Visual Analysis Algorithms for Embedded Systems

Visual search systems are very popular applications, but on-line versions in 3G wireless environments suffer from network constraint like unstable or limited bandwidth that entail latency in query delivery, significantly degenerating the user’s experience. An alternative is to exploit the ability of the newest mobile devices to perform heterogeneous activities, like not only creating but also processing images. Visual feature extraction and compression can be performed on on-board Graphical Processing Units (GPUs), making smartphones capable of detecting a generic object (matching) in an exact way or of performing a classification activity. The latest trends in visual search have resulted in dedicated efforts in MPEG standardization, namely the MPEG CDVS (Compact Descriptor for Visual Search) standard. CDVS is an ISO/IEC standard used to extract a compressed descriptor. As regards to classification, in recent years neural networks have acquired an impressive importance and have been applied to several domains. This thesis focuses on the use of Deep Neural networks to classify images by means of Deep learning. Implementing visual search algorithms and deep learning-based classification on embedded environments is not a mere code-porting activity. Recent embedded devices are equipped with a powerful but limited number of resources, like development boards such as GPGPUs. GPU architectures fit particularly well, because they allow to execute more operations in parallel, following the SIMD (Single Instruction Multiple Data) paradigm. Nonetheless, it is necessary to make good design choices for the best use of available hardware and memory. For visual search, following the MPEG CDVS standard, the contribution of this thesis is an efficient feature computation phase, a parallel CDVS detector, completely implemented on embedded devices supporting the OpenCL framework. Algorithmic choices and implementation details to target the intrinsic characteristics of the selected embedded platforms are presented and discussed. Experimental results on several GPUs show that the GPU-based solution is up to 7× faster than the CPU-based one. This speed-up opens new visual search scenarios exploiting entire real-time on-board computations with no data transfer. As regards to the use of Deep convolutional neural networks for off-line image classification, their computational and memory requirements are huge, and this is an issue on embedded devices. Most of the complexity derives from the convolutional layers and in particular from the matrix multiplications they entail. The contribution of this thesis is a self-contained implementation to image classification providing common layers used in neural networks. The approach relies on a heterogeneous CPU-GPU scheme for performing convolutions in the transform domain. Experimental results show that the heterogeneous scheme described in this thesis boasts a 50× speedup over the CPU-only reference and outperforms a GPU-based reference by 2×, while slashing the power consumption by nearly 30%

A Fast MPEG's CDVS Implementation for GPU Featured in Mobile Devices

The Moving Picture Experts Group's Compact Descriptors for Visual Search (MPEG's CDVS) intends to standardize technologies in order to enable an interoperable, efficient, and cross-platform solution for internet-scale visual search applications and services. Among the key technologies within CDVS, we recall the format of visual descriptors, the descriptor extraction process, and the algorithms for indexing and matching. Unfortunately, these steps require precision and computation accuracy. Moreover, they are very time-consuming, as they need running times in the order of seconds when implemented on the central processing unit (CPU) of modern mobile devices. In this paper, to reduce computation times and maintain precision and accuracy, we re-design, for many-cores embedded graphical processor units (GPUs), all main local descriptor extraction pipeline phases of the MPEG's CDVS standard. To reach this goal, we introduce new techniques to adapt the standard algorithm to parallel processing. Furthermore, to reduce memory accesses and efficiently distribute the kernel workload, we use new approaches to store and retrieve CDVS information on proper GPU data structures. We present a complete experimental analysis on a large and standard test set. Our experiments show that our GPU-based approach is remarkably faster than the CPU-based reference implementation of the standard, and it maintains a comparable precision in terms of true and false positive rates

Feature extraction using MPEG-CDVS and Deep Learning with application to robotic navigation and image classification

The main contributions of this thesis are the evaluation of MPEG Compact Descriptor for Visual Search in the context of indoor robotic navigation and the introduction of a new method for training Convolutional Neural Networks with applications to object classification. The choice for image descriptor in a visual navigation system is not straightforward. Visual descriptors must be distinctive enough to allow for correct localisation while still offering low matching complexity and short descriptor size for real-time applications. MPEG Compact Descriptor for Visual Search is a low complexity image descriptor that offers several levels of compromises between descriptor distinctiveness and size. In this work, we describe how these trade-offs can be used for efficient loop-detection in a typical indoor environment. We first describe a probabilistic approach to loop detection based on the standard’s suggested similarity metric. We then evaluate the performance of CDVS compression modes in terms of matching speed, feature extraction, and storage requirements and compare them with the state of the art SIFT descriptor for five different types of indoor floors. During the second part of this thesis we focus on the new paradigm to machine learning and computer vision called Deep Learning. Under this paradigm visual features are no longer extracted using fine-grained, highly engineered feature extractor, but rather using a Convolutional Neural Networks (CNN) that extracts hierarchical features learned directly from data at the cost of long training periods. In this context, we propose a method for speeding up the training of Convolutional Neural Networks (CNN) by exploiting the spatial scaling property of convolutions. This is done by first training a pre-train CNN of smaller kernel resolutions for a few epochs, followed by properly rescaling its kernels to the target’s original dimensions and continuing training at full resolution. We show that the overall training time of a target CNN architecture can be reduced by exploiting the spatial scaling property of convolutions during early stages of learning. Moreover, by rescaling the kernels at different epochs, we identify a trade-off between total training time and maximum obtainable accuracy. Finally, we propose a method for choosing when to rescale kernels and evaluate our approach on recent architectures showing savings in training times of nearly 20% while test set accuracy is preserved

Drug development progress in duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a severe, progressive, and incurable X-linked disorder caused by mutations in the dystrophin gene. Patients with DMD have an absence of functional dystrophin protein, which results in chronic damage of muscle fibers during contraction, thus leading to deterioration of muscle quality and loss of muscle mass over time. Although there is currently no cure for DMD, improvements in treatment care and management could delay disease progression and improve quality of life, thereby prolonging life expectancy for these patients. Furthermore, active research efforts are ongoing to develop therapeutic strategies that target dystrophin deficiency, such as gene replacement therapies, exon skipping, and readthrough therapy, as well as strategies that target secondary pathology of DMD, such as novel anti-inflammatory compounds, myostatin inhibitors, and cardioprotective compounds. Furthermore, longitudinal modeling approaches have been used to characterize the progression of MRI and functional endpoints for predictive purposes to inform Go/No Go decisions in drug development. This review showcases approved drugs or drug candidates along their development paths and also provides information on primary endpoints and enrollment size of Ph2/3 and Ph3 trials in the DMD space

Situational analysis study for the agriculture sector in Ghana

Agriculture is important for Ghana’s economy and the livelihoods of the majority of the rural population even though its level of contribution to GDP is declining. Its importance is not only in terms of the contribution to food and nutrition security, but also in providing a basis for agro-industrial activities and for exports. It provides jobs and livelihoods to a significant proportion of the population especially in the rural areas. Farmers cultivate major staples such as maize, cassava, yam, plantain, sorghum and rice. The cash crops grown include cocoa, oil palm, cashew and rubber among others. Ghana’s 2019 annual growth rate for agriculture was 4.6%. The crop sub-sector is the largest in the agricultural sector followed by livestock and fisheries. The impacts of climate change on agriculture are not just projected but are real. The sector is currently contending against erratic rainfall patterns, water stress, desertification/ degradation of ecological systems/ forest degradation; increasing temperatures; and disruption of seasonality. Climate change affects agricultural activities in diverse ways including changes in the onset of the rainy season, increase incidence and frequency in some regions, increase in post-harvest losses of agricultural commodities, decline in the availability and quality of forage and high mortality and morbidity of livestock. Managing the impacts of climate change is important in addressing the challenge of enhancing productivity in the agricultural sector. It is a multi-dimensional challenge; hence solutions must emanate from the identifiable components of the environment. Agriculture is given a high priority in Ghana’s political and socio-economic discourse with the President highlighting the agricultural programme of PFJ as the flagship of his government. The various national policy documents including the national development framework have underscored the importance of the agricultural sector. However, there is need to enhance policy coherence and strengthen policy implementation along the governance structures from the national through the regional to the municipal and district assemblies. Farmers and women must have stronger voices at the district level to articulate better their concerns. Besides, Ghana’s national budgetary allocation to the agricultural sector is still below the target of the Maputo Declaration at about 9.7% currently. However, the on-going programmes such as the PFJ and its constituent modules are likely to increase it. The funding from multi- and bilateral sources are also likely to increase agricultural expenditures. The key recommendations proposed include creating an enabling legal, institutional and policy framework to create a favorable environment for enhancing policy coherence and strengthening policy implementation along the governance structures from the national to regional through to the municipal and district assemblies. It is also important to increase national budgetary and finance flows from bilateral and multi-lateral sources into the agriculture sector to promote widespread adoption of Climate Smart Agriculture (CSA). Investments should take into account gender and youth considerations, supported by a strong extension services system. Farmers’ adoption of CSA is an important intervention area that economic planning must cater for. Market access and access to financial resources to finance their agricultural activities in crops, livestock, fishery and agroforestry, are crucial. Government must consider, adopt and implement this recommendation in collaboration with other stakeholders

The process of the new inter-organizational format of social franchising from a social network theory approach: Institutions, social entrepeneurship povile, innovation and the argument of embeddedness

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe inability of the public sector to satisfy social needs- like poverty alleviation, social inclusion of disadvantaged groups, unemployment, health and education - are redefining the relationship between the governments and their citizens by making the latter play an active role as the provider of the welfare state. Citizens through their entrepreneurial activity have been pulled to the third sector leading to the emergence of new organizational forms like social enterprises and social franchises. The main focus of this research study is the investigation of the new interorganizational format of social franchising which has received ‘scunt’ research attention up to now. The behaviour of actors and organizations in the social economy sector are influenced by the properties and dynamics of elements coming from the political, social, organizational and individual level. We have adopted a systems approach of social network theory. A grounded theory named Social Franchise Model (SoFraM) has been induced from an exploratory empirical mixed method study conducted at various stages and from different sources during a time frame of thirty months. Primary data were raised through six case studies in the UK and Greece, more than 143 interviews with social entrepreneurs and various stakeholders and three action research projects which were the subject of analytic induction supported by archival analysis of secondary data coming from governmental, European Commission, local authority and other sources. Our findings indicate that the formation, growth and success of social franchises is heavily shaped through: firstly, law, regulations, and incentives introduced by centralized or formal institutions- both supranational and national- as well as their driving logics; secondly, the relational and structural embeddedness of actors in networks and the social norms that subsequently emerge; thirdly, the characteristics of the individual social entrepreneurship profile; and finally elements of the social innovation model adopted. The properties of the system of informal or decentralised institutions of networks have been further explored through a pilot quantitative study on mainstream franchises in the UK and Greece. An online self-administered questionnaire has been created based on our conceptual framework of the Franchise Network Model (FNM) drawn from existing scales from literature. The findings indicate that relational and structural embeddedness of actors and organizations in networks determine choices of formation, partner selection, governance mode and the subsequent performance of franchise systems

The EU Cohesion policy and healthy national development: Management and Promotion in Ukraine

Монографія присвячена дослідженню сутності принципів реалізації політики згуртованості Європейського Союзу. Авторами проведено аналіз економічних, екологічних та соціальних аспектів інтеграції досвіду ЄС у державну політику України. У монографії узагальнено підходи до відновлення країни та здорового розвитку. Окрему увагу приділено питанням управління системою охорони здоров’я, тенденціям та перспективам досягнення стану стійкості системи медико-соціального забезпечення населення в умовах впливу COVID-19 на національну економіку. Узагальнено досвід використання маркетингових та інноваційних технологій у контексті здорового національного розвитку.Монография посвящена исследованию сущности принципов реализации политики сплоченности Европейского Союза. Авторами проведен анализ экономических, экологических и социальных аспектов интеграции опыта ЕС в государственную политику Украины. В монографии обобщены подходы к восстановлению и здоровому развитию. Отдельное внимание уделено вопросам управления здравоохранением, тенденциям и перспективам достижения состояния устойчивости системы медико-социального обеспечения населения в условиях влияния COVID-19 на национальную экономику. Обобщен опыт использования маркетинговых и инновационных технологий в контексте здорового национального развития.The monograph focused on the specifics of the principles of the EU Cohesion Policy implementation. The authors conducted an analysis of the economic, ecological and social aspects of the integration of the EU experience into the state policy of Ukraine. The monograph summarizes approaches to the restoration of the country and healthy development. Particular attention is paid to the issues of health care system management, the trends and prospects of achieving the state of resilience of the medical and social provision system of the population in the context of the impact of COVID-19 on the national economy. The experience of using marketing and innovative technologies in the context of healthy national development is summarized. The monograph is generally intended for government officials, entrepreneurs, researchers, graduate students, students of economic, medical, and other specialties