Cloud computing is a model for enabling convenient and on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction.

The concept of cloud computing fills a perpetual need of future computing:

-          A way to increase capacity or add capabilities on the fly without investing in new infrastructure, training new personnel, or licensing new software.

-          Cloud computing encompasses any subscription-based or pay-per-use service that, in real time over the Internet, extends IT's existing capabilities

SFI technology leverage cloud computing usage models that synergize compute power, networking, storage, and security solutions. Our vision is to implement cloud computing solutions that are seamless and automated. This vision offers the promise of swiftly responding to the demands of users. With SFI clouds support, we can rapidly enable the delivery of massive videos to end-user’s smart phone. It can also support high volume templates for pattern recognition applications.

Pattern recognition is generally categorized according to the type of learning procedure used to generate the output value.

Supervised assumes that a set of training data (the training set) has been provided, consisting of a set of instances that have been properly labeled by hand with the correct output. A learning procedure then generates a model that attempts to meet two sometimes conflicting objectives: Perform as well as possible on the training data, and generalize as well as possible to new data.

Unsupervised learning, on the other hand, assumes training data that has not been hand-labeled, and attempts to find inherent patterns in the data that can then be used to determine the correct output value for new data instances.

A combination of the two that has recently been explored is semi-supervised learning, which uses a combination of labeled and unlabeled data. Note that in cases of unsupervised learning, there may be no training data at all to speak of; in other words, the data to be labeled is the training data.

Many common pattern recognition algorithms are probabilistic in nature, in that they use statistical inference to find the best label for a given instance. Unlike other algorithms, which simply output a "best" label, often times probabilistic algorithms also output a probability of  the instance being described by the given label. In addition, many probabilistic algorithms output a list of the N-best labels with associated probabilities, for some value of N, instead of simply a single best label. When the number of possible labels is fairly small, N may be set so that the probability of all possible labels is output. Probabilistic algorithms have many advantages over non-probabilistic algorithms:

• They output a confidence value associated with their choice.
• Correspondingly, they can abstain when the confidence of choosing any particular output is too low.
• Because of the probabilities output, probabilistic pattern-recognition algorithms can be more effectively incorporated into larger machine-learning tasks, in a way that partially or completely avoids the problem of error propagation.

Techniques to transform the raw feature vectors are sometimes used prior to application of the pattern-matching algorithm. For example, feature extraction algorithms attempt to reduce a large-dimensionality feature vector into a smaller-dimensionality vector that is easier to work with and encodes less redundancy, using mathematical techniques such as principal components analysis. Feature selection algorithms, attempt to directly prune out redundant or irrelevant features. The distinction between the two is that the resulting features after feature extraction has taken place are of a different sort than the original features and may not easily be interpretable, while the features left after feature selection are simply a subset of the original features.

SFI Technology utilizes pattern recognition technique, algorithm and solution to provide our users with state of art products. Our vision is to implement pattern recognition solutions that are highly efficient and capable of working in high speed environment. This vision offers the promise of swiftly responding to the demands of users. Our approach is different from those of common DSP solution where pipeline processing is hard to achieve. In the mean while, when it comes to a big model data size, general purpose DSP’s small internal data cache register will bring down DSP efficiency.  SFI embedded DSP pattern recognition platform equipped with the following advantages:

-          High volume data computation

-          Sufficient storage space to support high speed applications

-          Capable of supporting real time environment

-          Small form factor highly portable

-          Low power usage

Distracted driving is a problem. With the increasing public focus on distracted driving, SFI’s technology will empower drivers to a safe and responsible way. The SFI products that will assist in auto collision avoidance, speed monitoring, text-to-voice, driver drowsiness detection and much more

-          SFI's automobile products are designed to lift a higher driving safety standard, providing consumers with a groundbreaking driving experience.

-          By capturing, processing a HD video content, SFI’s automobile products assist of avoiding accident in all kinds of driving conditions.

-          Our high-performance pattern recognition DSP solutions address all segments of the road conditions, while our innovative algorithm enables us to deliver a no exception solution.

-          SFI’s automobile products are custom designed to deliver optimal performance of video which capture and record real time driving historical data.

-          The SFI’s automobile products are the most advanced implementations of the current pattern recognition technology and deliver exceptional performance at the worst weather conditions.

-          The SFI’s automobile products utilize smart phone and cloud server for massive data storage and applications.