FPGA applications in signal and image processing

The increasing demand for real-time and smart digital signal processing (DSP) systems, calls for a better platform for their implementation. Most of these systems (e.g. digital image processing) are highly parallelisable, memory and processor hungry; such that the increasing performance of today�s general-purpose microprocessors are no longer able to handle them. A highly parallel hardware architecture, which offers enough memory resources, offers an alternative for such DSP implementations

A single-chip FPGA implementation of real-time adaptive background model

This paper demonstrates the use of a single-chip FPGA for the extraction of highly accurate background models in real-time. The models are based on 24-bit RGB values and 8-bit grayscale intensity values. Three background models are presented, all using a camcorder, single FPGA chip, four blocks of RAM and a display unit. The architectures have been implemented and tested using a Panasonic NVDS60B digital video camera connected to a Celoxica RC300 Prototyping Platform with a Xilinx Virtex II XC2v6000 FPGA and 4 banks of onboard RAM. The novel FPGA architecture presented has the advantages of minimizing latency and the movement of large datasets, by conducting time critical processes on BlockRAM. The systems operate at clock rates ranging from 57MHz to 65MHz and are capable of performing pre-processing functions like temporal low-pass filtering on standard frame size of 640X480 pixels at up to 210 frames per second

Digital signal processing: the impact of convergence on education, society and design flow

Design and development of real-time, memory and processor hungry digital signal processing systems has for decades been accomplished on general-purpose microprocessors. Increasing needs for high-performance DSP systems made these microprocessors unattractive for such implementations. Various attempts to improve the performance of these systems resulted in the use of dedicated digital signal processing devices like DSP processors and the former heavyweight champion of electronics design – Application Specific Integrated Circuits. The advent of RAM-based Field Programmable Gate Arrays has changed the DSP design flow. Software algorithmic designers can now take their DSP algorithms right from inception to hardware implementation, thanks to the increasing availability of software/hardware design flow or hardware/software co-design. This has led to a demand in the industry for graduates with good skills in both Electrical Engineering and Computer Science. This paper evaluates the impact of technology on DSP-based designs, hardware design languages, and how graduate/undergraduate courses have changed to suit this transition

A spatially distributed model for foreground segmentation

Foreground segmentation is a fundamental first processing stage for vision systems which monitor real-world activity. In this paper we consider the problem of achieving robust segmentation in scenes where the appearance of the background varies unpredictably over time. Variations may be caused by processes such as moving water, or foliage moved by wind, and typically degrade the performance of standard per-pixel background models. Our proposed approach addresses this problem by modeling homogeneous regions of scene pixels as an adaptive mixture of Gaussians in color and space. Model components are used to represent both the scene background and moving foreground objects. Newly observed pixel values are probabilistically classified, such that the spatial variance of the model components supports correct classification even when the background appearance is significantly distorted. We evaluate our method over several challenging video sequences, and compare our results with both per-pixel and Markov Random Field based models. Our results show the effectiveness of our approach in reducing incorrect classifications

Evaluation of herbs as potential alternatives for bear bile and rhino horn used in traditional Chinese medicines: chemical and biological analysis.

There is currently an unsustainable rate of exploitation of certain species of plants and animals for use in traditional medicines. Bear bile and rhino horn are derived from endangered species and are traditionally combined with medicinal herbs as anti-bacterial and anti-inflammatory agents. This study was designed to assess herbs for use as possible alternatives for these two products. Herbs were selected based on their traditional use as antiinflammatory and anti-bacterial agents. Chemical methods were used to confirm the plant species of the herbs and to measure concentrations of metals and pesticide residues as a means for assessing their quality. Antibacterial activities were determined using a direct bioautography technique. Anti-inflammatory activities of the herb extracts and isolated compounds were ascertained using an in vitro nuclear factor kappaB (NF-KB) activity, as assessed by IL-6 luciferase gene reporter assay. A novel assay was developed to estimate drug-herb interactions by measuring the effects of selected herbs and drugs on the production of eicosatrienoic acids from hepatic cytochrome P450 (CYP450) metabolism of arachidonic acid. In addition, CYP3A4 enzyme assays were conducted. The pesticide residue and heavy metal concentrations of the tested herbs was found not to exceed the existing legally permitted concentrations in foodstuffs, but the rhino horn sample contained elevated levels of Cd, Pb, Hg and Zn. Rhino horn was not found to be an effective anti-bacterial or anti-inflammatory agent In the assays used in this study. Seventeen herbs demonstrated anti-bacterial activity. Also, nine herbs demonstrated inhibitory NF-KB activity. Preliminary results indicate that co-administration of Scutel/aria baicalensis, Salvia miltiorrhiza, Rehmann/a glutinosa or Coptidis Rhizoma with drugs metabolised by CYP3A4, could lead to possible drug-herb interactions. Based on the Information obtained in these studies nine herbs are proposed as alternatives to rhino horn; eight herbs and two Kampo medicines are proposed as alternatives to bear bile

Interoperable services based on activity monitoring in ambient assisted living environments

Ambient Assisted Living (AAL) is considered as the main technological solution that will enable the aged and people in recovery to maintain their independence and a consequent high quality of life for a longer period of time than would otherwise be the case. This goal is achieved by monitoring human’s activities and deploying the appropriate collection of services to set environmental features and satisfy user preferences in a given context. However, both human monitoring and services deployment are particularly hard to accomplish due to the uncertainty and ambiguity characterising human actions, and heterogeneity of hardware devices composed in an AAL system. This research addresses both the aforementioned challenges by introducing 1) an innovative system, based on Self Organising Feature Map (SOFM), for automatically classifying the resting location of a moving object in an indoor environment and 2) a strategy able to generate context-aware based Fuzzy Markup Language (FML) services in order to maximize the users’ comfort and hardware interoperability level. The overall system runs on a distributed embedded platform with a specialised ceiling- mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels, to detect specific events such as potential falls and to deploy the right sequence of fuzzy services modelled through FML for supporting people in that particular context. Experimental results show less than 20% classification error in monitoring human activities and providing the right set of services, showing the robustness of our approach over others in literature with minimal power consumption

An FPGA-based infant monitoring system

We have designed an automated visual surveillance system for monitoring sleeping infants. The low-level image processing is implemented on an embedded Xilinx’s Virtex II XC2v6000 FPGA and quantifies the level of scene activity using a specially designed background subtraction algorithm. We present our algorithm and show how we have optimised it for this platform

Binary object recognition system on FPGA with bSOM

Tri-state Self Organizing Map (bSOM), which takes binary inputs and maintains tri-state weights, has been used for classification rather than clustering in this paper. The major contribution here is the demonstration of the potential use of the modified bSOM in security surveillance, as a recognition system on FPGA

Chinese Market Access Barriers of U.S Oilseeds and Grains

China was admitted into the WTO in December 2001 and this raised the hopes of the US that China will open up to agricultural trade with the US. However, this potential has not been realized. The goal of this study is to determine the impacts of trade impediments and barriers of the market access of US oilseeds and grains in China. A market access variable that was obtained by dividing the total value of U.S soybean and corn exports to China by U.S agricultural G.D.P was regressed on China’s per capita income, exchange rate of the yuan to the dollar, arable land to labor ratio in the U.S and a dummy variable representing China’s WTO accession. The result found per capita income to have a positive impact on market access of U.S oilseeds and grains in China. Exchange rate of the yuan to the dollar was found to be significant and has a negative impact on market access. However, China’s WTO accession and the arable land to labor ratio in the U.S did not have any significance on the market access of U.S oilseeds and grains.Market Access, Market Access Barriers, U.S Oil seeds and Grains, Import, International Development, International Relations/Trade,