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One of the commonly-used feature extraction algorithms in computer vision is the histogram of oriented gradients. Extracting the features from an image using this algorithm requires a large amount of computations. One way to boost the speed is to implement this algorithm on field programmable gate arrays, to benefit from flexible designs such as parallel computing. In this paper, we first, provide a summary of the steps of the histogram of oriented gradients algorithm. We then survey the implementation techniques of the histogram of oriented gradients on field-programmable gate arrays in the past decade. We group the different techniques into four main categories and analyze various enhancement methods in each category. The first group is the optimization of the algorithm computation which involves the steps of input selection, magnitude calculation, orientation and bin assignment, and normalization. The second category is data manipulation techniques which include numerical representation, data flow modification, and memory optimization. The third group contains modified features based on the histogram of oriented gradients and their hardware implementation, and the fourth one is the implementations in hardware-software co-design of the algorithm. We compare the different implementations using a speed metric called pixels per clock cycle, and resource utilization. Finally, we provide design summary tables for efficient implementation with respect to the speed metric, accuracy, and resource utilization. INDEX TERMS Histogram of oriented gradients, field programmable gate arrays, hardware acceleration, hardware design. KIN FUN LI is the Director of the two highly sought-after professional master of engineering programs in telecommunications and information security (MTIS) and applied data science (MADS) at the University of Victoria, Canada, where he teaches both hardware and software courses in the Department of Electrical and Computer Engineering. He dedicates his time to instructing and researching in computer architecture, hardware accelerators, education analytics, and data mining applications. He is actively involved in the organization of many international conferences, including the biennial IEEE Pacific Rim in Victoria and the internationally held IEEE AINA. He is also a passionate supporter and participant in numerous international activities to promote the engineering profession, education, and diversity.

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This article focus on optimal economic load dispatch based on an intelligent method of shark smell optimization (SSO). In this problem, the risk constrains has been considered which has root in uncertainity and unpredictable behavior of wind power. Regarding to increasing of this clean energy in power systems and un‐dispatchable behavior of wind power, its conditional value at risk index considered in this article which consists of loss from load and "spilling" wind energy connected with unpredictable imbalances among generation and load. This problem has been considered as an optimization problem based on SSO that evaluate the balance between cost and risk. This algorithm is based on distinct shark smell abilities for localizing the prey. In sharks' movement, the concentration of the odor is an important factor to guide the shark to the prey. In other words, the shark moves in the way with higher odor concentration. This characteristic is used in the proposed SSO algorithm to find the solution of an optimization problem. Effectiveness of the proposed method has been applied over 30‐bus power system in comparison with other techniques. © 2016 Wiley Periodicals, Inc. Complexity 21: 494–506, 2016