Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa

Cognitive scale-free networks as a model for intermittency in human natural language

We model certain features of human language complexity by means of advanced concepts borrowed from statistical mechanics. Using a time series approach, the diffusion entropy method (DE), we compute the complexity of an Italian corpus of newspapers and magazines. We find that the anomalous scaling index is compatible with a simple dynamical model, a random walk on a complex scale-free network, which is linguistically related to Saussurre's paradigms. The model yields the famous Zipf's law in terms of the generalized central limit theorem.Comment: Conference FRACTAL 200

유전알고리즘 및 강화학습을 사용한 고속 회로 설계 자동화 프레임워크

학위논문(석사) -- 서울대학교대학원 : 융합과학기술대학원 지능정보융합학과, 2022.2. 전동석.Although design automation is a key enabler of modern large-scale digital systems, automating the transistor-level circuit design process still remains a challenge. Some recent works suggest that deep learning algorithms could be adopted to find optimal transistor dimensions in relatively small circuitry such as analog amplifiers. However, those approaches are not capable of exploring different circuit structures to meet the given design constraints. In this work, we propose an automatic circuit design framework that can generate practical circuit structures from scratch as well as optimize the size of each transistor, considering performance and reliability. We employ the framework to design level shifter circuits, and the experimental results show that the framework produces novel level shifter circuit topologies and the automatically optimized designs achieve 2.8-5.3× lower PDP than prior arts designed by human experts.설계 자동화는 대규모 디지털 시스템을 가능하게 하는 핵심 요소이지만 트랜지스터 수준에서 회로 설계 프로세스를 자동화하는 것은 여전히 어려운 과제로 남아 있습니다. 최근 연구에서는 아날로그 앰프와 같은 비교적 작은 회로에서 최적의 성능을 보이는 트랜지스터 크기를 찾기 위해 deep learning 알고리즘을 활용할 수 있다고 말합니다. 그러나 이러한 접근 방식은 주어진 설계 constraint를 충족하는 다른 회로 구조 탐색에 적용하기 어렵습니다. 본 연구에서는 성능과 신뢰성을 고려하여 각 트랜지스터의 크기를 최적화할 뿐만 아니라 처음부터 실용적인 회로 구조를 생성할 수 있는 자동 회로 설계 framework를 제안합니다. 우리는 framework를 사용하여 level shifter 회로를 설계했으며 실험 결과는 프레임워크가 새로운 level shifter 회로 토폴로지를 생성하고 자동으로 최적화된 설계가 인간 전문가가 설계한 선행 기술보다 2.8-5.3배 더 낮은 PDP를 달성한다는 것을 보여줍니다.Abstract i Contents ii List of Tables iv List of Figures v List of Algorithms vi 1 Introduction 1 2 Related work 6 2.1 Genetic Algorithm 6 2.2 NeuroEvolution of Augmenting Topologies (NEAT) 7 2.3 Reinforcement Learning (RL) 10 2.4 DDPG, D4PG, and PPO 12 2.5 Level Shifter 14 3 Proposed circuit design framework 17 3.1 Topology Generator 17 3.2 Circuit Optimizer 25 4 Experiment Result 32 4.1 Level Shifter Design 32 4.2 Topology Generation 34 4.3 Circuit Optimization 36 4.4 Test Chip Fabrication 42 4.5 Applicability of Topology Generator 47 5 Conclusion 50 Abstract (In Korean) 57석

Batch and median neural gas

Neural Gas (NG) constitutes a very robust clustering algorithm given euclidian data which does not suffer from the problem of local minima like simple vector quantization, or topological restrictions like the self-organizing map. Based on the cost function of NG, we introduce a batch variant of NG which shows much faster convergence and which can be interpreted as an optimization of the cost function by the Newton method. This formulation has the additional benefit that, based on the notion of the generalized median in analogy to Median SOM, a variant for non-vectorial proximity data can be introduced. We prove convergence of batch and median versions of NG, SOM, and k-means in a unified formulation, and we investigate the behavior of the algorithms in several experiments.Comment: In Special Issue after WSOM 05 Conference, 5-8 september, 2005, Pari

Compact Model Representation for 3D Reconstruction

3D reconstruction from 2D images is a central problem in computer vision. Recent works have been focusing on reconstruction directly from a single image. It is well known however that only one image cannot provide enough information for such a reconstruction. A prior knowledge that has been entertained are 3D CAD models due to its online ubiquity. A fundamental question is how to compactly represent millions of CAD models while allowing generalization to new unseen objects with fine-scaled geometry. We introduce an approach to compactly represent a 3D mesh. Our method first selects a 3D model from a graph structure by using a novel free-form deformation FFD 3D-2D registration, and then the selected 3D model is refined to best fit the image silhouette. We perform a comprehensive quantitative and qualitative analysis that demonstrates impressive dense and realistic 3D reconstruction from single images.Comment: 9 pages, 6 figure

Decoding Social Influence and the Wisdom of the Crowd in Financial Trading Network

In this paper, we study roles of social mechanisms in a financial system. Our data come from a novel on-line foreign exchange trading brokerage for individual investors, which also allows investors to form social network ties between each other and copy others' trades. From the dataset, we analyze the dynamics of this connected social influence systems in decision making processes. We discover that generally social trades outperform individual trades, but the social reputation of the top traders is not completely determined by their performance due to social feedback even when users are betting their own money. We also find that social influence plays a significant role in users' trades, especially decisions during periods of uncertainty. We report evidences suggesting that the dynamics of social influence contribute to market overreaction