Optimized mask selection for person identification and camera distance measurement based on interocular distance

To promote underwater construction, the tools such as underwater robotic manipulator system, underwater vehicles and so on are necessary. Furthermore, these system need an accurate 3D position parameters. If no position information for automatic control system that system is difficult to be operated. Hence, it is important to take position information. Although 3D position camera can offer 3D position parameters, it has still some deviation. In addition, the manipulator system need accurate distance information for fast and accurate operation. Hence, an accurate distance measurement system is necessary for a remotely teleoperated robotic manipulator system for underwater constructions. This paper focus on the distance measurement study to offer a accurate target distance information. The information can combine with 3D position camera to obtain more accurate 3D position parameters to offer a useful position information for underwater constructions, with a remotely teleoperated robotic manipulator system. In this paper, three distance measurement system, includes commercial ultrasonic distance measurement system, fabricated ultrasonic system and a laser distance measurement system is study. The commercial distance measurement system have a distance measurement system rang 8~35 cm, plusmn0.1 cm. However, a short distance measurement is difficult to be measured with the measurement system. On the other side, the fabricated measurement system can measure a short distance. Information from 0~5 cm, accuracy plusmn0.3 cm. The fabricated distance measurement system fabricated with PZT thin film. In this study, a horn is added to improve the accuracy of distance measurement. In addition, the laser distance measurement system, measurement system is used to measure the distance information also in this study. The combination of these distance measurement system to form a multiple range distance measurement can offer a long measurement range for accurate distance measurement underwater. A detailed experiment is to be studied in the future. Furthermore, the distance measurement system has been offered a distance measurement in a remotely teleoperated robotic manipulator system for underwater constructions

This paper presents a novel person to camera distance measuring system based on eye-distance. The distance between centers of two eyes is used for measuring the person to camera distance. The variation in eye-distance (in pixels) with the changes in camera to person distance (in inches) is used to formulate the distance measuring system. The system starts with computing the distance between two eyes of a person and then person to camera distance is measured. The proposed distance measurement system is relatively simple and inexpensive to implement as it does not require any other external distance measuring tools. Experimental results show the effectiveness of the system with an average accuracy of 94.11%.

Introduction: Human radiography plays a major role for personal and gender identification in case of natural disasters such as earth quacks, tsunamis, etc. when human remains are decayed or deceased. The uniqueness in dimensions and patterns of the frontal sinus (FS) lead to advances in forensic dentistry. Aim: To evaluate the dimensions and patterns of FS as observed on posterior-anterior (PA) cephalogram for personal and gender identification. Materials and Methods: A total of 700 PA Cephlograms, 350 males and 350 females were selected. The mean dimensions and patterns of the FS in males and females were determined and evaluated. Results: On intergroup comparison, the difference in dimensions of males and females were statistically significant with p-value of 0.000. The present study revealed that out of 700, FS symmetry and asymmetry was observed in 386 (55.14%) and 165 (23.57%) individuals respectively. The FS aplasia was seen in 149 (21.2%) individuals. Conclusion: The morphometric analysis of FS can serve as an adjunct to other methods of personal and gender identification. Keywords: Personal identification, Frontal Sinus, PA cephalogram.

With the technology development on detecting circulating tumor cells (CTCs) and cell-free DNAs (cfDNAs) in blood, serum, and plasma, non-invasive diagnosis of cancer becomes promising. A few studies reported good correlations between signals from tumor tissues and CTCs or cfDNAs, making it possible to detect cancers using CTCs and cfDNAs. However, the detection cannot tell which cancer types the person has. To meet these challenges, we developed an algorithm, eTumorType, to identify cancer types based on copy number variations (CNVs) of the cancer founding clone. eTumorType integrates cancer hallmark concepts and a few computational techniques such as stochastic gradient boosting, voting, centroid, and leading patterns. eTumorType has been trained and validated on a large dataset including 18 common cancer types and 5327 tumor samples. eTumorType produced high accuracies (0.86–0.96) and high recall rates (0.79–0.92) for predicting colon, brain, prostate, and kidney cancers. In addition, relatively high accuracies (0.78–0.92) and recall rates (0.58–0.95) have also been achieved for predicting ovarian, breast luminal, lung, endometrial, stomach, head and neck, leukemia, and skin cancers. These results suggest that eTumorType could be used for non-invasive diagnosis to determine cancer types based on CNVs of CTCs and cfDNAs.

An automated AT89C55WD microcontroller based surface roughness and distance measurement system is developed. An IR diode and detector are used for the measurement. In this paper, the experimental arrangement for the measurement of distance and classification of surface roughness is explained. An artificial neural network (ANN), trained with a back propagation algorithm is employed for the data processing to classify the roughness of the surface and for the distance measurement. A three layer ANN is used to learn the type of surface and corresponding output using a back propagation algorithm. After training, the neural network is used to classify the roughness of the surface and to measure distance.

To prospectively evaluate the diagnostic performance of screening ABUS as the primary screening test for breast cancer among Korean women aged 40-49 years. This prospective, multicenter study included asymptomatic Korean women aged 40-49 years from three academic centers between February 2017 and October 2019. Each participant underwent ABUS without mammography, and the ABUS images were interpreted at each hospital with double-reading by two breast radiologists. Biopsy and at least 1 year of follow-up was considered the reference standard. Diagnostic performance of ABUS screening and subgroup analyses according to patient and tumor characteristics were evaluated. Reference standard data were available for 959 women. The recall rate was 9.8% (95% confidence interval [CI]: 7.9%, 11.7%; 94 of 959 women) and the cancer detection yield was 5.2 per 1000 women (95% CI: -0.6, 11.1; 5 of 959 women). There was only one interval cancer. The sensitivity was 83.3% (95% CI: 53.5%, 100%; 5 of 6 cancers) and the specificity was 90.7% (95% CI: 88.8%, 92.5%; 864 of 95. women). The positive predictive values of biopsies performed (PPV3) was 20.0% (95% CI: 4.3%, 35.7%; 5 of 25 women). Women with heterogeneous background echotexture had a higher recall rate (p = .009) and lower specificity (p = .036). Women with body mass index values < 25 kg/m2 had a higher mean recall rate (p = .046). In East Asia, screening automated breast US may be an alternative to screening mammography for detecting breast cancers in women aged 40-49 years. • Automated breast US screening for breast cancer in asymptomatic women aged 40-49 is effective with 5.2 per 1000 cancer detection yield. • Women with heterogeneous background echotexture had a higher recall rate and lower specificity. • Women with body mass index < 25 kg/m2 had a higher recall rate.

To develop methods to model the relationship between cancer detection and recall rates to inform professional standards. Annual screening programme information for each of the 80 English NHSBSP units (totalling 11.3 million screening tests) for the seven screening years from 1 April 2009 to 31 March 2016 and some Dutch screening programme information were used to produce linear and non-linear models. The non-linear models estimated the modelled maximum values (MMV) for cancers detected at different grades and estimated how rapidly the MMV was reached (the modelled 'slope' (MS)). Main outcomes include the detection rate for combined invasive/micro-invasive and high-grade DCIS (IHG) detection rate and the low/intermediate grade DCIS (LIG) detection rate. At prevalent screens for IHG cancers, 99% of the MMV was reached at a recall rate of 7.0%. The LIG detection rate had no discernible plateau, increasing linearly at a rate of 0.12 per 1000 for every 1% increase in recall rate. At incident screens, 99% of the MMV for IHG cancer detection was 4.0%. LIG DCIS increased linearly at a rate of 0.18 per 1000 per 1% increase in recall rate. Our models demonstrate the diminishing returns associated with increasing recall rates. The screening programme in England could use the models to set recall rate ranges, and other countries could explore similar methodology. • Question: How can we determine optimum recall rates in breast cancer screening? • Findings: In this large observational study, we show that increases in recall rates above defined levels are almost exclusively associated with false positive recalls and a very small increase in low/intermediate grade DCIS. • Meaning: High recall rates are not associated with increases in detection of life-threatening cancers. The models developed in this paper can be used to help set recall rate ranges that maximise benefit and minimise harm.

Fiber probe is an important component in optical coherence tomography (OCT) and distance measurement system, its collection efficiency of the light reflected from target surface directly affects the signal-to-noise ratio (SNR) of the system, while the target distance will have direct influence on the collection efficiency. However, there are few researches on specific analysis of the collection efficiency variation of fiber probe when target distance change. In this paper, we proposed the theoretical model of fiber probe, and we deduce the collection efficiency of the light reflected from the target surface which is deviated from the ideal image plane of the fixed-focus fiber probe with Δl in the direction of the optical axis. The theoretical result is testified by ZEMAX simulation, and the difference between the simulation result and the theoretical calculation is 0.0023 when Δl = -5mm. In addition, ZEMAX is utilized to simulate the light collection efficiency when target distance change for reflective target surface. The results of simulation are basically consistent with theoretical calculation. It is concluded that the rate of descent for the collection efficiency is greater when target surface is placed in front of the ideal image plane, and the working range of the fiber probe increases with the increase of ideal image distance. The findings can guide us to make effective use of fiber probe and chose a detector with the right dynamic range to improve distance measurement precision.

Estimation of harvested fruit weight using volume measurements with distance sensors: A case study with olives in a big box

Recently, the distance measurement system that can measure a big object such as a clay model or a die at high speed, in non-contact and with high accuracy is demanded. The distance measurement system which used a laser and triangulation has been used for such distance measurement until now. But it was difficult to reconcile the large distance measurement such as about 100mm and high resolution such as about 10μm because large focal depth is incompatible with a small beam diameter in illumination optics of conventional distance measurement system. So we develop the distance measurement system that used axicon lens for illumination optics to realize large focal depth and small beam diameter. In this paper, it is reported about principle of an axicon lens, experimental result using axicon lens, method of distance calculation and experimental result using this system.

Semantic Segmentation (SS) is the task to assign a semantic label to each pixel of the observed images, which is of crucial significance for autonomous vehicles, navigation assistance systems for the visually impaired, and augmented reality devices. However, there is still a long way for SS to be put into practice as there are two essential challenges that need to be addressed: efficiency and evaluation criterions for practical application. For specific application scenarios, different criterions need to be adopted. Recall rate is an important criterion for many tasks like autonomous vehicles. For autonomous vehicles, we need to focus on the detection of the traffic objects like cars, buses, and pedestrians, which should be detected with high recall rates. In other words, it is preferable to detect it wrongly than miss it, because the other traffic objects will be dangerous if the algorithm miss them and segment them as safe roadways. In this paper, our main goal is to explore possible methods to attain high recall rate. Firstly, we propose a real-time SS network named Swift Factorized Network (SFN). The proposed network is adapted from SwiftNet, whose structure is a typical U-shape structure with lateral connections. Inspired by ERFNet and Global Convolution Networks (GCNet), we propose two different blocks to enlarge valid receptive field. They do not take up too much calculation resources, but significantly enhance the performance compared with the baseline network. Secondly, we explore three ways to achieve higher recall rate, i.e loss function, classifier and decision rules. We perform a comprehensive set of experiments on state-of-the-art datasets including CamVid and Cityscapes. We demonstrate that our SS convolutional neural networks reach excellent performance. Furthermore, we make a detailed analysis and comparison of the three proposed methods on the promotion of recall rate.

With the increasing development of internet, the security of personal information becomes more and more important. Thus, variety of personal recognition methods have been introduced to ensure persons' information security. Traditional recognition methods such as Personal Identification Number (PIN), or Identification tag (ID) are vulnerable to hackers. Then the biometric technology, which uses the unique physiological characteristics of human body to identify user information has been proposed. But the biometrics widely used at present such as human face, fingerprint, iris, and voice can also be forged and falsified. The biometric with living body features such as electromyography (EMG) signal is a good method to achieve aliveness detection and prevent the spoofing attacks. However, there are few studies on personal recognition based on EMG signal. According to the application context, personal recognition system may operate either in identification mode or verification mode. In the personal identification mode, the system recognizes an individual by searching the templates of all the users in the database for a match. While in the personal verification mode, the system validates a person's identity by comparing the captured features with her or his own template(s) stored in the system database. In this paper, both EMG-based personal identification method and EMG-based personal verification method are investigated. First, the Myo armband is placed on the right forearm (specifically, the height of the radiohumeral joint) of 21 subjects to collect the surface EMG signal under hand-open gesture. Then, two different methods are proposed for EMG-based personal identification, i.e., personal identification method based on Discrete Wavelet Transform (DWT) and ExtraTreesClassifier, and personal identification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Networks (CNN). Experiments with 21 subjects show that the identification accuracy of this two methods can achieve 99.206% and 99.203% respectively. Then based on the identification method using CWT and CNN, transfer learning algorithm is adopted to solve the model update problem when new data is added. Finally, an EMG-based personal verification method using CWT and siamese networks is proposed. Experiments show that the verification accuracy of this method can achieve 99.285%.

A sole biometric identifier in construction an individual identification is often not able to meet the preferred performance requirements. Biometric identification pedestal on multiple biometrics correspond to an up-and-coming trend. computerized biometric scheme for person identification gauge a “signature” of the human body, compare the resulting characteristic to a database, and cause to be an request dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are classically characteristic, even though point in time varying, such as Face recognition, Iris recognition, Fingerprint verification, Palm print verification in making a personal identification. Multi-biometric systems, which consolidate in sequence from multiple biometric sources, are in advance popularity since they are able to conquer limitations such as non-universality, noisy sensor data, large intra-user difference and vulnerability to spoof attacks that are commonly encountered in uni-biometric systems. In this paper, it addresses the concept issues and the applications strategies of multi-biometric systems.

A design concept of a goggle type HMD (Head Mount Display) which is capable of controlling automatically user’s interocular distance is introduced. A linear motor is hired for each of the left and right pupillary distance control based on the measurement of the interocular distance with a micro-camera located at the top of the microprojector for each eye. A half mirror for each eye is used to connect the projector/camera to a corresponding eye. Each camera measures its corresponding eye’s pupil with a high accuracy under the illumination of infrared light located at near the camera. The distance range of the controlling is 55 mm to 75 mm. The maximum travelling distance of each linear motor with the four optical components is 10 mm.

Ultrasonic system for accurate distance measurement in the air