International consensus on the management of large (≥20 mm) colorectal laterally spreading tumors: World Endoscopy Organization Delphi study.

OBJECTIVES: There have been significant advances in the management of large (≥20 mm) laterally spreading tumors (LSTs) or nonpedunculated colorectal polyps; however, there is a lack of clear consensus on the management of these lesions with significant geographic variability especially between Eastern and Western paradigms. We aimed to provide an international consensus to better guide management and attempt to homogenize practices. METHODS: Two experts in interventional endoscopy spearheaded an evidence-based Delphi study on behalf of the World Endoscopy Organization Colorectal Cancer Screening Committee. A steering committee comprising six members devised 51 statements, and 43 experts from 18 countries on six continents participated in a three-round voting process. The Grading of Recommendations, Assessment, Development and Evaluations tool was used to assess evidence quality and recommendation strength. Consensus was defined as ≥80% agreement (strongly agree or agree) on a 5-point Likert scale. RESULTS: Forty-two statements reached consensus after three rounds of voting. Recommendations included: three statements on training and competency; 10 statements on preresection evaluation, including optical diagnosis, classification, and staging of LSTs; 14 statements on endoscopic resection indications and technique, including statements on en bloc and piecemeal resection decision-making; seven statements on postresection evaluation; and eight statements on postresection care. CONCLUSIONS: An international expert consensus based on the current available evidence has been developed to guide the evaluation, resection, and follow-up of LSTs. This may provide guiding principles for the global management of these lesions and standardize current practices.

Design of Network-on-Chip-Based Restricted Coulomb Energy Neural Network Accelerator on FPGA Device.

Sensor applications in internet of things (IoT) systems, coupled with artificial intelligence (AI) technology, are becoming an increasingly significant part of modern life. For low-latency AI computation in IoT systems, there is a growing preference for edge-based computing over cloud-based alternatives. The restricted coulomb energy neural network (RCE-NN) is a machine learning algorithm well-suited for implementation on edge devices due to its simple learning and recognition scheme. In addition, because the RCE-NN generates neurons as needed, it is easy to adjust the network structure and learn additional data. Therefore, the RCE-NN can provide edge-based real-time processing for various sensor applications. However, previous RCE-NN accelerators have limited scalability when the number of neurons increases. In this paper, we propose a network-on-chip (NoC)-based RCE-NN accelerator and present the results of implementation on a field-programmable gate array (FPGA). NoC is an effective solution for managing massive interconnections. The proposed RCE-NN accelerator utilizes a hierarchical-star (H-star) topology, which efficiently handles a large number of neurons, along with routers specifically designed for the RCE-NN. These approaches result in only a slight decrease in the maximum operating frequency as the number of neurons increases. Consequently, the maximum operating frequency of the proposed RCE-NN accelerator with 512 neurons increased by 126.1% compared to a previous RCE-NN accelerator. This enhancement was verified with two datasets for gas and sign language recognition, achieving accelerations of up to 54.8% in learning time and up to 45.7% in recognition time. The NoC scheme of the proposed RCE-NN accelerator is an appropriate solution to ensure the scalability of the neural network while providing high-performance on-chip learning and recognition.

Hand Gesture Recognition Using FSK Radar Sensors.

Hand gesture recognition, which is one of the fields of human-computer interaction (HCI) research, extracts the user's pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted continuous-wave (CW) radar, which only shows a good performance at a fixed distance, which is due to its limitation of not seeing the distance. This paper proposes a hand gesture recognition system that utilizes frequency-shift keying (FSK) radar, allowing for a recognition method that can work at the various distances between a radar sensor and a user. The proposed system adopts a convolutional neural network (CNN) model for the recognition. From the experimental results, the proposed recognition system covers the range from 30 cm to 180 cm and shows an accuracy of 93.67% over the entire range.

Is colorectal endoscopic submucosal dissection safe and effective for 15-19-mm tumors?

PURPOSE: The outcomes of colorectal endoscopic submucosal dissection (ESD) in 15-19-mm tumors are unclear. This study compared the effectiveness and safety of colorectal ESD for 15-19-mm tumors and tumors exceeding that size. METHODS: From August 2018 to December 2020, 213 cases of colorectal tumors removed by colorectal ESD at a tertiary hospital were enrolled in this study. The cases were divided into two groups according to the pathologically measured size of the resected lesion: an intermediate group (15-19 mm, n = 62) and a large group (≥ 20 mm, n = 151). The en bloc resection rate, complete resection rate, and complications were investigated retrospectively. RESULTS: The en bloc resection rate was significantly higher in the intermediate than large group (100% vs. 94%, p = 0.049), and the mean total procedure time was shorter in the intermediate than large group (29.2 [Formula: see text] 12.6 vs. 48.4 [Formula: see text] 28.8 min, p < 0.001). However, the mean procedure speed was significantly lower in the intermediate than large group (0.25 [Formula: see text] 0.10 vs. 0.28 [Formula: see text] 0.11 cm2/min, p = 0.031). The complete resection rate, post-procedural bleeding, and perforation rate were not significantly different between the two groups. In multivariate analyses, the total procedure time and mean procedure speed were significantly associated with lesion size. CONCLUSION: Colorectal ESD of 15-19-mm lesions is effective, and has a shorter procedure time and higher en bloc resection rate than the same procedure for larger lesions.

FMCW Radar Sensors with Improved Range Precision by Reusing the Neural Network.

This paper addresses the challenge of enhancing range precision in radar sensors through supervised learning. However, when the range precision surpasses the range resolution, it leads to a rapid increase in the number of labels, resulting in elevated learning costs. The removal of background noise in indoor environments is also crucial. In response, this study proposes a methodology aiming to increase range precision while mitigating the issue of a growing number of labels in supervised learning. Neural networks learned for a specific section are reused to minimize learning costs and maximize computational efficiency. Formulas and experiments confirmed that identical fractional multiple patterns in the frequency domain can be applied to analyze patterns in other FFT bin positions (representing different target positions). In conclusion, the results suggest that neural networks trained with the same data can be repurposed, enabling efficient hardware implementation.

FPGA Implementation of Keyword Spotting System Using Depthwise Separable Binarized and Ternarized Neural Networks.

Keyword spotting (KWS) systems are used for human-machine communications in various applications. In many cases, KWS involves a combination of wake-up-word (WUW) recognition for device activation and voice command classification tasks. These tasks present a challenge for embedded systems due to the complexity of deep learning algorithms and the need for optimized networks for each application. In this paper, we propose a depthwise separable binarized/ternarized neural network (DS-BTNN) hardware accelerator capable of performing both WUW recognition and command classification on a single device. The design achieves significant area efficiency by redundantly utilizing bitwise operators in the computation of the binarized neural network (BNN) and ternary neural network (TNN). In a complementary metal-oxide semiconductor (CMOS) 40 nm process environment, the DS-BTNN accelerator demonstrated significant efficiency. Compared with a design approach where BNN and TNN were independently developed and subsequently integrated as two separate modules into the system, our method achieved a 49.3% area reduction while yielding an area of 0.558 mm2. The designed KWS system, which was implemented on a Xilinx UltraScale+ ZCU104 field-programmable gate array (FPGA) board, receives real-time data from the microphone, preprocesses them into a mel spectrogram, and uses this as input to the classifier. Depending on the order, the network operates as a BNN or a TNN for WUW recognition and command classification, respectively. Operating at 170 MHz, our system achieved 97.1% accuracy in BNN-based WUW recognition and 90.5% in TNN-based command classification.

FPGA Implementation of Efficient CFAR Algorithm for Radar Systems.

The constant false-alarm rate (CFAR) algorithm is essential for detecting targets during radar signal processing. It has been improved to accurately detect targets, especially in nonhomogeneous environments, such as multitarget or clutter edge environments. For example, there are sort-based and variable index-based algorithms. However, these algorithms require large amounts of computation, making them difficult to apply in radar applications that require real-time target detection. We propose a new CFAR algorithm that determines the environment of a received signal through a new decision criterion and applies the optimal CFAR algorithms such as the modified variable index (MVI) and automatic censored cell averaging-based ordered data variability (ACCA-ODV). The Monte Carlo simulation results of the proposed CFAR algorithm showed a high detection probability of 93.8% in homogeneous and nonhomogeneous environments based on an SNR of 25 dB. In addition, this paper presents the hardware design, field-programmable gate array (FPGA)-based implementation, and verification results for the practical application of the proposed algorithm. We reduced the hardware complexity by time-sharing sum and square operations and by replacing division operations with multiplication operations when calculating decision parameters. We also developed a low-complexity and high-speed sorter architecture that performs sorting for the partial data in leading and lagging windows. As a result, the implementation used 8260 LUTs and 3823 registers and took 0.6 µs to operate. Compared with the previously proposed FPGA implementation results, it is confirmed that the complexity and operation speed of the proposed CFAR processor are very suitable for real-time implementation.

FPGA Implementation of the Chirp-Scaling Algorithm for Real-Time Synthetic Aperture Radar Imaging.

Synthetic aperture radar (SAR), which can generate images of regions or objects, is an important research area of radar. The chirp scaling algorithm (CSA) is a representative SAR imaging algorithm. The CSA has a simple structure comprising phase compensation and fast Fourier transform (FFT) operations by replacing interpolation for range cell migration correction (RCMC) with phase compensation. However, real-time processing still requires many computations and a long execution time. Therefore, it is necessary to develop a hardware accelerator to improve the speed of algorithm processing. In addition, the demand for a small SAR system that can be mounted on a small aircraft or drone and that satisfies the constraints of area and power consumption is increasing. In this study, we proposed a CSA-based SAR processor that supports FFT and phase compensation operations and presents field-programmable gate array (FPGA)-based implementation results. We also proposed a modified CSA flow that simplifies the traditional CSA flow by changing the order in which the transpose operation occurs. Therefore, the proposed CSA-based SAR processor was designed to be suitable for modified CSA flow. We designed the multiplier for FFT to be shared for phase compensation, thereby achieving area efficiency and simplifying the data flow. The proposed CSA-based SAR processor was implemented on a Xilinx UltraScale+ MPSoC FPGA device and designed using Verilog-HDL. After comparing the execution times of the proposed SAR processor and the ARM cortex-A53 microprocessor, we observed a 136.2-fold increase in speed for the 4096 × 4096-pixel image.

sEMG-Based Hand Gesture Recognition Using Binarized Neural Network.

Recently, human-machine interfaces (HMI) that make life convenient have been studied in many fields. In particular, a hand gesture recognition (HGR) system, which can be implemented as a wearable system, has the advantage that users can easily and intuitively control the device. Among the various sensors used in the HGR system, the surface electromyography (sEMG) sensor is independent of the acquisition environment, easy to wear, and requires a small amount of data. Focusing on these advantages, previous sEMG-based HGR systems used several sensors or complex deep-learning algorithms to achieve high classification accuracy. However, systems that use multiple sensors are bulky, and embedded platforms with complex deep-learning algorithms are difficult to implement. To overcome these limitations, we propose an HGR system using a binarized neural network (BNN), a lightweight convolutional neural network (CNN), with one dry-type sEMG sensor, which is implemented on a field-programmable gate array (FPGA). The proposed HGR system classifies nine dynamic gestures that can be useful in real life rather than static gestures that can be classified relatively easily. Raw sEMG data collected from a dynamic gesture are converted into a spectrogram with information in the time-frequency domain and transferred to the classifier. As a result, the proposed HGR system achieved 95.4% classification accuracy, with a computation time of 14.1 ms and a power consumption of 91.81 mW.

Risk of Metachronous Colorectal Advanced Neoplasia and Cancer in Patients With 3-4 Nonadvanced Adenomas at Index Colonoscopy: A Systematic Review and Meta-Analysis.

INTRODUCTION: This systematic review and meta-analysis evaluated the available evidence on the risk of metachronous advanced neoplasia (AN) and colorectal cancer (CRC) in patients with 3-4 nonadvanced adenomas (NAAs). METHODS: We searched MEDLINE, EMBASE, and Cochrane Library databases up to January 2021 for studies evaluating metachronous AN and CRC risk by comparing 3 groups (1-2 vs 3-4 vs ≥5 NAAs) at index colonoscopy. The estimates for risk of metachronous AN and CRC were evaluated using random-effects models. RESULTS: Fifteen studies (n = 36,375) were included. The risk of metachronous AN was significantly higher in the 3-4 NAAs group than in the 1-2 NAAs group (relative risk [RR] 1.264, 95% confidence interval [CI] 1.053-1.518, P = 0.012; I2 = 0%); there was no difference between the ≥ 5 NAAs and 3-4 NAAs groups (RR 1.962, 95% CI 0.972-3.958, P = 0.060; I2 = 68%). The risks of metachronous CRC between the 1-2 NAAs and 3-4 NAAs groups (RR 2.663, 95% CI 0.391-18.128, P = 0.317; I2 = 0%) or the 3-4 NAAs and ≥ 5 NAAs groups (RR 1.148, 95% CI 0.142-9.290, P = 0.897; I2 = 0%) were not significantly different. DISCUSSION: Although the risk of metachronous AN was greater in the 3-4 NAAs group than in the 1-2 NAAs group, the risk of metachronous AN and CRC between the 3-4 NAAs and ≥ 5 NAAs groups was not different. This suggests that further studies on metachronous AN and CRC risk in the 3-4 NAAs group are warranted to confirm a firm ≥5-year interval surveillance colonoscopy.

Improving the tolerability and safety of 1-L polyethylene glycol plus low-dose ascorbic acid for bowel preparation in a healthy population: a randomized multicenter clinical trial.

BACKGROUND AND AIMS: One-liter polyethylene glycol plus ascorbic acid (PEG-ASC) improves patient tolerability, but some patients still show low tolerability to a relatively high content of ASC. This study aimed to improve the tolerability and safety of 1-L PEG with low-dose ASC in comparison with standard 1-L and 2-L PEG-ASC. METHODS: This was a randomized, controlled, double-blinded, multicenter, noninferiority trial involving 215 healthy adults who underwent colonoscopy from June 2020 to January 2021. Efficacy, tolerability, and safety were compared among 1-L PEG with low-dose ASC (50% lower ASC concentration in group A and 25% lower ASC concentration in groups B and C) and standard 1-L and 2-L PEG-ASC with all split regimens. RESULTS: One-liter PEG with low-dose ASC (groups A-C) had similar bowel cleansing efficacies according to the Harefield Cleansing Scale and Boston Bowel Preparation Scale, without negative clinical performance, compared with standard 1-L and 2-L PEG-ASC preparation (all P > .1). One-liter PEG with low-dose ASC had better tolerability compared with 2-L PEG-ASC and less residual fluid retention in the stomach compared with 1-L PEG-ASC, proportional to the amount of ASC. No significant differences were found in the incidences of overall adverse events, mild adverse events, or death or in the occurrence of gastroduodenal erosion or ulcer in upper endoscopy. CONCLUSIONS: One-liter PEG with low-dose ASC (25%-50% reduction in dose) for bowel cleansing showed similar efficacy and safety compared with standard 1-L or 2-L PEG-ASC, better tolerability compared with 2-L PEG-ASC, and less residual gastric fluid retention compared with standard 1-L PEG-ASC. (Clinical trial registration number: KCT0005490.).

Does precutting prior to endoscopic piecemeal resection of large colorectal neoplasias reduce local recurrence? A KASID multicenter study.

BACKGROUND: It would be expected that local recurrence could be reduced by performing precutting (with sufficient margins) prior to endoscopic piecemeal mucosal resection (EPMR). We explored the clinical outcomes and local recurrence after precutting EPMR of large colorectal neoplasias. METHODS: Between January 2005 and December 2015, in total, 223 patients with colorectal neoplasias ≥ 2 cm in diameter removed via EPMR in four tertiary hospitals were enrolled. The patients were divided into a precut EPMR group (n = 62) and a non-precut EPMR group (n = 161). We retrospectively evaluated clinical outcomes and factors associated with local recurrence. RESULTS: The mean total procedure time was significantly shorter in the non-precut EPMR group than in the precut EPMR group. However, the number of pieces, and the complete resection and recurrence rates, did not differ significantly [for the latter, precut 8.1% vs. non-precut 9.9%, P = 0.668]. The complete resection rate, number of pieces, and use of argon plasma coagulation (APC) were significantly associated with the local recurrence rate on univariate analysis. In the Cox's proportional hazards model, prophylactic APC [hazard ratio 0.307, 95% confidence interval (CI) 0.114-0.823; P = 0.019] and complete resection rate (odds ratio 0.083, 95% CI 0.011-0.655; P = 0.018) were significantly associated with the local recurrence rate. CONCLUSION: Precutting prior to EPMR did not significantly reduce the local recurrence rate or the number of resected pieces. Histologically complete resection, reducing the number of pieces, and prophylactic APC seem to be important in terms of reducing local recurrence.

Safety of Gastrointestinal Endoscopy in Korea: A Nationwide Survey and Population-Based Study.

BACKGROUND: Attention should be paid to endoscopy-related complications and safety-related accidents that may occur in the endoscopy unit. This study investigated the current status of complications associated with diagnostic and therapeutic endoscopy in Korea. METHODS: A questionnaire survey on endoscopy-related complications was conducted in a total of 50 tertiary or general hospitals in Korea. The results were compared to the population-level claims data from the Health Insurance Review & Assessment Service (HIRA), which analyzed endoscopy procedures conducted in 2017 in Korea. RESULTS: The incidences of bleeding associated with diagnostic and therapeutic esophagogastroduodenoscopy (EGD) and with diagnostic and therapeutic colonoscopy were 0.224% and 3.155% and 0.198% and 0.356%, respectively, in the 2017 HIRA claims data, compared to 0.012% and 1.857%, and 0.024% and 0.717%, in the 50 hospitals surveyed. The incidences of perforation associated with diagnostic and therapeutic EGD and with diagnostic and therapeutic colonoscopy were 0.023% and 0.613%, and 0.007% and 0.013%, respectively, in the 2017 HIRA claims data compared to 0.001% and 0.325%, and 0.017% and 0.206%, in the 50 hospitals surveyed. In the HIRA claims data, the incidence of bleeding/perforation after diagnostic colonoscopy in clinics, community hospitals, general hospitals, and tertiary hospitals was 0.129%/0.000%, 0.088%/0.004%, 0.262%/0.009%, and 0.479%/0.030% respectively, and the corresponding incidence of bleeding/perforation after therapeutic colonoscopy was 0.258%/0.004%, 0.401%/0.007%, 0.408%/0.024%, and 0.731%/0.055%. CONCLUSION: The incidences of complications associated with diagnostic and therapeutic EGD or colonoscopy tended to increase with the hospital volume in Korea. TRIAL REGISTRATION: Clinical Research Information Service Identifier: KCT0001728.

High-Speed Continuous Wavelet Transform Processor for Vital Signal Measurement Using Frequency-Modulated Continuous Wave Radar.

This paper proposes a high-speed continuous wavelet transform (CWT) processor to analyze vital signals extracted from a frequency-modulated continuous wave (FMCW) radar sensor. The proposed CWT processor consists of a fast Fourier transform (FFT) module, complex multiplier module, and inverse FFT (IFFT) module. For high-throughput processing, the FFT and IFFT modules are designed with the pipeline FFT architecture of radix-2 single-path delay feedback (R2SDF) and mixed-radix multipath delay commutator (MRMDC) architecture, respectively. In addition, the IFFT module and the complex multiplier module perform a four-channel operation to reduce the processing time from repeated operations. Simultaneously, the MRMDC IFFT module minimizes the circuit area by reducing the number of non-trivial multipliers by using a mixed-radix algorithm. In addition, the proposed CWT processor can support variable lengths of 8, 16, 32, 64, 128, 256, 512, and 1024 to analyze various vital signals. The proposed CWT processor was implemented in a field-programmable gate array (FPGA) device and verified through the measurement of heartbeat and respiration from an FMCW radar sensor. Experimental results showed that the proposed CWT processor can reduce the processing time by 48.4-fold and 40.7-fold compared to MATLAB software with Intel i7 CPU. Moreover, it can be confirmed that the proposed CWT processor can reduce the processing time by 73.3% compared to previous FPGA-based implementations.

Method for Improving Range Resolution of Indoor FMCW Radar Systems Using DNN.

Various studies on object detection are being conducted, and in this regard, research on frequency-modulated continuous wave (FMCW) RADAR is being actively conducted. FMCW RADAR requires high-distance resolution to accurately detect objects. However, if the distance resolution is high, a high-modulation bandwidth is required, which has a prohibitively high cost. To address this issue, we propose a two-step algorithm to detect the location of an object through DNN using many low-cost FMCW RADARs. The algorithm first infers the sector by measuring the distance to the object for each FMCW RADAR and then measures the position through the grid according to the inferred sector. This improves the distance resolution beyond the modulation bandwidth. Additionally, to detect multiple targets, we propose a Gaussian filter. Multiple targets are detected through an ordered-statistic constant false-alarm rate (OS-CFAR), and there is an 11% probability that multiple targets cannot be detected. In the lattice structure proposed in this paper, the performance of the proposed algorithm compared to those in existing works was confirmed with respect to the cost function. The difference in performance versus complexity was also confirmed when the proposed algorithm had the same complexity and the same performance, and it was confirmed that there was a performance improvement of up to five-fold compared to those in previous papers. In addition, multi-target detection was shown in this paper. Through MATLAB simulation and actual measurement on a single target, RMSEs were 0.3542 and 0.41002 m, respectively, and through MATLAB simulation and actual measurement on multiple targets, RMSEs were confirmed to be 0.548265 and 0.762542 m, respectively. Through this, it was confirmed that this algorithm works in real RADAR.

Usefulness of narrow-band imaging for the detection of remnant sessile-serrated adenoma (SSA) tissue after endoscopic resection: the KASID multicenter study.

BACKGROUND: A sessile-serrated adenoma (SSA) has a high risk for incomplete resection. Little is known regarding how to immediately detect remnant SSA tissue after endoscopic resection. We investigated the usefulness of narrow-band imaging (NBI) to detect remnant SSA tissue after endoscopic mucosal resection (EMR). METHODS: We performed a prospective randomized study on 138 patients who had suspicious SSA on colonoscopy at five centers. After EMR on the suspected SSA determined on the endoscopic morphology, all lesions were randomized into two inspection methods, NBI and white light endoscopy (WLE), to detect remnant tissue on the resected margin. If remnant tissue was detected, an additional resection was performed. Finally, we obtained quadrant biopsies on the resection margin to evaluate the incomplete resection. The proportion of incomplete resection was calculated by combining the detection of remnant tissue and the positivity of SSA cells on the final quadrant biopsies. The primary outcome was the proportion of remnant tissue detection, and the secondary outcome was the proportion of incomplete resection of SSA. RESULTS: In all, 145 lesions from 138 patients were removed. The diagnostic rate of SSA was 87.6% (127/145). After randomization, NBI inspection was performed on 69 lesions, and WLE inspection was performed on 76 lesions. The histologic diagnostic rate of SSA was 89.9% (62/69) in the NBI group and 85.5% (65/76) in the WLE group (p > 0.05). There were no significant differences in the detection of remnant tissue (12.9% (8/62) vs. 15.4% (10/65), p > 0.05), the proportion of SSA in remnant tissue (11.3% (7/62) vs. 12.3% (8/65), p > 0.05), or the proportion of incomplete resection (6.5 (4/62) vs. 10.8 (7/65), p > 0.05) between the NBI and WLE inspection groups, respectively. CONCLUSION: NBI was not superior to WLE for detecting remnant SSA tissue after EMR and could not decrease the proportion of incomplete resection of SSA.

A PAPR Reduction Technique for Fast Touch Sensors Adopting a Multiple Frequency Driving Method on Large Display Panels.

The multiple frequency driving method (MFDM) capacitive touch system (CTS), which drives transmit (TX) electrodes in parallel, has been developed to improve the touch-sensitivity of large touch screens at high speed. However, when driving multiple TX electrodes at the same time, TX signals are merged through the touch panel, which results in increasing the peak-to-average power ratio (PAPR) of combined signals. Due to the high PAPR, the signal is distorted out of the power amplifier's linear range, causing a touch malfunction. The MFDM CTS can avoid this problem by reducing the drive voltage or partially driving the TX electrodes in parallel. However, these methods cause a significant performance drop with respect to signal-to-noise ratio (SNR) in the MFDM systems. This paper proposes a stack method which reduces PAPR effectively without the performance degradation of MFDM and achieves real-time touch sensitivity in large display panels. The proposed method allocates a suitable phase for each TX electrode to reduce the peak power of combined signals. Instead of investigating all of the phases for the total number of TX electrodes, the optimal phase is estimated from the highest frequency to the lowest one and fixed one by one, which can reduce the required time to find a suitable phase considerably. As a result, it enables high-speed sensing of multi-touch on a large touch screen and effectively reduces PAPR to secure high signal-to-noise-ratio (SNR). Through experiments, it was verified that the proposed method in this paper has an SNR of 39.36 dB, achieving a gain of 19.35 and 5.98 dB compared to the existing touch system method and the algorithm used in the communication system, respectively.

FPGA Implementation of an Efficient FFT Processor for FMCW Radar Signal Processing.

This paper presents the design and implementation results of an efficient fast Fourier transform (FFT) processor for frequency-modulated continuous wave (FMCW) radar signal processing. The proposed FFT processor is designed with a memory-based FFT architecture and supports variable lengths from 64 to 4096. Moreover, it is designed with a floating-point operator to prevent the performance degradation of fixed-point operators. FMCW radar signal processing requires windowing operations to increase the target detection rate by reducing clutter side lobes, magnitude calculation operations based on the FFT results to detect the target, and accumulation operations to improve the detection performance of the target. In addition, in some applications such as the measurement of vital signs, the phase of the FFT result has to be calculated. In general, only the FFT is implemented in the hardware, and the other FMCW radar signal processing is performed in the software. The proposed FFT processor implements not only the FFT, but also windowing, accumulation, and magnitude/phase calculations in the hardware. Therefore, compared with a processor implementing only the FFT, the proposed FFT processor uses 1.69 times the hardware resources but achieves an execution time 7.32 times shorter.

Compressive Sensing-Based SAR Image Reconstruction from Sparse Radar Sensor Data Acquisition in Automotive FMCW Radar System.

In this paper, we propose a method for reconstructing synthetic aperture radar (SAR) images by applying a compressive sensing (CS) technique to sparsely acquired radar sensor data. In general, SAR image reconstruction algorithms require radar sensor data acquired at regular spatial intervals. However, when the speed of the radar-equipped platform is not constant, it is difficult to consistently perform regular data acquisitions. Therefore, we used the CS-based signal recovery method to efficiently reconstruct SAR images even when regular data acquisition was not performed. In the proposed method, we used the l1-norm minimization to overcome the non-uniform data acquisition problem, which replaced the Fourier transform and inverse Fourier transform in the conventional SAR image reconstruction method. In addition, to reduce the phase distortion of the recovered signal, the proposed method was applied to each of the in-phase and quadrature components of the acquired radar sensor data. To evaluate the performance of the proposed method, we conducted experiments using an automotive frequency-modulated continuous wave radar sensor. Then, the quality of the SAR image reconstructed with data acquired at regular intervals was compared with the quality of images reconstructed with data acquired at non-uniform intervals. Using the proposed method, even if only 70% of the regularly acquired radar sensor data was used, a SAR image having a correlation of 0.83 could be reconstructed.

Signal Expansion Method in Indoor FMCW Radar Systems for Improving Range Resolution.

As various unmanned autonomous driving technologies such as autonomous vehicles and autonomous driving drones are being developed, research on FMCW radar, a sensor related to these technologies, is actively being conducted. The range resolution, which is a parameter for accurately detecting an object in the FMCW radar system, depends on the modulation bandwidth. Expensive radars have a large modulation bandwidth, use the band above 77 GHz, and are mainly used as in-vehicle radar sensors. However, these high-performance radars have the disadvantage of being expensive and burdensome for use in areas that require precise sensors, such as indoor environment motion detection and autonomous drones. In this paper, the range resolution is improved beyond the limited modulation bandwidth by extending the beat frequency signal in the time domain through the proposed Adaptive Mirror Padding and Phase Correction Padding. The proposed algorithm has similar performance in the existing Zero Padding, Mirror Padding, and Range RMSE, but improved results were confirmed through the ρs indicating the size of the side lobe compared to the main lobe and the accurate detection rate of the OS CFAR. In the case of ρs, it was confirmed that with single targets, Adaptive Mirror Padding was improved by about 3 times and Phase Correct Padding was improved by about 6 times compared to the existing algorithm. The results of the OS CFAR were divided into single targets and multiple targets to confirm the performance. In single targets, Adaptive Mirror Padding improved by about 10% and Phase Correct Padding by about 20% compared to the existing algorithm. In multiple targets, Phase Correct Padding improved by about 20% compared to the existing algorithm. The proposed algorithm was verified through the MATLAB Tool and the actual FMCW radar. As the results were similar in the two experimental environments, it was verified that the algorithm works in real radar as well.