Earticle

Imbalanced datasets are common in industrial internet of things (IIoT) systems due to challenges in acquiring faulty labels. Augmentation and graph-based methods have been proposed to improve classification accuracy of deep learning-based systems. However, the conventional approaches can be limited by training complexity and inefficient memory usage. In this paper, GraphSAGE with contrastive encoder (GCE) is proposed to improve classification accuracy and memory utilization efficiency. From the simulation results, it is confirmed that the GCE can improve classification accuracy by up to 23% compared to conventional approaches.

In this study, we explore the potential of leveraging machine learning techniques, specifically auto-encoders (AE), for the decoding of linear block codes. Our findings suggest that this approach can outperform the conventional ordered statistics decoding (OSD) method, especially in a Rayleigh fading channel environment. We have rigorously trained the AE under both additive white Gaussian noise and Rayleigh fading channel conditions to ensure robustness in its performance. The output of the AE is combined with the received vector in a suitable manner to perform OSD. Through our experiments, we demonstrate that this proposed decoding approach yields better results than the conventional OSD method in Rayleigh fading channel when we used (23,12) Golay code.

This paper proposes two new design approaches for decimal to binary-coded-decimal (BCD) encoder using reversible logic through Peres gate (PG) and Feynman gate (FG) which consume 10 and 11 gates respectively to realize such circuitry. Gates have been arranged properly to minimize the gate count (GC) followed by the reduction of the quantum cost (QC) and garbage outputs (GO). The proposed designs have an improvement at least up to 45%, 40%, and 50% of GC, GO and QC respectively from the best state of the art designs.

One of the most common task of electromyography (EMG)-based human?machine interface (HMI) is the hand gesture recognition. Robust and accurate predictions leveraging the surface EMG (sEMG) is a key challenge. In this paper, we present a 3D hand gesture prediction application, leveraging the sEMG signal and the optical hand tracking information. A transformer?encoder classifier (TEC) module is introduced in an IPC-system to predict the 3D-hand gestures using eight monopolar channels from sEMG as input. An experimental testbed is setup to acquire, train, and predict the 3D hand gestures within a feasible range of performance. The performance has been evaluated in terms of percentage of correctly classified keypoints (PCK). PCK is measured by first estimating the euclidean distance between the actual and the predicted keypoints. The percentage of keypoints within a threshold distance value are then calculated. Results from the ablation study indicate that the proposed scheme shows a percentage of correctly classified keypoints of up to 72.8%, 92.7%, 97.2%, and 98.6% with a PCK threshold of 5 mm, 10 mm, 20 mm, and 30 mm, respectively.

Reliable channel estimation is critical for achieving high-speed and energy-efficient communication in Unmanned Aerial Vehicle-Free Space Optical (UAV-FSO) systems, particularly under dynamic impairments such as atmospheric turbulence (AT) and pointing errors (PEs). This paper proposes a pilot-free channel estimation framework based on a Variational Autoencoder (VAE). The system employs Intensity Modulation/Direct Detection (IM/DD) with?-ary one-hot encoded symbols (). The VAE encodes noisy received signals into a 128-dimensional latent space and reconstructs the transmitted data, effectively learning the joint effects of AT, PEs, and AWGN. Unlike prior works that primarily consider boresight or Gaussian-jitter PEs, this study explicitly incorporates a Nakagami-modeled PE distribution, capturing UAV-induced beam misalignment under mobility, vibration, and turbulence coupling. Simulation results show that the proposed VAE significantly outperforms conventional estimators (LS, MMSE, LMMSE) and deep learning baselines (AE, DNN, CNN) across various turbulence strengths. Under strong turbulence and PEs, the VAE attains nearly two-fold lower MSE compared to CNN and DNN. In addition, evaluation on real turbulence-impaired datasets further validates robustness and generalization. The proposed pilot-free scheme delivers accurate channel estimation, reduced BER, and improved spectral efficiency, making it suitable for real-time adaptive UAV-FSO communication.

일반적인 평판 프린터 시스템은 PC와 전용 S/W로 구성되어 사용에 불편함이 존재한다. 결국, 평판 프린터 시스템 구성의 간소화, 스마트화 등을 통해 다양한 형태의 프린팅을 쉽고 편리하게 사용할 수 있는 기술이 필요하 다. 즉, 한 대의 프린터로 여러 종류의 소재에 인쇄가 가능하며, 다양한 형태의 상품을 인쇄할 수 있는 다입체 프린 터에 대한 수요가 증가하고 있다. 따라서, 본 논문에서는 Head Encoder/Trigger 제어를 이용하여 다입체 인쇄가 가능한 평판 프린터 시스템을 개발하였다. 이를 위해, 평판 프린터 내부 모듈을 입력 형태 감지 센서를 연계시키고, 별도의 메인 콘트롤러를 통해 프린터의 Head Encoder와 Head Trigger 신호에 의해 모든 동작 상태를 제어하는 평판 프린터를 개발하였다. 이를 통해, IoT 기술의 발전 및 보급의 확산으로 산업 전반에 걸쳐 스마트 환경의 프린 터 제어가 발전된 형태로 확대될 것이며, 향후 3D 프린팅 산업 발전에 기여할 것으로 기대된다.

The general flatbed printer system is composed of a PC and a dedicated S/W, which is inconvenient to use. In the end, there is a need for a technology that can easily and conveniently use various types of printing through simplification, smartization, etc. of a flatbed printer system configuration. That is, there is an increasing demand for multi-dimensional printer capable of printing on various types of materials with one printer and capable of printing various types of products. Therefore, in this paper, we developed a flatbed printer system capable of multi-dimensional printing using Head Encoder/Trigger control. To this end, we developed a flatbed printer that connects the internal module of the flatbed printer with an input type detection sensor and controls all operating states by the head encoder and head trigger signals of the printer through separate main controllers. Through this, the development and diffusion of IoT technology will expand the printer control of the smart environment to the developed form throughout the industry. It is expected to contribute to the development of the 3D printing industry in the future.

최근 인공지능(AI) 기술의 발전으로 기존 침입 탐지 시스템(NIDS)의 한계를 극복하기 위한 연구가 활발히 이루어지고 있 다. 기존 서명 기반 또는 단일 딥러닝 모델은 새로운 공격 유형이나 제로데이 공격 탐지에 취약하며 네트워크 플로우 간 구조 적 관계를 충분히 반영하지 못한다. 이에 본 연구는 SAGEConv-GNN과 Transformer Encoder를 결합한 하이브리드 NIDS를 제안한다. GNN은 국부 구조 정보를, Transformer는 장기 의존성과 전역 문맥을 학습하며 Gated Fusion으로 이를 통합한다. 또 한 positional encoding, Early Stopping, label smoothing, class weight 등을 적용해 학습 안정성을 확보하였다. UNSW-NB15 (약 50,000 flows) 실험 결과 본 모델은 GCN, GAT, GraphSAGE, Transformer보다 우수했으며 Accuracy 99.70%, Precision 96.44%, Recall 99.08%, F1-score 97.2%를 달성했다. 이는 불균형 데이터 환경에서도 높은 탐지 성능과 낮은 오탐지율을 유지 함을 보이며 AI 기반 하이브리드 NIDS의 실용 가능성을 제시한다.

With recent advances in AI, overcoming the limits of traditional NIDS has become crucial: signature-based or single deep learning models struggle with novel/zero-day attacks and fail to capture structural relations among flows. We propose a hybrid NIDS combining SAGEConv-GNN (local structure) and a Transformer Encoder (global context/long-range dependencies), integrated via Gated Fusion, with positional encoding, early stopping, label smoothing, and class weighting for stable training. On UNSW-NB15 (~50k flows), our model outperforms GCN, GAT, GraphSAGE, and Transformer baselines, achieving 99.70% Accuracy, 96.44% Precision, 99.08% Recall, and 97.2% F1. This demonstrates strong detection performance and low false positives in imbalanced settings, underscoring the practicality of AI-based hybrid NIDS.

고령화 사회에 접어들면서 거동이 어려운 장애인과 고령자의 개인 교통수단에 대한 수요가 증가하고 있다. 실제로 2017년 기준 전국 전동보장구 보급수는 9만여 대로 지속해서 증가하는 추세다. 하지만 장애인 및 고령자의 판단 능력과 조정 능력은 정상인보다 상대적으로 차이가 있는 관계로 주행 중 사고 발생의 가능성이 크다. 다양한 사고의 원인 중 하나는 도로 노면 상태의 불균형으로 인해 개인 이동 수단 조향 제어의 간섭이다. 본 논문에서는 이 같은 사고를 예방하고자 도로 노면 상태를 고속으로 인지할 수 있는 암호화 형식 의미론적 분할 알고리즘 을 소개한다. 이를 위하여 도로 노면 파손이 포함된 1,500여 장의 학습용 데이터와 150여 장의 테스트용 데이터를 새롭게 구성하였다. 그리고 이를 활용하여 기존의 Encoder와 Decoder 단계 로 구성된 Auto-encoder 방식과 달리 Encoder 단계로 이루어진 심층 신경망을 제안하였다. 이 심층 신경망은 기존의 방식과 비교했을 때 평균 정확도(Mean Accuracy)는 4.45% 증가하였고 파라미터는 59.2% 감소하였으며 연산 속도는 11.9% 향상되었다. 이 같은 고속 알고리즘을 활 용하여 안전한 개인 이동 수단이 확대 적용되길 기대한다.

As we face an aging society, the demand for personal mobility for disabled and aged people is increasing. In fact, as of 2017, the number of electric wheelchair in the country continues to increase to 90,000. However, people with disabilities and seniors are more likely to have accidents while driving, because their judgment and coordination are inferior to normal people. One of the causes of the accident is the interference of personal vehicle steering control due to unbalanced road surface conditions. In this paper, we introduce a encoder type semantic segmentation algorithm that can recognize road conditions at high speed to prevent such accidents. To this end, more than 1,500 training data and 150 test data including road surface damage were newly secured. With the data, we proposed a deep neural network composed of encoder stages, unlike the Auto-encoding type consisting of encoder and decoder stages. Compared to the conventional method, this deep neural network has a 4.45% increase in mean accuracy, a 59.2% decrease in parameters, and an 11.9% increase in computation speed. It is expected that safe personal transportation will be come soon by utilizing such high speed algorithm.

딥러닝 기반 의료 영상 진단 모델의 신뢰성을 확보하기 위해 설명 가능한 AI 기술이 필수적으로 요구되고 있다. 다 양한 XAI 방법 중, 반사실적 설명은 직관적인 해석을 제공하지만, 기존 생성 모델 기반 방법론은 학습이 불안정하 고 특정 아키텍처에 의존하는 문제를 가진다. 이러한 불안정성과 의존성은 다양한 최신 분류기 모델에 대한 설명 생 성기의 적용을 어렵게 하고 신뢰성을 저해한다. 이 문제를 해결하기 위해, 본 연구는 분류기의 인코더를 판별기의 인 코더와 공유하는 새로운 반사실 설명 생성 프레임워크를 제안한다. 이 구조는 판별기가 픽셀 레벨이 아닌 분류기의 의 미론적 특징 공간 내에서 현실성을 판별하도록 유도하여, GAN 학습을 안정화시키고 일반화 성능을 확보한다. 흉부 X-ray(CheXpert) 데이터셋을 이용한 실험에서, 제안 프레임워크가 ResNet, ResneXt, DenseNet, ConvNeXt 등 다양한 분류기에서 학습 안정성을 크게 향상시키고, 높은 타당성(Validity)과 현실성(FID)을 달성함을 확인하 였다.

While Deep Learning models show high accuracy in medical image diagnosis, their 'black box' nature limits clinical adoption, making eXplainable AI (XAI) essential. Among XAI methods, Counterfactual Explanations offer intuitive interpretations, but existing generative approaches suffer from training instability and architecture-dependency. This instability and dependency hinder the application of explainers to various modern classifiers and reduce their reliability. To address this, this paper proposes a novel counterfactual generation framework that shares the classifier's encoder with the discriminator's encoder. This architecture stabilizes GAN training by forcing the discriminator to assess realism within the classifier's semantic feature space, rather than at the pixel level, thus ensuring generalization. Experiments on the CheXpert (Chest X-ray) dataset demonstrate that the proposed framework significantly improves training stability and achieves high validity and realism (FID) across various classifiers, including ResNet, ResNeXt, DenseNet, and ConvNeXt.

발리스는 열차의 안전한 운행을 위하여 철도의 레일 사이에 설치하여 운영하는 장치로, 텔레그램이라 칭하는 정보(거리ㆍ구배ㆍ속도ㆍ임시 속도제한 등의 가변정보)를 무선으로 통과하는 열차에 전송하는 기능을 갖는다. 본 연구는 이에 필요한 DBPL 인코더의 설계에 대한 것이다. 일반적으로 발리스를 위한 DBPL 인코더는 전용 ASIC이나 FPGA를 통하여 구현하고 있으나, 본 연구에서는 범용의 저전력 마이크로컨트롤러(STM32L4 Series)를 활용하여 설계하였다. 본 연구의 DBPL인코더는 펌웨어 방식의 논리적 1차 처리 수단과 마이크로컨트롤러 내장 SPI Bus 등을 이용한 물리적 출력수단으로 구분하여 설계하였다. 본 연구의 DBPL 인코더는 유로발리스 표준에서 요구하는 564.48Kbps의 속도로 동작 가능함을 확인하였다.

The balise is a device for the railroad signal control systems, which is installed between both rail. The balise sends fixed or variable data, named telegram, to the train with wireless method. The telegram includes the position information, the movable distance under the signal status, the gradient, the speed, the temporary speed limit, etc. This research is on a design of the DBPL encoder for the balise. Normally the DBPL encoder for the balise is with the ASIC or FPGA technology. In this research, the DBPL encoder is designed with commercial low power operable micro-controller. The firmware(logic level encode) and the SPI Bus function block(physical level output) of the micro-controller are used for the DBPL encode. Under the european standard, the required working speed of the DBPL encoder is 564.48Kbps. The DBPL encoder of this research is tested under the speed of 564.48Kbps, and it worked properly.

분산 환경에서 다양하게 사용되고 있는 IoT 장치는 의료·환경·교통·바이오·공공장소 등 사용 분야가 다양해지면 서 IoT 장치에서 송·수신되는 데이터의 중요도가 점점 증가하고 있다. 본 논문에서는 IoT 데이터의 신뢰성을 보장하기 위한 방법으로 수 많은 데이터들을 다양한 중요 속성별로 분류·처리하도록 오토인코더 기반의 IoT 연계 처리 기법을 제안한다. 제안 기법은 오토인코더 기반의 IoT 연계 처리를 위해서 IoT 데이터를 특성별로 블록체인으로 묶어 처리하 도록 IoT 데이터별로 상관관계 지수를 사용한다. 제안 기법은 IoT 데이터의 신뢰성을 보장하기 위해서 상관관계 지수 에 적용된 블록체인 기반의 n-계층 구조로 확장 운영한다. 또한, 제안 기법은 IoT 데이터의 상관관계 지수에 따라 IoT 수집 데이터에 가중치를 적용하여 IoT 데이터를 선택할 수 있을 뿐만 아니라 실시간으로 IoT 데이터의 무결성을 검증 하는 비용을 낮출 수 있다. 제안 기법은 n-계층 구조로 IoT 데이터를 확장할 수 있도록 IoT 데이터의 처리 비용을 유지한다.

IoT devices, which are used in various ways in distributed environments, are becoming more important in data transmitted and received from IoT devices as fields of use such as medical, environment, transportation, bio, and public places are diversified. In this paper, as a method to ensure the reliability of IoT data, an autoencoder-based IoT-linked processing technique is proposed to classify and process numerous data by various important attributes. The proposed technique uses correlation indices for each IoT data so that IoT data is grouped and processed by blockchain by characteristics for IoT linkage processing based on autoencoder. The proposed technique expands and operates into a blockchain-based n-layer structure applied to the correlation index to ensure the reliability of IoT data. In addition, the proposed technique can not only select IoT data by applying weights to IoT collection data according to the correlation index of IoT data, but also reduce the cost of verifying the integrity of IoT data in real time. The proposed technique maintains the processing cost of IoT data so that IoT data can be expanded to an n-layer structure.

동시적 위치 추정 및 지도 작성(Simultaneous Localization And Mapping, SLAM)은 주변 환경의 지도를 구 축하며 현재 위치를 추정하는 기술로, 자율주행 로봇, 드론, 증강현실(AR) 등 다양한 분야에 활용된다. 그러나 센 서 종류와 환경에 따라 성능이 저하될 수 있는데, 예를 들어 비전 기반 SLAM은 질감이 부족한 환경에서, 라이다 기반 SLAM은 구조물이 드문 공간에서 한계를 가진다. 본 연구에서는 이러한 센서의 한계를 보완하기 위해 비전과 라이다를 융합하고, 엔코더 기반의 오도메트리(odometry)를 결합한 SLAM 기법을 제안한다. 또한, 명시적인 보정 (calibration) 과정 없이 호모그래피(homography)를 적용하여 각 센서의 좌표계를 정렬하는 방법도 함께 제안한 다. 실내외 다양한 장애물이 존재하는 환경에서 실험한 결과, 평균 위치 오차는 약 3.7cm로 측정되어 효과적으로 잘 작동함을 확인하였다.

Simultaneous Localization and Mapping(SLAM) is a technique that builds a map of the surrounding environment while estimating the current position, and it is widely used in various fields such as autonomous driving, drones, and augmented reality(AR). However, its performance can degrade depending on the type of sensor and the environment. For example, vision-based SLAM struggles in textureless environments, while LiDAR-based SLAM has limitations in areas lacking sufficient structural features. To overcome these limitations, this study proposes a SLAM method that fuses visual and LiDAR data and integrates encoder-based odometry. In addition, a homography-based approach is introduced to align the coordinate systems of each sensor without an explicit calibration process. Experiments conducted in various indoor and outdoor environments with obstacles confirmed that the proposed method operates effectively, achieving an average localization error of approximately 3.7cm.

본 논문에서는 인코더-디코더 모델(encoder-decoder model)에서 잠재 벡터(latent vector)의 분류 성능을 비교 분석한다. 오토인코더와 같은 일반적인 인코더-디코더 모델은 인코더 입력을 잠재 벡터로 변환하고 이를 디코더에 입력하여 인코더 입력과 유사한 출력을 생성하도록 학습한다. 이와 같은 인코더-디코더 모델의 잠재 벡터는 인코더 입력의 특징을 추상화하여 잘 보존한다고 고려할 수 있다. 나아가 잠재 벡터가 특징 공간에서 클러스터들 사이에서 구분이 가능한 거리를 보장한다면 이를 비지도 학습에 적용하는 것이 가능하다. 본 논문에서는 인코더-디코더 모델 에서의 잠재 벡터를 비지도 학습 및 점진적 학습에 적용하기 위한 기초 연구로서 잠재 벡터의 분류 성능을 분석한 다. 이를 위해 스택트 오토인코더(stacked autoencoder)와 2가지 종류의 CNN(Convolutional Neural Network) 기반 오토인코더를 바탕으로 각각 구해지는 잠재 벡터를 KNN(K-Nearest Neighbor)과 랜덤 포레스 트(random forest)를 포함하는 4가지 종류의 분류기에 적용한다. 실험 결과 완전 연결 계층(fully connected or dense layer)를 가지는 CNN 기반의 오토인코더를 사용한 결과 평균 정확률은 약 97%이고 스택트 오토인코더의 결과는 약 95%로 2% 정도 우수한 분류 성능을 보이는 것을 확인할 수 있다. 본 논문에서의 연구를 바탕으로 완전 연결 계층을 가지는 CNN 기반의 오토 인코더를 이용하여 구해지는 잠재 벡터를 비지도 학습에 적용하는 것으로 확 장하는 것이 가능하다.

This paper compares and analyzes the classification performance of latent vectors in the encoder-decoder model. A typical encoder-decoder model, such as an autoencoder, transforms the encoder input into a latent vector and feeds it into the decoder. In this process, the encoderdecoder model learns to produce an decoder output similar to the encoder input. We can consider that the latent vector of the encoder-decoder model is well preserved by abstracting the characteristics of the encoder input. Further, it is possible to apply to unsupervised learning, if the latent vector guarantees a sufficient distance between clusters in the feature space. In this paper, the classification performance of latent vectors is analyzed as a basic study for applying latent vectors in encoder-decoder models to unsupervised and continual learning. The latent vectors obtained by the stacked autoencoder and 2 types of CNN-based autoencoder are applied to 4 kinds of classifiers including KNN and random forest. Experimental results show that the latent vector using the CNN-based autoencoder with a dense layer(about 97%) shows superior classification performance by up to 2% compared to the result of the stacked autoencoder(about 95%). Based on the results in this paper, it is possible to extend the latent vector obtained by using a CNN-based auto-encoder with dense layer to unsupervised learning.

빅데이터 시대를 맞이하여 인공지능 분야는 괄목할만한 성장을 보이고 있으며 특히 딥러닝에 의한 이미지 분류 학습방법이 중요한 영역으로 자리하고 있다. 이미지 분류에서 많이 사용되어 온 CNN의 성능을 더욱 개선하기 위해 다양한 연구가 활발하게 진행되었는데, 이 중에서 대표적인 방법이 CRNN(Convolutional Recurrent Neural Network) 알고리즘이다. CRNN 알고리즘은 이미지 분류를 위한 CNN과 시계열적 요소를 인식하기 위한 RNN의 조합으로 구성되는데, CRNN의 RNN영역에서 사용하는 입력값은 학습 대상의 이미지를 합성곱과 풀링 기법을 적용하여 추출된 결과물을 flatten한 값이고, 이 입력값들은 이미지 내 동일 위상에 있는 픽셀값들이 서로 다른 순서로 나타나기 때문에, RNN에서 의도한 이미지 내 배열 순서를 제대로 학습하기 어렵다는 한계점을 지닌다. 따라서 본 연구는 인코더와 디코더의 개념을 응용한 CNN과 RNN의 새로운 하이브리드 방법을 제안하여, 이미지 분류 성능을 향상시키는 것을 목적으로 하였다. 본 연구에서는 다양한 알고리즘 비교 실험을 통해, 새로운 하이브리드 방법의 효과성을 검증하였다. 본 연구는 인코더와 디코더 개념의 적용 가능성을 넓히고, 제안한 방법이 기존 하이브리드 방법에 비해, 복잡도가 크게 증가하지 않아 모델 학습 시간과 인프라 구축 비용 측면에서 이점을 있다는 점에서 학문적 시사점을 가진다. 또한, 정확한 이미지 분류가 필요한 다양한 분야에서 제공되는 서비스의 품질을 높일 수 있는 가능성을 제시하였다는 점에서 실무적 시사점을 가진다.

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses a study on the speculative navigation using auxiliary encoder during the development of QR Code-aware indoor mobility robots.

In this work, we are going to use thermal aware approach in Encoder design and also testing thermal stability by working on different ambient temperatures 298.15K, 308.15K, 318.15K, 328,15K, 338.15K and 348.15K and 358.15K. We have observe the compatibility of our device with wireless network by working on different I/O standards (LVCMOS15 and LVCMOS25) . There is 30.29% reduction in leakage power, when we scale down temperature from 358.15K to 298.15K using LVCMOS15 as I/O standard on 40nm Virtex FPGA. Leakage power is calculated for 65nm FPGA and 90nm FPGA as well .In this work, we are using Verilog Hardware Description Language.

This paper describes the steps involved in the tip control and automation of a hydraulic boom and knuckle crane which is used to position loads with accuracy and repeatability in shortest possible time. The crane is required to perform erection and installation of a modular bridge consisting of number of identical modules which have to be picked up or raised, moved along a predefined path and lowered at a drop point sequentially by the crane. Automation and tip control of the crane will reduce the burden on the operator and also save time. The requirement of raising and lowering of the loads require synchronised operation of two joints of the crane. For this purpose two different algorithms were derived, and the one which was feasible with the crane was used in the actual trials. For modelling and simulation of the crane, the kinematic equations of the crane were developed in MATLAB & the hydraulic circuit simulation & 3D Model of the crane simulation was developed in ITI_SimulationX. Actual system trials were done using dummy loads with the crane. The sensors to obtain feedback of joint angles were retrofitted on the existing crane structure. In place of the manual joystick controller, a new MMI based control system based on PLC was designed. The adapted crane can be operated manually by the individual valve joysticks as well as in automatic mode by the MMI and PLC based controller. The positions of the crane are taught to the controller using ‘teach’ mode in the controller. The controller calculates the new positions based on the kinematic equations and gives command to the valves to position the load accordingly.