements based on the D-SCFlip decoder. First, we propose the LLR fidelity criterion to define the LLR threshold and investigate confidence probability to calculate the LLR threshold indirectly. The information bits whose LLR values are smaller than the LLR threshold will be excluded from the range of candidate bits, which reduces the complexity of constructing the flip-bits list without the loss of error-correction performance. Second, we improve the calculation method for flip-bits metric with two perturbation parameters, which locates the channel-induced error bits faster, thus improving the error-correction performance. Then, TD-SCFlip-ω decoder is also proposed, which is limited to correcting up to ω bits in each extra decoding attempt. Simulation results show that the TD-SCFlip decoding is slightly better than the D-SCFlip decoding in terms of error-correction performance and decoding complexity, while the error-correction performance of TD-SCFlip-ω decoding is comparable to that of D-SCFlip-ω decoding but with lower decoding complexity.

Previously, convolutional neural networks have made tremendous progress in target recognition based on micro-Doppler radar. However, these studies only considered the presence of one target at a time in the surveillance area. Simultaneous multi-targets recognition for surveillance radar remains a pretty challenging issue. To alleviate this issue, this letter develops a multi-instance multi-label (MIML) learning strategy, which can automatically locate the crucial input patterns that trigger the labels. Benefitting from its powerful target-label relation discovery ability, the proposed framework can be trained with limited supervision. We emphasize that only echoes from single targets are involved in training data, avoiding the preparation and annotation of multi-targets echo in the training stage. To verify the validity of the proposed method, we model two representative ground moving targets, i.e., person and wheeled vehicles, and carry out numerous comparative experiments. The result demonstrates that the developed framework can simultaneously recognize multiple targets and is also robust to variation of the signal-to-noise ratio (SNR), the initial position of targets, and the difference in scattering coefficient.

In this letter, we propose a feature-based knowledge distillation scheme which transfers knowledge between intermediate blocks of teacher and student with flow-based architecture, specifically Normalizing flow in our implementation. In addition to the knowledge transfer scheme, we examine how configuration of the distillation positions impacts on the knowledge transfer performance. To evaluate the proposed ideas, we choose two knowledge distillation baseline models which are based on Normalizing flow on different domains: CS-Flow for anomaly detection and SRFlow-DA for super-resolution. A set of performance comparison to the baseline models with popular benchmark datasets shows promising results along with improved inference speed. The comparison includes performance analysis based on various configurations of the distillation positions in the proposed scheme.

Video-based action recognition encompasses the recognition of appearance and the classification of action types. This work proposes a discrete-temporal-sequence-based motion tendency clustering framework to implement motion clustering by extracting motion tendencies and self-supervised learning. A published traffic intersection dataset (inD) and a self-produced gesture video set are used for evaluation and to validate the motion tendency action recognition hypothesis.

The design and adjustment of the divergence in audio applications using nonnegative matrix factorization (NMF) is still open problem. In this study, to deal with this problem, we explore a representation of the divergence using neural networks (NNs). Instead of the divergence, our approach extends the multiplicative update algorithm (MUA), which estimates the NMF parameters, using NNs. The design of the extended MUA incorporates NNs, and the new algorithm is referred to as the deep MUA (DeMUA) for NMF. While the DeMUA represents the algorithm for the NMF, interestingly, the divergence is obtained from the incorporated NN. In addition, we propose theoretical guides to design the incorporated NN such that it can be interpreted as a divergence. By appropriately designing the NN, MUAs based on existing divergences with a single hyper-parameter can be represented by the DeMUA. To train the DeMUA, we applied it to audio denoising and supervised signal separation. Our experimental results show that the proposed architecture can learn the MUA and the divergences in sparse denoising and speech separation tasks and that the MUA based on generalized divergences with multiple parameters shows favorable performances on these tasks.

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.

In this paper, a novel reconfigurable intelligent surface (RIS)-aided full-duplex (FD) cooperative non-orthogonal multiple access (CNOMA) network is investigated over Nakagami-m fading channels, where two RISs are employed to help the communication of paired users. To evaluate the potential benefits of our proposed scheme, we first derive the closed-form expressions of the outage probability. Then, we derive users' diversity orders according to the asymptotic approximation at high signal-to-noise-ratio (SNR). Simulation results validate our analysis and reveal that users' diversity orders are affected by their channel fading parameters, the self-interference of FD, and the number of RIS elements.

In this paper, we investigate the sum rate (SR) maximization problem for downlink cooperative non-orthogonal multiple access (C-NOMA) system under in-phase and quadrature-phase (IQ) imbalance at the base station (BS) and destination. The BS communicates with users by a half-duplex amplified-and-forward (HD-AF) relay under imperfect IQ imbalance. The sum rate maximization problem is formulated as a non-convex optimization with the quality of service (QoS) constraint for each user. We first use the variable substitution method to transform the non-convex SR maximization problem into an equivalent problem. Then, a joint power and rate allocation algorithm is proposed based on successive convex approximation (SCA) to maximize the SR of the systems. Simulation results verify that the algorithm can improve the SR of the C-NOMA compared with the cooperative orthogonal multiple access (C-OMA) scheme.

In industry, automatic speech recognition has come to be a competitive feature for embedded products with poor hardware resources. In this work, we propose a tiny end-to-end speech recognition model that is lightweight and easily deployable on edge platforms. First, instead of sophisticated network structures, such as recurrent neural networks, transformers, etc., the model we propose mainly uses convolutional neural networks as its backbone. This ensures that our model is supported by most software development kits for embedded devices. Second, we adopt the basic unit of MobileNet-v3, which performs well in computer vision tasks, and integrate the features of the hidden layer at different scales, thus compressing the number of parameters of the model to less than 1 M and achieving an accuracy greater than that of some traditional models. Third, in order to further reduce the CPU computation, we directly extract acoustic representations from 1-dimensional speech waveforms and use a self-supervised learning approach to encourage the convergence of the model. Finally, to solve some problems where hardware resources are relatively weak, we use a prefix beam search decoder to dynamically extend the search path with an optimized pruning strategy and an additional initialism language model to capture the probability of between-words in advance and thus avoid premature pruning of correct words. In our experiments, according to a number of evaluation categories, our end-to-end model outperformed several tiny speech recognition models used for embedded devices in related work.

Our group has developed terahertz(THz)-waves emitting devices utilizing single crystals of high temperature superconductor Bi2Sr2CaCu2O8+δ (Bi2212). The working principle of the device is based on the AC Josephson effect which is originated in the intrinsic Josephson junctions (IJJs) constructed in Bi2212 single crystals. In principle, based on the superconducting gap of the compound and the AC Josephson effect, the emission frequency range from 0.1 to 15 THz can be generated by simply adjusting bias voltages to the IJJs. In order to improve the device performances, we have performed continuous improvement to the device structures. In this paper, we present our recent approaches to high performance Bi2212 THz-waves emitters. Firstly, approaches to the reduction of self Joule heating of the devices is described. In virtue of improved device structures using Bi2212 crystal chips, the device characteristics, such as the radiation frequency and the output power, become better than previous structures. Secondly, developments of THz-waves emitting devices using IJJs-mesas coupled with external structures are explained. The results clearly indicate that the external structures are very useful not only to obtain desired radiation frequencies higher than 1 THz but also to control radiation frequency characteristics. Finally, approaches to further understanding of the spontaneous synchronization of IJJs is presented. The device characteristics obtained through the approaches would play important roles in future developments of THz-waves emitting devices by use of Bi2212 single crystals.

Uncovered muck truck detection aims to detect the muck truck and distinguish whether it is covered or not by dust-proof net to trace the source of pollution. Unlike traditional detection problem, recalling all uncovered trucks is more important than accurate locating for pollution traceability. When two objects are very close in an image, the occluded object may not be recalled because the non-maximum suppression (NMS) algorithm can remove the overlapped proposal. To address this issue, we propose a Location First NMS method to match the ground truth boxes and predicted boxes by position rather than class identifier (ID) in the training stage. Firstly, a box matching method is introduced to re-assign the predicted box ID using the closest ground truth one, which can avoid object missing when the IoU of two proposals is greater than the threshold. Secondly, we design a loss function to adapt the proposed algorithm. Thirdly, a uncovered muck truck detection system is designed using the method in a real scene. Experiment results show the effectiveness of the proposed method.