Codes Andn-ary Relations

The aim of this thesis is to develop a general mechanism for the construction of codes and to extract general properties of classes of codes. This mechanism makes it unnecessary to study various classes of codes separately--at least to some extent--by different constructions and properties.;To achieve this goal, the mechanism of characterizing classes of languages by binary relations is studied. Some general properties related to binary relations and languages are obtained. Moreover, three new classes of codes, n-shuffle codes, solid codes, and intercodes are constructed. Solid codes and intercodes have the synchronous decoding property which is very useful in the design of circuits of coders and decoders.;The studies of codes, n-codes, and intercodes indicate that these three classes of codes cannot be characterized by binary relations. We introduce a more general mechanism, that is, to characterize classes of languages by finitary relations. This mechanism can be used to characterize more classes of languages, such as the classes of n-codes and intercodes. Sometimes, it is difficult to show inclusion relations between classes of codes and hierarchy properties of classes of codes. Results derived in this thesis provide a mechanism which can simplify this task

Using Capacitance Sensor to Extract Characteristic Signals of Dozing from Skin Surface

Skin is the largest organ of the human body and a physiological structure that is directly exposed to the environment. From a theoretical perspective, numerous physiological and psychological signals use the skin as a medium for input and output with the outside world. Therefore, the skin is considered an optimal signal interception point when developing noninvasive, direct, and rapid signal exploration devices. To date, skin signal interceptions are predominantly performed by measuring skin impedance. However, this method is prone to interference such as sweat secretion, salt accumulation on the skin, and muscle contractions, which may result in a substantial amount of interference and erroneous results. The present study proposes novel and effective methods for skin signal interception, such as using a nested probe as a sensor to measure capacitance to be further processed as physiological and psychological signals. The experimental results indicate that the capacitance curve for the transition between wakefulness and dozing exhibits significant changes. This change in the curve can be analyzed by computer programs to clearly and rapidly determine whether the subject has entered the initial phases of sleep

Ciprofloxacin-resistant Salmonella enterica Typhimurium and Choleraesuis from Pigs to Humans, Taiwan

We evaluated the disk susceptibility data of 671 nontyphoid Salmonella isolates collected from different parts of Taiwan from March 2001 to August 2001 and 1,261 nontyphoid Salmonella isolates from the National Taiwan University Hospital from 1996 to 2001. Overall, ciprofloxacn resistance was found in 2.7% (18/671) of all nontyphoid Salmonella isolates, in 1.4% (5/347) of Salmonella enterica serotype Typhimurium and in 7.5% (8/107) in S. enterica serotype Choleraesuis nationwide. MICs of six newer fluoroquinolones were determined for the following isolates: 37 isolates of ciprofloxacin-resistant (human) S. enterica Typhimurium (N = 26) and Choleraesuis (N = 11), 10 isolates of ciprofloxacin-susceptible (MIC <1 μg/mL) (human) isolates of these two serotypes, and 15 swine isolates from S. enterica Choleraesuis (N = 13) and Typhmurium (N = 2) with reduced susceptibility to ciprofloxacin (MIC >0.12 μg/mL). Sequence analysis of the gryA, gyrB, parC, parE, and acrR genes, ciprofloxacin accumulation; and genotypes generated by pulsed-field gel electrophoresis with three restriction enzymes (SpeI, XbaI, and BlnI) were performed. All 26 S. enterica Typhimurium isolates from humans and pigs belonged to genotype I. For S. enterica Choleraesuis isolates, 91% (10/11) of human isolates and 54% (7/13) of swine isolates belonged to genotype B. These two genotypes isolates from humans all exhibited a high-level of resistance to ciprofloxacin (MIC 16–64 μg/mL). They had two-base substitutions in the gyrA gene at codons 83 (Ser83Phe) and 87 (Asp87Gly or Asp87Asn) and in the parC gene at codon 80 (Ser80Arg, Ser80Ile, or Ser84Lys). Our investigation documented that not only did these two S. enterica isolates have a high prevalence of ciprofloxacin resistance nationwide but also that some closely related ciprofloxacin-resistant strains are disseminated from pigs to humans

Adaptive DE-based reversible steganographic technique using bilinear interpolation and simplified location map

In this paper, an adaptive DE-based reversible steganographic scheme with bilinear interpolation and simplified location map is proposed. In traditional reversible difference expansion (DE) scheme, it suffers from two problems: the embeddable location is considered insufficient and the embedding payload control capability in single layer embedding is weak. For the first problem, the kernel of bilinear interpolation is applied to effectively improve the number of the embeddable location while the quality of the stego-image can be maintained at a good level. In addition, the proposed simplified location map is used for the existing adaptive embedding rule to improve the second problem where the secret data can be adaptively embedded and also the load of additional information can be reduced. The experimental results revealed that the proposed scheme presented better visual quality of the stego-image and carried larger embedding payload than some other revised DE schemes, such as Alattar's and Lee's schemes

A Fovea Localization Scheme Using Vessel Origin-Based Parabolic Model

At the center of the macula, fovea plays an important role in computer-aided diagnosis. To locate the fovea, this paper proposes a vessel origin (VO)-based parabolic model, which takes the VO as the vertex of the parabola-like vasculature. Image processing steps are applied to accurately locate the fovea on retinal images. Firstly, morphological gradient and the circular Hough transform are used to find the optic disc. The structure of the vessel is then segmented with the line detector. Based on the characteristics of the VO, four features of VO are extracted, following the Bayesian classification procedure. Once the VO is identified, the VO-based parabolic model will locate the fovea. To find the fittest parabola and the symmetry axis of the retinal vessel, an Shift and Rotation (SR)-Hough transform that combines the Hough transform with the shift and rotation of coordinates is presented. Two public databases of retinal images, DRIVE and STARE, are used to evaluate the proposed method. The experiment results show that the average Euclidean distances between the located fovea and the fovea marked by experts in two databases are 9.8 pixels and 30.7 pixels, respectively. The results are stronger than other methods and thus provide a better macular detection for further disease discovery

A statistical and learning based oncogene detection and classification scheme using human cDNA expressions for ovarian carcinoma

In this paper, a human ovarian cDNA expression database is analyzed for detecting oncogenes and then selected oncogenes are used to identify pathological stages of ovarian carcinoma. This human ovarian cDNA expression database collects 41 patient samples which includes 13 samples of normal ovarian tumors (OVT), six samples of borderline of cancers (BOT), seven samples of ovarian cancer at stage I (OVCA-I) and 15 samples of ovarian cancer at stage III (OVCA-III). Each pathological sample contains a large number of genes (9600 genes). Hence oncogene analyzing and discovering is difficult. For this reason, a statistical testing method, t-test, is used to cull most of unconcerned genes in five different pathological stage classification cases. Then, these selected oncogenes are further used by artificial neural network (ANN) with five different classifications according to their gene expressions of pathological stages to set up a recognition system. This recognition system is used to show the efficiency of the proposed classification scheme. From the experimental results, the highest and lowest accuracy of five classification experiments is 100% and 89.47%. Moreover, this paper also proposed a novel t-test strategy to select more important oncogenes and increase lowest classification accuracy to 94.74%. The proposed scheme also can be used to develop a graphical user interface (GUI) bio-statistical or automatic diagnosis system for gene expression analysis to assist doctors and pathologists to analyze and diagnose ovarian cancer

Data Imbalance Immunity Bone Age Assessment System Using Independent Autoencoders

Bone age assessment (BAA) is an important indicator of child maturity. Generally, a person is evaluated for bone age mostly during puberty stage; compared to toddlers and post-puberty stages, the data of bone age at puberty stage are much easier to obtain. As a result, the amount of bone age data collected at the toddler and post-puberty stages are often much fewer than the amount of bone age data collected at the puberty stage. This so-called data imbalance problem affects the prediction accuracy. To deal with this problem, in this paper, a data imbalance immunity bone age assessment (DIIBAA) system is proposed. It consists of two branches, the first branch consists of a CNN-based autoencoder and a CNN-based scoring network. This branch builds three autoencoders for the bone age data of toddlers, puberty, and post-puberty stages, respectively. Since the three types of autoencoders do not interfere with each other, there is no data imbalance problem in the first branch. After that, the outputs of the three autoencoders are input into the scoring network, and the autoencoder which produces the image with the highest score is regarded as the final prediction result. In the experiments, imbalanced training data with a positive and negative sample ratio of 1:2 are used, which has been alleviated compared to the original highly imbalanced data. In addition, since the scoring network converts the classification problem into an image quality scoring problem, it does not use the classification features of the image. Therefore, in the second branch, we also add the classification features to the DIIBAA system. At this time, DIIBAA considers both image quality features and classification features. Finally, the DenseNet169-based autoencoders are employed in the experiments, and the obtained evaluation accuracies are improved compared to the baseline network

Data Imbalance Immunity Bone Age Assessment System Using Independent Autoencoders

Bone age assessment (BAA) is an important indicator of child maturity. Generally, a person is evaluated for bone age mostly during puberty stage; compared to toddlers and post-puberty stages, the data of bone age at puberty stage are much easier to obtain. As a result, the amount of bone age data collected at the toddler and post-puberty stages are often much fewer than the amount of bone age data collected at the puberty stage. This so-called data imbalance problem affects the prediction accuracy. To deal with this problem, in this paper, a data imbalance immunity bone age assessment (DIIBAA) system is proposed. It consists of two branches, the first branch consists of a CNN-based autoencoder and a CNN-based scoring network. This branch builds three autoencoders for the bone age data of toddlers, puberty, and post-puberty stages, respectively. Since the three types of autoencoders do not interfere with each other, there is no data imbalance problem in the first branch. After that, the outputs of the three autoencoders are input into the scoring network, and the autoencoder which produces the image with the highest score is regarded as the final prediction result. In the experiments, imbalanced training data with a positive and negative sample ratio of 1:2 are used, which has been alleviated compared to the original highly imbalanced data. In addition, since the scoring network converts the classification problem into an image quality scoring problem, it does not use the classification features of the image. Therefore, in the second branch, we also add the classification features to the DIIBAA system. At this time, DIIBAA considers both image quality features and classification features. Finally, the DenseNet169-based autoencoders are employed in the experiments, and the obtained evaluation accuracies are improved compared to the baseline network

Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery

Rice is a staple food in many Asian countries, but its production requires a high water demand. Moreover, more attention should be paid to the water management of rice due to global climate change and frequent droughts. To address this problem, we propose a rice water stress identification system. Since water irrigation usually affects the opening and closing of rice leaf stomata which directly affects leaf temperature, rice leaf temperature is a suitable index for evaluating rice water stress. The proposed rice water stress identification system uses a CNN (convolutional neural network) to identify water stress in thermal images of rice fields and to classify the irrigation situation into three classes: 100%, 90%, and 80% irrigation. The CNN was applied to extract the temperature level score from each thermal image based on the degree of difference between the three irrigation situations, then these scores were used to further classify the water-stress situation. In the experiments in this study, we compare CNN classification results without considering the degree between each class. The proposed method considerably improves water stress identification. Since rice leaf temperature is relative to air temperature and is not an absolute value, the background temperature is also important reference information. We combine two different methods for background processing to extract more features and achieve more accurate identification