Concurrent self-assembly of RGB microLEDs for next-generation displays.

MicroLED displays have been in the spotlight as the next-generation displays owing to their various advantages, including long lifetime and high brightness compared with organic light-emitting diode (OLED) displays. As a result, microLED technology1,2 is being commercialized for large-screen displays such as digital signage and active R&D programmes are being carried out for other applications, such as augmented reality3, flexible displays4 and biological imaging5. However, substantial obstacles in transfer technology, namely, high throughput, high yield and production scalability up to Generation 10+ (2,940 × 3,370 mm2) glass sizes, need to be overcome so that microLEDs can enter mainstream product markets and compete with liquid-crystal displays and OLED displays. Here we present a new transfer method based on fluidic self-assembly (FSA) technology, named magnetic-force-assisted dielectrophoretic self-assembly technology (MDSAT), which combines magnetic and dielectrophoresis (DEP) forces to achieve a simultaneous red, green and blue (RGB) LED transfer yield of 99.99% within 15 min. By embedding nickel, a ferromagnetic material, in the microLEDs, their movements were controlled by using magnets, and by applying localized DEP force centred around the receptor holes, these microLEDs were effectively captured and assembled in the receptor site. Furthermore, concurrent assembly of RGB LEDs were demonstrated through shape matching between microLEDs and receptors. Finally, a light-emitting panel was fabricated, showing damage-free transfer characteristics and uniform RGB electroluminescence emission, demonstrating our MDSAT method to be an excellent transfer technology candidate for high-volume production of mainstream commercial products.

Fluidic self-assembly for MicroLED displays by controlled viscosity.

Displays in which arrays of microscopic 'particles', or chiplets, of inorganic light-emitting diodes (LEDs) constitute the pixels, termed MicroLED displays, have received considerable attention1,2 because they can potentially outperform commercially available displays based on organic LEDs3,4 in terms of power consumption, colour saturation, brightness and stability and without image burn-in issues1,2,5-7. To manufacture these displays, LED chiplets must be epitaxially grown on separate wafers for maximum device performance and then transferred onto the display substrate. Given that the number of LEDs needed for transfer is tremendous-for example, more than 24 million chiplets smaller than 100 µm are required for a 50-inch, ultra-high-definition display-a technique capable of assembling tens of millions of individual LEDs at low cost and high throughput is needed to commercialize MicroLED displays. Here we demonstrate a MicroLED lighting panel consisting of more than 19,000 disk-shaped GaN chiplets, 45 µm in diameter and 5 µm in thickness, assembled in 60 s by a simple agitation-based, surface-tension-driven fluidic self-assembly (FSA) technique with a yield of 99.88%. The creation of this level of large-scale, high-yield FSA of sub-100-µm chiplets was considered a significant challenge because of the low inertia of the chiplets. Our key finding in overcoming this difficulty is that the addition of a small amount of poloxamer to the assembly solution increases its viscosity which, in turn, increases liquid-to-chiplet momentum transfer. Our results represent significant progress towards the ultimate goal of low-cost, high-throughput manufacture of full-colour MicroLED displays by FSA.

Human-Unrecognizable Differential Private Noised Image Generation Method.

Differential privacy has emerged as a practical technique for privacy-preserving deep learning. However, recent studies on privacy attacks have demonstrated vulnerabilities in the existing differential privacy implementations for deep models. While encryption-based methods offer robust security, their computational overheads are often prohibitive. To address these challenges, we propose a novel differential privacy-based image generation method. Our approach employs two distinct noise types: one makes the image unrecognizable to humans, preserving privacy during transmission, while the other maintains features essential for machine learning analysis. This allows the deep learning service to provide accurate results, without compromising data privacy. We demonstrate the feasibility of our method on the CIFAR100 dataset, which offers a realistic complexity for evaluation.

Validation of resonance Raman spectroscopy-measured skin carotenoid status as a biomarker for fruit and vegetable intake in Korean adults.

Blood carotenoid concentration measurement is considered the gold standard for fruit and vegetable (F&V) intake estimation; however, this method is invasive and expensive. Recently, skin carotenoid status (SCS) measured by optical sensors has been evaluated as a promising parameter for F&V intake estimation. In this cross-sectional study, we aimed to validate the utility of resonance Raman spectroscopy (RRS)-assessed SCS as a biomarker of F&V intake in Korean adults. We used data from 108 participants aged 20-69 years who completed SCS measurements, blood collection and 3-d dietary recordings. Serum carotenoid concentrations were quantified using HPLC, and dietary carotenoid and F&V intakes were estimated via 3-d dietary records using a carotenoid database for common Korean foods. The correlations of the SCS with serum carotenoid concentrations, dietary carotenoid intake and F&V intake were examined to assess SCS validity. SCS was positively correlated with total serum carotenoid concentration (r = 0·52, 95 % CI = 0·36, 0·64, P < 0·001), serum ß-carotene concentration (r = 0·60, 95 % CI = 0·47, 0·71, P < 0·001), total carotenoid intake (r = 0·20, 95 % CI = 0·01, 0·37, P = 0·04), ß-carotene intake (r = 0·30, 95 % CI = 0·11, 0·46, P = 0·002) and F&V intake (r = 0·40, 95 % CI = 0·23, 0·55, P < 0·001). These results suggest that SCS can be a valid biomarker of F&V intake in Korean adults.

Deep learning approach to detection of colonoscopic information from unstructured reports.

BACKGROUND: Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information embedded in the reports can be used for various purposes, including colorectal cancer risk prediction, follow-up recommendation, and quality measurement. However, the availability and accessibility of unstructured text data are still insufficient despite the large amounts of accumulated data. We aimed to develop and apply deep learning-based natural language processing (NLP) methods to detect colonoscopic information. METHODS: This study applied several deep learning-based NLP models to colonoscopy reports. Approximately 280,668 colonoscopy reports were extracted from the clinical data warehouse of Samsung Medical Center. For 5,000 reports, procedural information and colonoscopic findings were manually annotated with 17 labels. We compared the long short-term memory (LSTM) and BioBERT model to select the one with the best performance for colonoscopy reports, which was the bidirectional LSTM with conditional random fields. Then, we applied pre-trained word embedding using large unlabeled data (280,668 reports) to the selected model. RESULTS: The NLP model with pre-trained word embedding performed better for most labels than the model with one-hot encoding. The F1 scores for colonoscopic findings were: 0.9564 for lesions, 0.9722 for locations, 0.9809 for shapes, 0.9720 for colors, 0.9862 for sizes, and 0.9717 for numbers. CONCLUSIONS: This study applied deep learning-based clinical NLP models to extract meaningful information from colonoscopy reports. The method in this study achieved promising results that demonstrate it can be applied to various practical purposes.

Task-Specific Adaptive Differential Privacy Method for Structured Data.

Data are needed to train machine learning (ML) algorithms, and in many cases often include private datasets that contain sensitive information. To preserve the privacy of data used while training ML algorithms, computer scientists have widely deployed anonymization techniques. These anonymization techniques have been widely used but are not foolproof. Many studies showed that ML models using anonymization techniques are vulnerable to various privacy attacks willing to expose sensitive information. As a privacy-preserving machine learning (PPML) technique that protects private data with sensitive information in ML, we propose a new task-specific adaptive differential privacy (DP) technique for structured data. The main idea of the proposed DP method is to adaptively calibrate the amount and distribution of random noise applied to each attribute according to the feature importance for the specific tasks of ML models and different types of data. From experimental results under various datasets, tasks of ML models, different DP mechanisms, and so on, we evaluate the effectiveness of the proposed task-specific adaptive DP method. Thus, we show that the proposed task-specific adaptive DP technique satisfies the model-agnostic property to be applied to a wide range of ML tasks and various types of data while resolving the privacy-utility trade-off problem.

Bypassing Heaven's Gate Technique Using Black-Box Testing.

In recent years, the number and sophistication of malware attacks on computer systems have increased significantly. One technique employed by malware authors to evade detection and analysis, known as Heaven's Gate, enables 64-bit code to run within a 32-bit process. Heaven's Gate exploits a feature in the operating system that allows the transition from a 32-bit mode to a 64-bit mode during execution, enabling the malware to evade detection by security software designed to monitor only 32-bit processes. Heaven's Gate poses significant challenges for existing security tools, including dynamic binary instrumentation (DBI) tools, widely used for program analysis, unpacking, and de-virtualization. In this paper, we provide a comprehensive analysis of the Heaven's Gate technique. We also propose a novel approach to bypass the Heaven's Gate technique using black-box testing. Our experimental results show that the proposed approach effectively bypasses and prevents the Heaven's Gate technique and strengthens the capabilities of DBI tools in combating advanced malware threats.

Clustering Approach for Detecting Multiple Types of Adversarial Examples.

With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model. As an adversarial example has recently been considered in the most severe problem of deep learning technology, its defense methods have been actively studied. Such effective defense methods against adversarial examples are categorized into one of the three architectures: (1) model retraining architecture; (2) input transformation architecture; and (3) adversarial example detection architecture. Especially, defense methods using adversarial example detection architecture have been actively studied. This is because defense methods using adversarial example detection architecture do not make wrong decisions for the legitimate input data while others do. In this paper, we note that current defense methods using adversarial example detection architecture can classify the input data into only either a legitimate one or an adversarial one. That is, the current defense methods using adversarial example detection architecture can only detect the adversarial examples and cannot classify the input data into multiple classes of data, i.e., legitimate input data and various types of adversarial examples. To classify the input data into multiple classes of data while increasing the accuracy of the clustering model, we propose an advanced defense method using adversarial example detection architecture, which extracts the key features from the input data and feeds the extracted features into a clustering model. From the experimental results under various application datasets, we show that the proposed method can detect the adversarial examples while classifying the types of adversarial examples. We also show that the accuracy of the proposed method outperforms the accuracy of recent defense methods using adversarial example detection architecture.

Proton pump inhibitors use and the risk of fatty liver disease: A nationwide cohort study.

BACKGROUND AND AIM: Proton pump inhibitor (PPI)-induced hypochondria can change the composition of the gut microbiota, inducing overgrowth of small bowel bacteria, which has been suggested to promote the development of fatty liver disease through the gut-liver axis. In this study, we aimed to investigate the association between PPI use and the risk of fatty liver disease. METHODS: A retrospective cohort study was conducted using the Korean National Health Insurance Service-National Sample Cohort, a nationwide population-based representative sample, from January 1, 2002, to December 31, 2015. PPI use was identified from treatment claims and considered as a time-varying variable. RESULTS: During 1 463 556 person-years of follow-up, 75 727 patients had at least one PPI prescription, and 3735 patients developed fatty liver disease. The hazard ratio for fatty liver disease comparing PPI users with non-PPI users was 1.68 (95% confidence interval, 1.61-1.75). When adjusted for multiple confounders, including age, sex, body mass index, smoking, alcohol intake, exercise, income level, and comorbidities, the association was still significant (hazard ratio, 1.50; 95% confidence interval, 1.44-1.57). After considering the amounts of PPIs stratified by cumulative defined daily dose, the dose-response effect was observed until 180 days. Subgroup analysis also revealed that PPI use was correlated to an increased risk of fatty liver disease. CONCLUSIONS: This current national wide cohort study suggests that PPI use was associated with an increased risk of fatty liver disease compared with non-use of PPIs. Clinicians should consider fatty liver as a potential risk when prescribing PPI.

Is Homomorphic Encryption-Based Deep Learning Secure Enough?

As the amount of data collected and analyzed by machine learning technology increases, data that can identify individuals is also being collected in large quantities. In particular, as deep learning technology-which requires a large amount of analysis data-is activated in various service fields, the possibility of exposing sensitive information of users increases, and the user privacy problem is growing more than ever. As a solution to this user's data privacy problem, homomorphic encryption technology, which is an encryption technology that supports arithmetic operations using encrypted data, has been applied to various field including finance and health care in recent years. If so, is it possible to use the deep learning service while preserving the data privacy of users by using the data to which homomorphic encryption is applied? In this paper, we propose three attack methods to infringe user's data privacy by exploiting possible security vulnerabilities in the process of using homomorphic encryption-based deep learning services for the first time. To specify and verify the feasibility of exploiting possible security vulnerabilities, we propose three attacks: (1) an adversarial attack exploiting communication link between client and trusted party; (2) a reconstruction attack using the paired input and output data; and (3) a membership inference attack by malicious insider. In addition, we describe real-world exploit scenarios for financial and medical services. From the experimental evaluation results, we show that the adversarial example and reconstruction attacks are a practical threat to homomorphic encryption-based deep learning models. The adversarial attack decreased average classification accuracy from 0.927 to 0.043, and the reconstruction attack showed average reclassification accuracy of 0.888, respectively.

Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack.

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.

Physical Activity Protects Against the Risk of Erosive Esophagitis on the Basis of Body Mass Index.

GOAL: To determine whether physical activity lowers the risk for erosive esophagitis on the basis of body mass index (BMI). BACKGROUND: Although previous studies have shown that physical activity is closely associated with erosive esophagitis, these data may be confounded by obesity. STUDY: In this retrospective study, we included 182,409 patients who underwent an upper endoscopy and were diagnosed with erosive esophagitis. The impact of the amount and intensity of physical activity on the risk for erosive esophagitis was analyzed based on BMI groups. Subjects were classified into three BMI groups with equal numbers in each group. RESULTS: Overall, 10.3% (n=18,859) of patients were diagnosed with erosive esophagitis. After adjusting for confounding factors, a greater amount of exercise [lower tertile: odd ratio (OR), 0.86; 95% confidence interval (CI), 0.77-0.96; middle tertile: OR, 0.91; 95%, CI 0.84-1.00; upper tertile: OR, 0.79; 95% CI, 0.73-0.85) and increased exercise intensity (lower tertile, moderate: OR, 0.61; 95% CI, 0.52-0.71; vigorous: OR, 0.51; 95% CI, 0.44-0.58; middle tertile, moderate: OR, 0.62; 95% CI, 0.55-0.70; vigorous: OR, 0.58; 95% CI, 0.51-0.65; upper tertile, moderate: OR, 0.58; 95% CI, 0.53-0.65; vigorous: OR, 0.58; 95% CI, 0.53-0.64) was associated with a decreased risk for erosive esophagitis in all 3 BMI groups. In addition, we observed that increased physical activity intensity notably decreased the risk for erosive esophagitis in subjects performing lesser physical activity, but slightly decreased the risk for erosive esophagitis in subjects performing more physical activity. CONCLUSION: Physical activity is inversely associated with erosive esophagitis.

Risk of comorbid cardiovascular disease in Korean long-term cancer survivors.

BACKGROUND: Cardiovascular disease (CVD) is a common cause of death in cancer survivors. We evaluated the risk of comorbid CVD in long-term cancer survivors according to specific cancer site. METHODS: Study subjects included 47,171 Koreans aged over 40 years who visited a health promotion centre between 2010 and 2012. Information on CVD and cancer was obtained from self-report. Comorbid CVD was defined as stroke, myocardial infarction or angina pectoris. The risk of comorbid CVD in survivors was compared with that in non-cancer subjects using multiple logistic regression analysis. RESULTS: Among cancer survivors (n = 3,753), 330 events of CVD were reported. Age- and sex-adjusted analysis showed that odds ratio (OR) for comorbid CVD in survivors was 1.52 (95% confidence interval: 1.34-1.72) compared to non-cancer subjects (n = 43,418). After adjusting for health-related behaviour and medical history, only lung cancer survivors had 2.44 (1.01-5.89)-fold higher OR for CVD. In stratified analysis, significantly higher OR for CVD was evident in lung cancer survivors with hypertension who did not perform regular physical exercise. CONCLUSIONS: Adult cancer survivors may have an increased risk of comorbid CVD that might be mediated in part by known cardiovascular risk factors depending on the specific cancer site.

Secure and Blockchain-Based Emergency Driven Message Protocol for 5G Enabled Vehicular Edge Computing.

Basic safety message (BSM) are messages that contain core elements of a vehicle such as vehicle's size, position, speed, acceleration and others. BSM are lightweight messages that can be regularly broadcast by the vehicles to enable a variety of applications. On the other hand, event-driven message (EDM) are messages generated at the time of occurrence such as accidents or roads sliding and can contain much more heavy elements including pictures, audio or videos. Security, architecture and communication solutions for BSM use cases have been largely documented on in the literature contrary to EDM due to several concerns such as the variant size of EDM, the appropriate architecture along with latency, privacy and security. In this paper, we propose a secure and blockchain based EDM protocol for 5G enabled vehicular edge computing. To offer scalability and latency for the proposed scenario, we adopt a 5G cellular architecture due to its projected features compared to 4G tong-term evaluation (LTE) for vehicular communications. We consider edge computing to provide local processing of EDM that can improve the response time of public agencies (ambulances or rescue teams) that may intervene to the scene. We make use of lightweight multi-receiver signcryption scheme without pairing that offers low time consuming operations, security, privacy and access control. EDM records need to be kept into a distributed system which can guarantee reliability and auditability of EDM. To achieve this, we construct a private blockchain based on the edge nodes to store EDM records. The performance analysis of the proposed protocol confirms its efficiency.

Software-Defined Network-Based Vehicular Networks: A Position Paper on Their Modeling and Implementation.

There is a strong devotion in the automotive industry to be part of a wider progression towards the Fifth Generation (5G) era. In-vehicle integration costs between cellular and vehicle-to-vehicle networks using Dedicated Short Range Communication could be avoided by adopting Cellular Vehicle-to-Everything (C-V2X) technology with the possibility to re-use the existing mobile network infrastructure. More and more, with the emergence of Software Defined Networks, the flexibility and the programmability of the network have not only impacted the design of new vehicular network architectures but also the implementation of V2X services in future intelligent transportation systems. In this paper, we define the concepts that help evaluate software-defined-based vehicular network systems in the literature based on their modeling and implementation schemes. We first overview the current studies available in the literature on C-V2X technology in support of V2X applications. We then present the different architectures and their underlying system models for LTE-V2X communications. We later describe the key ideas of software-defined networks and their concepts for V2X services. Lastly, we provide a comparative analysis of existing SDN-based vehicular network system grouped according to their modeling and simulation concepts. We provide a discussion and highlight vehicular ad-hoc networks' challenges handled by SDN-based vehicular networks.

Breast Cancer Risk Associations with Digital Mammographic Density by Pixel Brightness Threshold and Mammographic System.

Purpose To compare three mammographic density measures defined by different pixel intensity thresholds as predictors of breast cancer risk for two different digital mammographic systems. Materials and Methods The Korean Breast Cancer Study included 398 women with invasive breast cancer and 737 control participants matched for age at mammography (±1 year), examination date, mammographic system, and menopausal status. Mammographic density was measured by using the automated Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software and the semiautomated Cumulus software at the conventional threshold (Cumulus) and at increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were Box-Cox-transformed and adjusted for age, body mass index, and menopausal status. Conditional logistic regression was used to estimate risk associations. For calculation of measures of predictive value, the change in odds per standard deviation (OPERA) and the area under the receiver operating characteristic curve (AUC) were used. Results For dense area, with use of the direct conversion system the OPERAs were 1.72 (95% confidence interval [CI]: 1.38, 2.15) for LIBRA, 1.58 (95% CI: 1.27, 1.97) for Cumulus, 2.04 (95% CI: 1.60, 2.59) for Altocumulus, and 3.48 (95% CI: 2.45, 4.47) for Cirrocumulus (P < .001). The corresponding AUCs were 0.70, 0.69, 0.76, and 0.89, respectively. With use of the indirect conversion system, the corresponding OPERAs were 1.50 (95% CI: 1.28, 1.76), 1.36 (95% CI: 1.16, 1.59), 1.40 (95% CI: 1.19, 1.64), and 1.47 (95% CI: 1.25, 1.73) (P < .001) and the AUCs were 0.64, 0.60, 0.61, and 0.63, respectively. Conclusion It is possible that mammographic density defined by higher pixel thresholds could capture more risk-predicting information with use of a direct conversion mammographic system; the mammographically bright, rather than white, regions are etiologically important. © RSNA, 2017.

Young Age and Risk of Lymph Node Metastasis in Differentiated Type Early Gastric Cancer.

BACKGROUND: Young patients with gastric cancer reportedly have a worse prognosis than older patients due to delayed diagnosis and more aggressive tumor behavior. However, it is unclear whether this applies to early gastric cancer (EGC), for which endoscopic resection is indicated. We investigated the association between age and lymph node metastasis (LNM). METHODS: We identified 4055 patients diagnosed with EGC of differentiated histology who underwent surgery. The association between age and LNM was examined using logistic regression for each T stage separately with adjustments for multiple covariates. We compared LNM rates for each of the Japanese Endoscopic Resection Guidelines criteria in younger (< 40 years) and older patients (40 years). RESULTS: The median number of lymph nodes examined was the same for T1a and T1b stages (n = 34). The median number of lymph nodes examined was not significantly different within T1a stage (P = 0.093), but within T1b stage, the number of lymph nodes examined was significantly different (P = 0.019). The highest number was between 50 and 59 years (median = 37), and the lowest number was in the 20 to 49 years and older than 70 age brackets (median = 34). LNM rate and age were not significantly associated within each stage (P values 0.269, 0.783 for T1a and T1b, respectively). Among patients fulfilling endoscopic resection criteria, the LNM rate in younger patients was lower than in older patients. CONCLUSIONS: In differentiated-type EGC, young age at diagnosis was not associated with LNM rate. Therefore, endoscopic resection criteria for early gastric cancer can be applied to younger patients.

Identification of risk factors for sessile and traditional serrated adenomas of the colon by using big data analysis.

BACKGROUND AND AIM: Little is known about the risk factors associated with serrated polyps, because the early studies, which occurred before the new World Health Organization classification was introduced, included mixtures of serrated polyps. This study aimed to evaluate the risk factors associated with the presence of sessile serrated adenomas (SSAs) and traditional serrated adenomas (TSAs) using big data analytics. METHODS: Using a case-control design, we evaluated the risk factors associated with the presence of SSAs and TSAs. Subjects who underwent colonoscopies from 2002 to 2012 as part of the comprehensive health screening programs undertaken at the Samsung Medical Center, Korea, participated in this study. RESULTS: Of the 48 677 individuals who underwent colonoscopies, 183 (0.4%) had SSAs and 212 (0.4%) had TSAs. The multivariate analysis determined that being aged ≥ 50 years (odds ratio [OR] 1.91, 95% confidential interval [CI] 1.27-2.90, P = 0.002) and a history of colorectal cancer among first-degree relatives (OR 3.14, 95% CI 1.57-6.27, P = 0.001) were significant risk factors associated with the presence of SSAs and that being aged ≥ 50 years (OR 2.61, 95% CI 1.79-3.80, P < 0.001), obesity (OR 1.63, 95% CI 1.12-2.36, P = 0.010), and a higher triglyceride level (OR 1.63, 95% CI 1.12-2.36, P = 0.010) were independent risk factors associated with the presence of TSAs. CONCLUSIONS: We used big data analytics to determine the risk factors associated with the presence of specific polyp subgroups, and individuals who have these risk factors should be carefully scrutinized for the presence of SSAs or TSAs during screening colonoscopies.

Efficient Proximity Computation Techniques Using ZIP Code Data for Smart Cities †.

In this paper, we are interested in computing ZIP code proximity from two perspectives, proximity between two ZIP codes (Ad-Hoc) and neighborhood proximity (Top-K). Such a computation can be used for ZIP code-based target marketing as one of the smart city applications. A naïve approach to this computation is the usage of the distance between ZIP codes. We redefine a distance metric combining the centroid distance with the intersecting road network between ZIP codes by using a weighted sum method. Furthermore, we prove that the results of our combined approach conform to the characteristics of distance measurement. We have proposed a general and heuristic approach for computing Ad-Hoc proximity, while for computing Top-K proximity, we have proposed a general approach only. Our experimental results indicate that our approaches are verifiable and effective in reducing the execution time and search space.

An Enhanced Map-Matching Algorithm for Real-Time Position Accuracy Improvement with a Low-Cost GPS Receiver.

This paper proposes a real-time position accuracy improvement method for a low-cost global positioning system (GPS), which uses geographic data for forming a digital road database in the digital map information. We link the vehicle's location to the position on the digital map using the map-matching algorithm to improve the position accuracy. In the proposed method, we can distinguish the vehicle direction on the road and enhance the horizontal accuracy using the geographic data composed of the vector point set of the digital map. We use the iterative closest point (ICP) algorithm that calculates the rotation matrix and the translation vector to compensate for the disparity between the GPS and the digital map information. We also use the least squares method to correct the error caused by the rotation of the ICP algorithm and link on the digital map to eliminate the residual disparity. Finally, we implement the proposed method in real time with a low-cost embedded system and demonstrate the effectiveness of the proposed method through various experiments.