Information leakage in Wyner's wiretap channel model is usually defined as the mutual information between the secret message and the eavesdropper's received signal. We define a new quantity called “conditional information leakage given the eavesdropper's received signals,” which expresses the amount of information that an eavesdropper gains from his/her received signal. A benefit of introducing this quantity is that we can develop a fast algorithm for computing the conditional information leakage, which has linear complexity in the code length n, while the complexity for computing the usual information leakage is exponential in n. Validity of such a conditional information leakage as a security criterion is confirmed by studying the cases of binary symmetric channels and binary erasure channels.

Compressed Sensing (CS) is known to provide better channel estimation performance than the Least Square (LS) method for channel estimation. However, multipath delays may not be resolved if they span between the grids. This grid problem of CS is an obstacle to super resolution channel estimation. An Atomic Norm (AN) minimization is one of the methods for estimating continuous parameters. The AN minimization can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. There are studies showing that the AN minimization method has better resolution than conventional CS methods. In this paper, we propose a channel estimation method based on the AN minimization for Spread Spectrum (SS) systems. The accuracy of the proposed channel estimation is compared with the conventional LS method and Dantzig Selector (DS) of the CS. In addition to the application of channel estimation in wireless communication, we also show that the AN minimization can be applied to Global Positioning System (GPS) using Gold sequence.

We consider a circuit composed of linear capacitors, nonlinear resistors, and dc voltage sources and show the possibility that the total energy dissipated at resistors in the above circuit is smaller than the energy stored at capacitors. Linear passive circuits cannot possess such a property.

We assess how well the recently created MRI reconstruction technique, Multi-Resolution Convolutional Neural Network (MRCNN), performs in the core medical vision field (classification). The primary goal of MRCNN is to identify the best k-space undersampling patterns to accelerate the MRI. In this study, we use the Figshare brain tumor dataset for MRI classification with 3064 T1-weighted contrast-enhanced MRI (CE-MRI) over three categories: meningioma, glioma, and pituitary tumors. We apply MRCNN to the dataset, which is a method to reconstruct high-quality images from under-sampled k-space signals. Next, we employ the pre-trained VGG16 model, which is a Deep Neural Network (DNN) based image classifier to the MRCNN restored MRIs to classify the brain tumors. Our experiments showed that in the case of MRCNN restored data, the proposed brain tumor classifier achieved 92.79% classification accuracy for a 10% sampling rate, which is slightly higher than that of SRCNN, MoDL, and Zero-filling methods have 91.89%, 91.89%, and 90.98% respectively. Note that our classifier was trained using the dataset consisting of the images with full sampling and their labels, which can be regarded as a model of the usual human diagnostician. Hence our results would suggest MRCNN is useful for human diagnosis. In conclusion, MRCNN significantly enhances the accuracy of the brain tumor classification system based on the tumor location using under-sampled k-space signals.

We show some results concerning the number of solutions of the equation y+Ax=b (yTx=0, y0, x0) which plays a central role in the dc analysis of transistor circuits. In particular, we give sufficient conditions for the equation to possess exactly 2l (ln) solutions, where n is the dimension of the vector x.

We propose a post-filter (digital filter applied after the correlator) to reduce multiple-access interference (MAI) in the correlator output in asynchronous communications. Optimum filter coefficients are derived for Markov and i.i.d. codes. It is shown that post-filter is not needed for Markov case. Variance of MAI is reduced in i.i.d. codes and it becomes equal to that of Markov codes; thus, both will have the same bit error rate (BER) performance. This post-filter reduces level of MAI in the correlator output for Gold codes as well.

A detector based on calculation of a posteriori probability is proposed for code acquisition in singleuser direct sequence code division multiple access (DS/CDMA) systems. Available information is used for decision making, unlike conventional methods which only use a part of it. Although this increases the overhead in terms of additional memory and computational complexity, significant performance improvements are achieved. The frame work is extended to multiuser systems and again mean acquisition time/correct acquisition probability performance is superior to the conventional systems although computational complexity is high. An approximate multiuser method with significantly less complexity is also derived.