Optimal Detection Method Research Articles

Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols. To this end, we develop a DTL detection framework which consists of offline learning, transfer learning, and online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is derived based on the minimum error probability (MEP) criterion. As a realization of the developed framework, we then apply convolutional neural networks (CNN) to intelligently explore the features of the sample covariance matrix, which facilitates the design of a CNN-based algorithm for tag signal detection. Exploiting the powerful capability of CNN in extracting features of data in the matrix formation, the proposed method is able to further improve the system performance. In addition, an asymptotic explicit expression is also derived to characterize the properties of the proposed CNN-based method when the number of samples is sufficiently large. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.

OPTIMAL CONDITION FOR MULTIPLEX POLYMERASE CHAIN REACTION (PCR) IN DETECTING ASCARIS LUMBRICOIDES, TRICHURIS TRICHIURA, AND NECATOR AMERICANUS IN PRESERVED STOOL

Background: Multiplex PCR examination is one of the molecular examination methodologies applied to detect soil-transmitted helminth (STH) infection. Optimization of the multiplex PCR method is a complex process, but it is necessary to obtain both a correct detection process and a satisfactory DNA product.
 Objective: To determine whether multiplex PCR can be optimized to diagnose STH from Indonesian isolates, and to find the optimal method for detection of A. lumbricoides, T. trichiura, and N. americanus infections in stool that have been stored for 3 years.
 Methods: A total of 15 samples were examined, and these samples were previously examined by using a microscopic method, then continued with optimization steps.
 Result: The optimal PCR mixture used primers targeting COI gene for A. lumbricoides, 18S rDNA for T. trichiura and ITS1 for N. americanus, 15 µl of Go Taq Green Master Mix, 5 µl of the 3 pairs of primers, 5 µl of DNA template and 4 µl of DdH2O, and the condition was 30 minutes of 950C denaturation, 30 second of 530C annealing and 1 minute of 720C extension, repeated for 35 cycles.
 Conclusion: Multiplex PCR can be optimized for STH detection from Indonesian isolates. The successful detection using the multiplex PCR method was influenced by sample preparation prior to DNA isolation, which includes several steps i.e. homogenization of samples using bead beaters and passing samples on liquid nitrogen rapidly.

Sensor fault detection in a class of nonlinear systems using modal Kalman filter

Kalman filter and its different variants are commonly used as optimal methods for fault detection in various types of system components. In this paper, a newly introduced type of aforementioned filters, called modal Kalman filter, is extended and utilized in order to estimate the states of nonlinear systems, for sensor fault detection purposes, in a class of nonlinear certain systems. This method, in contrast to the extended Kalman filter, which employs only the linear term of Taylor expansion, retains higher-order terms; as a result, the estimation error will reduce accordingly. Practicality and effectivity of this method, and its superiority over Kalman filter, in terms of accuracy and promptness of sensor fault detection, are also verified with simulation results.

WASTE MANAGEMENT AND THEIR ENVIRONMENTAL IMPACT: CHALLENGES AND OPPORTUNITIES AT NATIONAL LEVEL

Both nationally and globally, waste generation increases with increasing product consumption. Thus, addressing the issue of waste is an ongoing challenge for researchers around the world. This challenge derives from the fact that in many European countries, including Romania, the amount of waste generated is very high compared to current technologies for treatment, recycle or recovery. Therefore, the most convenient management methods remain storage and incineration. These methods generate significant amounts of hazardous pollutants with a significant impact on the environment and human health. The paper presents a series of results related to waste management obtained within National Research and Development Institute for Industrial Ecology – ECOIND Bucharest in the last 10 years. The paper is focused on four topics: analytical detection and monitoring of waste pollutants; environmental impact and risk assessments; harmonized waste classification and management; waste treatment and recovery technologies. The results obtained within these topics refer to optimal methods for detection and characterization of complex waste matrices, methods for assessing the impact on the environment generated by non-compliant landfills and risk prediction and reliable solutions and technologies for waste treatment and recovery. The topics approached represent a challenge for starting the implementation of circular economy concept promoted by EU and adopted by countries and governments all over the world.

Etest Methods for Screening Heterogeneous Vancomycin-Intermediate Staphylococcus aureus (hVISA) strains.

The importance of heterogeneous vancomycin-intermediate Staphylococcus aureus (hVISA) as a preceding stage for the development of vancomycin-resistant S. aureus is growing. We analyzed the prevalence of hVISA among bacteremia with methicillin-resistant S. aureus (MRSA) using two Etests and evaluated the clinical characteristics and outcomes. Ninety-eight MRSA isolates from blood were collected at two University hospitals in Korea. Macrodilution Etest and glycopeptide resistance detection Etests were used for detection of hVISA. Staphylococcal cassette chromosome mec (SCCmec) typing was performed by multiplex PCR. Clinical data were collected retrospectively from patient medical records. About 30% of MRSA strains were identified as hVISA. Diabetes mellitus was associated (P = 0.047) with hVISA infections. The hVISA isolates were associated with high teicoplanin MIC and multidrug resistance (P = 0.001). SCCmec type II accounted for the majority (79.3%) of hVISA strains. The prevalence of hVISA strains was increased and can lead to the development of multidrug-resistant strains. Patients with diabetes were found to have a greater risk for infection with hVISA strains. As the impact of hVISA on clinical outcome is not yet clear, large-scale studies about clinical outcomes and optimal detection methods of hVISA are needed. In conclusion, hVISA strains have a high prevalence in bloodstream MRSA infections. Awareness of the increase in hVISA strains should motivate laboratories to establish a system to detect and monitor hVISA.

A Graph-constrained Changepoint Detection Approach for ECG Segmentation.

Electrocardiogram (ECG) signal is the most commonly used non-invasive tool in the assessment of cardiovascular diseases. Segmentation of the ECG signal to locate its constitutive waves, in particular the R-peaks, is a key step in ECG processing and analysis. Over the years, several segmentation and QRS complex detection algorithms have been proposed with different features; however, their performance highly depends on applying preprocessing steps which makes them unreliable in realtime data analysis of ambulatory care settings and remote monitoring systems, where the collected data is highly noisy. Moreover, some issues still remain with the current algorithms in regard to the diverse morphological categories for the ECG signal and their high computation cost. In this paper, we introduce a novel graph-based optimal changepoint detection (GCCD) method for reliable detection of Rpeak positions without employing any preprocessing step. The proposed model guarantees to compute the globally optimal changepoint detection solution. It is also generic in nature and can be applied to other time-series biomedical signals. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed method achieves overall sensitivity Sen = 99.76, positive predictivity PPR = 99.68, and detection error rate DER = 0.55 which are comparable to other state-of-the-art approaches.1 2.

ФАЗОЧАСТОТНОЕ ОБНАРУЖЕНИЕ СЕЙСМИЧЕСКИХ СИГНАЛОВ

Актуальность. В настоящее время при поиске нефтяных и газовых месторождений большое внимание уделяется более активному вовлечению в разведочный процесс слабо исследованных территорий, а также доисследованию природных резервуаров в районах промышленного освоения месторождений. Для этих целей широко применяются методы сейсморазведки, в результате чего формируются огромные массивы данных, подлежащие последующей обработке и интерпретации. Одной из задач, решаемой на этапе обработки, является надежное обнаружение сейсмических сигналов, особенно важное значение эта задача имеет при прослеживании волн, так как от достоверности обнаружения зависит качество построения отражающих границ. Поэтому построение эффективных методов и алгоритмов обнаружения сигналов на основе анализа ФЧХ, обладающих более высокой степенью устойчивости к помехам, является весьма актуальной задачей. Цель: на основе оптимальной и субоптимальной обработки ФЧХ сейсмических волн, получаемых при поиске нефтяных и газовых месторождений, построить методы обнаружения сигналов и исследовать их эффективность на моделях сейсмоимпульсов путем вычисления вероятностных характеристик ошибок обнаружения. Методы и средства исследования: методы статистического анализа и синтеза алгоритмов, теория случайных процессов, дискретное преобразование Фурье, методы обработки и интерпретации сейсмических данных, математическое моделирование и вычислительный эксперимент. Результаты. На основе метода максимального правдоподобия построена оптимальная процедура обнаружения сейсмических сигналов по их фазочастотным характеристикам. Получены аналитические выражения для вероятностей ошибок обнаружения. Показано, что оптимальное фазочастотное обнаружение обеспечивает результаты, близкие к результатам абсолютно оптимального метода обнаружения сигналов. Для практической реализации разработанного метода предложена субоптимальная обработка ФЧХ (равновесная обработка), не требующая информации о распределении энергии сигнала в частотной области. Проведены исследования помехоустойчивости метода обнаружения с равновесной обработкой, и получены оценки потерь в зависимости от числа использованных частотных компонент в спектре сигнала. Показано место применения разработанных методов обнаружения при решении задач прослеживания сейсмических волн.

Stillbirth at term: Does size really matter?

Placental dysfunction has a deleterious influence on fetal size and is associated with higher rates of perinatal morbidity and mortality. This association underpins the strategy of fetal size evaluation as a mechanism to identify placental dysfunction and prevent stillbirth. The optimal method of routine detection of small for gestational age (SGA) remains to be clarified with choices between estimation of symphyseal-fundal height versus routine third-trimester ultrasound, various formulae for fetal weight estimation by ultrasound, and the variable use of national, customized, or international fetal growth references. In addition to these controversies, the strategy for detecting SGA is further undermined by data demonstrating that the relationship between fetal size and adverse outcome weakens significantly with advancing gestation such that near term, the majority of stillbirths and adverse perinatal outcomes occur in normally sized fetuses. The use of maternal serum biochemical and Doppler parameters near term appears to be superior to fetal size in the identification of fetuses compromised by placental dysfunction and at increased risk of damage or demise. Multiparameter models and predictive algorithms using maternal risk factors, and biochemical and Doppler parameters have been developed, but need to be prospectively validated to demonstrate their effectiveness.

Analysis of Tissue Abnormality in Mammography Images Using Gray Level Co-occurrence Matrix Method

One of the dangerous diseases is breast cancer, which threatens women and men to the same extent. But women are more affected by this disease. Computer-Aided Diagnosis (CAD) is the optimal method for the early detection of breast cancer. It can reduce the false positives in radiologist diagnosis, which leads to reduce the death-rate. This paper presents a feature extraction technique with mammography images to breast mass recognition. Then, distinguishing normal tissue and abnormal breast masses. The mini-MIAS database of mammograms was used in this paper. Gray Level Co-occurrence Matrix is the method that was used to extract features from the region of interest. The best sensitivity, specificity, and accuracy are observed with a k nearest neighbor classifier.

A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar.

In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.

External Quality Assessment Schemes for Biomarker Testing in Oncology: Comparison of Performance between Formalin-Fixed, Paraffin-Embedded–Tissue and Cell-Free Tumor DNA in Plasma

Liquid biopsies have emerged as a useful addition to tissue biopsies in molecular pathology. Literature has shown lower laboratory performances when a new method of variant analysis is introduced. This study evaluated the differences in variant analysis between tissue and plasma samples after the introduction of liquid biopsy in molecular analysis. Data from a pilot external quality assessment scheme for the detection of molecular variants in plasma samples and from external quality assessment schemes for the detection of molecular variants in tissue samples were collected. Laboratory performance and error rates by sample were compared between matrices for variants present in both scheme types. Results showed lower overall performance [65.6% (n=276) versus 89.2% (n=1607)] and higher error rates [21.0% to 43.5% (n=138) versus 8.7% to 16.7% (n=234 to 689)] for the detection of variants in plasma compared to tissue, respectively. In the plasma samples, performance was decreased for variants with an allele frequency of 1% compared to 5% [56.5% (n=138) versus 74.6% (n=138)]. The implementation of liquid biopsy in the detection of circulating tumor DNA in plasma was associated with poor laboratory performance. It is important both to apply optimal detection methods and to extensively validate new methods for testing circulating tumor DNA before treatment decisions are made.

A new fuzzy rule based pixel organization scheme for optimal edge detection and impulse noise removal

Fuzzy sets provide a framework for incorporating human knowledge as an efficient unsupervised machine learning tool for problem solving. The approach discussed in this paper introduces a generalized transfer learning scheme using rule based fuzzy logic for edge detection in digital images. The spatial domain statistical properties of the image are explored as training data set and expressed in fuzzy format to obtain a decision function for optimal edge detection along with reduction of impulse noise. During fuzzy inference process, a specific linguistic value in input fuzzy set is selected in order to obtain an optimal range of second order difference which discriminates the edge pixels from the non-edge pixels. The proposed fuzzy rule based optimal edge pixel detection method in the presence of random valued impulse noise tends to sufficiently extract the edge pixels with out boosting the noisy pixels. The effectiveness of the proposed fuzzy rule based edge detection scheme is verified by testing it on various standard test images and comparing with existing edge detection techniques at different noise densities.

Выбор оптимального метода распознавания сельскохозяйственных культур по космоснимкам высокого разрешения (на примере Саратовского Заволжья)

Выбор оптимального метода распознавания сельскохозяйственных культур по космоснимкам высокого разрешения (на примере Саратовского Заволжья)

Performance Degradation Effect Countermeasures in Residence Times Difference (RTD) Fluxgate Magnetic Sensors

This paper aims to explore the detection defect of residence times difference (RTD) fluxgate working in low-power mode and present the countermeasures for sensor resolution improvement and linearity enhancement. The main defects are amplitude and symmetry changes induced in the output spikes of fluxgate probe due to the magnetic field. These defects lead to thresholds deviation and asymmetry, then cause severe performance degradation especially on detection resolution and linearity according to the RTD theory. To overcome such effects, the optimized RTD method based on voltage extraction and feedback technology is proposed to implement magnetic field compensation and achieve a zero-field running regime of the RTD fluxgate. In this regard, the sensor linearity is improved by a factor of 38, and the resolution degradation effect is suppressed more than 6 times, verified by the laboratory experiments. The optimized detection method proposed in this paper demonstrated a great potential to achieve lower power consumption and will make the RTD fluxgate more promising technology among bio-magnetic applications.

Robust Data Detection for MIMO Systems With One-Bit ADCs: A Reinforcement Learning Approach

The use of one-bit analog-to-digital converters (ADCs) at a receiver is a power-efficient solution for future wireless systems operating with a large signal bandwidth and/or a massive number of receive radio frequency chains. This solution, however, induces a high channel estimation error and therefore makes it difficult to perform the optimal data detection that requires perfect knowledge of likelihood functions at the receiver. In this paper, we propose a likelihood function learning method for multiple-input multiple-output (MIMO) systems with one-bit ADCs using a reinforcement learning approach. The key idea is to exploit input-output samples obtained from data detection, to compensate the mismatch in the likelihood function. The underlying difficulty of this idea is a label uncertainty in the samples caused by a data detection error. To resolve this problem, we define a Markov decision process (MDP) to maximize the accuracy of the likelihood function learned from the samples. We then develop a reinforcement learning algorithm that efficiently finds the optimal policy by approximating the transition function and the optimal state of the MDP. Simulation results demonstrate that the proposed method provides significant performance gains for the optimal data detection methods that suffer from the mismatch in the likelihood function.

State of kidneys of the population of the industrial city

The assessment of renal functions of the adult population of Atyrau (West Kazakhstan) exposed to Atyrau oil refinery and other industrial objects (enterprises in the city), as well as employees of this refinery has been performed by virtue of the identification ofpathological changes in the urine with the use ofphotometric screening, determination of serum levels of creatinine, urea, uric acid and the calculation of glomerular filtration rate. There were obtained evident data testifying to the development of statistically significant exertion of renal function in residents of this city as compared with those of control area residents. There was revealed a statistically significant decrease in the relative density of urine in in 42% of the population, leucocyturia - in 20%, erythrocyturia - in 25%. Decreased glomerularfiltration rate was noted in these people. A statistically significant elevation of the level of uric acid in the blood in combination with an acidic reaction and sharply acidic urine reaction indicates to the possibility of the development of urate urolithiasis which is confirmed by cases of discharge of stones in the urine in 20% of the population according to data of previously executed questionnaire survey. The above mentioned changes in the functional renal reserve and hyperuricemia are equally often found among the residents of the city - Atyrau refinery workers, and the rest of the city people as well. The results of the study allow to recommend the screening ofurine with quantitative registration ofpathological changes as a mean of the most optimal objective methods for early detection of renal dysfunction in population exposed to nephrotoxic impact of environmental techno-genic pollutants.

Comparison of geophysical and botanical results in simulated clandestine graves in rural and tropical environments in Colombia, South America

Abstract In most Latin American countries there are significant numbers of both missing people and forced disappearances, c. 84 000 alone in Colombia. Successful detection of buried human remains by forensic search teams can be difficult in varying terrain and climates. This paper reports on the geophysical and botanical monitoring of simulated clandestine graves in two different environments over 24 months to establish optimal detection methods and equipment configurations. Twelve clandestine burials were simulated at three different burial depths (0.5, 0.8 and 1.2 m) commonly encountered in Latin America. The simulated targets were three pig carcasses, three human skeletons, three graves with burnt human beheaded skeletons and finally three empty graves to act as control. Geophysical detection methods included ground-penetrating radar, magnetic susceptibility, bulk ground conductivity and electrical resistivity. Geophysical results showed that apparent electrical resistivity was optimal to detect targets (85% success) in semi-rural areas and ground-penetrating radar was optimal to detect targets (92%) in tropical rainforest areas, followed by magnetic susceptibility and bulk ground conductivity. Botanical results evidenced variations in both study sites, which should be noted by forensic search investigators, with rapid growth of Raphanus in the tropical rainforest graves and Malvaceae and Petiveria in the rural study site. This paper shows the importance of conducting long-term controlled studies to assist forensic search teams with expected results and optimum equipment configuration(s).

National trends of synchronous bilateral breast cancer incidence in the United States.

Increase in breast cancer survivorship, advancements in diagnostic imaging and standardization of contralateral breast screening before breast cancer surgery have resulted in increased detection of contralateral breast cancer (CBC). The aim of this study was to assess national trends of synchronous bilateral breast cancer (sBBC) and metachronous bilateral breast cancer (mBBC) incidence in newly diagnosed breast cancer patients. The Surveillance, Epidemiology, and End Results (SEER) database (1973-2014) was used to identify 11,177 women diagnosed with CBC. CBC was classified as sBBC when primary breast cancer in both breasts is diagnosed in the same year, or as mBBC, when diagnosed more than one year from primary breast cancer. Temporal trends in sBBC incidence were then evaluated using the Cochran-Armitage test for trend. Of the 11,177 women diagnosed with CBC, 4228 (38%) had sBBC and 6949 (62%) had mBBC. The incidence of sBBC increased significantly from 1.4% in 1975 to 2.9% in 2014 (p < 0.001). sBBC was more likely to be diagnosed as early stage in recent years (78% in 1975 vs. 90% in 2014 [p < 0.001]), and 69% of patients were treated with mastectomy in 2014. The number of sBBC has increased, and contralateral tumors are more likely to be detected at an early stage with the first primary breast cancer. Despite the early stage findings, most were treated with mastectomy. Further studies are needed to define the best therapy for patients with contralateral disease and optimal surveillance and detection methods.

Combining set-theoretic UIO and invariant sets for optimal guaranteed robust fault detection and isolation

This paper proposes an optimal guaranteed robust fault detection and isolation (FDI) method combing the set-theoretic unknown input observer (SUIO) and the robust positively invariant (RPI) sets for linear time-invariant (LTI) systems. The optimality of the proposed FDI method is achieved under a two-layer framework. The first layer allows to design a single optimal FDI-oriented SUIO with RPI set separation-based guaranteed FDI conditions to reduce the FDI conservatism of single observer. The second layer consists in designing an optimal observer configuration for a bank of optimal SUIOs to reduce the FDI conservatism of all the SUIOs together. Finally, the effectiveness of the proposed guaranteed robust FDI method is illustrated by using a four-tank system.

Dual-antenna GNSS spoofing detection method based on Doppler frequency difference of arrival

Recent years have witnessed the emergence of portable global navigation satellite spoofing devices. A variety of anti-spoofing techniques have been dedicated to detecting the presence of spoofing signals, most of which solely utilize either signal characteristics or navigational information. However, since the GNSS system is under continuous monitoring and control, there is a subtle consistency between the signal and the navigational information in authentic GNSS signals, which the spoofing signal can hardly counterfeit. Here, we present a new dual-antenna spoofing detection technique based on this consistency. The essence of this method is the precise estimations of the frequency difference of arrival between a pair of fixed antennas from both the carrier phase observations and the navigational information. When there is no spoofing signal, the observation should be consistent with the prediction. Otherwise, spoofing signals would probably break the consistency because of the geometric and motion discrepancies between GNSS satellites and the spoofing device(s). Theoretically, since the proposed method does not make any presumptions about the spoofer, it can detect moving spoofers and multi-antenna spoofers. We analyze the optimal detection method in three different situations, and the corresponding detection performances are both theoretically and numerically investigated. We also intensively conduct experiments with various configurations to prove the detection capabilities against various spoofing techniques and strategies.