Boosting the hardware-efficiency of cascade support vector machines for embedded classification applications

Support Vector Machines (SVMs) are considered as a state-of-the-art classification algorithm capable of high accuracy rates for a different range of applications. When arranged in a cascade structure, SVMs can efficiently handle problems where the majority of data belongs to one of the two classes, such as image object classification, and hence can provide speedups over monolithic (single) SVM classifiers. However, the SVM classification process is still computationally demanding due to the number of support vectors. Consequently, in this paper we propose a hardware architecture optimized for cascaded SVM processing to boost performance and hardware efficiency, along with a hardware reduction method in order to reduce the overheads from the implementation of additional stages in the cascade, leading to significant resource and power savings. The architecture was evaluated for the application of object detection on 800×600 resolution images on a Spartan 6 Industrial Video Processing FPGA platform achieving over 30 frames-per-second. Moreover, by utilizing the proposed hardware reduction method we were able to reduce the utilization of FPGA custom-logic resources by ∼30%, and simultaneously observed ∼20% peak power reduction compared to a baseline implementation

A scalable dataflow accelerator for real time onboard hyperspectral image classification

© Springer International Publishing Switzerland 2016.Real-time hyperspectral image classification is a necessary primitive in many remotely sensed image analysis applications. Previous work has shown that Support Vector Machines (SVMs) can achieve high classification accuracy, but unfortunately it is very computationally expensive. This paper presents a scalable dataflow accelerator on FPGA for real-time SVM classification of hyperspectral images.To address data dependencies, we adapt multi-class classifier based on Hamming distance. The architecture is scalable to high problem dimensionality and available hardware resources. Implementation results show that the FPGA design achieves speedups of 26x, 1335x, 66x and 14x compared with implementations on ZYNQ, ARM, DSP and Xeon processors. Moreover, one to two orders of magnitude reduction in power consumption is achieved for the AVRIS hyperspectral image datasets

Image Feature Extraction Acceleration

Image feature extraction is instrumental for most of the best-performing algorithms in computer vision. However, it is also expensive in terms of computational and memory resources for embedded systems due to the need of dealing with individual pixels at the earliest processing levels. In this regard, conventional system architectures do not take advantage of potential exploitation of parallelism and distributed memory from the very beginning of the processing chain. Raw pixel values provided by the front-end image sensor are squeezed into a high-speed interface with the rest of system components. Only then, after deserializing this massive dataflow, parallelism, if any, is exploited. This chapter introduces a rather different approach from an architectural point of view. We present two Application-Specific Integrated Circuits (ASICs) where the 2-D array of photo-sensitive devices featured by regular imagers is combined with distributed memory supporting concurrent processing. Custom circuitry is added per pixel in order to accelerate image feature extraction right at the focal plane. Specifically, the proposed sensing-processing chips aim at the acceleration of two flagships algorithms within the computer vision community: the Viola-Jones face detection algorithm and the Scale Invariant Feature Transform (SIFT). Experimental results prove the feasibility and benefits of this architectural solution.Ministerio de Economía y Competitividad TEC2012-38921-C02, IPT-2011- 1625-430000, IPC-20111009Junta de Andalucía TIC 2338-2013Xunta de Galicia EM2013/038Office of NavalResearch (USA) N00014141035

Evaluation of a group based cognitive behavioural therapy programme for menstrual pain management in young women with intellectual disabilities: protocol for a mixed methods controlled clinical trial

BACKGROUND: Menstrual pain which is severe enough to impact on daily activities is very common amongst menstruating females. Research suggests that menstrual pain which impacts on daily functioning may be even more prevalent amongst those with intellectual disabilities. Despite this, little research attention has focused on pain management programmes for those with intellectual disabilities. The aims of this pilot study were to develop and evaluate a theory-based cognitive behavioural therapy (CBT) programme for menstrual pain management in young women with intellectual disabilities. METHODS/DESIGN: The study utilised a mixed methods controlled clinical trial to evaluate elements from a CBT programme called Feeling Better (McGuire & McManus, 2010). The Feeling Better programme is a modular, manualised intervention designed for people with an intellectual disability and their carers. The programme was delivered to 36 young women aged 12 – 30 years who have a Mild - Moderate Intellectual Disability, split between two conditions. The treatment group received the Feeling Better intervention and the control group received treatment as usual. To evaluate the effectiveness of the programme, measures were taken of key pain variables including impact, knowledge, self-efficacy and coping. Process evaluation was conducted to examine which elements of the programme were most successful in promoting change. DISCUSSION: Participants in the intervention group were expected to report the use of a greater number of coping strategies and have greater knowledge of pain management strategies following participation in the intervention and at three month follow-up, when compared to control group participants. A significant advantage of the study was the use of mixed methods and inclusion of process evaluation to determine which elements of a cognitive behavioural therapy programme work best for individuals with intellectual disabilities. TRIAL REGISTRATION: Current Controlled Trials ISRCTN7556775

Health Conditions and Their Impact among Adolescents and Young Adults with Down Syndrome

Objective: To examine the prevalence of medical conditions and use of health services among young adults with Down syndrome and describe the impact of these conditions upon their lives. Methods: Using questionnaire data collected in 2011 from parents of young adults with Down syndrome we investigated the medical conditions experienced by their children in the previous 12 months. Univariate, linear and logistic regression analyses were performed. Results: We found that in addition to the conditions commonly experienced by children with Down syndrome, including eye and vision problems (affecting 73%), ear and hearing problems (affecting 45%), cardiac (affecting 25%) and respiratory problems (affecting 36%), conditions also found to be prevalent within our young adult cohort included musculoskeletal conditions (affecting 61%), body weight (affecting 57%), skin (affecting 56%) and mental health (affecting 32%) conditions and among young women menstrual conditions (affecting 58%). Few parents reported that these conditions had no impact, with common impacts related to restrictions in opportunities to participate in employment and community leisure activities for the young people, as well as safety concerns. Conclusion: There is the need to monitor, screen and provide appropriate strategies such as through the promotion of healthy lifestyles to prevent the development of comorbidities in young people with Down syndrome and, where present, to reduce their impact

Embedded Hardware-Efficient Real-Time Classification With Cascade Support Vector Machines

Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the majority of the data belong to one of the two classes, such as image object classification, and hence can provide speedups over monolithic (single) SVM classifiers. However, SVM classification is a computationally demanding task and existing hardware architectures for SVMs only consider monolithic classifiers. This paper proposes the acceleration of cascade SVMs through a hybrid processing hardware architecture optimized for the cascade SVM classification flow, accompanied by a method to reduce the required hardware resources for its implementation, and a method to improve the classification speed utilizing cascade information to further discard data samples. The proposed SVM cascade architecture is implemented on a Spartan-6 field-programmable gate array (FPGA) platform and evaluated for object detection on 800 × 600 (Super Video Graphics Array) resolution images. The proposed architecture, boosted by a neural network that processes cascade information, achieves a real-time processing rate of 40 frames/s for the benchmark face detection application. Furthermore, the hardware-reduction method results in the utilization of 25% less FPGA custom-logic resources and 20% peak power reduction compared with a baseline implementation

Evaluation of HMFG(2) and thyroglobulin in the diagnosis of thyroid fine needle aspiration (FNA)

In order to appraise the usefulness of HMFG(2) and thyroglobulin (Tg) as specific markers for the diagnosis of thyroid disease, we studied 63 FNA smears. Cases tested included 30 benign (nine colloid goitres, six cases of Hashimoto’s thyroiditis, six Hurthle cell adenomas, nine follicular adenomas) and 33 malignant lesions (nine follicular carcinomas, 12 papillary carcinomas, nine anaplastic carcinomas, three medullary carcinomas). All cases with malignant lesions except the anaplastic carcinomas were positive for HMFG(2). Immunoreactive cells to HMFG(2) were also found in 15 adenomas out of 30 benign cases. Positive Tg reaction was found in benign and malignant thyroid lesions, except six cases of Hashimoto’s thyroiditis, nine anaplastic and three medullary carcinomas. The results obtained indicate that morphology paired with immunocytochemistry can usually depict a more specific profile of thyroid lesions for better evaluation of the pathology

DNA-PLOIDY AND VIMENTIN EXPRESSION IN PRIMARY BREAST-CANCER

The DNA content of 50 breast cancers of varying tumour type, grade and stage was measured using static image cytometry, and correlated with vimentin expression in the tumour cells. A tendency to increased vimentin expression and aneuploidy was observed in high grade and late stage tumours(1). A statistically significant difference was observed in DNA index and ploidy balance between grade 1 and grades 2 and 3 carcinomas (P < 0.05) and between stage I and stage II carcinomas (P < 0.05). There was a significant difference in the expression of vimentin between grades 1, 2, 3 (P < 0.001), and stages I, II and III ductal carcinomas (P < 0.05). No significant difference was observed in the proliferation index and the degree of hyperploidy (P > 0.05). Clonal heterogeneity was observed in 25% of breast carcinomas, and was associated with increased vimentin expression. These changes may be indicative of genomic alteration and tumour aggressiveness