Magnetic properties of the Old Crow tephra: Identification of a complex iron titanium oxide mineralogy

International audience[1] The mineralogy and grain-size distribution of the Fe-Ti oxide population of the Old Crow tephra bed, outcropping at the Halfway House loess deposit in central Alaska, are characterized through multiple low-and high-temperature magnetization experiments. The characterization is facilitated by heavy liquid separation of the bulk sample into a low-density ( 0.8 and may play an equally important role as magnetic indicator of titanomagnetite. Furthermore, we demonstrate the ability of low-temperature magnetism to locate a 1 mm thick tephra bed dispersed in loess over 10 cm depth, through the identification of very low concentrations of a titanohematite phase with y = 0.9. The potential for advancing regional correlation of sedimentary deposits through the identification of Fe-Ti oxides common to tephra beds by low-temperature magnetism is illustrated in this study. INDEX TERMS: 1540 Geomagnetism and Paleomagnetism: Rock and mineral magnetism; 1512 Geomagnetism and Paleomagnetism: Environmental magnetism; 1519 Geomagnetism and Paleomagnetism: Magnetic mineralogy and petrology; 8404 Volcanology: Ash deposits; 5109 Physical Properties of Rocks: Magnetic and electrical properties; KEYWORDS: low-temperature magnetism, frequency and amplitude dependence of AC susceptibility, ilmenite-hematite and magnetite-ulvospinel solid solution series, tephra, stratigraphic correlatio

Brain signatures: a modality for biometric authentication

Link to publisher's homepage at http://ieeexplore.ieee.orgIn this paper we investigate the use of brain signatures as a possible biometric authentication technique. Research on brain EEG signals has shown that individuals exhibit unique brain patterns for similar tasks. In this paper we use brain EEG signals recorded during the performance of three mental tasks to identify six individuals. PSD features using Welch algorithm is extracted from the EEG Beta waves. A feed forward neural classifier is used to identify the six individuals. The performance of the neural network is appreciable with an average accuracy of 94.4 to 97.5%. Results validate the usage of brain signatures as a possible modality for biometric verification

Designing a hybrid sensor system for a housekeeping robot

Link to publisher's homepage at http://ieeexplore.ieee.orgHousekeeping robots are service robots specially designed to perform housekeeping tasks such as cleaning and vacuuming, our research focuses on the design of a housekeeping robot to pick up waste objects in a home or office environment. In this paper a hybrid sensor system for the house keeping robot is proposed using vision and ultrasonic sensors to navigate around obstacles and to pick up objects. To pickup objects, ascertaining the exact location of the object is of prime importance. This can be accomplished by computing the 3D coordinates of the object. The navigation task for robots involves the detection of obstacles or objects in the traversable path. Images of objects and obstacles are captured using vision sensors, segmented from background and processed to extract features which are fed to a neural network to recognize and differentiate between obstacles and objects. A recognition accuracy of 100% with an error tolerance of 0.001 is achieved. The centroid of the segmented object is computed to give the x, y and z coordinates of the object location

Housekeeping robot: From concept to design

Link to publisher's homepage at http://ieeexplore.ieee.orgService robots are emerging from the laboratory as commercial products. Floor cleaning, material transporting in radioactive and other hostile environments and security robots are some of the facets of a service robot. This paper focuses on one such service robot for housekeeping purposes, the concept and a design aspect of the developed robot is presented. The design philosophy that emphasizes compromise and practicality in design is being explained. This philosophy is used in the design and integration of a housekeeping robot system and sensor systems to provide new functionality for the user. The robot navigation problem is solved through a hybrid sensor system. The developed robot system comprises of a mobile platform, hybrid sensor system and a gripper system. This paper also discusses the design concepts and realization of a housekeeping robot to perform picking and placing tasks

Object localization using stereo sensors for adept SCARA robot

Link to publisher's homepage at http://ieeexplore.ieee.orgIn this paper we present a stereo vision system for segmentation of partially occluded objects and computation of object grasping point in bin picking environments. The stereo vision system was interfaced with an Adept SCARA Robot to perform bin picking operations. Most researches on bin picking involve combination of vision and force sensors, however in this research an attempt is made to develop a bin picking system using only vision sensors for bin pick and place operation. An algorithm to segment partially occluded objects is proposed. The proposed stereo vision system was found to be effective for partially occluded objects and in the absence of albedo effects. The results are validated through real time bin picking experiments on the Adept Robot

Segmentation and location computation of bin objects

http://ezproxy.unimap.edu.my:2081/source/sourceInfo.url?sourceId=144749In this paper we present a stereo vision based system for segmentation and location computation of partially occluded objects in bin picking environments. Algorithms to segment partially occluded objects and to find the object location [midpoint,x, y and z co-ordinates] with respect to the bin area are proposed. The z co-ordinate is computed using stereo images and neural networks. The proposed algorithms is tested using two neural network architectures namely the Radial Basis Function nets and Simple Feedforward nets. The training results fo feedforward nets are found to be more suitable for the current application.The proposed stereo vision system is interfaced with an Adept SCARA Robot to perform bin picking operations. The vision system is found to be effective for partially occluded objects, in the absence of albedo effects. The results are validated through real time bin picking experiments on the Adept Robot

Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks

Link to publisher's homepage at http://ieeexplore.ieee.orgBrain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI) for communication, using the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental task EEG signals from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Principal component analysis is used for extracting features from the EEG signals. The EEG signal is classified into two tasks. Ten such task combinations are studied. Average classification accuracies varied from 75.5% to 100% with a testing error tolerance of 0.05. The classification performance of the proposed algorithm is found to be better compared to our earlier work using AR model features

Brain machine interface: A comparison between fuzzy and neural classifiers

Link to publisher's homepage at http://www.ijicic.org/Patients with neurodegenerative disease loose all motor movements including impairment of speech, leaving the patients totally locked-in. One possible option for rehabilitation of such patients is through a brain machine interfaces (BMI) which uses their active cognition capabilities to control external devices and the environment. BMIs are designed using the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. This paper compares the performances of two BMIs designed using neural and fuzzy classifiers. EEG signals collected from two subjects during five mental tasks are used to test the classifiers. Band power of the EEG signals is used as features for testing the classifiers. From the results it is observed that the neural classifiers outperformed the fuzzy classifiers marginally. The neural classifier showed an average classification efficiency of 86.15% for subject 1 and 84.09% for subject 2. On the other hand the fuzzy classifier showed an average classification efficiency of 84.5% for subject 1 and 83.0% for subject 2

Recognition of motor imagery of hand movements for a BMI using PCA features

Link to publisher's homepage at http://ieeexplore.ieee.orgMotor imagery is the mental simulation of a motor act that includes preparation for movement and mental operations of motor representations implicitly or explicitly. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through a brain machine interfaces (BMI). In other words a BMI can be used to rehabilitate people suffering from neuromuscular disorders as a means of communication or control. This paper presents a novel approach in the design of a four state BMI using two electrodes. The BMI is designed using Neural Network Classifiers. The performance of the BMI is evaluated using two network architectures. The performance of the proposed algorithm has an average classification efficiency of 93.5%

Functional link PSO neural network based classification of EEG mental task signals

Link to publisher's homepage at http://ieeexplore.ieee.orgClassification of EEG mental task signals is a technique in the design of Brain machine interface [BMI]. A BMI can provide a digital channel for communication in the absence of the biological channels and are used to rehabilitate patients with neurodegenerative diseases, a condition in which all motor movements are impaired including speech leaving the patients totally locked-in. BMI are designed using the electrical activity of the brain detected by scalp EEG electrodes. In this paper five different mental tasks from two subjects were studied, combinations of two tasks are used in the classification process. A novel functional link neural network trained by a PSO algorithm is proposed for classification of the EEG signals. Principal component analysis features are used in the training and testing of the neural network. The average classification accuracies were observed to vary from 80.25% to 93% for the 10 different task combinations for each of the subjects. The proposed network has an average training time of 0.16 sec. The results obtained validate the performance of the proposed algorithm for mental task classification