We developed a low-cost, low-noise, tunable, high-peak-power, ultrafast laser system based on a SESAM-modelocked, solid-state Yb tungstate laser plus spectral broadening via a microstructured fiber followed by pulse compression. The spectral selection, tuning, and pulse compression are performed with a simple prism compressor. The output pulses are tunable from 800 to 1250 nm, with the pulse duration down to 25 fs, and average output power up to 150 mW, at 80 MHz pulse repetition rate. We introduce the figure of merit (FOM) for the two-photon and multi-photon imaging (or other nonlinear processes), which is a useful guideline in discussions and for designing the lasers for an improved microscopy signal. Using a 40 MHz pulse repetition rate laser system, with twice lower FOM, we obtained high signal-to-noise ratio two-photon fluorescence images with or without averaging, of mouse intestine section and zebra fish embryo. The obtained images demonstrate that the developed system is capable of nonlinear (TPE, SHG) imaging in a multimodal operation. The system could be potentially used in a variety of other techniques including, THG, CARS and applications such as nanosurgery.

Fluorescence lifetime imaging microscopy (FLIM) which is capable of visualizing local molecular and physiological parameters in living cells, plays a significant role in biological sciences, chemistry, and medical research. In order to unveil dynamic cellular processes, it is necessary to develop high-speed FLIM technology. Thanks to the development of highly parallel time-to-digital convertor (TDC) arrays, especially when integrated with single-photon avalanche diodes (SPADs), the acquisition rate of high-resolution fluorescence lifetime imaging has been dramatically improved.

On the other hand, these technological advances and advanced data acquisition systems have generated massive data, which significantly increases the difficulty of FLIM analysis. Traditional FLIM systems rely on time-consuming iterative algorithms to retrieve the FLIM parameters. Therefore, lifetime analysis has become a bottleneck for high-speed FLIM applications, let alone real-time or video-rate FLIM systems. Although some simple algorithms have been proposed, most of them are only able to resolve a simple FLIM decay model. On the other hand, existing FLIM systems based on CPU processing do not make use of available parallel acceleration.

In order to tackle the existing problems, my study focused on introducing the state-of-art general purpose graphics processing units (GPUs) to the FLIM analysis, and building a data processing system based on both CPU and GPUs. With a large amount of parallel cores, the GPUs are able to significantly speed up lifetime analysis compared to CPU-only processing. In addition to transform the existing algorithms into GPU computing, I have developed a new high-speed and GPU friendly algorithm based on an artificial neural network (ANN). The proposed GPU-ANN-FLIM method has dramatically improved the efficiency of FLIM analysis, which is at least 1000-folder faster than some traditional algorithms, meaning that it has great potential to fuel current revolutions in high-speed high-resolution FLIM applications.

In this paper we report a novel biosensor based on electric field detection for recording cardiac electrical activity in zebrafish embryos. Using Sussex patented Electric Potential Sensing technology, a portable, non-invasive and cost-effective platform is developed to monitor in vivo electrocardiogram activity from the zebrafish heart. Cardiac activity signals were successfully detected from living zebrafish embryos starting at 3 days-post-fertilization

We present a bio-inspired unicast routing protocol for vehicular Ad Hoc Networks which uses the cellular attractor selection mechanism to select next hops. The proposed unicast routing protocol based on attractor selecting (URAS) is an opportunistic routing protocol, which is able to change itself adaptively to the complex and dynamic environment by routing feedback packets. We further employ a multi-attribute decision-making strategy, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), to reduce the number of redundant candidates for next-hop selection, so as to enhance the performance of attractor selection mechanism. Once the routing path is found, URAS maintains the current path or finds another better path adaptively based on the performance of current path, that is, it can self-evolution until the best routing path is found. Our simulation study compares the proposed solution with the state-of-the-art schemes, and shows the robustness and effectiveness of the proposed routing protocol and the significant performance improvement, in terms of packet delivery, end-to-end delay, and congestion, over the conventional method.

Electrical Impedance Mammography has been the field of interest in recent years in the area of breast care monitoring. This paper proposes a wireless planar array EIM data acquisition system ensuring low cost, reliable, accurate and efficient system. The proposed wireless planar array EIM system consists of wireless nodes, a gateway and a computer. This proposed system could not only reduces the noise interference from the conventional wired data acquisition system, which is one of the most talked about issues as well as increases the number of independent measurements which are very important for data acquisition and image reconstruction. The current planar EIM system’s limitation of using fixed number of electrodes can be improved by increasing the number of electrodes to improve the quality of information. Practical difficulty of applying large number of electrodes to the current system includes space limitation due the sealed synthetic covering comprising of driving circuitry and the programmable switching network. By the use of wireless nodes for transmitting and receiving the measurements, acquisition and the digital circuitry will be isolated from the system receiving and transmitting the measurements and the control signals through a radio link. Preliminary investigation has demonstrated that the system is successful; accurate and optimized for the current planar EIM system.

Epidemic dynamics, a kind of biological mechanisms describing microorganism propagation within populations, can inspire a wide range of novel designs of engineering technologies, such as advanced wireless communication and networking, global immunization on complex systems, and so on. There have been many studies on epidemic spread, but most of them focus on closed regions where the population size is fixed. In this paper, we proposed a susceptible-exposed-infected-recovered model with a variable contact rate to depict the dynamic spread processes of epidemics among heterogeneous individuals in open finite regions. We took the varied number of individuals and the dynamic migration rate into account in the model. We validated the effectiveness of our proposed model by simulating epidemics spread in different scenarios. We found that the average infected possibility of individuals, the population size of infectious individuals in the regions, and the infection ability of epidemics have great impact on the outbreak sizes of epidemics. The results demonstrate that the proposed model can well describe epidemics spread in open finite regions.

In this paper we report the development of the Micro-electroscope, a multimodal approach that allows the recording of morphological evolution occurring within the developing heart of zebrafish embryos, while simultaneously recording its functional counterpart: in vivo non-invasive heart electrical activity. The system is based on both a custom made bright field microscope aiming to gather morphological imaging and a built-in chamber containing a novel Electric Potential Sensing (EPS) microelectrode to record the heart functional activity.

Abstract Travelers' route choice behavior, a dynamical learning process based on their own experience, traffic information, and influence of others, is a type of cooperation optimization and a constant day-to-day evolutionary process. Travelers adjust their route choices to choose the best route, minimizing travel time and distance, or maximizing expressway use. Because route choice behavior is based on human beings, the most intelligent animals in the world, this swarm behavior is expected to incorporate more intelligence. Unlike existing research in route choice behavior, the influence of other travelers is considered for updating route choices on account of the reality, which makes the route choice behavior from individual to swarm. A new swarm intelligence algorithm inspired by travelers' route choice behavior for solving mathematical optimization problems is introduced in this paper. A comparison of the results of experiments with those of the classical global Particle Swarm Optimization (PSO) algorithm demonstrates the efficacy of the Route Choice Behavior Algorithm (RCBA). The novel algorithm provides a new approach to solving complex problems and new avenues for the study of route choice behavior.

A novel high-speed fluorescence lifetime imaging (FLIM) analysis method based on artificial neural networks (ANN) has been proposed. In terms of image generation, the proposed ANN-FLIM method does not require iterative searching procedures or initial conditions, and it can generate lifetime images at least 180-fold faster than conventional least squares curve-fitting software tools. The advantages of ANN-FLIM were demonstrated on both synthesized and experimental data, showing that it has great potential to fuel current revolutions in rapid FLIM technologies.

Currently, there is no effective sensing technology available to monitor the electrocardiogram(ECG) activity of the living zebrafish heart during early developmental stages. Most of the methods are based either on the use of simple visual inspections which are limited to quantifying the heart-rate or invasive methodologies which require the insertion of electrodes or heart explantation techniques, both requiring the use of expensive differential amplifiers and noise isolated environments.
In this paper we report the continuous detection of the cardiac electrical activity in embryonic zebrafish using a non-invasive approach. We present a portable and cost-effective platform based on the Sussex patented Electric Potential Sensing(EPS) technology, to monitor in vivo electrocardiogram activity from the zebrafish heart. We present results using the experimental prototype, which enables the acquisition of cardio-electrophysiological signals from in vivo 3, 4 and 5 days-post-fertilization(dpf) zebrafish embryos.

The characterization of the electrocardiographic activity of the living zebrafish heart during early developmental stages is a challenging task. Most of the available techniques are limited to heartbeat rate quantification being this inaccurate. Other invasive methodologies require the insertion of electrodes noise isolated environments and advanced amplification stages making these techniques very expensive. In this paper, we present a novel and non-invasive sensor development to characterize the functional activity of the developing heart of in vivo zebrafish embryos. The design is based on the Electric Potential Sensing technology patented at Sussex which has been developed to achieve reproducibility and continuous detection. We present preliminary functional characterization data of the developing zebrafish heart starting at 3 days-post-fertilization. Results show that using the proposed system for mapping the electrocardiographic activity of the zebrafish heart at early developmental stages is successfully accomplished. This is the first time that such a sensitive sensor has been developed for measuring the electrical changes occurring on micron sized (< 100 µm) living samples such as the zebrafish heart.

Currently, there is no effective sensing technology available to monitor the electrocardiogram activity of the living zebrafish heart during early developmental stages. Most of the methods are based either on the use of simple visual inspections which are limited to quantifying the heart rate or invasive methodologies which require the insertion of electrodes or heart explantation techniques both requiring the use of expensive differential amplifiers and noise isolated environments. In this paper we report the continuous detection of the cardiac electrical activity in embryonic zebrafish using a non-invasive approach. We present a portable and cost-effective platform based on the Electric Potential Sensing technology, to monitor in vivo electrocardiogram activity from the zebrafish heart. This proof of principle demonstration shows how electrocardiogram measurements from embryonic zebrafish may become accessible by using the electric field detection method. We present results obtained using the experimental prototype, which enables the acquisition of cardio-electrophysiological signals from in vivo zebrafish embryos.

Fluorescence lifetime imaging microscopy (FLIM) plays a significant role in biological sciences, chemistry, and medical research. We propose a Graphic Processing Units (GPUs) based FLIM analysis tool suitable for high-speed and flexible time-domain FLIM applications. With a large number of parallel processors, GPUs can significantly speed up lifetime calculations compared to CPU-OpenMP (parallel computing with multiple CPU cores) based analysis. We demonstrate how to implement and optimize FLIM algorithms on GPUs for both iterative and non-iterative FLIM analysis algorithms. The implemented algorithms have been tested on both synthesized and experimental FLIM data. The results show that at the same precision the GPU analysis can be up to 24-fold faster than its CPU-OpenMP counterpart. This means that even for high precision but time-consuming iterative FLIM algorithms, GPUs enable fast or even real-time analysis.

Despite the widespread use of insecticides, community engagement programmes and preventive measures mosquito borne diseases are growing and new tools to prevent the spread of disease are urgently needed. An alternative control measure for the eradication of Anopheles mosquitoes is suggested by the use of a Sustainable Control Model, which demonstrates the capability of Odonata, a natural beneficial predator, to exercise control over Anopheles mosquitoes in less than 140 days.

Plant pest recognition and detection is vital for food security, quality of life and a stable agricultural economy. This research demonstrates the combination of the k-means clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detection of the dataset is achieved by partitioning the data space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the objects (pests) from the background (pest habitat). The detection is established by extracting the variant distinctive attributes between the pest and its habitat (leaf, stem) and using the correspondence filter to identify the plant pests to obtain correlation peak values for different datasets. This work further establishes that the recognition probability from the pest image is directly proportional to the height of the output signal and inversely proportional to the viewing angles, which further confirmed that the recognition of plant pests is a function of their position and viewing angle. It is encouraging to note that the correspondence filter can achieve rotational invariance of pests up to angles of 360 degrees, which proves the effectiveness of the algorithm for the detection and recognition of plant pests.