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Radiation portal monitors comprising large-volume plastic scintillators are commonly used to monitor the smuggling of radioactive materials. Various applications have been proposed to perform radioisotope identification using these monitors. Such applications require calibration of the spectrum measured by the detector to obtain the physical energy spectrum. The relationship between the multichannel readout and energy bins depends on environmental conditions: it implies that energy calibration in radiation portal monitors should be performed periodically, even multiple times in a single day, thus demanding for a simple and fast energy calibration method. In this study, a deep learning model and a spectral remapping method were used to transform the raw detector output into an energy spectrum with constant energy bins. The deep learning model was designed to predict energy calibration parameters based on the channel spectrum of a single radioisotope. The dataset used to train the deep learning model was generated using the spectrum of the radiation portal monitor. A convolutional neural network model was utilized to evaluate the performance. The remapping method was designed to remap calibrated energy bins to fixed energy bins based on the linear interpolation of nearby bins. The performance of the neural network model and of the remapping method were then evaluated based on several measured spectra taken with different conditions, and found to be adequate to fulfill the requirements.

Accurate density measurement of molten refractory metals over 3000 K is very challenging, and difficult to achieve with conventional methods. Although containerless techniques have been the most effective and well-established methods to measure the density of molten metals at such high temperatures, a large discrepancy in the containerlessly measured density values has been reported. Here, we identify the uncertainty factors of the density measurement and their influence on the measured density of molten refractory metals over 3000 K using an electrostatic levitator (ESL). We find that intensely focused laser beams can cause rotation-induced deformation of a levitated droplet and thus the large uncertainty in the measured density. Moreover, the combination of sample rotation and precession seriously affects the measurements of density and temperature dependence of density (i.e., volume thermal expansion). By minimizing such rotation and precession, we successfully measure the density and volume expansion coefficient of refractory liquids (tantalum, molybdenum, and niobium) with significantly improved reproducibility and accuracy, and evaluate the uncertainties associated with the density measurement using ESL.

We present a powerful error control decoder which can be used in all kinds of digital versatile disk (DVD) systems. The decoder exploits the error information from the modulation decoder in order to increase the error correcting capability. We can identify that the modulation decoder in DVD system can detect errors more than 60% of total errors when burst errors are occurred. In results, for a decoded block, error correcting capability of the proposed scheme is improved up to 25% more than that of the original error control decoder. Also, a pipeline-balanced Reed?Solomon Product Code (RSPC) decoder with a low hardware complexity is designed to maximize the throughput. The maximum throughput of the RSPC decoder is 740 Mbps at 100 MHz and the number of gate counts is 20.3 K for RS(182,172,11) decoder and 30.7 K for RS(208,192,17) decoder, respectively.

Optical storage systems require the channel data sequence having DC-free characteristic. DC-free coding schemes for binary sequence are difficult to apply directly for high-rate multi-level optical recording systems. Thus, we present an effective coding scheme that DC-content is sufficiently controlled by inserting redundancy bits less than 1.25% for multi-level optical recording systems. This method is very simple and can be used for all multi-level run-length-limited codes.

In this paper, we propose the M=9 run-length-limited (d,k)=(1,3) code for multilevel recording channels. The code rate is 5/3 (bits/symbol), and the code has a simple 3-state encoder and a sliding block decoder. During the decoding process, the maximum error propagation of input data is seven bits. The structures of the encoder and decoder of the proposed code are very simple, so it may be easily implemented for high-density multilevel recording systems.

One of the major concerns regarding data detection in holographic data storage systems is overcoming interpage interference (IPI). In general, the IPI can be reduced by making the number of 1 (light) and 0 (dark) pixels the same in a recorded page and to have as many transitions as possible produced in each row and column. Such requirements can be satisfied by the guided scrambling (GS) method. In this paper, we investigate the optimum number of control bits to form a page.

For high-density optical recording systems, the effectiveness of the partial–response maximum-likelihood (PRML) detection scheme is limited by the channel asymmetry. Hence, to solve the problem and improve detection performance, we present an adaptive equalization scheme using the output of an expanded maximum-likelihood (ML) detector. The proposed PRML detection method adapts well to the asymmetric optical recording channels and has better bit error rate performance than the conventional PRML detection method.

In this paper, we propose a modified calculation method of the branch metric in partial response maximum likelihood (PRML) detection for high-density optical storage systems. The scheme is related to the relative probability of the run-length limited code. Hence, we also present the relative probabilities of the 17PP coded sequences for 2nd-, 3rd- and 4th-orders of PR targets. The modified PRML detection using the relative probability of the 17PP coded sequence has better bit error rate (BER) performance than conventional PRML detection.