Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

We propose a novel method to merge convolutional neural-nets for the inference stage. Given two well-trained networks that may have different architectures that handle different tasks, our method aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights. The shared weights are further re-trained to fine-tune the performance of the merged model. The proposed method effectively produces a compact model that may run original tasks simultaneously on resource-limited devices. As it preserves the general architectures and leverages the co-used weights of well-trained networks, a substantial training overhead can be reduced to shorten the system development time. Experimental results demonstrate a satisfactory performance and validate the effectiveness of the method.Comment: To appear in the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, 2018. (IJCAI-ECAI 2018

An Analogue Front-End System with a Low-Power On-Chip Filter and ADC for Portable ECG Detection Devices

Medical diagnostic instruments can be made into portable devices for the purpose of home care, such as the diagnosis of heart disease. These assisting devices are not only used to monitor patients but are also beneficial as handy and convenient medical instruments. Hence, for reasons of both portability and durability, designers should reduce the power consumption of assistant devices as much as possible to extend their battery lifetime. However, achieving the low power requirement of the ECG sensing and the processing board for the ECG with commercial discrete components (A21-0003) is difficult because the low power consumer electronics for ECG acquisition systems are not yet available. With the help of the integrated circuit technology, the power-saving requirement of portable and durable equipment gives circuit designers the impetus to reduce the power consumption of analogue front-end circuits in ECG acquisition systems. In addition, the analogue front-end circuits, which are the interface between physical signals and the digital processor, must be operated at a low-supply voltage to be integrated into the low-voltage system-on-a-chip (SOC) system (Eshraghian, 2006). Therefore, the chapter will present two design examples of low-voltage (1 V) and low-power (<1 W) on-chip circuits including a low-pass filter (LPF) and an analogue-to-digital converter (ADC) to demonstrate the possibility of developing the low-voltage low-power ECG acquisition SO

Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers

Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers

Estimation of Sediment Yield and Transportation in a Watershed River Reach Due to Climate Change

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Electronic band structures of Ge1−xSnx semiconductors: A first-principles density functional theory study

[[abstract]]We conduct first-principles total-energy density functional calculations to study the band structures in Ge 1− x Sn x infrared semiconductor alloys. The norm-conserving optimized pseudopotentials of Ge and Sn have been constructed for electronic structure calculations. The composition-bandgap relationships in Ge 1−x Sn x lattices are evaluated by a detailed comparison of structural models and their electronic band structures. The critical Sn composition related to the transition from indirect- to direct-gap in Ge 1−x Sn x alloys is estimated to be as low as x∼ 0.016 determined from the parametric fit. Our results show that the crossover Sn concentration occurs at a lower critical Sn concentration than the values predicted from the absorption measurements. However, early results indicate that the reliability of the critical Sn concentration from such measurements is hard to establish, since the indirect gap absorption is much weaker than the direct gap absorption. We find that the direct band gap decreases exponentially with the Sn composition over the range 0 0.375, in very good agreement with the theoretical observed behavior [D. W. Jenkins and J. D. Dow, Phys. Rev. B 36, 7994, 1987]. For homonuclear and heteronuclear complexes of Ge 1−x Sn x alloys, the indirect band gap at L-pointis is found to decrease homonuclear Ge-Ge bonds or increase homonuclear Sn-Sn bonds as a result of the reduced L valley. All findings agree with previously reported experimental and theoretical results. The analysis suggests that the top of valence band exhibits the localization of bond charge and the bottom of the conduction band is composed of the Ge 4s4p and/or Sn 5s5p atomic orbits.[[booktype]]紙本[[booktype]]電子

IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management

Curbing road accidents has always been one of the utmost priorities in every country. In Malaysia, Traffic Investigation and Enforcement Department reported that Malaysia’s total number of road accidents has increased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidents is driver’s behaviours. However, drivers’ behaviour was challenging to regulate by the enforcement team or fleet operators, especially heavy vehicles. We proposed adopting the Internet of Things (IoT) and its’ emerging technologies to monitor and alert driver’s behavioural and driving patterns in reducing road accidents. In this work, we proposed a lane tracking and iris detection algorithm to monitor and alert the driver’s behaviour when the vehicle sways away from the lane and the driver feeling drowsy, respectively. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as an intelligent transportation system in the vehicle. We implemented face recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring, respectively. With the GPS module, we monitored and alerted against permissible vehicle’s speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driver’s behavioural activities in real-time through the user access portal. We validated it successfully on Malaysian roads.  The outcome of this pilot project benefits the safety of drivers, public road users, and passengers. The impact of this framework leads to a new regulation by the government agencies towards merit and demerit system, real-time fleet monitoring of intelligent transportation systems, and socio-economy such as cheaper health premiums. The big data can be used to predict the driver’s behavioural in the future