Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiency Optimization of Deep Workout Recognition with Accelerometer Sensor for a Mobile Environment

Recent advancements in deep learning have created numerous possibilities for real-world application, among which the recognition of human motions with sensors. We employed a convolutional neural network (CNN) to process workout motion data to solve the segmentation and recognition problem. We focused on deploying the network architecture in a mobile environment characterized by limited resources. Our experimental results were promising in terms of both segmentation and recognition. Furthermore, we analyzed the performance correlation between sampling the rate and recognition rate. This result indicated 55Hz to provide an appropriate amount of information for workout motion. Then we investigated the computational cost and memory space usage of the hyper-parameter selection. The experimental results implied that appropriate hyper-parameter selection could reduce the computational burden on the mobile environment. Subsequently, we suggest an efficiency score regarding the computational cost, memory usage, and the recognition rate. Our suggested efficiency score shows that our method could find hyper-parameters that offer minimum loss of accuracy and are computationally inexpensive with optimal utilization of memory space.
Warpage Prediction and Lifetime Analysis for Large Size Through-silicon-via (TSV) Interposer Package

In the study, warpage as a serious problem need to be investigated for large size TSV interposer package technology. The most important task for TSV interposer package is to discuss the warpage influence on some critical assembly processes. In the paper, two kinds of assembly processes were described in detail: one is based on strip-level assembly processes, the other is based on individual package-level one. By using finite element method, warpage simulation results were analyzed for TSV interposer attach, die attach, stiffener attach, etc. Besides, board-level lifetime prediction under the harsh TC condition (−65 °C ~ 150 °C) was investigated in the study based on Darveaux’s model of strain energy density.
Classification of Heterogeneous Scenes in POL-SAR Image Based on Statistical Analysis

In this paper, a new method is presented based on statistical framework for the classification of heterogeneous scenes in Polarimetric SAR (POL-SAR) image. Firstly, a new measurement on the heterogeneous scenes is presented based on statistical analysis to describe the local space complexity and variability. Then, an adaptive threshold is set to classify the heterogeneous scenes of POL-SAR images. The performance of our method is tested by three real POL-SAR data sets. The experiment results show that our method works well for the heterogeneous scenes classification, and can effectively suppress noise.
Learning Two-level Features for Fine-grained Image Classification

Fine-grained images categorization is a challenging task due to the difficulty of recognizing the subtle difference among the sub-categories. Existing approaches mainly focus on using the manual annotations or the attention algorithm to localize the discriminative regions and have achieved impressive performance. But many methods regard these multi-scale regions equally even they may contain only one small part of the object. This may decrease the performance of the network. To address the problem, we propose the two-level attention network to generate object-level and part-level regions by a CNN model which has been fine-tuned by the full-size image train set of current task. We also train two subnetworks which combine these two-level features together and achieve better performance than two individual networks. The method consists of two parts: the first part is the attention network which localizes the area of the target in the image and its discriminative regions; the second part is the classification network which learns the two-level feature representations. We conduct experiments on the CUB-200-2011, Stanford Dogs, Stanford Cars and FGVC-Aircraft dataset to evaluate the effectiveness of the proposed method.
A Cross-Platform Study on IoT Malware

Attacks towards the Internet of Things (IoT) devices are on the rise. For the lack of basic security monitoring and protection mechanisms, many of these devices are infected with malware and forced to join the attack campaigns on the Internet. Efficient precaution and mitigation of emerging IoT malware could only be pursued after in-depth analysis of captured malware samples. To enable efficient countermeasure against IoT malware, in this paper, we present a multi-level analysis of IoT malware programs based on static/dynamic analysis. To do so, we first use an entropy-based method to differentiate packed malware samples from non-packed ones. Then, characterizing information from static and dynamic analysis are vectorized and examined by t-SNE, which provides a visual hint on the interpretability of different features. Finally, an efficient classifier, namely support vector machine (SVM), is applied to the vector presentations of the malware for quantitative evaluation. Experiment show that opcode sequences obtained from static analysis provide sufficient discriminant information such that IoT malware can be classified with near optimal accuracy.
A Survey on Multi-hop Routing Protocols for Efficient Resource Allocation in IoTs

Nowadays, most encouraging strategy for best resources utilization and allocation in wireless multi-hop networks is BackPressure (BP) based routing. Throughput optimality, load-aware routing, priority aware routing, adaptive resource allocation and its simplicity of understanding are the key features of BP based routing protocols. In this article, firstly we explain the working of classical BP and then classify the possible design of BP based routing and scheduling. In addition, we interrogate that how BP tackles our challenging problems efficiently, their merits, and their deficiencies by summarizing several key issues faced in the design of efficient BP based routing protocols. Meanwhile, this paper presents a complete survey of state of the art BP based routing protocols for wireless multi-hop networks. Furthermore, this paper points out the future directions regarding BP to increase the efficacy of BP based routing and scheduling.
High-Order Cumulant-Based Particle Filtering Algorithm for Pedestrian Object Tracking

A novel particle filtering algorithm based on high-order cumulants is proposed for pedestrian tracking to overcome the object occlusion problem. In the proposed algorithm, principal component analysis is employed to construct the object appearance model in the particle filtering frame, and incremental learning is used to update the object model. A subspace strategy is used to build the reconstruction error of the object image, and then an effective occlusion detector based on the third-order cumulants of the reconstruction error is proposed for the detection of the occlusion object. The experimental results show that the proposed algorithm can effectively detect and track the occluded object, and its performance outperforms conventional approaches; namely, the incremental visual tracking algorithm, the tracking-learning-detection algorithm, and the visual tracking decomposition algorithm.
Smart Parking System using Cloud based Computation and Raspberry Pi

The internet of things plays a vital role in interconnection and automation of various physical devices, vehicles, home appliances and other things. With the help of software, various sensors, actuators, these objects connect and exchange data. This automation of devices enhances a person’s standard of life and way of living, which is a need of future. A similar need is discussed in this paper. In this project a smart parking feature is discussed which enables a user of find a parking location and a free slot in that parking space inside a city. This project focuses on reducing time wasted on finding parking space nearby and on going through the filled parking slots. This in turn reduces the fuel consumption and standard of living.
Permittivity of Undoped Silicon in the Millimeter Wave Range

With the rapid development of millimeter wave technology, it is a fundamental requirement to understand the permittivity of materials in this frequency range. This paper describes the dielectric measurement of undoped silicon in the E-band (60–90 GHz) using a free-space quasi-optical system. This system is capable of creating local plane wave, which is desirable for dielectric measurement in the millimeter wave range. Details of the design and performance of the quasi-optical system are presented. The principle of dielectric measurement and retrieval process are described incorporating the theories of wave propagation and scattering parameters. Measured results of a sheet of undoped silicon are in agreement with the published results in the literature, within a discrepancy of 1%. It is also observed that silicon has a small temperature coefficient for permittivity. This work is helpful for understanding the dielectric property of silicon in the millimeter wave range. The method is applicable to other electronic materials as well as liquid samples.
Automatic Optic Disc Localization Using Particle Swarm Optimization Technique

There is a growing need for plenarily automated algorithms that expeditiously localize the optic disc region in retinal fundus images for the analysis of retinal pathologies such as glaucoma. In this paper, we propose a methodology based on particle swarm optimization for automatic localization of optic disc region from retinal fundus images, where minimization of the fitness function is utilized to resolve optimization quandaries. Here, kernels are modeled as particles and they test the region-of-interest based on the fitness function, in the respective databases, where it is likely that the optic disc exists. The proposed method is validated on a total of 1670 fundus images obtained from various publicly available fundus image datasets. The optic disc localization accuracy obtained by the proposed method are 100%, 98.01%, 96.15%, 98.87%, 100%, and 100% on DRIVE, DRISHTI-GS, DIARETDB0, DIARETDB1, DRIONS-DB, and MESSIDOR fundus image databases, respectively. The precision of localization was improved with initialization of kernel particles within bright region-of-interest in fundus images.
Mutitask Learning Based Muti-examples Keywords Spotting in Low Resource Condition

Keywords Spotting (KWS) is a critical task in speech recognition, aiming to spot the pre-selected keywords out of a continuous speech. In a typical low resource condition, we can only obtain dozens of examples of each keyword. How to make full use of multi-examples information to build an effective keyword spotting system is the present challenge since traditional keyword spotting technologies are not suitable. In this paper, we propose a multi-examples keywords spotting system, which gains a significant performance improvement by applying multitask learning technologies to extract feature and build model. A post-processing method is also used to reduce the false alarm rate.
Neural Network Reliability Enhancement Approach Using Dropout Underutilization in GPU

Recently, the researches on DNN (deep neural network) using GPUs has been actively conducted. The reason for using GPUs in DNN is that it reduces the learning time by using many computational cores. However, GPUs have no implements to support the reliable computing operations. It can exacerbate the reliability of the deep neural network. To ensure the reliability of the deep neural network, the proposed approach is to utilize the dropout technique used in MLP (Multi-Layer Perceptron) learning. In case of the dropout method, some threads in GPUs do not participate in the calculation and it causes GPUs underutilization. The proposed method uses the GPUs underutilization to support a reliable deep neural network. The proposed approach is available through using the idle neurons with adjacent calculating neurons in the dropout process. The experiment results show that the proposed approach is able to support the reliability issues in GPUs while executing deep neural network algorithms.
Realization of Switched-Current Wavelet Filter Using Hybrid Genetic Algorithm

A novel method for the realization of switched-current wavelet filter has been proposed in this paper. The transfer function of wavelet filter is approximated by hybrid genetic algorithm and then synthesized by the switched-current filter structure with input distribution and output feedback configuration which is constructed based on signal flow graph and second generation switched-current integrator. The design formulae for circuit parameters are also presented. The Gaussian wavelet filter is used as an example in this paper to illustrate the approximation procedure of transfer function and the construction procedure of switched-current filter structure, in which five switched-current bilinear mapping lossless integrators are employed. Simulation results by ASIZ show the feasibility of the proposed method.
A Novel Patterned Fabric Defect Detection Algorithm based on Dual Norm Low Rank Decomposition

Fabric defect detection plays an important role in the quality control of textile manufacturing. In order to accurately detect the patterned fabric defects, a novel pattern fabric defect detection method based on the dual norm low rank decomposition is proposed in this paper. Firstly, the feature matrix is generated using Gabor transform. It makes the background lie in a low-rank subspace. Then, the dual norm low rank decomposition model is adopted to divide the feature matrix into low-rank part (background) and non-low-rank part (defect). The adopted model uses the dual norm of nuclear norm as the regular term to substitute for the original "sparse" constraint in the low rank sparse decomposition model, thus can realize the effective separation of the defect part by minimizing the inner product between background part and defect part. Finally, the saliency map generated by the non-low-rank part is segmented via the improved adaptive threshold to locate the defect regions. The experimental results show that the proposed algorithm has a high detection accuracy and robustness to various patterned fabric images comparing with the state-of-the-art.
Web Services and GSM based Smart Home Control System

In recent years, smart home control system have seen integration of different and promising technologies. Wireless protocols, web services architectures, mobile communications standard and much more technologies can be integrated in such smart system. The choice of the technologies which will be integrated to the future smart home must be done correctly. In this paper, an advanced smart home control system is presented. The proposed system permits to users to control and monitor the home equipments and devices remotely and flexibly. In this proposed system, user has the possibility to take control of the system via internet or GSM as an alternative remote control solution. This system is designed using XBee ZigBee modules, Raspberry pi, developed smart plugs and GSM modem.
Compression Method of Position Information for IoT-based Bus Location System Using LoRaWAN

The authors aim to realize an Internet of Things-based bus location system that can be achieved with low operation cost using Long Range Wide Area Network (LoRaWAN). However, in Japan, when LoRaWAN is operated in the mode where the communication distance is the longest, the data size that can be transmitted at once is limited to 11 bytes. This paper proposes a location information compression method to transmit the time information and the traveling position of the bus under this constraint. When transmitting time and location information acquired from GPS, 280 bits are required in the general method, whereas in the proposed method it can be compressed to 49 bits.
Social Champion Identification for NGOs

In this paper, we attempt to provide a recommender system to help NGOs select motivated and passionate individuals to promote their cause on social media by evaluating their digital social profiles. We have taken Twitter as the social medium due to its relatively easier access to user data and tweets. The best fit for a particular NGO would be the person who has both influence within her digital social network as well as writes and posts on matters relevant to the NGO. To achieve this ranking, we break down the profile of the candidate into three metrics: Influence, Engagement and Relevance. These three metrics provide quantitative scores of the influence the candidate has over her network, how engaged her follower base is and how relevant the topics the candidate talks about is to the NGO’s work respectively. The first metric uses the TwitterRank algorithm to measure the influence the user has on his follower base. Engagement quantifies the way the follower base reacts to the posts through likes and retweets. Finally, we implement topic modelling with LDA to quantify the relevance of the candidate’s tweets to the NGO’s areas of interest. We consider whichever topic the NGO provides as the domain on which it requires candidates and hence, this system can be used by any organization to find persons of interest on Twitter.
The Proposal and It’s Evalution of Biometric Authentication Method by EEG Analysis Using Image Stimulation

In recent years, techniques of Brain Machine Interface (BMI) which conducts human communication and robot manipulation using human brain activity are widely researched. This is the result of a noninvasive electroencephalograph device that can measure Electroencephalogram (EEG) in real time. However, there is a present condition that the authentication method when BMI is not much researched. In our research, we propose a biometric authentication method of electroencephalogram using image stimulation. In this research, we propose a biometric authentication method of electroencephalogram using image stimulation. In this paper, we construct and then evaluate a system that performs biometric authentication using EEG at image stimulus. We perform feature extraction using cross-correlation coefficient, and SVM for classification / authentication. Moreover We considered the method for preprocessing (digital filter, artifact countermeasure, epoch), we verify more appropriate preprocessing method. We verified the proposed method. In our proposed system, EER: 2.0% was obtained when artifact countermeasure, digital filter (IIR filter), and epoch method were used. From the result of FAR and FRR, our system was suggested that accuracy is improved by taking artifact countermeasure.
A Rule-of-thumb Condition to Avoid Large HRS Current in ReRAM Crossbar Array Design

This paper presents a rule-of-thumb condition on the size of an ReRAM crossbar array to avoid large HRS current in read operation. Although HRS current must be small for large read margin, the worst HRS current can be too large significantly reducing the read margin if the array size is too large. According to our analysis, the worst HRS current starts steeply increasing as the array size increases beyond a certain size limit. We derived an approximate condition on the size limit in terms of design parameters to avoid large HRS current. The derived formula is verified with SPICE simulation demonstrating that engineers can nicely estimate the maximum array size without causing large HRS current.
Combining CNN with Hand-Crafted Features for Image Classification

Convolutional neural networks (CNN) have achieved outstanding performance in image recognition tasks, but many hand-crafted features still play important roles in some areas. Hand-crafted features are designed to describe image content from specific aspects, which may provide complementary information for CNN in image classification tasks. This paper explores feature fusion methods and proposes a novel framework for combining CNN with hand-crafted features. The framework has two main advantages. First, feature encoder can encode non-normalized features in CNN, which takes advantage of some good edge, texture and local features. Second, joint training strategy makes features fuse better in CNN. We validate that many handcrafted features help to improve the performance of origin CNN. Experiments show our method outperforms the origin CaffeNet on Cifar10 dataset with 79.16% accuracy.
Investigation of TSV Liner Interface With Multiwell Structured TSV to Suppress Noise Propagation in Mixed-Signal 3D-IC

Mixed-signal 3D-ICs have a stacked structure of digital and analog circuit chips. In this study, the effect of noise propagation from a digital circuit on an analog circuit was evaluated using an actual mixed-signal 3D-IC. The noise propagation via through-silicon vias (TSVs) was measured, with a ring-oscillator as a noise source. For a comprehensive investigation, TSV-liner interface states were evaluated along the depth direction using unique multiwell-structured TSVs and a charge-pumping method. It was considered that the interface traps and nonconformal thickness of the TSV liner increased the noise propagation among stacked chips.
Employment Effects of Financial Constraints during the Great Recession

Abstract Employment declined substantially during the 2007–2009 recession, especially in small and young firms. Using confidential firm-level data of the universe of firms and a difference-in-differences methodology, this paper estimates that financial constraints reduced employment growth by 4 to 8 percentage points in small firms relative to large firms and by 7 to 9 percentage points in young relative to old firms. I find that the effect of financial constraints on small firms is driven to a large extent by young firms. I then document that financial constraints affected employment growth in small and young firms strongly through the entry and exit of firms.
Highly Stretchable, Durable, and Printable Textile Conductor

This paper describes the development of a highly stretchable and durable textile electrode fabricated by simple stencil and screen printing methods. It specifically investigated the effects of an interface layer as a planarization layer between the conductive electrode and the textile on the durability of the textile electrode. A stretchable conductive paste was synthesized by mixing Ag flake powder in a polyester. The conductive electrode was encapsulated with Ecoflex material. The stretchability and durability of the textile electrodes were evaluated via stretching, MIT folding, and dynamic endurance tests. The stretching and MIT folding tests indicated that the interface layer significantly enhanced the durability of the textile electrode. A highly stretchable and flexible textile electrode exhibited a low sheet resistance of $0.05 \Omega /$square, excellent stretchability of 70%. The textile electrodes also withstood stretching endurance tests of 10,000 cycles. The illumination of an LED with the conductive electrode was also stable under 70% tensile strain and in water.