Square Root Algorithm

Multiple Input -Multiple Output (MIMO) wireless technology involves highly complex vectors and matrix computations which are directly related to increased power and area consumption. This paper proposes an area and power efficient VLSI architecture that can serve the dual purpose of minimum norm sorting of rows as well as upper/lower block tri-angularization of matrices. The resources inside the architecture are shared among both operations and only primitive computations are used. Results indicate saving in silicon real estate as well as power consumption compared to previous architecture without degrading performance.

Multiple Input-Multiple Output (MIMO) wireless technology involves highly complex vectors and matrix computations which are directly related to increased power and area consumption. This paper proposes an area and power efficient VLSI architecture that can serve the dual purpose of minimum norm sorting of rows as well as upper/lower block tri-angularization of matrices. The resources inside the architecture are shared among both operations and only primitive computations are used. Results indicate saving in silicon real estate as well as power consumption compared to previous architecture without degrading performance.

Multiple Input-Multiple Output (MIMO) wireless technology involves highly complex vectors and matrix computations which are directly related to increased power and area consumption. This paper proposes an area and power efficient VLSI architecture that can serve the dual purpose of minimum norm sorting of rows as well as upper/lower block tri-angularization of matrices. The resources inside the architecture are shared among both operations and only primitive computations are used. Results indicate saving in silicon real estate as well as power consumption compared to previous architecture without degrading performance.

Multiple Input-Multiple Output (MIMO) wireless technology involves highly complex vectors and matrix computations which are directly related to increased power and area consumption. This paper proposes an area and power efficient VLSI architecture that can serve the dual purpose of minimum norm sorting of rows as well as upper/lower block tri-angularization of matrices. The resources inside the architecture are shared among both operations and only primitive computations are used. Results indicate saving in silicon real estate as well as power consumption compared to previous architecture without degrading performance.

Multiple Input-Multiple Output (MIMO) wireless technology involves highly complex vectors and matrix computations which are directly related to increased power and area consumption. This paper proposes an area and power efficient VLSI architecture that can serve the dual purpose of minimum norm sorting of rows as well as upper/lower block tri-angularization of matrices. The resources inside the architecture are shared among both operations and only primitive computations are used. Results indicate saving in silicon real estate as well as power consumption compared to previous architecture without degrading performance.

Multiple Input-Multiple Output (MIMO) wireless technology involves highly complex vectors and matrix computations which are directly related to increased power and area consumption. This paper proposes an area and power efficient VLSI architecture that can serve the dual purpose of minimum norm sorting of rows as well as upper/lower block tri-angularization of matrices. The resources inside the architecture are shared among both operations and only primitive computations are used. Results indicate saving in silicon real estate as well as power consumption compared to previous architecture without degrading performance.

Multiple Input-Multiple Output (MIMO) wireless technology involves highly complex vectors and matrix computations which are directly related to increased power and area consumption. This paper proposes an area and power efficient VLSI architecture that can serve the dual purpose of minimum norm sorting of rows as well as upper/lower block tri-angularization of matrices. The resources inside the architecture are shared among both operations and only primitive computations are used. Results indicate saving in silicon real estate as well as power consumption compared to previous architecture without degrading performance.

Multiple Input-Multiple Output (MIMO) wireless technology involves highly complex vectors and matrix computations which are directly related to increased power and area consumption. This paper proposes an area and power efficient VLSI architecture that can serve the dual purpose of minimum norm sorting of rows as well as upper/lower block tri-angularization of matrices. The resources inside the architecture are shared among both operations and only primitive computations are used. Results indicate saving in silicon real estate as well as power consumption compared to previous architecture without degrading performance.

Multiple Input -Multiple Output (MIMO) wireless technology involves highly complex vectors and matrix computations which are directly related to increased power and area consumption. This paper proposes an area and power efficient VLSI architecture that can serve the dual purpose of minimum norm sorting of rows as well as upper/lower block tri-angularization of matrices. The resources inside the architecture are shared among both operations and only primitive computations are used. Results indicate saving in silicon real estate as well as power consumption compared to previous architecture without degrading performance.

Multiple Input-Multiple Output (MIMO) wireless technology involves highly complex vectors and matrix computations which are directly related to increased power and area consumption. This paper proposes an area and power efficient VLSI architecture that can serve the dual purpose of minimum norm sorting of rows as well as upper/lower block tri-angularization of matrices. The resources inside the architecture are shared among both operations and only primitive computations are used. Results indicate saving in silicon real estate as well as power consumption compared to previous architecture without degrading performance.

In this paper, a new Playfair cipher built on bits level symmetric key cryptographic was proposed for the purpose of converting pairs of letters (digraphs) into single letters. The proposed algorithm is capable to overcome many of the shortcoming and vulnerabilities that exist in the current classical version of Playfair algorithm. The Playfair cipher is exceedingly complex than a classical substitution cipher, but still simple to hack using automated tactics. It is famous as a digraph cipher because two letters are exchanged by other two letters. This destroys any solo letter occurrence statistics, but the digraph statistics still unaffected (frequencies of two letters). Unluckily letter pairs have a flatter distribution than the one letter frequencies, so this intricacy matters for solving the code using pen and paper procedures. The suggested encryption process is conducted as follows; letters are first arranged in a spiral manner in Polybius square, afterwards, each pair will be replaced utilizing before-after technique if we are arranging pairs horizontally and down-up technique (vertically). The former process produces pairs of Plaintext that will be converted to binary bit stream then will be divided over blocks with stable sizes. Bits of these blocks are taken from pairs then fit them into square matrix of suitable order to put the concept of row-wise and revers row-wise matrix. Bits of this matrix are split into 2x2 square matrixes. The sub-matrixes are formed 8 bits. Here the XNOR operation is taken into consideration for bitwise operation to generate the keys for decryption and produce the cipher-text.

The lossless embedding problem, also known as the Darlington synthesis or unitary extension problem, considers the extension of a given contractive system to become the partial input-output operator of a lossless system. In the paper, the embedding problem is solved for discrete-time time-varying systems with finite but possibly timevarying state dimensions, for the strictly contractive as well as the boundary case. The construction is done in a state space context and gives rise to a time-varying Riccati difference equation which is shown to have a closedform solution. As a corollary, a discrete-time Bounded Real Lemma is formulated, linking contractiveness of an input-output operator to conditions on its state realization.