In the field of machine learning security, as one of the attack surfaces especially for edge devices, the application of side-channel analysis such as correlation power/electromagnetic analysis (CPA/CEMA) is expanding. Aiming to evaluate the leakage resistance of neural network (NN) model parameters, i.e. weights and biases, we conducted a feasibility study of CPA/CEMA on floating-point (FP) operations, which are the basic operations of NNs. This paper proposes approaches to recover weights and biases using CPA/CEMA on multiplication and addition operations, respectively. It is essential to take into account the characteristics of the IEEE 754 representation in order to realize the recovery with high precision and efficiency. We show that CPA/CEMA on FP operations requires different approaches than traditional CPA/CEMA on cryptographic implementations such as the AES.

Orbital angular momentum (OAM) multiplexing technology is being investigated for high-capacity point-to-point (PtP) wireless transmission toward beyond 5G systems. OAM multiplexing is a spatial multiplexing technique that utilizes the twisting of electromagnetic waves. Its advantage is that it reduces the computational complexity of the signal processing on spatial multiplexing. Meanwhile point-to-multi point (PtMP) wireless transmission, such as integrated access and backhaul (IAB) will be expected to simultaneously accommodates a high-capacity prioritized backhaul-link and access-links. In this paper, we study the extension of OAM multiplexing transmission from PtP to PtMP to meet the above requirements. We propose a backhaul prioritized resource control algorithm that maximizes the received signal-to-interference and noise ratio (SINR) of the access-links while maintaining the backhaul-link. The proposed algorithm features adaptive mode selection that takes into account the difference in the received power of each OAM mode depending on the user equipment position and the guaranteed power allocation of the backhaul capacity. We then evaluate the performance of the proposed method through computer simulation. The results show that throughput of the access-links improved compared with the conventional multi-beam multi-user multi-input multi-output (MIMO) techniques while maintaining the throughput of the backhaul-link above the required value with minimal feedback information.

The ideal point scattering model requires that each scattering center is isotropic, the position of the scattering center corresponding to the target remains unchanged, and the backscattering amplitude and phase of the target do not change with the incident frequency and incident azimuth. In fact, these conditions of the ideal point scattering model are difficult to meet, and the scattering models are not ideal in most cases. In order to understand the difference between non-ideal scattering center and ideal scattering center, this paper takes a metal plate as the research object, carries out two-dimensional imaging of the metal plate, compares the difference between the imaging position and the theoretical target position, and compares the shape of the scattering center obtained from two-dimensional imaging of the plate from different angles. From the experimental results, the offset between the scattering center position and the theoretical target position corresponding to the two-dimensional imaging of the plate under the non-ideal point scattering model is less than the range resolution and azimuth resolution. The deviation between the small angle two-dimensional imaging position and the theoretical target position using the ideal point scattering model is small, and the ideal point scattering model is still suitable for the two-dimensional imaging of the plate. In the imaging process, the ratio of range resolution and azimuth resolution affects the shape of the scattering center. The range resolution is equal to the azimuth resolution, the shape of the scattering center is circular; the range resolution is not equal to the azimuth resolution, and the shape of the scattering center is elliptic. In order to obtain more accurate two-dimensional image, the appropriate range resolution and azimuth resolution can be considered when using the ideal point scattering model for two-dimensional imaging. The two-dimensional imaging results of the plate at different azimuth and angle can be used as a reference for the study of non-ideal point scattering model.

In this paper, multi-input multi-output (MIMO) signal detection with random walk along a gradient descent direction using an intermediate search point is presented. As a low complexity MIMO signal detection schemes, a gradient descent algorithm with Metropolis-Hastings (MH) methods has been proposed. Random walk along a gradient descent direction speeds up the MH based search using the gradient of a least-squares cost function. However, the gradient vector may be discarded through QAM constellation quantization in some cases. For further performance improvement, this paper proposes an improved search scheme in which the gradient vector is stored for the next search iteration to generate an intermediate search point. The performance of the proposed scheme improves with higher order modulation symbols as compared with that of a conventional scheme. Numerical results obtained through computer simulation show that a bit error rate (BER) performance improves by 5dB at a BER of 10-3 for 64QAM symbols in a 16×16 MIMO system.

We propose a non-photorealistic rendering method for automatically generating point-light-style (PLS) images from photographic images using peripheral difference filters with different window sizes. The proposed method can express PLS patterns near the edges of photographic images as dots. To verify the effectiveness of the proposed method, experiments were conducted to visually confirm PLS images generated from various photographic images.

In the cellular system, the Worst Case User (WCU), whose distances to three nearest BSs are the similar, usually achieves the lowest performance. Improving user performance, especially the WCU, is a big problem for both network designers and operators. This paper works on the WCU in terms of coverage probability analysis by the stochastic geometry tool and data rate optimization with the transmission power constraint by the reinforcement learning technique under the Stretched Pathloss Model (SPLM). In analysis, only fast fading from the WCU to the serving Base Stations (BSs) is taken into the analysis to derive the lower bound coverage probability. Furthermore, the paper assumes that the Coordinated Multi-Point (CoMP) technique is only employed for the WCU to enhance its downlink signal and avoid the explosion of Intercell Interference (ICI). Through the analysis and simulation, the paper states that to improve the WCU performance under bad wireless environments, an increase in transmission power can be a possible solution. However, in good environments, the deployment of advanced techniques such as Joint Transmission (JT), Joint Scheduling (JS), and reinforcement learning is an suitable solution.

Video-based point cloud compression (V-PCC) utilizes video compression technology to efficiently encode dense point clouds providing state-of-the-art compression performance with a relatively small computation burden. V-PCC converts 3-dimensional point cloud data into three types of 2-dimensional frames, i.e., occupancy, geometry, and attribute frames, and encodes them via video compression. On the other hand, the quality of these frames may be degraded due to video compression. This paper proposes an adaptive neural network-based post-processing filter on attribute frames to alleviate the degradation problem. Furthermore, a novel training method using occupancy frames is studied. The experimental results show average BD-rate gains of 3.0%, 29.3% and 22.2% for Y, U and V respectively.

This paper refers to the optimal voltage pair, which minimizes the energy consumption of LSI circuits under a target delay constraint, as a Minimum Energy Point (MEP). This paper proposes an approximation-based implementation method for an MEP tracking system over a wide voltage region. This paper focuses on the MEP characteristics that the energy loss is sufficiently small even though the voltage point changes near the MEP. For example, the energy loss is less than 5% even though the estimated MEP differs by a few tens of millivolts in comparison with the actual MEP. Therefore, the complexity for determining the MEP is relaxed by approximating complex operations such as the logarithmic or the exponential functions in the MEP tracking algorithm, which leads to hardware-/software-efficient implementation. When the MEP tracking algorithm is implemented in software, the MEP estimation time is reduced from 1ms to 13µs by the proposed approximation. When implemented in hardware, the proposed method can reduce the area of an MEP estimation circuit to a quarter. Measurement results of a 32-bit RISC-V processor fabricated in a 65-nm SOTB process technology show that the energy loss introduced by the proposed approximation is less than 2% in comparison with the MEP operation. Furthermore, we show that the MEP can be tracked within about 45 microseconds by the proposed MEP tracking system.

In wireless sensor networks (WSNs), wireless power transfer (WPT) has been studied as an energy-harvesting technique for prolonging their network lifetime. The WPT can supply power resources to sensor nodes (SNs) wirelessly, however, the reception (harvesting) power at SNs depends on their distance from a WPT equipment (WPTE), leading to the location-dependent non-uniformity in the reception power among SNs. For the fixed-located WPTE, SNs distant from the WPTE suffer from insufficient reception power. To handle this problem, this paper proposes a novel network structure introducing multiple hybrid access points (HAPs), which equip two functions of conventional cluster head function, including data collection and relay transmission, and WPT function. Then, these HAPs take terms providing both functions. By periodically rotating the HAP providing the WPT function, the location of the WPTE can be changed, which reduces the non-uniformity in the SN reception power. Also, this paper proposes a clustering scheme based on the residual power at SNs to reduce their power depletion under the proposed network structure. The evaluation results through computer simulation show that the proposed system reduces the non-uniformity in the SN reception power and the power depletion at the SNs and then improves the data collection rate, compared with the conventional systems.

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.

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.

Currently there has been increasing demand for real-time training on resource-limited IoT devices such as smart sensors, which realizes standalone online adaptation for streaming data without data transfers to remote servers. OS-ELM (Online Sequential Extreme Learning Machine) has been one of promising neural-network-based online algorithms for on-chip learning because it can perform online training at low computational cost and is easy to implement as a digital circuit. Existing OS-ELM digital circuits employ fixed-point data format and the bit-widths are often manually tuned, however, this may cause overflow or underflow which can lead to unexpected behavior of the circuit. For on-chip learning systems, an overflow/underflow-free design has a great impact since online training is continuously performed and the intervals of intermediate variables will dynamically change as time goes by. In this paper, we propose an overflow/underflow-free bit-width optimization method for fixed-point digital circuits of OS-ELM. Experimental results show that our method realizes overflow/underflow-free OS-ELM digital circuits with 1.0x - 1.5x more area cost compared to the baseline simulation method where overflow or underflow can happen.

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.

In secure multiparty computation (MPC), floating-point numbers should be handled in many potential applications, but these are basically expensive. In particular, for MPC based on secret sharing (SS), the floating-point addition takes many communication rounds though the addition is the most fundamental operation. In this paper, we propose an SS-based two-party protocol for floating-point addition with 13 rounds (for single/double precision numbers), which is much fewer than the milestone work of Aliasgari et al. in NDSS 2013 (34 and 36 rounds, respectively) and also fewer than the state of the art in the literature. Moreover, in contrast to the existing SS-based protocols which are all based on “roundTowardZero” rounding mode in the IEEE 754 standard, we propose another protocol with 15 rounds which is the first result realizing more accurate “roundTiesToEven” rounding mode. We also discuss possible applications of the latter protocol to secure Validated Numerics (a.k.a. Rigorous Computation) by implementing a simple example.

A typical approach to reconstructing a 3D environment model is scanning the environment with a depth sensor and fitting the accumulated point cloud to 3D models. In this kind of scenario, a general 3D environment reconstruction application assumes temporally continuous scanning. However in some practical uses, this assumption is unacceptable. Thus, a point cloud matching method for stitching several non-continuous 3D scans is required. Point cloud matching often includes errors in the feature point detection because a point cloud is basically a sparse sampling of the real environment, and it may include quantization errors that cannot be ignored. Moreover, depth sensors tend to have errors due to the reflective properties of the observed surface. We therefore make the assumption that feature point pairs between two point clouds will include errors. In this work, we propose a feature description method robust to the feature point registration error described above. To achieve this goal, we designed a deep learning based feature description model that consists of a local feature description around the feature points and a global feature description of the entire point cloud. To obtain a feature description robust to feature point registration error, we input feature point pairs with errors and train the models with metric learning. Experimental results show that our feature description model can correctly estimate whether the feature point pair is close enough to be considered a match or not even when the feature point registration errors are large, and our model can estimate with higher accuracy in comparison to methods such as FPFH or 3DMatch. In addition, we conducted experiments for combinations of input point clouds, including local or global point clouds, both types of point cloud, and encoders.

This paper presents a novel method for weakly supervised semantic segmentation of 3D point clouds using a novel graph and edge convolutional neural network (GECNN) towards 1% and 10% point cloud with labels. Our general framework facilitates semantic segmentation by encoding both global and local scale features via a parallel graph and edge aggregation scheme. More specifically, global scale graph structure cues of point clouds are captured by a graph convolutional neural network, which is propagated from pairwise affinity representation over the whole graph established in a d-dimensional feature embedding space. We integrate local scale features derived from a dynamic edge feature aggregation convolutional neural networks that allows us to fusion both global and local cues of 3D point clouds. The proposed GECNN model is trained by using a comprehensive objective which consists of incomplete, inexact, self-supervision and smoothness constraints based on partially labeled points. The proposed approach enforces global and local consistency constraints directly on the objective losses. It inherently handles the challenges of segmenting sparse 3D point clouds with limited annotations in a large scale point cloud space. Our experiments on the ShapeNet and S3DIS benchmarks demonstrate the effectiveness of the proposed approach for efficient (within 20 epochs) learning of large scale point cloud semantics despite very limited labels.

Elliptic curve cryptography (ECC), one of the asymmetric cryptography, is widely used in practical security applications, especially in the Internet of Things (IoT) applications. This paper presents a low-power reconfigurable architecture for ECC, which is capable of resisting simple power analysis attacks (SPA) and can be configured to support all of point operations and modular operations on 160/192/224/256-bit field orders over GF(p). Point multiplication (PM) is the most complex and time-consuming operation of ECC, while modular multiplication (MM) and modular division (MD) have high computational complexity among modular operations. For decreasing power dissipation and increasing reconfigurable capability, a Reconfigurable Modular Multiplication Algorithm and Reconfigurable Modular Division Algorithm are proposed, and MM and MD are implemented by two adder units. Combining with the optimization of operation scheduling of PM, on 55 nm CMOS ASIC platform, the proposed architecture takes 0.96, 1.37, 1.87, 2.44 ms and consumes 8.29, 11.86, 16.20, 21.13 uJ to perform one PM on 160-bit, 192-bit, 224-bit, 256-bit field orders. It occupies 56.03 k gate area and has a power of 8.66 mW. The implementation results demonstrate that the proposed architecture outperforms the other contemporary designs reported in the literature in terms of area and configurability.

A method for runtime energy optimization based on the supply voltage (Vdd) and the threshold voltage (Vth) scaling is proposed. This paper refers to the optimal voltage pair, which minimizes the energy consumption of LSI circuits under a target delay constraint, as a Minimum Energy Point (MEP). The MEP dynamically fluctuates depending on the operating conditions determined by a target delay constraint, an activity factor and a chip temperature. In order to track the MEP, this paper proposes a closed-form continuous function that determines the MEP over a wide operating performance region ranging from the above-threshold region down to the sub-threshold region. Based on the MEP determination formula, an MEP tracking algorithm is also proposed. The MEP tracking algorithm estimates the MEP even though the operating conditions widely change. Measurement results based on a 32-bit RISC processor fabricated in a 65-nm Silicon On Thin Buried oxide (SOTB) process technology show that the proposed method estimates the MEP within a 5% energy loss in comparison with the actual MEP operation.

This paper proposes an area- and energy-efficient DLL-based body bias generator (BBG) for minimum energy operation that controls p-well and n-well bias independently. The BBG can minimize total energy consumption of target circuits under a skewed process condition between nMOSFETs and pMOSFETs. The proposed BBG is composed of digital cells compatible with cell-based design, which enables energy- and area-efficient implementation without additional supply voltages. A test circuit is implemented in a 65-nm FDSOI process. Measurement results using a 32-bit RISC processor on the same chip show that the proposed BBG can reduce energy consumption close to a minimum within a 3% energy loss. In this condition, energy and area overheads of the BBG are 0.2% and 0.12%, respectively.

This paper investigates the sum rate (SR) maximization problem for downlink cooperative non-orthogonal multiple access (C-NOMA) systems with hardware impairments (HIs). The source node communicates with users via a half-duplex amplified-and-forward (HD-AF) relay with HIs. First, we derive the SR expression of the systems under HIs. Then, SR maximization problem is formulated under maximum power of the source, relay, and the minimum rate constraint of each user. As the original SR maximization problem is a non-convex problem, it is difficult to find the optimal resource allocation directly by tractional convex optimization method. We use variable substitution method to convert the non-convex SR maximization problem to an equivalent convex optimization problem. Finally, a joint power and rate allocation based on interior point method is proposed to maximize the SR of the systems. Simulation results show that the algorithm can improve the SR of the C-NOMA compared with the cooperative orthogonal multiple access (C-OMA) scheme.