FootSLAM meets adaptive thresholding

The is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordCalibration of the zero-velocity detection threshold is an essential prerequisite for zero-velocity-aided inertial navigation. However, the literature is lacking a self-contained calibration method, suitable for large-scale use in unprepared environments without map information or pre-deployed infrastructure. In this paper, the calibration of the zero-velocity detection threshold is formulated as a maximum likelihood problem. The likelihood function is approximated using estimation quantities readily available from the FootSLAM algorithm. Thus, we obtain a method for adaptive thresholding that does not require map information, measurements from supplementary sensors, or user input. Experimental evaluations are conducted using data with different gait speeds, sensor placements, and walking trajectories. The proposed calibration method is shown to outperform fixed-threshold zero-velocity detectors and a benchmark using a speed-based threshold classifier.National Institute of Standards and Technology (NIST

Rancang Bangun Alat Pereduksi Particulate Matter (PM) Gas Buang Mesin Diesel Dengan Metode Cyclone

Gas buang dari hasil proses pembakaran berpengaruh terhadap pencemaran udara dan lingkungan khususnya motor diesel. Proses pembakaran bahan bakar pada motor bakar menghasilkan gas buang yang mengandung unsur Nitrogen Oksida (NOx), Sulfur Oksida (SOx), Particulate Matter (PM), Karbon Monoksida (CO), dan Hidrokarbon (HC) yang bersifat mencemari udara. Agar motor diesel yang digunakan tidak mengakibatkan pencemaran udara berlebih, perlu dilakukan suatu penelitian menurunkan emisi gas buang motor diesel dengan pemilihan teknologi dan metode yang tepat. Penelitian cylone separator ini berdasarkan prinsip kerja separator yang memanfaatkan gaya sentrifugal dan perbedaan massa jenis. Karena massa jenis PM lebih besar dari pada massa jenis gas buang, PM akan terpisah dari gas buang karena gaya sentrifugal dan adanya perbedaan massa jenis. Pada tahap awal penelitian ini yaitu dibuat desain dan kemudian dilakukan CFD analisis untuk dicari yang paling efisien dari segi kecepatan dan jenis aliran. Pada hasil analisa CFD disimpulkan bahwa metode Perry lebih efisien dibanding 3 metode yang lain. Setelah didapat desain yang efisien maka dilanjutkan dengan pembuatan prototipe, untuk selanjutnya dilakukan uji ekperimen. Berdasar uji eksperimen, cyclone separator dapar mereduksi PM gas Buang motor diesel pada beban 2000 watt sebesar 8,71%. Sedangkan pada beban 2500 watt, cylone separator dapat mereduksi PM sebesar 34,49%

Mammography screening: views from women and primary care physicians in Crete

Background: Breast cancer is the most commonly diagnosed cancer among women and a leading cause of death from cancer in women in Europe. Although breast cancer incidence is on the rise worldwide, breast cancer mortality over the past 25 years has been stable or decreasing in some countries and a fall in breast cancer mortality rates in most European countries in the 1990s was reported by several studies, in contrast, in Greece have not reported these favourable trends. In Greece, the age-standardised incidence and mortality rate for breast cancer per 100.000 in 2006 was 81,8 and 21,7 and although it is lower than most other countries in Europe, the fall in breast cancer mortality that observed has not been as great as in other European countries. There is no national strategy for screening in this country. This study reports on the use of mammography among middleaged women in rural Crete and investigates barriers to mammography screening encountered by women and their primary care physicians. Methods: Design: Semi-structured individual interviews. Setting and participants: Thirty women between 45–65 years of age, with a mean age of 54,6 years, and standard deviation 6,8 from rural areas of Crete and 28 qualified primary care physicians, with a mean age of 44,7 years and standard deviation 7,0 serving this rural population. Main outcome measure: Qualitative thematic analysis. Results: Most women identified several reasons for not using mammography. These included poor knowledge of the benefits and indications for mammography screening, fear of pain during the procedure, fear of a serious diagnosis, embarrassment, stress while anticipating the results, cost and lack of physician recommendation. Physicians identified difficulties in scheduling an appointment as one reason women did not use mammography and both women and physicians identified distance from the screening site, transportation problems and the absence of symptoms as reasons for non-use. Conclusion: Women are inhibited from participating in mammography screening in rural Crete. The provision of more accessible screening services may improve this. However physician recommendation is important in overcoming women's inhibitions. Primary care physicians serving rural areas need to be aware of barriers preventing women from attending mammography screening and provide women with information and advice in a sensitive way so women can make informed decisions regarding breast caner screening

Robust attentional aggregation of deep feature sets for multi-view 3D reconstruction

We study the problem of recovering an underlying 3D shape from a set of images. Existing learning based approaches usually resort to recurrent neural nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean poolings, to fuse multiple deep features encoded from input images. However, GRU based approaches are unable to consistently estimate 3D shapes given different permutations of the same set of input images as the recurrent unit is permutation variant. It is also unlikely to refine the 3D shape given more images due to the long-term memory loss of GRU. Commonly used pooling approaches are limited to capturing partial information, e.g., max/mean values, ignoring other valuable features. In this paper, we present a new feedforward neural module, named AttSets, together with a dedicated training algorithm, named FASet, to attentively aggregate an arbitrarily sized deep feature set for multi-view 3D reconstruction. The AttSets module is permutation invariant, computationally efficient and flexible to implement, while the FASet algorithm enables the AttSets based network to be remarkably robust and generalize to an arbitrary number of input images. We thoroughly evaluate FASet and the properties of AttSets on multiple large public datasets. Extensive experiments show that AttSets together with FASet algorithm significantly outperforms existing aggregation approaches

Demo: IMU−Aided Magneto−Inductive Localization

In this work, we propose an infrastructure-based indoor localization system that exploits the predictable spatio-temporal features of a local magnetic field. The system also relies on inertial data in order to map the environment and track the user's location. Additionally, WiFi access points may be used to improve the performance

Map-aided navigation for emergency searches

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordReal-time positioning of emergency personnel has been an active research topic for many years. However, studies on how to improve navigation accuracy by using prior information on the idiosyncratic motion characteristics of firefighters are scarce. This paper presents an algorithm for generating pseudo observations of position and orientation based on standard search patterns used by firefighters. The iterative closest point algorithm is used to compare walking trajectories estimated from inertial odometry with search patterns generated from digital maps. The resulting fitting errors are then used to integrate the pseudo observations into a map-aided navigation filter. Specifically, we present a sequential Monte Carlo solution where the pattern comparison is used to both update particle weights and create new particle samples. Experimental results involving professional firefighters demonstrate that the proposed pseudo observations can achieve a stable localization error of about one meter, and offer increased robustness in the presence of map errors

DeepTIO: a deep thermal-inertial odometry with visual hallucination

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordVisual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fail. Thermal cameras are commonly used for perception and inspection when the environment has low visibility. However, their use in odometry estimation is hampered by the lack of robust visual features. In part, this is as a result of the sensor measuring the ambient temperature profile rather than scene appearance and geometry. To overcome this issue, we propose a Deep Neural Network model for thermal-inertial odometry (DeepTIO) by incorporating a visual hallucination network to provide the thermal network with complementary information. The hallucination network is taught to predict fake visual features from thermal images by using Huber loss. We also employ selective fusion to attentively fuse the features from three different modalities, i.e thermal, hallucination, and inertial features. Extensive experiments are performed in hand-held and mobile robot data in benign and smoke-filled environments, showing the efficacy of the proposed model

Towards Learning Group-Equivariant Features for Domain Adaptive 3D Detection

The performance of 3D object detection in large outdoor point clouds deteriorates significantly in an unseen environment due to the inter-domain gap. To address these challenges, most existing methods for domain adaptation harness self-training schemes and attempt to bridge the gap by focusing on a single factor that causes the inter-domain gap, such as objects’ sizes, shapes, and foreground density variation. However, the resulting adaptations suggest that there is still a substantial inter-domain gap left to be minimized. We argue that this is due to two limitations: 1) Biased pseudo-label collection from self-training. 2) Multiple factors jointly contributing to how the object is perceived in the unseen target domain. In this work, we propose a grouping-exploration strategy framework, Group Explorer Domain Adaptation (GroupEXP-DA), to addresses those two issues. Specifically, our grouping divides the available label sets into multiple clusters and ensures all of them have equal learning attention with the group-equivariant spatial feature, avoiding dominant types of objects causing imbalance problems. Moreover, grouping learns to divide objects by considering inherent factors in a data-driven manner, without considering each factor separately as existing works. On top of the group-equivariant spatial feature that selectively detects objects similar to the input group, we additionally introduce an explorative group update strategy that reduces the false negative detection in the target domain, further reducing the inter-domain gap. During inference, only the learned group features are necessary for making the group-equivariant spatial feature, placing our method as a simple add-on that can be applicable to most existing detectors. We show how each module contributes to substantially bridging the inter-domain gaps compared to existing works across large urban outdoor datasets such as NuScenes, Waymo, and KITTI

Vascular structures in dermoscopy

AbstractDermoscopy is an aiding method in the visualization of the epidermis and dermis. It is usually used to diagnose melanocytic lesions. In recent years, dermoscopy has increasingly been used to diagnose non-melanocytic lesions. Certain vascular structures, their patterns of arrangement and additional criteria may demonstrate lesion-specific characteristics. In this review, vascular structures and their arrangements are discussed separately in the light of conflicting views and an overview of recent literature