The LoRaWAN network is widely employed in agricultural Internet of Things (IoT) applications requiring long-range wireless communication and low-energy consumption. However, challenges arise in dynamic environments like woodlands, where obstacles such as tree foliage disrupt the Fresnel zone and absorb signals. A previous study proposes a solution utilizing a LoRa Mesh Network (LoRa Meshnet) capable of establishing connections under tree canopies. Still, LoRa Meshnet is less battery-efficient than LoRaWAN. Thus, relying solely on LoRa Meshnet is inefficient in situations where LoRaWAN communication is affected by dynamic obstacles like seasonally varying foliage. To address this, we introduce a hybrid LoRa network architecture that utilizes LoRaWAN as the primary network and LoRa Meshnet as the backup, automatically switching between them. This approach ensures energy efficiency in sparse foliage situations by using LoRaWAN while guaranteeing stable data transmission in dense foliage scenarios with the use of LoRa Meshnet. Additionally, the system offers easy network deployment and cost-effectiveness.

Abstract As the pandemic situation continues, many people exercise at home. Mat Pilates is a popular workout and effective core strengthening. Although many researchers have conducted pose recognition studies for exercise posture correction, the study on Pilates exercise is only one case on static images. Therefore, for the purpose of exercise monitoring, we propose a real-time Pilates posture recognition system on a smartphone for exercise monitoring. We aimed to recognize 8 Pilates exercises—Bridge, Head roll-up, Hundred, Roll-up, Teaser, Plank, Thigh stretch, and Swan. First, the Blazepose model is used to extract body joint features. Then, we designed a deep neural network model that recognizes Pilates based on the extracted body features. It also measures the number of workouts, duration, and similarity to experts in video sequences. The precision, recall, and f1-score of the posture recognition model are 0.90, 0.87, and 0.88, respectively. The introduced application is expected to be used for exercise management at home.

This paper presents a new method for generating speed profiles for autonomous vehicles using a Transformer-based network that predicts the coefficients of quintic polynomials. To train and validate the network, we curate a dataset of 500K simulated urban driving scenarios, where the ground truths are obtained by running offline model predictive control (MPC) optimization. We also present tailored loss functions to emulate MPC behavior and constrain upper-and-lower boundary conditions to provide feasible speed profiles. Extensive experimental results demonstrate the efficacy of the proposed method in providing high-quality speed profiles for a large number of path candidates and long planning horizons. The proposed method is capable of generating efficient speed profiles for 1024 path candidates within 30ms.

To limit the spread of COVID-19, thermal screening cameras were installed everywhere. These cameras observe many thermal faces. These thermal face data are generally used to monitor strange temperatures for COVID-19 screening or to maintain social distancing. Big data of Thermal face generated everywhere should be used in the more practical functions. We proposed a method to measure non-contact breathing signals using thermal face data. In addition, breathing signals data estimated from thermal face data was converted to DICOM waveform Information Object Definitions (IODs) for interoperability management of medical data. The proposed method was tested on a golden reference (chest belt) with a mean accuracy of 93.52 %. a proposed method that can extract breathing signals using thermal screening cameras that are widely available around the world and manage data as healthcare interoperability information can show important potential in the public, telemedicine field in the future.

In advanced nodes, scaling of critical dimension and pitch has not progressed at historical Moore’s Law rates. Thus,scaling boostersare explored to improve achievable power, performance, area, and cost (PPAC) in new technologies. However, scaling boosters increase complexity of standard-cell architectures, power delivery, design rules, and other aspects of the design enablement, and may not result in design-level benefits. Therefore, design-technology co-optimization (DTCO) methodologies are required to evaluate design-level benefits of scaling boosters. The key challenge for DTCO is that large engineering efforts and long timelines are needed to develop design enablements (e.g., cell libraries) and perform implementation studies in order to assess technology options. We describe a new framework that can systematically evaluate a measure of intrinsic routability,$K_{\mathrm{ th}}$, across both technology and design choices. We focus on routability since it is a critical factor in the scaling of area and cost. Our framework includes realistic standard-cell libraries that are automatically generated using satisfiability modulo theory (SMT) methods, and a new pin shape selection method. Routability assessments are based on the PROBE approach and an improved construction of underlying netlist topologies. Our experimental studies demonstrate the assessment of routability impacts for advanced-node technology and design options. We demonstrate learning-based$K_{\mathrm{ th}}$prediction to reduce runtime, disk space and commercial tool licenses needed to implement our framework. Our work enables faster and more comprehensive evaluation of technology options early in the technology development process.

In this article, we propose an automated standard cell synthesis framework, SP&R, which simultaneously solves P&R without deploying any sequential/separate operations, by a novel dynamic pin allocation scheme. The proposed SP&R utilizes the multiobjective optimization feature of satisfiability modulo theories (SMT) to obtain optimal cell layouts. To achieve practical scalability of the framework, we develop various search-space reduction techniques, including breaking symmetry, conditional assignment/localization, and cell/objective function partitioning. Compared to the previous work, SP&R achieves 20.8× to 131.7× runtime improvements on average across the design-rule sets. As a result, SP&R successfully produces cell layouts up to 36 field-effect transistors (FETs) and 27 nets within 1.75 h by orchestrating all innovative tactics together, resulting in the generation of a whole 7-nm standard cell library. Compared to the known layouts, our work improves cell size and # M2 tracks by 0.1 contacted poly pitch and 0.3 tracks, respectively.

The safety of an automated vehicle requires accurate information of surrounding conditions, because a false sensor output can lead to a fatal accident during driving. Thus, monitoring of abnormalities in every sensor is important for robust perception of the environment. Since it is difficult to obtain anomalous data, it is hard to develop a robust detection algorithm using only a relatively small number of anomalies. In this paper, we propose a data augmentation method for oversampling minority anomalies in lane detection. Using a generative adversarial network that makes the generator learn to estimate the distribution of anomalous data, it generates synthesized minority anomalies. The generated anomalies are used to train an anomaly detection network while minimizing latency for use in real situations. During training, the generated anomalies, with various mixed quality, are sampled differently according to their quality. This helps the detection network to be optimized with better quality data. Experimental result shows that when using the proposed anomaly detection framework for monitoring lane abnormality, it improves the performance by 12% when compared to the vanilla recurrent neural network.

Physical layer key generation is based on reciprocal channels providing common randomness, which was so far known from TDD wireless channels. This paper opens the door to wireline physical-layer security, especially focusing on power-line connections. Additionally to the known reciprocity, we now also provide randomization by terminating idle branching connections (e.g., empty sockets) with random (reactive) loads. Alternatively, unused pairs at the power-line modem's end may be terminated by random (reactive) loads. Simulation and measurement results are shown. We also indicate, how the actual key generation can be realized by quantizing a frequency range and using the position of notches of the transfer function or of transmission coefficients selecting a quantization interval and with it a binary label as a key segment. Key reconciliation can simply be realized by a publicly announced shift of the quantization grid. Applications are seen for in-home and industrial devices required to exchange data over the power-line network securely.

This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction. We propose a simple encoder-decoder architecture based on multihead attention. The proposed model generates the distribution of the predicted trajectories for multiple vehicles in parallel. Our approach to model the interactions can learn to attend to a few influential vehicles in an unsupervised manner, which can improve the interpretability of the network. The experiments using naturalistic trajectories at highway show the clear improvement in terms of positional error on both longitudinal and lateral direction.

For the path planning of autonomous vehicles, it is important to predict the future trajectory of the surrounding vehicles. However, predicting future trajectory is difficult because it needs to consider the invisible interaction between the vehicles in a dynamic driving environment. In this paper, a new approach, which considers the interaction between surrounding vehicles, is proposed for accurate prediction of the future trajectory. The proposed method provides continuous predicted trajectories over time in the longitudinal and lateral directions, respectively. The deep ensemble technique is also used to predict the uncertainty of the estimated trajectory. This paper performs the training and verification of the algorithm using NGSIM dataset, which is the vehicle driving data obtained through actual vehicle driving.

With the commercialization of various Driver Assistance Systems (DAS), those vehicles have some autonomous functions like Smart Cruise Control (SCC) and Lane Keeping System (LKS). It is believed that autonomous driving can be achieved by combining the DAS functions in the limited situations such as on highways. However, in order to coordinate the DAS functions for autonomous driving, a supervisor is needed to select an appropriate DAS function. In this paper, we propose a method for training a supervisor that selects proper DAS by deep reinforcement learning. The driving policy operates based on camera images and LIDAR data that are accessible in autonomous vehicles. Therefore, deep reinforcement learning network model is designed to analyze both camera image and LIDAR data. This system aims to drive in simulated traffic situation of highway without collision and with high speed. Unlike the systems which learn how to throttle, brake and steering directly, the proposed method can guarantee safe driving because the learned driving policy is based on the existing commercialized DAS functions. In order to verify the algorithms, a simulation tool is developed using Unity for highway environment with multiple vehicles and autonomous driving performance is compared with the proposed supervisor.

This paper proposes semi-autonomous control that minimizes intervention considering driver's steering and braking intentions. The biggest challenge of this problem is how to fairly judge driver's intentions that appear differently in the lateral and longitudinal directions and how to minimize controller intervention. A decoupled model predictive control (MPC) and optimal intervention decision methods are proposed considering driver incompatibility. Several MPCs are designed first considering the fact that the driver can avoid obstacles either by braking or moving to the left or right lanes. The control input to avoid the collision is calculated for each MPC such that its intervention can be minimized reflecting the driver's intention. After driver incompatibility is formalized, the optimal input is selected to minimize the incompatibility among the paths that can avoid accidents. The proposed algorithm is validated in simulations where collision can be avoided while minimizing the intervention.

Timing margin to cover process variation is one of the most critical factors that limit the amount of supply voltage reduction thereby power consumption. To remove too conservative timing margin, Bubble Razor was introduced to dynamically detect and correct errors in two-phase transparent latch designs [13]. However, it does not fully exploit the potential of two-phase transparent latch design, e.g. time borrowing. Thus, especially at low supply voltage where the effect of process variation becomes significant, the existing Bubble Razor can suffer from significant overhead in performance and power consumption due to too frequent occurrence of bubble generations. We present a design methodology for coarse-grained Bubble Razor which exploits the time-borrowing characteristic of two-phase transparent latch design. By selectively inserting error checkpoints, i.e., shadow latches and error management logic, in the circuit, time borrowing can be applied between error checkpoints thereby avoiding bubbles which could occur in the existing Bubble Razor design with a checkpoint at every latch on the critical path. We present a methodology to choose the grain size (the number of stages between error checkpoints) based on 3-sigma delay distribution. We also verify the benefits of coarse-grained Bubble Razor with a real microprocessor, Core-A design [15] using 20nm Predictive Technology Model (PTM) [16]. The proposed methodology offers 62% improvement in performance (MIPS) and 49% less energy consumption (per instruction) at 0.6V operation (zero frequency margin) over the original Bubble Razor scheme. In addition, it gives 25% area reduction in core design.