National Electronics and Computer Technology Center

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The accuracy of a source location estimate is very sensitive to the accurate knowledge of receiver locations. This paper performs analysis and develops a solution for locating a moving source using time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements in the presence of random errors in receiver locations. The analysis starts with the Cramér–Rao lower bound (CRLB) for the problem, and derives the increase in mean-square error (MSE) in source location estimate if the receiver locations are assumed correct but in fact have error. A solution is then proposed that takes the receiver error into account to reduce the estimation error, and it is shown analytically, under some mild approximations, to achieve the CRLB accuracy for far-field sources. The proposed solution is closed form, computationally efficient, and does not have divergence problem as in iterative techniques. Simulations corroborate the theoretical results and the good performance of the proposed method. </para>

As CBCT is widely used in dental and maxillofacial imaging, it is important for users as well as referring practitioners to understand the basic concepts of this imaging modality. This review covers the technical aspects of each part of the CBCT imaging chain. First, an overview is given of the hardware of a CBCT device. The principles of cone beam image acquisition and image reconstruction are described. Optimization of imaging protocols in CBCT is briefly discussed. Finally, basic and advanced visualization methods are illustrated. Certain topics in these review are applicable to all types of radiographic imaging (e.g. the principle and properties of an X-ray tube), others are specific for dental CBCT imaging (e.g. advanced visualization techniques).

Graphene oxide (GO) has recently attracted great attention due to its unique chemical and physical properties. In this work, the GO nanosheets were prepared by a chemical exfoliation technique. The structural and optical properties of the as-prepared GO nanosheets were characterized by Raman, FTIR, UV-vis and photoluminescence spectroscopy. The FTIR results confirmed the existence of oxygen-containing groups on the GO nanosheets and the photoluminescence spectra of GO nanosheets showed the emission peak in the visible regions. These results indicate that the GO nanosheets could be used as a promising new material for biological applications such as biofunctionalization and fluorescence biosensors.

Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.

This paper conducts a performance analysis of two popular private blockchain platforms, Hyperledger Fabric and Ethereum (private deployment), to assess the performance and limitations of these state-of-the-art platforms. Blockchain, a decentralized transaction and data management technology, is said to be the technology that will have similar impacts as the Internet had on people's lives. Many industries have become interested in adopting blockchain in their IT systems, but scalability is an often- cited concern of current blockchain technology. Therefore, the goals of this preliminary performance analysis are twofold. First, a methodology for evaluating a blockchain platform is developed. Second, the analysis results are presented to inform practitioners in making decisions regarding adoption of blockchain technology in their IT systems. The experimental results, based on varying number of transactions, show that Hyperledger Fabric consistently outperforms Ethereum across all evaluation metrics which are execution time, latency and throughput. Additionally, both platforms are still not competitive with current database systems in term of performances in high workload scenarios.

The development of simple fluorescent and colorimetric assays that enable point-of-care DNA and RNA detection has been a topic of significant research because of the utility of such assays in resource limited settings. The most common motifs utilize hybridization to a complementary detection strand coupled with a sensitive reporter molecule. Here, a paper-based colorimetric assay for DNA detection based on pyrrolidinyl peptide nucleic acid (acpcPNA)-induced nanoparticle aggregation is reported as an alternative to traditional colorimetric approaches. PNA probes are an attractive alternative to DNA and RNA probes because they are chemically and biologically stable, easily synthesized, and hybridize efficiently with the complementary DNA strands. The acpcPNA probe contains a single positive charge from the lysine at C-terminus and causes aggregation of citrate anion-stabilized silver nanoparticles (AgNPs) in the absence of complementary DNA. In the presence of target DNA, formation of the anionic DNA-acpcPNA duplex results in dispersion of the AgNPs as a result of electrostatic repulsion, giving rise to a detectable color change. Factors affecting the sensitivity and selectivity of this assay were investigated, including ionic strength, AgNP concentration, PNA concentration, and DNA strand mismatches. The method was used for screening of synthetic Middle East respiratory syndrome coronavirus (MERS-CoV), Mycobacterium tuberculosis (MTB), and human papillomavirus (HPV) DNA based on a colorimetric paper-based analytical device developed using the aforementioned principle. The oligonucleotide targets were detected by measuring the color change of AgNPs, giving detection limits of 1.53 (MERS-CoV), 1.27 (MTB), and 1.03 nM (HPV). The acpcPNA probe exhibited high selectivity for the complementary oligonucleotides over single-base-mismatch, two-base-mismatch, and noncomplementary DNA targets. The proposed paper-based colorimetric DNA sensor has potential to be an alternative approach for simple, rapid, sensitive, and selective DNA detection.

With the fast development in ad hoc wireless communications and vehicular technology, it is foreseeable that, in the near future, traffic information will be collected and disseminated in real-time by mobile sensors instead of fixed sensors used in the current infrastructure-based traffic information systems. A distributed network of vehicles such as a vehicular ad hoc network (VANET) can easily turn into an infrastructure-less self-organizing traffic information system, where any vehicle can participate in collecting and reporting useful traffic information such as section travel time, flow rate, and density. Disseminating traffic information relies on broadcasting protocols. Recently, there have been a significant number of broadcasting protocols for VANETs reported in the literature. In this paper, we classify and provide an in-depth review of these protocols.

Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address.

We propose to use real-time EEG signal to classify happy and unhappy emotions elicited by pictures and classical music. We use PSD as a feature and SVM as a classifier. The average accuracies of subject-dependent model and subject-independent model are approximately 75.62% and 65.12%, respectively. Considering each pair of channels, temporal pair of channels (T7 and T8) gives a better result than the other area. Considering different frequency bands, high-frequency bands (Beta and Gamma) give a better result than low-frequency bands. Considering different time durations for emotion elicitation, that result from 30 seconds does not have significant difference compared with the result from 60 seconds. From all of these results, we implement real-time EEG-based happiness detection system using only one pair of channels. Furthermore, we develop games based on the happiness detection system to help user recognize and control the happiness.

It is common practice for software developers to use examples to guide development efforts. This largely unwritten, yet standard, practice of "develop by example" is often supported by examples bundled with library or framework packages, provided in textbooks, and made available for download on both official and unofficial web sites. However, the vast number of examples that are embedded in the billions of lines of already developed library and framework code are largely untapped. We have developed XSnippet, a context-sensitive code assistant framework that allows developers to query a sample repository for code snippets that are relevant to the programming task at hand. In particular, our work makes three primary contributions. First, a range of queries is provided to allow developers to switch between a context-independent retrieval of code snippets to various degrees of context-sensitive retrieval for object instantiation queries. Second, a novel graph-based code mining algorithm is provided to support the range of queries and enable mining within and across method boundaries. Third, an innovative context-sensitive ranking heuristic is provided that has been experimentally proven to provide better ranking for best-fit code snippets than context-independent heuristics such as shortest path and frequency. Our experimental evaluation has shown that XSnippet has significant potential to assist developers, and provides better coverage of tasks and better rankings for best-fit snippets than other code assistant systems.

Smartphones have become a useful tool in agriculture because their mobility matches the nature of farming, the cost of the device is highly accessible, and their computing power allows a variety of practical applications to be created. Moreover, smartphones are nowadays equipped with various types of physical sensors which make them a promising tool to assist diverse farming tasks. This paper systematically reviews smartphone applications mentioned in research literature that utilize smartphone built-in sensors to provide agricultural solutions. The initial 1,500 articles identified through database search were screened based on exclusion criteria and then reviewed thoroughly in full text, resulting in 22 articles included in this review. The applications are categorized according to their agricultural functions. Those articles reviewed describe 12 farming applications, 6 farm management applications, 3 information system applications, and 4 extension service applications. GPS and cameras are the most popular sensors used in the reviewed papers. This shows an opportunity for future applications to utilize other sensors such as accelerometer to provide advanced agricultural solutions.

In this work, flame-spray-made SnO2 nanoparticles are systematically studied by doping with 0.1-2 wt % nickel (Ni) and loading with 0.1-5 wt % electrolytically exfoliated graphene for acetone-sensing applications. The sensing films (∼12-18 μm in thickness) were prepared by a spin-coating technique on Au/Al2O3 substrates and evaluated for acetone-sensing performances at operating temperatures ranging from 150 to 350 °C in dry air. Characterizations by X-ray diffraction, transmission/scanning electron microscopy, Brunauer-Emmett-Teller analysis, X-ray photoelectron spectroscopy and Raman spectroscopy demonstrated that Ni-doped SnO2 nanostructures had a spheriodal morphology with a polycrystalline tetragonal SnO2 phase, and Ni was confirmed to form a solid solution with SnO2 lattice while graphene in the sensing film after annealing and testing still retained its high-quality nonoxidized form. Gas-sensing results showed that SnO2 sensing film with 0.1 wt % Ni-doping concentration exhibited an optimal response of 54.2 and a short response time of ∼13 s toward 200 ppm acetone at an optimal operating temperature of 350 °C. The additional loading of graphene at 5 wt % into 0.1 wt % Ni-doped SnO2 led to a drastic response enhancement to 169.7 with a very short response time of ∼5.4 s at 200 ppm acetone and 350 °C. The superior gas sensing performances of Ni-doped SnO2 nanoparticles loaded with graphene may be attributed to the large specific surface area of the composite structure, specifically the high interaction rate between acetone vapor and graphene-Ni-doped SnO2 nanoparticles interfaces and high electronic conductivity of graphene. Therefore, the 5 wt % graphene loaded 0.1 wt % Ni-doped SnO2 sensor is a promising candidate for fast, sensitive and selective detection of acetone.

Indoor localization has been actively researched recently due to security and safety as well as service matters. Previous research and development for indoor localization includes infrared, wireless LAN and ultrasonic. However, these technologies suffer either from the limited accuracy or lacking of the infrastructure. Radio Frequency Identification (RFID) is very attractive because of reasonable system price, and reader reliability. The RFID localization can be categorized into tag and reader localizations. In this paper, major localization techniques for both tag and reader localizations are reviewed to provide the readers state of the art of the indoor localization algorithms. The advantage and disadvantage of each technique for particular applications were also discussed.

A novel wearable electronic nose for armpit odor analysis is proposed by using a low-cost chemical sensor array integrated in a ZigBee wireless communication system. We report the development of a carbon nanotubes (CNTs)/polymer sensor array based on inkjet printing technology. With this technique both composite-like layer and actual composite film of CNTs/polymer were prepared as sensing layers for the chemical sensor array. The sensor array can response to a variety of complex odors and is installed in a prototype of wearable e-nose for monitoring the axillary odor released from human body. The wearable e-nose allows the classification of different armpit odors and the amount of the volatiles released as a function of level of skin hygiene upon different activities.

In this work, a novel method for electrode modification based on inkjet-printing of electrochemically synthesized graphene-PEDOT:PSS (GP-PEDOT:PSS) nanocomposite is reported for the first time. GP-PEDOT:PSS dispersed solution is prepared for use as an ink by one-step electrolytic exfoliation from a graphite electrode. GP-PEDOT:PSS layers are then printed on screen printed carbon electrodes (SPCEs) by a commercial inkjet material printer (Dimatrix Inc.) and their electrochemical behaviors towards three common electroactive analytes, including hydrogen peroxide (H2O2), nicotinamide adenine dinucleotide (NAD+/NADH) and ferri/ferro cyanide (Fe(CN)63−/4−) redox couples, are characterized. It is found that the oxidation signals for H2O2, NADH and K2Fe(CN)6 of PEDOT:PSS modified and GP-PEDOT:PSS modified SPCEs are ∼2–4 and ∼3–13 times higher than those of unmodified SPCE, respectively. In addition, excellent analytical features with relatively wide dynamic ranges, high sensitivities and low detection limits have been achieved. Therefore, the inkjet-printed GP-PEDOT:PSS electrode is a promising candidate for advanced electrochemical sensing applications.

Abstract Principal Component Analysis (PCA) is one of the most widely used data analysis methods in machine learning and AI. This manuscript focuses on the mathematical foundation of classical PCA and its application to a small-sample-size scenario and a large dataset in a high-dimensional space scenario. In particular, we discuss a simple method that can be used to approximate PCA in the latter case. This method can also help approximate kernel PCA or kernel PCA (KPCA) for a large-scale dataset. We hope this manuscript will give readers a solid foundation on PCA, approximate PCA, and approximate KPCA.

Indoor positioning systems are platforms required to provide location information for indoor location-aware computing. This study focuses on a type of indoor positioning systems called location fingerprinting technique that utilizes received signal strength indication (RSSI) of wireless local area network (WLAN) interfaces. Thus far, many characteristics of RSSI have not been thoroughly investigated. Many researchers have relied on the RSSI as sensor information to determine objects location while ignoring the characteristic of RSSI. Therefore, this paper looks into underlying mechanism of RSSI from the indoor positioning systems perspective. In particular, the distributions of RSSI from five different IEEE 802.11b/g WLAN interfaces are investigated using extensive measurement experiments. Desired properties of WLAN interfaces for location fingerprinting are also identified based on the measurement results. The ultimate goal is to use additional knowledge gained here to improve positioning performance and to model location fingerprinting based indoor positioning systems.

Since, cancer is curable when diagnosed at an early stage, lung cancer screening plays an important role in preventive care. Although both low dose computed tomography (LDCT) and computed tomography (CT) scans provide greater medical information than normal chest x-rays, access to these technologies in rural areas is very limited. There is a recent trend toward using computer-aided diagnosis (CADx) to assist in the screening and diagnosis of cancer from biomedical images. In this study, the 121-layer convolutional neural network, also known as DenseNet-121 by G. Huang et. al., along with the transfer learning scheme is explored as a means of classifying lung cancer using chest x-ray images. The model was trained on a lung nodule dataset before training on the lung cancer dataset to alleviate the problem of using a small dataset. The proposed model yields 74.43±6.01% of mean accuracy, 74.96±9.85% of mean specificity, and 74.68±15.33% of mean sensitivity. The proposed model also provides a heatmap for identifying the location of the lung nodule. These findings are promising for further development of chest x-ray-based lung cancer diagnosis using the deep learning approach. Moreover, they solve the problem of a small dataset.

Parking problems are commonplace in most major cities. The limited availability of parking results in traffic congestion, air pollution as well as driver frustration. The price for parking expansion is usually prohibitive or extremely high. Recently researchers turned to applying technologies for efficient parking management. In our previous work, we have developed an optical wireless sensor network (WSN) for traffic monitoring. We have demonstrated our system to the public in several exhibitions. It is realized that this simple invention could be applied to monitor vehicles in a parking garage. The system can then inform drivers of the number of available parking spaces and in which area should they be directed to. This kind of system should avoid driver's frustration in trying endlessly to find a parking space in a crowded parking garage. This paper describes our work to modify our original WSN and applies it to parking garages. Problems associated to applying our original WSN to parking garage are explained, and solutions are proposed

One activity performed by developers during regression testing is test-suite augmentation, which consists of assessing the adequacy of a test suite after a program is modified and identifying new or modified behaviors that are not adequately exercised by the existing test suite and, thus, require additional test cases. In previous work, we proposed MATRIX, a technique for test-suite augmentation based on dependence analysis and partial symbolic execution. In this paper, we present the next step of our work, where we (I) improve the effectiveness of our technique by identifying all relevant change-propagation paths, (2) extend the technique to handle multiple and more complex changes, (3) introduce the first tool that fully implements the technique, and (4) present an empirical evaluation performed on real software. Our results show that our technique is practical and more effective than existing test-suite augmentation approaches in identifying test cases with high fault-detection capabilities.