This paper describes a novel low-latency 4K 60 fps HEVC (high efficiency video coding)/H.265 multi-channel encoding system with content-aware bitrate control for live streaming. Adaptive bitrate (ABR) streaming techniques, such as MPEG-DASH (dynamic adaptive streaming over HTTP) and HLS (HTTP live streaming), spread widely on Internet video streaming. Live content has increased with the expansion of streaming services, which has led to demands for traffic reduction and low latency. To reduce network traffic, we propose content-aware dynamic and seamless bitrate control that supports multi-channel real-time encoding for ABR, including 4K 60 fps video. Our method further supports chunked packaging transfer to provide low-latency streaming. We adopt a hybrid architecture consisting of hardware and software processing. The system consists of multiple 4K HEVC encoder LSIs that each LSI can encode 4K 60 fps or up to high-definition (HD) ×4 videos efficiently with the proposed bitrate control method. The software takes the packaging process according to the various streaming protocol. Experimental results indicate that our method reduces encoding bitrates obtained with constant bitrate encoding by as much as 56.7%, and the streaming latency over MPEG-DASH is 1.77 seconds.

A waveguide 2-plane hybrid coupler with two operating bands is proposed. The cross-sectional shape of the coupled region inside the proposed coupler is designed with a two-dimensional arbitrary geometry sorting method. Simulations of the proposed hybrid coupler has a fractional bandwidth (FBW) of 2.17% at the center of 24.99GHz, and at the center of 28.28GHz an FBW of 6.13%. The proposed coupler is analyzed by the mode-matching finite-element hybrid method, and the final result is obtained using a genetic algorithm. The analyzed result of the coupling for the main modes in the coupled region is presented. The design result is confirmed by measurements.

Although intervention practices like mindfulness meditation have proven effective in treating psychosis, there is no clarity on the mechanism of information propagation in the brain. In this study, we formulated a network optimization problem and searched for the optimal solution using Digital Annealer developed by Fujitsu Ltd. This is inspired by quantum computing and is effective in solving large-scale combinatorial optimization problems to find the information propagation pathway in the brain that contributes to the realization of mindfulness. Specifically, we defined the optimal network state as the state of the brain network that is considered to be associated with the mindfulness state. We formulated the problem into two network optimization problems — the minimum vertex-cover problem and the maximum-flow problem — to search for the information propagation pathway that is important for realizing the state. In the minimum vertex-cover problem, we aimed to identify brain regions that are important for the realization of the mindfulness state, and identified eight regions, including four that were suggested to be consistent with previous studies. We formulated the problem as a maximum-flow problem to identify the information propagation pathways in the brain that contribute to the activation of these four identified regions. As a result, approximately 30% of the connections in the brain network structure of this study were identified, and the pathway with the highest flow rate was considered to characterize the bottom-up emotion regulation during mindfulness. The findings of this study could be useful for more direct interventions in the context of mindfulness, which are being investigated by neurofeedback and other methods. This is because existing studies have not clarified the information propagation pathways that contribute to the realization of the brain network states that characterize mindfulness states. In addition, this approach may be useful as a methodology to identify information propagation pathways in the brain that contribute to the realization of higher-order human cognitive activities, such as mindfulness, within large-scale brain networks.

The Euclidean projection operation is the most complex and time-consuming of the alternating direction method of multipliers (ADMM) decoding algorithms, resulting in a large number of resources when deployed on hardware platforms. We propose a simplified line segment projection algorithm (SLSA) and present the hardware design and the quantization scheme of the SLSA. In simulation results, the proposed SLSA module has a better performance than the original algorithm with the same fixed bitwidths due to the centrosymmetric structure of SLSA. Furthermore, the proposed SLSA module with a simpler structure without hypercube projection can reduce time consuming by up to 72.2% and reduce hardware resource usage by more than 87% compared to other Euclidean projection modules in the experiments.

In this paper, a reference-voltage self-selected pseudo-differential sensing scheme suitable for single-ended SRAM is proposed. The proposed sensing scheme can select different reference voltage according to the offset direction. With the employment of the new sensing scheme, the swing of the read bit-line in the read operation is reduced by 74.6% and 45.5% compared to the conventional domino and the pseudo-differential sense amplifier sensing scheme, respectively. Therefore, the delay and power consumption of the read operation are significantly improved. Simulation results based on a standard 55nm CMOS show that compared with the conventional domino and pseudo-differential sensing schemes, the sensing delay is improved by 66.4% and 47.7%, and the power consumption is improved by 31.4% and 22.5%, respectively. Although the area of the sensing scheme is increased by 50.8% compared with the pseudo-differential sense amplifier sensing scheme, it has little effect on the entire SRAM area.

Multiple human tracking is a fundamental problem in understanding the context of a visual scene. Although both accuracy and speed are required in real-world applications, recent tracking methods based on deep learning focus on accuracy and require a substantial amount of running time. We aim to improve tracking running speeds by performing human detections at certain frame intervals because it accounts for most of the running time. The question is how to maintain accuracy while skipping human detection. In this paper, we propose a method that interpolates the detection results by using an optical flow, which is based on the fact that someone's appearance does not change much between adjacent frames. To maintain the tracking accuracy, we introduce robust interest point detection within the human regions and a tracking termination metric defined by the distribution of the interest points. On the MOT17 and MOT20 datasets in the MOTChallenge, the proposed SDOF-Tracker achieved the best performance in terms of total running time while maintaining the MOTA metric. Our code is available at https://github.com/hitottiez/sdof-tracker.

In this paper, we propose new radio access network (RAN) architecture for reliable millimeter-wave (mmWave) communications, which has the flexibility to meet users' diverse and fluctuating requirements in terms of communication quality. This architecture is composed of multiple radio units (RUs) connected to a common distributed unit (DU) via fronthaul links to virtually enlarge its coverage. We further present grant-free non-orthogonal multiple access (GF-NOMA) for low-latency uplink communications with a massive number of users and robust coordinated multi-point (CoMP) transmission using blockage prediction for uplink/downlink communications with a high data rate and a guaranteed minimum data rate as the technical pillars of the proposed RAN. The numerical results indicate that our proposed architecture can meet completely different user requirements and realize a user-centric design of the RAN for beyond 5G/6G.

A power-scalable wideband distributed amplifier is proposed. For reducing the power consumption of this power-hungry amplifier, it is efficient to lower the supply voltage. However, there is a hurdle owing to the transistor threshold voltage. In this work, a CMOS deeply depleted channel process is employed to overcome the hurdle.

We propose a sampling method that incorporates a normally distributed sampling series for EEG measurements using compressed sensing. We confirmed that the ADC sampling count and amount of wirelessly transmitted data can be reduced by 11% while maintaining a reconstruction accuracy similar to that of the conventional method.

In this paper, a non-linear precoding algorithm with low out-of-band (OOB) radiation is proposed for massive multiple-input multiple-output (MIMO) systems. Massive MIMO sets more than one hundred antennas at each base station to achieve higher spectral efficiency and throughput. Full digital massive MIMO may constrain the resolution of digital-to-analog converters (DACs) since each DAC consumes a large amount of power. In massive MIMO systems with low resolution DACs, designing methods of DAC output signals by nonlinear processing are being investigated. The conventional scheme focuses only on a sum rate or errors in the received signals and so triggers large OOB radiation. This paper proposes an optimization criterion that takes OOB radiation power into account. Gibbs sampling is used as an algorithm to find sub-optimal solutions given this criterion. Numerical results obtained through computer simulation show that the proposed criterion reduces mean OOB radiation power by a factor of 10 as compared with the conventional criterion. The proposed criterion also reduces OOB radiation while increasing the average sum rate by optimizing the weight factor for the OOB radiation. As a result, the proposed criterion achieves approximately 1.3 times higher average sum rates than an error-based criterion. On the other hand, as compared with a sum rate based criterion, the throughput on each subcarrier shows less variation which reduces the number of link adaptation options needed although the average sum rate of the proposed criterion is smaller.

Low-temperature deposition of Y2O3 at 80°C is studied using an yttrium precursor of tris(butylcyclopentadienyl)yttrium (Y(BuCp)3) and plasma exited humidified argon oxidizer. The deposition is demonstrated using an atomic-layer-deposition sequence; the Y(BuCp)3 and the oxidizing gases are time separately introduced to the reaction chamber and these injections are repeated. To determine the gas introduction conditions, surface reactions of Y(BuCp)3 adsorption and its oxidization are observed by an in-situ IR absorption spectroscopy. The deposited film is confirmed as fully oxidized Y2O3 by X-ray photoelectron spectroscopy. The present deposition is applicable for the deposition of Y2O3 film on flexible polyethylene terephthalate films.

The low temperature deposition of AlN at 160 °C is examined by using trimethyl aluminum (TMA) and NH radicals from plasma excited Ar diluted ammonia. For the deposition, a plasma tube separated from the reaction chamber is used to introduce the neutral NH radicals on the growing surface without the direct impacts of high-speed species and UV photons, which might be effective in suppressing the plasma damage to the sample surfaces. To maximize the NH radical generation, the NH3 and Ar mixing ratio is optimized by plasma optical emission spectroscopy. To determine the saturated condition of TMA and NH radical irradiations, an in-situ surface observation of IR absorption spectroscopy (IRAS) with a multiple internal reflection geometry is utilized. The low temperature AlN deposition is performed with the TMA and NH radical exposures whose conditions are determined by the IRAS experiment. The spectroscopic ellipsometry indicates the all-round surface deposition in which the growth per cycles measured from front and backside surfaces of the Si sample are of the same range from 0.39∼0.41nm/cycle. It is confirmed that the deposited film contains impurities of C, O, N although we discuss the method to decrease them. X-ray diffraction suggests the AlN polycrystal deposition with crystal phases of AlN (100), (002) and (101). From the saturation curves of TMA adsorption and its nitridation, their chemical reactions are discussed in this paper. In the present paper, we discuss the possibility of the low temperature AlN deposition.

Real-time applications are becoming more and more popular, and due to the demand for more compact and portable user devices, offloading terminal processes to edge servers is being considered. Moreover, it is necessary to process packets with low latency on edge servers, which are often virtualized for operability. When trying to achieve low-latency networking, the increase in server power consumption due to performance tuning and busy polling for fast packet receiving becomes a problem. Thus, we design and implement a low-latency and energy-efficient networking system, energy-efficient kernel busy poll (EE-KBP), which meets four requirements: (A) low latency in the order of microseconds for packet forwarding in a virtual server, (B) lower power consumption than existing solutions, (C) no need for application modification, and (D) no need for software redevelopment with each kernel security update. EE-KBP sets a polling thread in a Linux kernel that receives packets with low latency in polling mode while packets are arriving, and when no packets are arriving, it sleeps and lowers the CPU operating frequency. Evaluations indicate that EE-KBP achieves microsecond-order low-latency networking under most traffic conditions, and 1.4× to 3.1× higher throughput with lower power consumption than NAPI used in a Linux kernel.

Running IoT applications on edge computing infrastructures has the benefits of low response times and efficient bandwidth usage. System verification on a testbed is required to deploy IoT applications in production environments. In a testbed, Docker containers are preferable for a smooth transition of tested application programs to production environments. In addition, the round-trip times (RTT) of Docker containers to clients must be ensured, according to the target application's response time requirements. However, in existing testbed systems, the RTTs between Docker containers and clients are not ensured. Thus, we must undergo a large amount of configuration data including RTTs between all pairs of wireless base station nodes and servers to set up a testbed environment. In this paper, we present an edge computing testbed system with simple application programming interfaces (API) for testbed users that ensures RTTs between Docker containers and clients. The proposed system automatically determines which servers to place Docker containers on according to virtual regions and the RTTs specified by the testbed users through APIs. The virtual regions provide reduced size information about the RTTs in a network. In the proposed system, the configuration data size is reduced to one divided by the number of the servers and the command arguments length is reduced to approximately one-third or less, whereas the increased system running time is 4.3s.

Although end-to-end based speech recognition research for Mandarin-English code-switching has attracted increasing interests, it remains challenging due to data scarcity. Meta-learning approach is popular with low-resource modeling using high-resource data, but it does not make full use of low-resource code-switching data. Therefore we propose a two-fold cross-validation training framework combined with meta-learning approach. Experiments on the SEAME corpus demonstrate the effects of our method.

Low-density parity-check (LDPC) codes are widely used in communication systems for their high error-correcting performance. This survey introduces the elements of LDPC codes: decoding algorithms, code construction, encoding algorithms, and several classes of LDPC codes.

This paper presents a fully integrated yet compact receiver front-end for Sub-GHz applications such as Internet-of-Things (IoT). The low noise amplifier (LNA) matching network leverages an inductance boosting technique. A relatively small on-chip inductor with a compact area achieves impedance matching in such a low frequency. Moreover, a passive-mixer-first mode bypasses the LNA to extend the receiver dynamic-range. The passive mixer provides matching to the 50Ω antenna interface to eliminate the need for additional passive components. Therefore, the receiver can be fully-integrated without any off-chip matching components. The flipped-voltage-follower (FVF) cell is adopted in the low pass filter (LPF) and the variable gain amplifier (VGA) for its high linearity and low power consumption. Fabricated in 65nm LP CMOS process, the proposed receiver front-end occupies 0.37mm2 core area, with a tolerable input power ranging from -91.5dBm to -1dBm for 500kbps GMSK signal at 924MHz frequency. The power consumption is 1mW power under a 1.2V supply.

To alleviate the problem of the dependency on the quantity of the training sample data in speech emotion recognition, a weighted gradient pre-train algorithm for low-resource speech emotion recognition is proposed. Multiple public emotion corpora are used for pre-training to generate shared hidden layer (SHL) parameters with the generalization ability. The parameters are used to initialize the downsteam network of the recognition task for the low-resource dataset, thereby improving the recognition performance on low-resource emotion corpora. However, the emotion categories are different among the public corpora, and the number of samples varies greatly, which will increase the difficulty of joint training on multiple emotion datasets. To this end, a weighted gradient (WG) algorithm is proposed to enable the shared layer to learn the generalized representation of different datasets without affecting the priority of the emotion recognition on each corpus. Experiments show that the accuracy is improved by using CASIA, IEMOCAP, and eNTERFACE as the known datasets to pre-train the emotion models of GEMEP, and the performance could be improved further by combining WG with gradient reversal layer.

This paper proposes a novel multi-layer substrate integrated waveguide (SIW) resonator loaded with asymmetric E-shaped slot-lines and shows a tri-band band-pass filter (BPF) using the proposed structure. In the previous literature, various SIW resonators have been proposed to simultaneously solve the problems of large area and high insertion loss. Although these SIWs have a lower insertion loss than planar-type resonators using a printed circuit board, the size of these structures tends to be larger. A multi-layer SIW resonator loaded with asymmetric E-shaped slot-lines can solve the above problems and realize a tri-band BPF without increasing the size to realize further miniaturization. The theoretical design method and the structural design are shown. Moreover, the configured structure is fabricated and measured for showing the validity of the design method in this paper.

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.