Multiplication schemes based on Toeplitz matrix-vector product (TMVP) have been proposed by many researchers. TMVP can be computed using the recursive two-way and three-way split methods, which are composed of four blocks. Among them, we improve the space complexity of the component matrix formation (CMF) block. This result derives the improvements of multiplication schemes based on TMVP. Also, we present a subquadratic space complexity$GF(2^m)$multiplier with even type Gaussian normal basis (GNB). In order to design the multiplier, we formulate field multiplication as a sum of two TMVPs and efficiently compute the sum. As a result, for type 2 and 4 GNBs, the proposed multipliers outperform other similar schemes. The proposed type 6 GNB is the first subquadrtic space complexity multiplier with its explicit complexity formula.

In this paper, we present new parallel polynomial multiplication formulas which result in subquadratic space complexity. The schemes are based on a recently proposed block recombination of polynomial multiplication formula. The proposed two-way, three-way, and four-way split polynomial multiplication formulas achieve the smallest space complexities. Moreover, by providing area-time tradeoff method, the proposed formulas enable one to choose a parallel formula for polynomial multiplication which is suited for a design environment.

We propose block decompositions for the Toeplitz matrix-vector product (TMVP) using the$k$-way splitting method presented in the above paper. As a result, we show that the space complexity for TMVP can be improved.

In the above paper, Akleylekproposed an efficient multiplication algorithm for polynomials in Chebyshev form. In this comment, we show that a recombination of the above proposed algorithm induces more efficient algorithm for the multiplications of polynomials in Chebyshev form.

In this paper, we present a new method to compute the Mastrovito matrix forGF(2m) generated by an arbitrary irreducible polynomial using weakly dual basis of shifted polynomial basis. In particular, we derive the explicit formulas of the proposed multiplier for special type of irreducible pentanomialxm+xk3+xk2+xk1+1 withk1k2≤ (k1+k3)/2k3k1,m/2). As a result, the time complexity of the proposed multiplier matches or outperforms the previously known results. On the other hand, the number of XOR gates of the proposed multiplier is slightly greater than the best known results.

Given vehicle images, we suggest a way to recognize the color of the vehicle contained in the image. The color feature of a vehicle is represented by a color histogram, and we decide the appropriate number of color histogram bins, which mainly affects the successful recognition rate. After generating the histograms, template matching is used to decide the vehicle color. In HSI (hue saturation intensity) color space, experimental results show that the partition of H, S, and I into 8, 4, 4, respectively, achieves the highest success rate up to 88.34%.

In this paper, we suggest a method to identify the clothing as one approach for identifying a person in the grayscale image captured by a CCTV camera. We use the edge information of the clothing area for extracting the textural feature of the clothing. The edge filter is applied to the clothing area, and the histograms on the edge values are constructed. The feature vector is composed from the histograms. We used Euclidean distance as a similarity measure between feature vectors, and successfully identified the clothing with 77.8% of success rate.

In this paper, we present a bit-parallel multiplier for GF(2^m) defined by an irreducible pentanomial x^m+x^{k_3}+x^{k_2}+x^{k_1}+1, where 1\leq k_1 < k_2