Robotics, Vision and Control : Fundamental Algorithms in MATLAB

The practice of robotics and computer vision each involve the application of computational algorithms to data. The research community has developed a very large body of algorithms but for a newcomer to the field this can be quite daunting. For more than 10 years the author has maintained two open-source MATLAB® Toolboxes, one for robotics and one for vision. They provide implementations of many important algorithms and allow users to work with real problems, not just trivial examples. This new book makes the fundamental algorithms of robotics, vision and control accessible to all. It weaves together theory, algorithms and examples in a narrative that covers robotics and computer vision separately and together. Using the latest versions of the Toolboxes the author shows how complex problems can be decomposed and solved using just a few simple lines of code. The topics covered are guided by real problems observed by the author over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes over 1000 MATLAB® and Simulink® examples and figures. The book is a real walk through the fundamentals of mobile robots, navigation, localization, arm-robot kinematics, dynamics and joint level control, then camera models, image processing, feature extraction and multi-view geometry, and finally bringing it all together with an extensive discussion of visual servo systems.

High frequency of BRAF mutations in nevi

To evaluate the timing of mutations in BRAF (v-raf murine sarcoma viral oncogene homolog B1) during melanocytic neoplasia, we carried out mutation analysis on microdissected melanoma and nevi samples. We observed mutations resulting in the V599E amino-acid substitution in 41 of 60 (68%) melanoma metastases, 4 of 5 (80%) primary melanomas and, unexpectedly, in 63 of 77 (82%) nevi. These data suggest that mutational activation of the RAS/RAF/MAPK pathway in nevi is a critical step in the initiation of melanocytic neoplasia but alone is insufficient for melanoma tumorigenesis.

Vibration characteristics of concrete-steel composite floor structures

This paper discusses the vibration characteristics of a concrete-steel composite multi-panel floor structure; the use of these structures is becoming more common. These structures have many desirable properties but are prone to excessive and complex vibration, which is not currently well understood. Existing design codes and practice guides provide generic advice or simple techniques that cannot address the complex vibration in these types of low-frequency structures. The results of this study show the potential for an adverse dynamic response from higher and multi-modal excitation influenced by human-induced pattern loading, structural geometry, and activity frequency. Higher harmonics of the load frequency are able to excite higher modes in the composite floor structure in addition to its fundamental mode. The analytical techniques used in this paper can supplement the current limited code and practice guide provisions for mitigating the impact of human-induced vibrations in these floor structures.

Bioimpedance in the assessment of unilateral lymphedema of a limb: the optimal frequency

Background: Bioimpedance techniques provide a reliable method of assessing unilateral lymphedema in a clinical setting. Bioimpedance devices are traditionally used to assess body composition at a current frequency of 50 kHz. However, these devices are not transferable to the assessment of lymphedema, as the sensitivity of measuring the impedance of extracellular fluid is frequency dependent. It has previously been shown that the best frequency to detect extracellular fluid is 0 kHz (or DC). However, measurement at this frequency is not possible in practice due to the high skin impedance at DC, and an estimate is usually determined from low frequency measurements. This study investigated the efficacy of various low frequency ranges for the detection of lymphedema. Methods and Results: Limb impedance was measured at 256 frequencies between 3 kHz and 1000 kHz for a sample control population, arm lymphedema population, and leg lymphedema population. Limb impedance was measured using the ImpediMed SFB7 and ImpediMed L-Dex® U400 with equipotential electrode placement on the wrists and ankles. The contralateral limb impedance ratio for arms and legs was used to calculate a lymphedema index (L-Dex) at each measurement frequency. The standard deviation of the limb impedance ratio in a healthy control population has been shown to increase with frequency for both the arm and leg. Box and whisker plots of the spread of the control and lymphedema populations show that there exists good differentiation between the arm and leg L-Dex measured for lymphedema subjects and the arm and leg L-Dex measured for control subjects up to a frequency of about 30 kHz. Conclusions: It can be concluded that impedance measurements above a frequency of 30 kHz decrease sensitivity to extracellular fluid and are not reliable for early detection of lymphedema.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

That's what governments do : exploring fundamental barriers to public–philanthropic interaction : the example of indigenous well-being

Decades of intervention have made variable impact on the inequality between indigenous and non-indigenous well-being across the world. Unacceptable differences in life expectancy alone mark indigenous need as an area where greater understanding of public and private funding approaches and their interaction may deliver real benefits. Both the public and the third sector have been active in trying to address the disadvantage experienced by Australia’s indigenous people. The interaction between the indigenous cause philanthropy system and the wider geopolitical landscape in Australia is revealing barriers and insights that may apply in other challenging policy terrain. The research reported here draws upon two empirical studies aimed at understanding the issues facing philanthropy in Australia,including the impact of government agency both independently and as it contrasts with philanthropy. The two different cultures are evident and two levers (greater system flexibility and closer engagement) are suggested as important in moving forward the philanthropy/government relationship in this area.

Adaptive and optimal under frequency load shedding

Power systems in many countries are stressed towards their stability limit. If these stable systems experience any unexpected serious contingencies, or disturbances, there is a significant risk of instability, which may lead to wide-spread blackout. Frequency is a reliable indicator for such instability condition exists on the power system; therefore under-frequency load shedding technique is used to stable the power system by curtail some load. In this paper, the SFR-UFLS model redeveloped to generate optimal load shedding method is that optimally shed load following one single particular contingency event. The proposed optimal load shedding scheme is then tested on the 39-bus New England test system to show the performance against random load shedding scheme.

Effects of corrugation frequency and aspect ratio on natural convection within an enclosure having sinusoidal corrugation over a heated top surface

Natural convection of a two-dimensional laminar steady-state incompressible fluid flow in a modified rectangular enclosure with sinusoidal corrugated top surface has been investigated numerically. The present study has been carried out for different corrugation frequencies on the top surface as well as aspect ratios of the enclosure in order to observe the change in hydrodynamic and thermal behavior with constant corrugation amplitude. A constant flux heat source is flush mounted on the top sinusoidal wall, modeling a wavy sheet shaded room exposed to sunlight. The flat bottom surface is considered as adiabatic, while the both vertical side walls are maintained at the constant ambient temperature. The fluid considered inside the enclosure is air having Prandtl number of 0.71. The numerical scheme is based on the finite element method adapted to triangular non-uniform mesh element by a non-linear parametric solution algorithm. The results in terms of isotherms, streamlines and average Nusselt numbers are obtained for the Rayleigh number ranging from 10^3 to 10^6 with constant physical properties for the fluid medium considered. It is found that the convective phenomena are greatly influenced by the presence of the corrugation and variation of aspect ratios.

Evaluation of modal analysis techniques using physical models to detect osseointegration of implants in transfemoral amputees

Non-invasive vibration analysis has been used extensively to monitor the progression of dental implant healing and stabilization. It is now being considered as a method to monitor femoral implants in transfemoral amputees. This paper evaluates two modal analysis excitation methods and investigates their capabilities in detecting changes at the interface between the implant and the bone that occur during osseointegration. Excitation of bone-implant physical models with the electromagnetic shaker provided higher coherence values and a greater number of modes over the same frequency range when compared to the impact hammer. Differences were detected in the natural frequencies and fundamental mode shape of the model when the fit of the implant was altered in the bone. The ability to detect changes in the model dynamic properties demonstrates the potential of modal analysis in this application and warrants further investigation.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

Three carrier ambiguity resolution : distance-independent performance demonstrated using semi-generated triple frequency GPS signals

In spite of significant research in the development of efficient algorithms for three carrier ambiguity resolution, full performance potential of the additional frequency signals cannot be demonstrated effectively without actual triple frequency data. In addition, all the proposed algorithms showed their difficulties in reliable resolution of the medium-lane and narrow-lane ambiguities in different long-range scenarios. In this contribution, we will investigate the effects of various distance-dependent biases, identifying the tropospheric delay to be the key limitation for long-range three carrier ambiguity resolution. In order to achieve reliable ambiguity resolution in regional networks with the inter-station distances of hundreds of kilometers, a new geometry-free and ionosphere-free model is proposed to fix the integer ambiguities of the medium-lane or narrow-lane observables over just several minutes without distance constraint. Finally, the semi-simulation method is introduced to generate the third frequency signals from dual-frequency GPS data and experimentally demonstrate the research findings of this paper.