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Ion beam analysis (IBA) is a group of PIXE (particle induced X-ray emission) techniques dedicated to the analysis of material for geological sample of coastal sediment samples. Coastal/Beach samples collected from South East Coast of Tamilnadu were analyzed by PIXE technique and obtain the elemental composition of coastal sediments. The PIXE analysis was carried out using 1.7 MV Tandem accelerators with the energy of 2 MeV protons at Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, Tamilnadu, India. The data analysis is normally done in an interactive mode of GUPIX analytical software computer code. The identified elements are present in the fingerprint X-ray spectrum in the range of ppm to ppb level. Therefore in this paper, introductory results concerning determination of concentration of chosen elements such as K, P, Ca, Ti, Fe, Cr, Co, Zn, Mn, Ni, Cu, Rb, Sr, As, Zr, Hf, Pb and Hg of coastal sediment samples from the east coast of Tamilnadu, India are presented. The validation of the technique is assessed using standard reference material (SRM) NIST 1646a estuarine sediment and the result shows the good agreement with the certified one. The results were used to assess environmental toxicity of heavy metals and radiation hazard in the study area.

This paper deals with the design of a true random number generator (TRNG) using the fingerprint as an entropy source and its implementation in substitution box (S-box) of Advanced Encryption Standard (AES). Considering fingerprint as a unique and random arrangement of minutiae, these minutiae points are used as the source of entropy. The proposed design utilizes fewer resources minimizing hardware redundancy and enhancing the level of randomness. This TRNG has been designed and validated using Artix-7 FPGA. The data rate, speed and latency have been obtained as 40 Mbps, 5 Mbps and 305 ns, respectively. The generated random bit stream had also been sampled and converted to a binary format in MATLAB and tested through the National Institute of Standards and Technology (NIST) 800.22 statistical suite for validation. The proposed TRNG design pass efficiency achieved is more than 95% for a sample size of 10 binary sequences.

This paper introduces an FPGA implementation of a pseudo-random number generator (PRNG) using Chen’s chaotic system. This paper mainly focuses on the development of an efficient VLSI architecture of PRNG in terms of bit rate, area resources, latency, maximum length sequence, and randomness. First, we analyze the dynamic behavior of the chaotic trajectories of Chen’s system and set the parameter’s value to maintain low hardware design complexity. A circuit realization of the proposed PRNG is presented using hardwired shifting, additions, subtractions, and multiplexing schemes. The benefit of this architecture, all the binary multiplications (except $X_i\cdot Y_i$ and $X_i\cdot Z_i)$ operations are performed using hardwired shifting. Moreover, the generated sequences pass all the 15 statistical tests of NIST, while it generates pseudo-random numbers at a uniform clock rate with minimum hardware complexity. The proposed architecture of PRNG is realized using Verilog HDL, prototyped on the Virtex-5 FPGA (XC5VLX50T) device, and its analysis has been done using the Matlab tool. Performance analysis confirms that the proposed Chen chaotic attractor-based PRNG scheme is simple, secure, and hardware efficient, with high potential to be adopted in cryptography applications.

This paper presents a reconfigurable image confusion scheme, which uses Linear Congruential Generators (LCGs)-based Pseudorandom Bits Generator (PRBG). The PRBG is based on the variable input-coupled LCG with a reconfigurable clock divider. The proposed algorithm encrypts the input image up to four times successively using different random sequences in every attempt. This new scheme aims to efficiently extract statistically strong pseudorandom sequences from a proposed PRBG with a large keyspace and simultaneously increase the security level of the encrypted image. This PRBG was initially designed on Virtex-5 (XC5VLX110T), Virtex-7 (XC7VX330T) and Artix-7 (XC7A100T) Field Programmable Gate Arrays (FPGAs). The statistical properties of the proposed PRBG for four different configurations are verified by the National Institute of Standards and Technology (NIST) tests. Thereafter, a reconfigurable encryption/decryption algorithm that uses the proposed PRBG is developed for secure image encryption. The encryption process was accomplished using the MATLAB tool after obtaining the PRBG keys from the FPGA. To show the quality and strength of the encryption process, security analysis [correlations and Number of Pixels Change Rate (NPCR)] is performed. Security analysis results are compared with the conventional encryption algorithm to show that the developed reconfigurable encryption scheme provides better results in security tests.

Practical random number generation (RNG) circuits are typically achieved with analog devices or digital approaches. Digital-based techniques, which use field programmable gate array (FPGA) and graphics processing units (GPU) etc. usually have better performances than analog methods as they are programmable, efficient and robust. However, digital realizations suffer from the effect of finite precision. Accordingly, the generated random numbers (RNs) are actually periodic instead of being real random. To tackle this limitation, in this paper we propose a novel digital-analog hybrid scheme that employs the digital unit as the main body, and minimum analog devices to generate physical RNs. Moreover, the possibility of realizing the proposed scheme with only one memory element is discussed. Without loss of generality, we use the capacitor and the memristor along with FPGA to construct the proposed hybrid system, and a chaotic true random number generator (TRNG) circuit is realized, producing physical RNs at a throughput of Gbit/s scale. These RNs successfully pass all the tests in the NIST SP800-22 package, confirming the significance of the scheme in practical applications. In addition, the use of this new scheme is not restricted to RNGs, and it also provides a strategy to solve the effect of finite precision in other digital systems.

Cellular automaton (CA) as a discrete and deterministic dynamic system has been widely used in cryptography. In this paper, a novel triple-coupling CA model is proposed. We first apply the triple-coupling model to 1D CA to design a high-quality pseudo-random number generator (PRNG). Numerical simulation and analyses show that triple-coupling CA exhibits more complex evolutionary behavior than uncoupling CA. The pseudo-random sequence generated by triple-coupling 1D CA can pass all NIST SP800-22 tests. Subsequently, the triple-coupling model is also applied to 2D CA, and a novel image encryption scheme based on this model is proposed. Thanks to the triple-coupling model, the diffusion operation can be completed between different color channels of the color image. Experimental simulations and extensive cryptanalysis show that the image encryption algorithm using the triple-coupling model has superior security and high efficiency.

The high speed quantum-random-number generation based on measuring the quantum phase noise of DFB diode laser is investigated experimentally in this paper. The quantum randomness of the generated random string is physically guaranteed by measuring the random phase fluctuation of laser. The generated random string can pass all the National Institute of Standards and Technology (NIST) tests and the random number generation rate is up to 1.25 Gbps.

Most industrialized countries subsidize private sector R&D, even under some circumstances when the firm is owned by foreigners. The present paper, using a simple theoretical analysis of a monopoly firm selling only to the U.S. market, argues that such subsidies are welfare enhancing—as long, of course—as the funding agency chooses the projects it funds wisely. The paper suggests that a subsidy rate of 50% might be warranted under some circumstances.

Several significant sets of labeled samples of image data are surveyed that can be used in the development of algorithms for offline and online handwriting recognition as well as for machine printed text recognition. The method used to gather each data set, the numbers of samples they contain, and the associated truth data are discussed. In the domain of offline handwriting, the CEDAR, NIST, and CENPARMI data sets are presented. These contain primarily isolated digits and alphabetic characters. The UNIPEN data set of online handwriting was collected from a number of independent sources and it contains individual characters as well as handwritten phrases. The University of Washington document image databases are also discussed. They contain a large number of English and Japanese document images that were selected from a range of publications.