Modular Filter Structures Using Current Feedback Operational Amplifiers

The concept of the wave filtering is followed in the derivation of high-order filter topologies by employing Current Feedback Operational Amplifiers as active blocks. For this purpose, the wave equivalent of an appropriate passive element chosen to be the elementary building block is introduced. As the wave equivalents of the other passive elements are derived by performing appropriate manipulations in the configuration of the wave equivalent of the elementary building block, an attractive characteristic offered by the derived filter topologies is the modularity of their structures. The validity of the proposed method is verified through experimental results in the case of a 3rd-order lowpass filter

2d frustrated Ising model with four phases

In this paper we consider a 2d random Ising system on a square lattice with nearest neighbour interactions. The disorder is short range correlated and asymmetry between the vertical and the horizontal direction is admitted. More precisely, the vertical bonds are supposed to be non random while the horizontal bonds alternate: one row of all non random horizontal bonds is followed by one row where they are independent dichotomic random variables. We solve the model using an approximate approach that replace the quenched average with an annealed average under the constraint that the number of frustrated plaquettes is keep fixed and equals that of the true system. The surprising fact is that for some choices of the parameters of the model there are three second order phase transitions separating four different phases: antiferromagnetic, glassy-like, ferromagnetic and paramagnetic.Comment: 17 pages, Plain TeX, uses Harvmac.tex, 4 ps figures, submitted to Physical Review

Non-Mean-Field Behavior of Realistic Spin Glasses

We provide rigorous proofs which show that the main features of the Parisi solution of the Sherrington-Kirkpatrick spin glass are not valid for more realistic spin glass models in any dimension and at any temperature.Comment: LaTeX file, 8 page

Complex Random Energy Model: Zeros and Fluctuations

The partition function of the random energy model at inverse temperature $\beta$ is a sum of random exponentials $Z_N(\beta)=\sum_{k=1}^N \exp(\beta \sqrt{n} X_k)$, where $X_1,X_2,...$ are independent real standard normal random variables (= random energies), and $n=\log N$. We study the large $N$ limit of the partition function viewed as an analytic function of the complex variable $\beta$. We identify the asymptotic structure of complex zeros of the partition function confirming and extending predictions made in the theoretical physics literature. We prove limit theorems for the random partition function at complex $\beta$, both on the logarithmic scale and on the level of limiting distributions. Our results cover also the case of the sums of independent identically distributed random exponentials with any given correlations between the real and imaginary parts of the random exponent.Comment: 31 pages, 1 figur

Regularity Properties and Pathologies of Position-Space Renormalization-Group Transformations

We reconsider the conceptual foundations of the renormalization-group (RG) formalism, and prove some rigorous theorems on the regularity properties and possible pathologies of the RG map. Regarding regularity, we show that the RG map, defined on a suitable space of interactions (= formal Hamiltonians), is always single-valued and Lipschitz continuous on its domain of definition. This rules out a recently proposed scenario for the RG description of first-order phase transitions. On the pathological side, we make rigorous some arguments of Griffiths, Pearce and Israel, and prove in several cases that the renormalized measure is not a Gibbs measure for any reasonable interaction. This means that the RG map is ill-defined, and that the conventional RG description of first-order phase transitions is not universally valid. For decimation or Kadanoff transformations applied to the Ising model in dimension $d \ge 3$, these pathologies occur in a full neighborhood $\{ \beta > \beta_0 ,\, |h| < \epsilon(\beta) \}$ of the low-temperature part of the first-order phase-transition surface. For block-averaging transformations applied to the Ising model in dimension $d \ge 2$, the pathologies occur at low temperatures for arbitrary magnetic-field strength. Pathologies may also occur in the critical region for Ising models in dimension $d \ge 4$. We discuss in detail the distinction between Gibbsian and non-Gibbsian measures, and give a rather complete catalogue of the known examples. Finally, we discuss the heuristic and numerical evidence on RG pathologies in the light of our rigorous theorems.Comment: 273 pages including 14 figures, Postscript, See also ftp.scri.fsu.edu:hep-lat/papers/9210/9210032.ps.

Local difference patterns for drunk person identification

In this work, facial thermal infrared images are employed for intoxicated person discrimination. Specifically, the region of the forehead of the face of the sober and the corresponding intoxicated person is used to test if the employed Local Difference Patterns (LDPs) constitute discriminative features. For an intoxicated person, vessels on the forehead become more active so that the intensity of the pixels in this region is affected accordingly. The LDPs employed ignore orientation of the pixels distribution and give emphasis on the first and second norms of the differences as well as the ordered values of the pixels in the employed kernels. The statistics of the LDPs for the drunk person are different from those of the sober one and accordingly drunkenness can be ascertained by comparing the thermal infrared image of the corresponding sober and intoxicated person. Six from the eight LDPs examined to be used as features for drunk identification were proved successful. Their classification success rate was over 73 and up to 85%. The proposed method can be incorporated into a non-invasive inspection commercial system to be used by the police as a first step for intoxicated person detection. Forty one participants in the experiment have contributed to the creation of the unique sober–drunk database which is available on the web and contains over 4.000 images. © 2017, Springer Science+Business Media New York

Fusion using neural networks for intoxication identification

Fusion of dissimilar features by means of neural networks is demonstrated in this work aiming at improving the performance of these features for drunk person identification. The features are coming from the thermal images of the face of the inspected persons and have been derived using different image analysis techniques. Thus, they convey dissimilar information, which has to be transferred onto the same framework and fused to result into a decision with improved reliability. Conventional data association techniques are employed to explore the available information. After that, fusion of the information is carried out using Neural Networks. The resulting decision is of higher reliability compared to those achieved using the individual features separately. Experimental results are provided based on an existing sober-drunk database. The main advantage of the method is that it is not invasive and all the information is acquired remotely. In practice, an electronic system incorporating the proposed approach will point out to the police to whom an extended inspection for alcohol consumption is due. © 2018 IEEE-CONFERENCE. All rights reserved