Sangjin Kim

This study examined socioeconomic change, social institutions, and serious property crime in transitional Russia. Durkheim's anomie theory and recent research on violence in Russia led us to expect an association between negative socioeconomic change and property crime. Based upon institutional anomie theory, we also tested the hypothesis that the association between change and crime is conditioned by the strength of non-economic social institutions. Using crime data from the Russian Ministry of

In-memory computing (IMC) processors show significant energy and area efficiency for deep neural network (DNN) processing [1–3]. As shown in Fig. 16.5.1, despite promising macro-level efficiency and throughput, there remain three main challenges to extending gains to system performance with a high integration level. First, most previous works had a fixed configuration and fixed size of IMC macros, and when the size of macro was smaller than the DNN layer's dimension, repetitive memory accesses w

This paper presents a novel wavelet-domain color image enhancement using filtered directional bases and frequency-adaptive shrinkage. Most traditional noise reduction methods tend to over-suppress high-frequency details. For overcoming this problem we first decompose the input image into flat and edge regions, and remove noise using the alpha map computed from wavelet transform coefficients of LH, HL, and HH bands. After removing noise in the flat region, we further remove noise in edge regions

This paper presents a novel approach to depth estimation using a multiple color-filter aperture (MCA) camera and its application to multifocusing. An image acquired by the MCA camera contains spatially varying misalignment among RGB color channels, where the direction and length of the misalignment is a function of the distance of an object from the plane of focus. Therefore, if the misalignment is estimated from the MCA output image, multifocusing and depth estimation become possible using a se

An efficient and high-speed 3D point cloud-based neural network processing unit (PNNPU) is proposed using the block-based point processing. It has three key features: 1) page-based point block memory management unit (PMMU) with linked list-based page table (LLPT) for on-chip memory footprint reduction, 2) hierarchical block-wise farthest point sampling (HFPS), and block skipping ball-query (BSBQ) for fast and efficient point processing, 3) Skipping-based max-pooling prediction (SMPP) for through

Computing-in-memory (CIM) has emerged as an energy-efficient hardware solution for machine learning and AI. While static random access memory (SRAM)-based CIM has been prevalent, growing attention is directed towards leveraging dynamic random access memory (DRAM) and non-volatile memory (NVM) with its unique characteristics such as high-density and non-volatility. This brief reviews the evolving trends in DRAM and NVM-based CIM, which have faced unique challenges that arise from SRAM despite the

This article presents DynaPlasia, a reconfigurable eDRAM-based in- memory computing (IMC) processor with a novel triple-mode cell. It enables higher system-level performance and efficiency in a resource-limited environment. DynaPlasia proposes five key features that can enhance the energy efficiency and area efficiency of the IMC accelerator: 1) dynamic reconfigurable core architecture (DRECA), which dynamically reconfigures the effective IMC macro size according to DNN workloads; 2) the triple-

An inverse kinematics solution that utilizes the differential relationship between the joint space and Cartesian space of redundant manipulators is proposed. Until now, this solution could be obtained using the pseudo inverse of the Jacobian matrix. However the computation of the pseudo inverse is complex so that it is not easy to implement it on a digital computer for online motion planning of the robot. As an alternative to the pseudo inverse solution, an inverse kinematics solution using fuzz

This article presents Scaling-computing-in-memory (CIM), an energy-efficient embedded dynamic random access memory (eDRAM)-based in-memory-computing (IMC) accelerator with a dynamic-scaling readout for signal-to-quantization-noise ratio (SQNR) boosting and analog-to-digital converter (ADC) overhead reduction. It greatly saves the ADC cost by reducing the required number of ADC-bit and ADC operations by codesigning the algorithm and hardware. Scaling-CIM proposes three key features: 1) dynamic sc

In this paper, we present a novel real-time image restoration approach using a truncated constrained least-squares (TCLS) filter and spatially adaptive noise smoothing (SANS) algorithm based on alpha map for the extended depth of field (EDoF) system in an image signal processing (ISP) chain. The proposed TCLS filter and the alpha map-based SANS algorithm can be implemented in the Bayer-domain by using a general finite impulse response (FIR) structure. The TCLS filter coefficients are priori dete

Image restoration plays a major role in improving image quality as a preprocessing step in various imaging systems. While conventional image restoration techniques, such as the constrained least-squares (CLS) and the Wiener filters, often exhibit either noise amplification or over-smoothing problem. On the other hand, advanced image restoration techniques, such as the iterative regularization and the combined Fourier and wavelet domain thresholding filters, are not suitable for real-time applica

A multiple color-filter aperture (MCA) can provide a single camera with depth information and multifocusing. However, the original version of the MCA system exhibits inherent limitations such as manual, empirical tuning parameters for the color channel registration and fusion (CRF) process. Furthermore, a CRF output image still contains undesired out-of-focus blur because of the finite-sized apertures and the lateral displacement of each color-filter aperture, which results in low exposure, colo

Resolved motion rate control (RMRC) is used to map the Cartesian space trajectory to the joint space trajectory. The RMRC for the redundant robot requires the pseudo-inverse of the Jacobian matrix. However the pseudo-inverse is not easy to implement on a digital computer is real time and is mathematically complex. A simple fuzzy RMRC (FRMRC) that can replace the RMRC using the pseudo-inverse of the Jacobian is proposed. An FRMRC with appropriate fuzzy rules, membership functions, and reasoning m