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Launched in 2018, NASA's Parker Solar Probe (PSP) mission has been described as “The Mission to Touch the Sun”. Over a 7 year prime mission, the spacecraft will make 24 close encounters with the Sun. In order to meet requirements, PSP's telecom subsystem needed to incorporate a Ka-band downlink for radiometrics and science data return. PSP uses a 0.6 meter High Gain Antenna (HGA). This antenna is used only for a Ka-band downlink, and during its use is nominally the only time the science data is transmitted. Aspects of the mission design also restrict the use of the HGA to short periods in between each solar flyby. These constraints require the RF team and Mission Operations team to be extra diligent in optimizing the downlink data return. This paper provides a summary of PSP's first year of operations at Ka-band. In addition to presenting the overall Ka-band performance, other items that will be focused on include: planning considerations for Ka-band coverage with the DSN, observed effectiveness of a HGA calibration, pre-launch link assumptions, and possible future work. With multiple future deep space missions utilizing a Ka-band downlink, this information will hopefully be beneficial to help with planning and optimizing the data return.

This paper deals with 3D shape reconstruction based on a structured-light projector and two cameras using Evolutionary Algorithms (EAs). Two main problems are solved: first, the correspondence problem (also called the stereo matching problem) which allows the matching of different views of a same point in the two images, and second, the camera calibration which takes into account the camera parameters and the camera relative locations through a fundamental matrix. Concerning the correspondence problem, the main issue is the detection of the points to be matched, i.e. the points of interest (POIs). In this paper we propose a structured pattern to be projected on the object to analyse. The structured pattern is composed of triangles with different colors and different orientations. The POIs to be matched are the vertices of the triangles. Then we define an encoding method allowing the recovery of all the POIs that are detected in both images. In previous work, we showed that EAs can be used to obtain the fundamental matrix, thus providing an uncalibrated 3D reconstruction approach. However the method processed too small a number of POIs. The proposed acquisition system allows a high number of POIs to be obtained. The initial EAs are improved to use the new set of POIs. The experiments show that the proposed encoding method allows an efficient matching. In addition, accurate 3D reconstruction results are obtained and the process converges correctly.

Sparse coding is a very powerful method to learn high-level features from raw data input. It is able to learn an over complete basis that has the potential to capture robust and discriminative patterns within the data. However, like many other feature learning algorithms, it is unable to detect very similar features or stimuli on different input channels. In this paper, we propose a novel method to build general features that can be applicable to different sets of channels. This succinct representational model will express the stimuli independent of the locality in which they appeared. As a result, it prepares the groundwork for transferring the learned features from a set of input channels to other possible sets of input channels.

Motor current signature analysis is the reference method for the diagnosis of induction machines. However, it features some drawbacks, as it is bound to steady-state conditions, and fault signatures may be created by different overlapping phenomena. This paper presents a full-time domain-based method for the quantitative evaluation of electrical faults in induction machines. It relies on stator current measurements; thus, it is compliant with switching power converter drives, provided that a proper current measurement technique is available. Moreover, it allows an accurate quantification of faults without requiring spectrum analysis; hence, it achieves high resolution even with a low time acquisition period. A fault index is computed as the energy of the signal in a suitable ldquofault bandwidth,rdquo thus reducing the effects of frequency variations. The aforementioned advantages are perfectly suited to time varying signals and to embedded drive diagnostic systems.

Information and Communications Technologies (ICT) plays an essential role in today’s public administration. Countries, which are in the phase of recovering from conflicts and reinstating key functions of state administration, benefit from the utilization of ICT in order to offer services more quickly and efficiently to their citizens. This case study describes and discusses an ICT based solution Crisis Management Initiative (CMI) and the Liberia Ministry of Health and Social Welfare have developed with a view to supporting the State to strengthen its capacities in service delivery and thereby strengthen peace in the society. The solution is developed to support the reinstating of one central public service, birth registration, by developing a solution which addresses the need to access, collect, transfer, and store Birth Registration (BR) data with smartphones to complement a parallel process of using paper forms. The birth registration data is transferred by using the existing mobile networks. The case study describes why this solution was selected in a country where mobile networks cover pretty much the entire country and landline infrastructure does not exist. The case study describes how the project takes into consideration the fact that other key functions of the state will confront similar kind of challenges and, therefore a more generic approach can be developed based on the results and lessons learnt of the BR project. This observation then raises an important question how to support post conflict states in a structured way to set up public services to optimize use of their limited resources and time. This paper concentrates on demonstrating the mobile solutions for the birth registration process in Liberia and how the Liberia case can yield valuable input for CMI to further develop its framework theory for post conflict statebuilding.

Identifying butterfly species by image patterns is a challenging task in computer vision and pattern recognition community due to many butterfly species having similar shape patterns with complex interior structures and considerable pose variation. In additional, geometrical transformation and illumination variation also make this task more difficult. In this paper, a novel image descriptor, named Gaussian convolution angle (GCA) is proposed for butterfly species classification. The proposed GCA projects the butterfly vein image function and intensity image function along a group of vectors that start from a common contour points and ends at the remaining contour points which results a group of vectors that capture the complex vein patterns and texture patterns of butterfly images. The Gaussian convolutions of different scales are conducted to the resulting vector functions to generate a multiscale GCA descriptors. The proposed GCA is not only invariant to geometrical transformation including rotation, scaling and translation, but also invariant to lighting change. The proposed method has been tested on a publicly available butterfly image dataset that has 832 samples of 10 species. It achieves a classification accuracy of 92.03% which is higher than the benchmark methods.