A modification of the descriptor in a human detector using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is presented. The proposed modification requires inserting the values of average cell brightness resulting in the increase of the descriptor length from 3780 to 3908 values, but it is easy to compute and instantly gives ≈ 25% improvement of the miss rate at 10‒4 False Positives Per Window (FPPW). The modification has been tested on two versions of HOG-based descriptors: the classic Dalal-Triggs and the modified one, where, instead of spatial Gaussian masks for blocks, an additional central cell has been used. The proposed modification is suitable for hardware implementations of HOG-based detectors, enabling an increase of the detection accuracy or resignation from the use of some hardware-unfriendly operations, such as a spatial Gaussian mask. The results of testing its influence on the brightness changes of test images are also presented. The descriptor may be used in sensor networks equipped with hardware acceleration of image processing to detect humans in the images.
One of the most important issues that power companies face when trying to reduce time and cost maintenance is condition monitoring. In electricity market worldwide, a significant amount of electrical energy is produced by synchronous machines. One type of these machines is brushless synchronous generators in which the rectifier bridge is mounted on rotating shafts. Since bridge terminals are not accessible in this type of generators, it is difficult to detect the possible faults on the rectifier bridge. Therefore, in this paper, a method is proposed to facilitate the rectifier fault detection. The proposed method is then evaluated by applying two conventional kinds of faults on rectifier bridges including one diode open-circuit and two diode open-circuit (one phase open-circuit of the armature winding in the auxiliary generator in experimental set). To extract suitable features for fault detection, the wavelet transform has been used on recorded audio signals. For classifying faulty and healthy states, K-Nearest Neighbours (KNN) supervised classification method was used. The results show a good accuracy of the proposed method.
In this paper, we present the methods to detect the channel delay profile and the Doppler spectrum of shallow underwater acoustic channels (SUAC). In our channel sounding methods, a short impulse in form of a sinusoid function is successively sent out from the transmitter to estimated the channel impulse response (CIR). A bandpass filter is applied to eliminate the interference from out-of-band (OOB). A threshould is utilized to obtain the maximum time delay of the CIR. Multipath components of the SUAC are specified by correlating the received signals with the transmitted sounding pulse with its shifted phases from 0 to 2#25;. We show the measured channel parameters, which have been carried out in some lakes in Hanoi. The measured results illustrate that the channel is frequency selective for a narrow band transmission. The Doppler spectrum can be obtained by taking the Fourier transform of the time correlation of the measured channel transfer function. We have shown that, the theoretical maximum Doppler frequency fits well to that one obtained from measurement results.
The article presents the detection of gases using an infrared imaging Fourier-transform spectrometer (IFTS). The Telops company has developed the IFTS instrument HyperCam, which is offered as a short- or long-wave infrared device. The principle of HyperCam operation and methodology of gas detection has been shown in the paper, as well as theoretical evaluation of gas detection possibility. Calculations of the optical path between the IFTS device, cloud of gases and background have been also discussed. The variation of a signal reaching the IFTS caused by the presence of a gas has been calculated and compared with the reference signal obtained without the presence of a gas in IFTS's field of view. Verification of the theoretical result has been made by laboratory measurements. Some results of the detection of various types of gases has been also included in the paper.
Diagnostics of composite castings, due to their complex structure, requires that their characteristics are tested by an appropriate description method. Any deviation from the specific characteristic will be regarded as a material defect. The detection of defects in composite castings sometimes is not sufficient and the defects have to be identified. This study classifies defects found in the structures of saturated metallic composite castings and indicates those stages of the process where such defects are likely to be formed. Not only does the author determine the causes of structural defects, describe methods of their detection and identification, but also proposes a schematic procedure to be followed during detection and identification of structural defects of castings made from saturated reinforcement metallic composites. Alloys examination was conducted after technological process, while using destructive (macroscopic tests, light and scanning electron microscopy) and non-destructive (ultrasonic and X-ray defectoscopy, tomography, gravimetric method) methods. Research presented in this article are part of author’s work on castings quality.
The article reports three experiments conducted to determine whether musicians possess better ability of recognising the sources of natural sounds than non-musicians. The study was inspired by reports which indicate that musical training develops not only musical hearing, but also enhances various non-musical auditory capabilities. Recognition and detection thresholds were measured for recordings of environmental sounds presented in quiet (Experiment 1) and in the background of a noise masker (Experiment 2). The listener’s ability of sound source recognition was inferred from the recognition-detection threshold gap (RDTG) defined as the difference in signal level between the thresholds of sound recognition and sound detection. Contrary to what was expected from reports of enhanced auditory abilities of musicians, the RDTGs were not smaller for musicians than for non-musicians. In Experiment 3, detection thresholds were measured with an adaptive procedure comprising three interleaved stimulus tracks with different sounds. It was found that the threshold elevation caused by stimulus interleaving was similar for musicians and non-musicians. The lack of superiority of musicians over non-musicians in the auditory tasks explored in this study is explained in terms of a listening strategy known as casual listening mode, which is a basis for auditory orientation in the environment.
In this paper we present the numerical simulation-based design of a new microfluidic device concept for electrophoretic mobility and (relative) concentration measurements of dilute mixtures. The device enables stationary focusing points for each species, where the locally applied pressure driven flow (PDF) counter balances the species electrokinetic velocity. The axial location of the focusing point, along with the PDF flowrate and applied electric field reveals the electrokinetic mobility of each species. Simultaneous measurement of the electroosmotic mobility of an electrically neutral specie can be utilized to calculate the electrophoretic mobility of charged species. The proposed device utilizes constant sample feeding, and results in time-steady measurements. Hence, the results are independent of the initial sample distribution and flow dynamics. In addition, the results are insensitive to the species diffusion for large Peclet number flows (Pe > 400), enabling relative concentration measurement of each specie in the dilute mixture.
This paper presents a methodology for contact detection between convex quadric surfaces using its implicit equations. With some small modifications in the equations, one can model superellipsoids, superhyperboloids of one or two sheets and supertoroids. This methodology is to be implemented on a multibody dynamics code, in order to simulate the interpenetration between mechanical systems, particularly, the simulation of collisions with motor vehicles and other road users, such as cars, motorcycles and pedestrians. The contact detection of two bodies is formulated as a convex nonlinear constrained optimization problem that is solved using two methods, an Interior Point method (IP) and a Sequential Quadratic Programming method (SQP), coded in MATLAB and FORTRAN environment, respectively. The objective function to be minimized is the distance between both surfaces. The design constraints are the implicit superquadrics surfaces equations and operations between its normal vectors and the distance itself. The contact points or the points that minimize the distance between the surfaces are the design variables. Computational efficiency can be improved by using Bounding Volumes in contact detection pre-steps. First one approximate the geometry using spheres, and then Oriented Bounding Boxes (OBB). Results show that the optimization technique suits for the accurate contact detection between objects modelled by implicit superquadric equations.
The paper presents analyses of current research projects connected with explosive material sensors. Sensors are described assigned to X and γ radiation, optical radiation sensors, as well as detectors applied in gas chromatography, electrochemical and chemical sensors. Furthermore, neutron techniques and magnetic resonance devices were analyzed. Special attention was drawn to optoelectronic sensors of explosive devices.
Biometric identification systems, i.e. the systems that are able to recognize humans by analyzing their physiological or behavioral characteristics, have gained a lot of interest in recent years. They can be used to raise the security level in certain institutions or can be treated as a convenient replacement for PINs and passwords for regular users. Automatic face recognition is one of the most popular biometric technologies, widely used even by many low-end consumer devices such as netbooks. However, even the most accurate face identification algorithm would be useless if it could be cheated by presenting a photograph of a person instead of the real face. Therefore, the proper liveness measurement is extremely important. In this paper we present a method that differentiates between video sequences showing real persons and their photographs. First we calculate the optical flow of the face region using the Farnebäck algorithm. Then we convert the motion information into images and perform the initial data selection. Finally, we apply the Support Vector Machine to distinguish between real faces and photographs. The experimental results confirm that the proposed approach could be successfully applied in practice.
The contribution presents a novel approach to the detection and tracking of lanes based on lidar data. Therefore, we use the distance and reflectivity data coming from a one-dimensional sensor. After having detected the lane through a temporal fusion algorithm, we register the lidar data in a world-fixed coordinate system. To this end, we also incorporate the data coming from an inertial measurement unit and a differential global positioning system. After that stage, an original image of the road can be inferred. Based on this data view, we are able to track the lane either with a Kalman filter or by using a polynomial approximation for the underlying lane model.
In this work the construction of experimental setup for MEMS/NEMS deflection measurements is presented. The system is based on intensity fibre optic detector for linear displacement sensing. Furthermore the electronic devices: current source for driving the light source and photodetector with wide-band preamplifier are presented.
This research was conducted to investigate the natural, quantitative composition of the most common Fusarium species directly in fields of northeastern Poland. The concentration of Fusarium spp. and grain quality traits (yield, 1,000 kernel weight, test weight, grain moisture, ergosterol content, protein content, gluten content and starch content) were compared in four wheat varieties (Mandaryna, Struna, Kandela and Arabella). Obtained results indicated a relation between grain moisture, test weight, ergosterol content, yield and fungi concentration. Protein, starch and gluten content was similar in all wheat varieties. Fusarium culmorum was the most common pathogen in Mandaryna and Struna and F. graminearum in Kandela and Arabella. Fusarium avenaceum and F. poae occurred in low amounts in all wheat varieties except Mandaryna. Fusarium oxysporum was found in comparable concentrations in Struna, Kandela and Arabella. Struna despite medium Fusarium spp. colonization possessed the most desirable grain quality compared to other varieties. We carried out real-time PCR detection of Fusarium spp. which is an efficient, cost effective and time saving method in evaluating the development of fungal diseases which are not visible in standard observations.
Perception takes into account the costs and benefits of possible interpretations of incoming sensory data. This should be especially pertinent for threat recognition, where minimising the costs associated with missing a real threat is of primary importance. We tested whether recognition of threats has special characteristics that adapt this process to the task it fulfils. Participants were presented with images of threats and visually matched neutral stimuli, distorted by varying levels of noise. We found threat superiority effect and liberal response bias. Moreover, increasing the level of noise degraded the recognition of the neutral images to higher extent than the threatening images. To summarise, recognising threats is special, in that it is more resistant to noise and decline in stimulus quality, suggesting that threat recognition is a fast ‘all or nothing’ process, in which threat presence is either confirmed or negated.
Automatic gender detection is a process of determining the gender of a human according to the characteristic properties that represent the masculine and feminine attributes of a subject. Automatic gender detection is used in many areas such as customer behaviour analysis, robust security system construction, resource management, human-computer interaction, video games, mobile applications, neuro-marketing etc., in which manual gender detection may be not feasible. In this study, we have developed a fully automatic system that uses the 3D anthropometric measurements of human subjects for gender detection. A Kinect 3D camera was used to recognize the human posture, and body metrics are used as features for classification. To classify the gender, KNN, SVM classifiers and Neural Network were used with the parameters. A unique dataset gathered from 29 female and 31 male (a total of 60 people) participants was used in the experiment and the Leave One Out method was used as the cross-validation approach. The maximum accuracy achieved is 96.77% for SVM with an MLP kernel function.
As the most recent video coding standard, High Efficiency Video Coding (HEVC) adopts various novel techniques, including a quad-tree based coding unit (CU) structure and additional angular modes used for intra encoding. These new techniques achieve a notable improvement in coding efficiency at the penalty of significant computational complexity increase. Thus, a fast HEVC coding algorithm is highly desirable. In this paper, we propose a fast intra CU decision algorithm for HEVC to reduce the coding complexity, mainly based on a key-point detection. A CU block is considered to have multiple gradients and is early split if corner points are detected inside the block. On the other hand, a CU block without corner points is treated to be terminated when its RD cost is also small according to statistics of the previous frames. The proposed fast algorithm achieves over 62% encoding time reduction with 3.66%, 2.82%, and 2.53% BD-Rate loss for Y, U, and V components, averagely. The experimental results show that the proposed method is efficient to fast decide CU size in HEVC intra coding, even though only static parameters are applied to all test sequences.
Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of objects, automatic navigation and analysis of biomedical images. Successful implementation of higher level image analysis tasks, however, is conditioned by reliable detection of characteristic image local regions termed keypoints. A large number of keypoint detection algorithms has been proposed and verified. In this paper we discuss the most important keypoint detection algorithms. The main part of this work is devoted to description of a keypoint detection algorithm we propose that incorporates depth information computed from stereovision cameras or other depth sensing devices. It is shown that filtering out keypoints that are context dependent, e.g. located at boundaries of objects can improve the matching performance of the keypoints which is the basis for object recognition tasks. This improvement is shown quantitatively by comparing the proposed algorithm to the widely accepted SIFT keypoint detector algorithm. Our study is motivated by a development of a system aimed at aiding the visually impaired in space perception and object identification.
Two low-cost methods of estimating the road surface condition are presented in the paper, the first one based on the use of accelerometers and the other on the analysis of images acquired from cameras installed in a vehicle. In the first method, miniature positioning and accelerometer sensors are used for evaluation of the road surface roughness. The device designed for installation in vehicles is composed of a GPS receiver and a multi-axis accelerometer. The measurement data were collected from recorded ride sessions taken place on diversified road surface roughness conditions and at varied vehicle speeds on each of examined road sections. The data were gathered for various vehicle body types and afterwards successful attempts were made in constructing the road surface classification employing the created algorithm. In turn, in the video method, a set of algorithms processing images from a depth camera and RGB cameras were created. A representative sample of the material to be analysed was obtained and a neural network model for classification of road defects was trained. The research has shown high effectiveness of applying the digital image processing to rejection of images of undamaged surface, exceeding 80%. Average effectiveness of identification of road defects amounted to 70%. The paper presents the methods of collecting and processing the data related to surface damage as well as the results of analyses and conclusions.
This paper presents the design process and the results of a novel fall detector designed and constructed at the Faculty of Electronics, Military University of Technology. High sensitivity and low false alarm rates were achieved by using four independent sensors of varying physical quantities and sophisticated methods of signal processing and data mining. The manuscript discusses the study background, hardware development, alternative algorithms used for the sensor data processing and fusion for identification of the most efficient solution and the final results from testing the Android application on smartphone. The test was performed in four 6-h sessions (two sessions with female participants at the age of 28 years, one session with male participants aged 28 years and one involving a man at the age of 49 years) and showed correct detection of all 40 simulated falls with only three false alarms. Our results confirmed the sensitivity of the proposed algorithm to be 100% with a nominal false alarm rate (one false alarm per 8 h).
Based on the mould temperature measured by thermocouples during slab continuous casting, a difference of temperature thermograph is developed to detect slab cracks. In order to detect abnormal temperature region caused by longitudinal crack, the suspicious regions are extracted and divided by virtue of computer image processing algorithms, such as threshold segmentation, connected region judgement and boundary tracing. The abnormal regions are then determined and labeled with the eight connected component labeling algorithm. The boundary of abnormal region is also extracted to depict characteristics of longitudinal crack. Based on above researches, longitudinal crack with abnormal temperature region can be detected and is different from other abnormalities. Four samples of temperature drop are picked up to compare with longitudinal crack on the abnormal region formation, length, width, shape, et al. The results show that the abnormal region caused by longitudinal crack has a linear and vertical shape. The height of abnormal region is more than the width obviously. The ratio of height to width is usually larger than that of other temperature drop regions. This method provides a visual and easy way to detect longitudinal crack and other abnormities. Meanwhile it has a positive meaning to the intelligent and visual mould monitoring system of continuous casting.
Detection of leakages in pipelines is a matter of continuous research because of the basic importance for a waterworks system is finding the point of the pipeline where a leak is located and − in some cases − a nature of the leak. There are specific difficulties in finding leaks by using spectral analysis techniques like FFT (Fast Fourier Transform), STFT (Short Term Fourier Transform), etc. These difficulties arise especially in complicated pipeline configurations, e.g. a zigzag one. This research focuses on the results of a new algorithm based on FFT and comparing them with a developed STFT technique. Even if other techniques are used, they are costly and difficult to be managed. Moreover, a constraint in the leak detection is the pipeline diameter because it influences accuracy of the adopted algorithm. FFT and STFT are not fully adequate for complex configurations dealt with in this paper, since they produce ill-posed problems with an increasing uncertainty. Therefore, an improved Tikhonov technique has been implemented to reinforce FFT and STFT for complex configurations of pipelines. Hence, the proposed algorithm overcomes the aforementioned difficulties due to applying a linear algebraic approach.
A sensing system utilizing a standard optical fiber as a distributed sensor for the detection and localization of mechanical vibrations is presented. Vibrations can be caused by various external factors, like moving people, cars, trains, and other objects producing mechanical vibrations that are sensed by a fiber. In our laboratory we have designed a sensing system based on the Φ-OTDR (phase sensitive Optical Time Domain Reflectometry) using an extremely narrow laser and EDFAs.
Videoplethysmography is currently recognized as a promising noninvasive heart rate measurement method advantageous for ubiquitous monitoring of humans in natural living conditions. Although the method is considered for application in several areas including telemedicine, sports and assisted living, its dependence on lighting conditions and camera performance is still not investigated enough. In this paper we report on research of various image acquisition aspects including the lighting spectrum, frame rate and compression. In the experimental part, we recorded five video sequences in various lighting conditions (fluorescent artificial light, dim daylight, infrared light, incandescent light bulb) using a programmable frame rate camera and a pulse oximeter as the reference. For a video sequence-based heart rate measurement we implemented a pulse detection algorithm based on the power spectral density, estimated using Welch’s technique. The results showed that lighting conditions and selected video camera settings including compression and the sampling frequency influence the heart rate detection accuracy. The average heart rate error also varies from 0.35 beats per minute (bpm) for fluorescent light to 6.6 bpm for dim daylight.