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The emerging technology revolution empowered by artificial intelligence related research has brought us closer to the implementation of systems only seen in science-faction movies before. Artificial vision systems built in complex modern artefacts are very important for supporting their execution and perception based on camera captured raw pixels is one of the keys to open the machine intelligence. Deep learning and visual object tracking are both hot topics for computer vision research in recent years. Many visual object tracking algorithms based on deep learning technique appeared. The effectiveness of deep features for visual tracking has been shown on benchmarks, these big guys showed more intelligent behaviour capturing target object in bounding box but the high computational cost and deep hungry for data contradicts our initial requirements for practical tracking algorithm. In this paper, we investigated several deep trackers to help discovering current trends in this research area and hope it could help researchers and practitioners to better understand and further enhance existing methods.

Traditional dimming methods require excessive amount of external components and connection pins of ICs; therefore limit the possibility of making compact dimming fluorescent ballasts. By going over these conventional methods and introducing a new dimming method, this paper provides a solution for making highly integrated dimmable fluorescent ballasts, minimizing usage of external components and connector pins of ICs.

In order to improve the performances of median filter, a median filter based on judging impulse noise by statistic and adaptive threshold is proposed. This method includes two steps of judging impulse noise pixels and restoring it. First judging impulse noise pixels and signal pixels of edge and smooth area in the corrupted image, then median filter is used for the pixels corrupted by impulse noise and signal pixels of edge is preserved to avoid making the image fuzzy. The experimental results show that the algorithm can denoise impulse noise effectively and preserve image detail very well. Through comparing image fuzzy degree, the peak signal-to-noise ratio and the mean absolute error, the algorithm has better restoration performances in getting rid of impulse noise.

In many security informatics applications, it is important to monitor traffic over various communication channels and efficiently identify those communications that are unusual for further investigation. This paper studies such anomaly detection problems using a graph-theoretic link prediction approach. Data from the publicly-available Enron email corpus were used to validate the proposed approach.

It is a usual request that one ballast being able to drive multiple fluorescent lamps; however, it introduces new issues in electronic ballast designing such as requirements for wider output range, new protection features, auto-restart capability, etc. Step by step, this paper goes through the design issues and as conclusion, defines a high performance electronic ballast platform for multiple lamps.

T5HO lamps introduce new issues in electronic ballast designing such as requirements for higher ignition voltage, End-of-Life protection, etc. Also it is usually desired to have universal input voltage with wide range of output for T5HO electronic ballasts. Step by step, this paper goes through the design issues and as conclusion, defines a wide range high performance electronic ballast platform for T5HO lamps.

Electronic ballasts using a controller integrated circuit (IC) and two FETs are more reliable than self-oscillating bipolar transistor solutions, however, they usually have higher cost and bigger size. This paper introduces a new high voltage IC (HVIC) and low cost power factor technologies that allow developing electronic ballasts with low cost and small size

Expertise management systems are being widely adopted in organizations to manage tacit knowledge embedded in employees' heads. These systems have successfully applied many information technologies developed in fields such as information retrieval and document management to support expertise information collection, processing, and distribution. In this paper, we investigate the potentials of applying visualization techniques to support exploration of an expertise space. We implemented two widely applied dimensionality reduction visualization techniques, the self-organizing map and multidimensional scaling; to generate expert map and expertise field map visualizations based on an expertise data set. Our proposed approach is generic for automatic mapping of expertise space of an organization, research field, scientific domain, etc. Our initial analysis on the visualization results indicated that the expert map and expertise field map captured useful underlying structures of the expertise space and had the potential to support more efficient and effective expertise information searching and browsing.