Towards a Scalable Hardware/Software Co-Design Platform for Real-time Pedestrian Tracking Based on a ZYNQ-7000 Device

Currently, most designers face a daunting task to research different design flows and learn the intricacies of specific software from various manufacturers in hardware/software co-design. An urgent need of creating a scalable hardware/software co-design platform has become a key strategic element for developing hardware/software integrated systems. In this paper, we propose a new design flow for building a scalable co-design platform on FPGA-based system-on-chip. We employ an integrated approach to implement a histogram oriented gradients (HOG) and a support vector machine (SVM) classification on a programmable device for pedestrian tracking. Not only was hardware resource analysis reported, but the precision and success rates of pedestrian tracking on nine open access image data sets are also analysed. Finally, our proposed design flow can be used for any real-time image processingrelated products on programmable ZYNQ-based embedded systems, which benefits from a reduced design time and provide a scalable solution for embedded image processing products

A Single Chip System for Sensor Data Fusion Based on a Drift-diffusion Model

Current multisensory system face data communication overhead in integrating disparate sensor data to build a coherent and accurate global phenomenon. We present here a novel hardware and software co-design platform for a heterogeneous data fusion solution based on a perceptual decision making approach (the drift-diffusion model). It provides a convenient infrastructure for sensor data acquisition and data integration and only uses a single chip Xilinx ZYNQ-7000 XC7Z020 AP SOC. A case study of controlling the moving speed of a single ground-based robot, according to physiological states of the operator based on heart rates, is conducted and demonstrates the possibility of integrated sensor data fusion architecture. The results of our DDM-based data integration shows a better correlation coefficient with the raw ECG signal compare with a simply piecewise approach

Interactive Reading Using Low Cost Brain Computer Interfaces

This work shows the feasibility for document reader user applications using a consumer grade non-invasive BCI headset. Although Brain Computer Interface (BCI) type devices are beginning to aim at the consumer level, the level at which they can actually detect brain activity is limited. There is however progress achieved in allowing for interaction between a human and a computer when this interaction is limited to around 2 actions. We employed the Emotiv Epoc, a low-priced BCI headset, to design and build a proof-of-concept document reader system that allows users to navigate the document using this low cast BCI device. Our prototype has been implemented and evaluated with 12 participants who were trained to navigate documents using signals acquired by Emotive Epoc

Unconstrained Face Detection and Open-Set Face Recognition Challenge

Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention.Comment: This is an ERRATA version of the paper originally presented at the International Joint Conference on Biometrics. Due to a bug in our evaluation code, the results of the participants changed. The final conclusion, however, is still the sam

Graph-search Based UNet-d For The Analysis Of Endoscopic Images

While object recognition in deep neural networks (DNN) has shown remarkable success in natural images, endoscopic images still cannot be fully analysed using DNNs, since analysing endoscopic images must account for occlusion, light reflection and image blur. UNet based deep convolutional neural networks (DNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction, which is especially useful for working with multimodal endoscopic images with white light and fluoroscopy in the diagnosis of esophageal disease. However, the currently reported inference time for DNNs is above 200ms, which is unsuitable to integrate into robotic control loops. This work addresses real-time object detection and semantic segmentation in endoscopic devices. We show that endoscopic assistive diagnosis can approach satisfy detection rates with a fast inference time

Rotational blended learning in computer system engineering courses

Contribution: An original application of individual rotation to blended learning, which mixes e-learning, discussion groups (seminars), practical laboratory work, and self-motivated tasks called ``mini-projects.'' Background: In examining the changing practices toward students' transferable skills in higher education, current teaching needs to devote much more attention to using multi-modular teaching methods to foster students' key transferable skills, such as logical, analytical, and creative thinking. Intended Outcomes: Rotational blended learning is intended to maximize students' engagement and improve educational outcomes during the learning process. Application Design: A rotated form of existing teaching methods--e-learning, seminars, and group projects--was proposed. A quasi-experimental design, involving classroom observation, student surveys, and overall results, was used with two cohorts of computer system engineering students, one a controlled cohort taught using traditional techniques and the other an experimental cohort taught using a novel rotational blended system. Findings: The influence of blended learning on the subsequent development of critical transferable skills was demonstrated. Results suggest that rotational blended learning is an ideal way to address these challenges, since it allows computer engineering students to reassess and enhance the core skills and competencies they need to acquire in their learning experiences

Autophagy protects against palmitate-induced apoptosis in hepatocytes

BACKGROUND: Non-alcoholic fatty liver disease, one of the most common liver diseases, has obtained increasing attention. Palmitate (PA)-induced liver injury is considered a risk factor for the development of non-alcoholic fatty liver disease. Autophagy, a cellular degradative pathway, is an important self-defense mechanism in response to various stresses. In this study, we investigated whether autophagy plays a protective role in the progression of PA-induced hepatocytes injury. RESULTS: Annexin V-FITC/PI staining by FCM analysis, TUNEL assay and the detection of PARP and cleaved caspase3 expression levels demonstrated that PA treatment prominently induced the apoptosis of hepatocytes. Meanwhile, treatment of PA strongly induced the formation of GFP-LC3 dots, the conversion from LC3I to LC3II, the decrease of p62 protein levels and the increase of autophagosomes. These results indicated that PA also induced autophagy activation. Autophagy inhibition through chloroquine pretreatment or Atg5shRNA infection led to the increase of cell apoptosis after PA treatment. Moreover, induction of autophagy by pretreatment with rapamycin resulted in distinct decrease of PA-induced apoptosis. Therefore, autophagy can prevent hepatocytes from PA-induced apoptosis. In the further study, we explored pathway of autophagy activation in PA-treated hepatocytes. We found that PA activated PKCα in hepatocytes, and had no influence on mammalian target of rapamycin and endoplasmic reticulum stress pathways. CONCLUSIONS: These results demonstrated that autophagy plays a protective role in PA-induced hepatocytes apoptosis. And PA might induce autophagy through activating PKCα pathway in hepatocytes

Tailoring properties of polythiophene-based electrochromic displays

The synthesis of sixteen thiophene-based copolymers was achieved, using four known 3,4-bis(alkyloxy)thiophene monomers copolymerised with four novel alkoxybenzyl/alkoxynaphthyl thiophene (3 and 4 position) monomers respectively. The polymerisation was performed using a ratio of known monomer to novel monomer of 10 to 1, using iron (III) chloride as catalyst. The steric hindrance of these copolymers can be changed marginally by varying the different positions of the substituent in the alkoxy thiophene (3 and 4 position) monomers. The colour and the properties of copolymers are also changed through the modulation of the steric hindrance. All synthesised copolymers were used to fabricate prototype electrochromic devices and subjected to spectroelectrochemical analysis. In particular, copolymer 75 exhibited excellent electrochromic properties which can be fully oxidised at very low applied voltage (+ 1.5 V) and exhibited fast switching times (0.6 s). It switched between a purple colour in the neutral state and a highly transparent colour in its oxidised state

Activity Classification Using Raw Range and I & Q Radar Data with Long Short Term Memory Layers

This paper presents the first initial results of using radar raw I & Q data and range profiles combined with Long Short Term Memory layers to classify human activities. Although tested only on simple classification problems, this is an innovative approach that enables to bypass the conventional usage of Doppler-time patterns (spectrograms) as inputs of the Long Short Term Memory layers, and adopt instead sequences of range profiles or even raw complex data as inputs. A maximum 99.56% accuracy and a mean accuracy of 97.67% was achieved by treating the radar data as these time sequences, in an effective scheme using a deep learning approach that did not require the pre-processing of the radar data to generate spectrograms and treat them as images. The prediction time needed for a given input testing sample is also reported, showing a promising path for real-time implementation once the Long Short Term Memory layers network is properly trained