Acceleration and energy consumption optimization in cascading classifiers for face detection on low-cost ARM big.LITTLE asymmetric architectures

This paper proposes a mechanism to accelerate and optimize the energy consumption of a face detection software based on Haar-like cascading classifiers, taking advantage of the features of low-cost Asymmetric Multicore Processors (AMPs) with limited power budget. A modelling and task scheduling/allocation is proposed in order to efficiently make use of the existing features on big.LITTLE ARM processors, including: (I) source-code adaptation for parallel computing, which enables code acceleration by applying the OmpSs programming model, a task-based programming model that handles data-dependencies between tasks in a transparent fashion; (II) different OmpSs task allocation policies which take into account the processor asymmetry and can dynamically set processing resources in a more efficient way based on their particular features. The proposed mechanism can be efficiently applied to take advantage of the processing elements existing on low-cost and low-energy multi-core embedded devices executing object detection algorithms based on cascading classifiers. Although these classifiers yield the best results for detection algorithms in the field of computer vision, their high computational requirements prevent them from being used on these devices under real-time requirements. Finally, we compare the energy efficiency of a heterogeneous architecture based on asymmetric multicore processors with a suitable task scheduling, with that of a homogeneous symmetric architecture

Optimized Fundamental Signal Processing Operations for Energy Minimization on Heterogeneous Mobile Devices

[EN] Numerous signal processing applications are emerging on both mobile and high-performance computing systems. These applications are subject to responsiveness constraints for user interactivity and, at the same time, must be optimized for energy efficiency. The increasingly heterogeneous power-versus-performance profile of modern hardware introduces new opportunities for energy savings as well as challenges. In this line, recent systems-on-chip (SoC) composed of low-power multicore processors, combined with a small graphics accelerator (or GPU), yield a notable increment of the computational capacity while partially retaining the appealing low power consumption of embedded systems. This paper analyzes the potential of these new hardware systems to accelerate applications that involve a large number of floating-point arithmetic operations mainly in the form of convolutions. To assess the performance, a headphone-based spatial audio application for mobile devices based on a Samsung Exynos 5422 SoC has been developed. We discuss different implementations and analyze the tradeoffs between performance and energy efficiency for different scenarios and configurations. Our experimental results reveal that we can extend the battery lifetime of a device featuring such an architecture by a 238% by properly configuring and leveraging the computational resources.This work was supported by the Spanish Ministerio de Economia y Competitividad projects under Grant TIN2014-53495-R and Grant TEC2015-67387-C4-1-R, in part by the University Project UJI-B2016-20, in part by the Project PROMETEOII/2014/003. The work of J. A. Belloch was supported by the GVA Post-Doctoral Contract under Grant APOSTD/2016/069. This paper was recommended by Associate Editor Y. Ha.Belloch Rodríguez, JA.; Badia Contelles, JM.; Igual Peña, FD.; Gonzalez, A.; Quintana Ortí, ES. (2017). Optimized Fundamental Signal Processing Operations for Energy Minimization on Heterogeneous Mobile Devices. IEEE Transactions on Circuits and Systems I Regular Papers. 65(5):1614-1627. https://doi.org/10.1109/TCSI.2017.2761909S1614162765

Case Report: Successful Lung Transplantation from a Donor Seropositive for Trypanosoma cruzi Infection (Chagas Disease) to a Seronegative Recipient

Lung transplantation; Seropositive donor; Trypanosoma cruziTrasplantament de pulmó; Donant seropositiu; Trypanosoma cruziTrasplante de pulmón; Donante seropositivo; Trypanosoma cruziThe increasing shortage of organs for transplantation has prompted transplant programs to investigate the use of extended criteria donors, such as those with transmissible infectious diseases. Successful cases of organ transplantation (mostly kidney and liver) from Trypanosoma cruzi seropositive donors to seronegative recipients have been reported. We present a case of lung transplantation from a donor serologically positive for Chagas disease to a seronegative recipient, and provide a review of the literature. Left single lung transplantation was performed in a 44-year-old Spanish woman presenting with interstitial lung disease in February 2016. The deceased donor was a Colombian immigrant living in Spain who was serologically positive for Chagas disease. Oral administration of 5 mg/kg/day benznidazole divided in three doses for 60 days was given for specific Chagas disease prophylaxis after transplantation. Periodic follow-up with serological reverse transcription polymerase chain reaction to detect T. cruzi DNA were performed until 6 months after the end of treatment. All results were negative, indicating that transmission of T. cruzi had not occurred. In a review of the literature, two similar cases were identified in Argentina and the United States. In both cases T. cruzi infection was detected posttransplant in the recipients, after which they were treated with benznidazole. The course of the patient described herein confirms that lungs from donors with chronic T. cruzi infection can be used successfully as allografts, and that posttransplant prophylaxis with benznidazole may reduce the probability of transmission of T. cruzi to the recipient

A System for In-Line 3D Inspection without Hidden Surfaces

[EN] This work presents a 3D scanner able to reconstruct a complete object without occlusions, including its surface appearance. The technique presents a number of differences in relation to current scanners: it does not require mechanical handling like robot arms or spinning plates, it is free of occlusions since the scanned part is not resting on any surface and, unlike stereo-based methods, the object does not need to have visual singularities on its surface. This system, among other applications, allows its integration in production lines that require the inspection of a large volume of parts or products, especially if there is an important variability of the objects to be inspected, since there is no mechanical manipulation. The scanner consists of a variable number of industrial quality cameras conveniently distributed so that they can capture all the surfaces of the object without any blind spot. The object is dropped through the common visual field of all the cameras, so no surface or tool occludes the views that are captured simultaneously when the part is in the center of the visible volume. A carving procedure that uses the silhouettes segmented from each image gives rise to a volumetric representation and, by means of isosurface generation techniques, to a 3D model. These techniques have certain limitations on the reconstruction of object regions with particular geometric configurations. Estimating the inherent maximum error in each area is important to bound the precision of the reconstruction. A number of experiments are presented reporting the differences between ideal and reconstructed objects in the system.This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiveness) and FEDER (European Regional Developement Fund) supports under project IMDEEA/2018/115.Perez-Cortes, J.; Pérez Jiménez, AJ.; Sáez Barona, S.; Guardiola Garcia, JL.; Salvador Igual, I. (2018). A System for In-Line 3D Inspection without Hidden Surfaces. Sensors. 18(9):1-25. https://doi.org/10.3390/s18092993S12518

iMAGING : a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope

Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it. In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide. Comparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application. The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases

Parasitemia Levels in Trypanosoma cruzi Infection in Spain, an Area Where the Disease Is Not Endemic: Trends by Different Molecular Approaches

Trypanosoma cruzi infection has expanded globally through human migration. In Spain, the mother-to-child route is the mode of transmission contributing to autochthonous Chagas disease (CD); however, most people acquired the infection in their country of origin and were diagnosed in the chronic phase (imported chronic CD). In this context, we assessed the quantitative potential of the Loopamp Trypanosoma cruzi detection kit (Sat-TcLAMP) based on satellite DNA (Sat-DNA) to determine parasitemia levels compared to those detected by real-time quantitative PCRs (qPCRs) targeting Sat-DNA (Sat-qPCR) and kinetoplast DNA minicircles (kDNA-qPCR). This study included 173 specimens from 39 autochthonous congenital and 116 imported chronic CD cases diagnosed in Spain. kDNA-qPCR showed higher sensitivity than Sat-qPCR and Sat-TcLAMP. According to all quantitative approaches, parasitemia levels were significantly higher in congenital infection than in chronic CD (1 × 10-1 to 5 × 105 versus >1 × 10-1 to 6 × 103 parasite equivalents/mL, respectively [P < 0.001]). Sat-TcLAMP, Sat-qPCR, and kDNA-qPCR results were equivalent at high levels of parasitemia (P = 0.381). Discrepancies were significant for low levels of parasitemia and older individuals. Differences between Sat-TcLAMP and Sat-qPCR were not qualitatively significant, but estimations of parasitemia using Sat-TcLAMP were closer to those by kDNA-qPCR. Parasitemia changes were assessed in 6 individual cases in follow-up, in which trends showed similar patterns by all quantitative approaches. At high levels of parasitemia, Sat-TcLAMP, Sat-qPCR, and kDNA-qPCR worked similarly, but significant differences were found for the low levels characteristic of late chronic CD. A suitable harmonization strategy needs to be developed for low-level parasitemia detection using Sat-DNA- and kDNA-based tests. IMPORTANCE: Currently, molecular equipment has been introduced into many health care centers, even in low-income countries. PCR, qPCR, and loop-mediated isothermal amplification (LAMP) are becoming more accessible for the diagnosis of neglected infectious diseases. Chagas disease (CD) is spreading worldwide, and in countries where the disease is not endemic, such as Spain, the parasite Trypanosoma cruzi is transmitted from mother to child (congenital CD). Here, we explore why LAMP, aimed at detecting T. cruzi parasite DNA, is a reliable option for the diagnosis of congenital CD and the early detection of reactivation in chronic infection. When the parasite load is high, LAMP is equivalent to any qPCR. In addition, the estimations of T. cruzi parasitemia in patients living in Spain, a country where the disease is not endemic, resemble natural evolution in areas of endemicity. If molecular tests are introduced into the diagnostic algorithm for congenital infection, early diagnosis and timely treatment would be accomplished, so the interruption of vertical transmission can be an achievable goal.This research was supported by the Foundation for Innovative New Diagnostics (FIND), Geneva, Switzerland (WO klob-0003), and the Surveillance Program of Chagas Disease of the National Centre for Microbiology (CNM), Instituto de Salud Carlos III (ISCIII). CNM-ISCIII research team is supported by Fundación Mundo Sano, Spain (MVP 237/19). The ISGlobal research team is supported by the Agència de Gestió d’Ajuts Universitaris i de Recerca AGAUR) (2017 SGR 00924). ISGlobal is a member of the Centres de Recerca de Catalunya (CERCA) Programme, Government of Catalonia (Spain).S

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools : A review

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases

Fast Development of Dense Linear Algebra Codes on Graphics Processors

Abstract—We present an application programming interface (API) for the C programming language that facilitates the development of dense linear algebra algorithms on graphics processors applying the FLAME methodology. The interface, built on top of the NVIDIA CUBLAS library, implements all the computational functionality of the FLAME/C interface. In addition, the API includes data transference routines to explicitly handle communication between the CPU and GPU memory spaces. The flexibility and simplicity-of-use of this tool are illustrated using a complex operation of dense linear algebra: the Cholesky factorization. For this operation, we implement and evaluate all existing variants on an NVIDIA G80 processor. Index Terms—Graphics processors, FLAME, linear algebra, high performance