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This paper presents an exploratory study aimed at identifying the pain points that novice programmers experience, from the software engineering perspective, when developing and deploying smart and distributed systems, that may be classified as Ambient Intelligence (AmI) systems. The exploratory study was conducted among undergraduate students, that worked in groups for developing AmI projects during...
Advances in nanotechnology, large scale computing and communications infrastructure, coupled with recent progress in big data analytics, have enabled linking several billion devices to the Internet. These devices provide unprecedented automation, cognitive capabilities, and situational awareness. This new ecosystem--termed as the Internet-of-Things (IoT)--also provides many entry points into the network...
The use of Recurrent Neural Networks for video captioning has recently gained a lot of attention, since they can be used both to encode the input video and to generate the corresponding description. In this paper, we present a recurrent video encoding scheme which can discover and leverage the hierarchical structure of the video. Unlike the classical encoder-decoder approach, in which a video is encoded...
Prognostics and health management (PHM) can ensure that a battery system is working safely and reliably. Remaining useful life (RUL) prediction, as one main approach of PHM, provides early warning of failures that can be used to determine the necessary maintenance and replacement of batteries in advance. The existing RUL prediction techniques for lithium-ion batteries are inefficient to learn the...
The digital transformation of industrial production is driven by the advance of Cyber-Physical Production Systems (CPPS) within which raw materials, machines and operations are interconnected to form a sophisticated network. Making such systems self-adaptable is a priority concern for the future implementation of Industry 4.0 application scenarios. In this position paper, we design a meta-model and...
Urrently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer. This is usually actualized through feedforward multilayer neural networks, e.g. ConvNets, where each layer forms one of such successive representations. However, an alternative that can achieve the same goal is a feedback based approach in which the representation...
Recently, skeleton based action recognition gains more popularity due to cost-effective depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches based on handcrafted features are limited to represent the complexity of motion patterns. Recent methods that use Recurrent Neural Networks (RNN) to handle raw skeletons only focus on the contextual dependency in the temporal...
Prognostics and Health Management (PHM) can enhance reliability and reduce maintenance costs of the target system by providing advance warning of failure. To achieve the goals of PHM, prognostics is the most important and crucial. Prognostic approaches can be roughly divided into two categories: model-based methods and data-driven methods, both of which have advantages and limitations. To overcome...
Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamics for single-person action recognition. However, existing RNN models only focus on capturing the temporal dynamics of the person-person interactions by naively combining the activity dynamics of individuals or modeling them as a whole. This neglects the inter-related dynamics of how person-person interactions...
Underwater sensor networks are bounded by data sensing, transmitting, and forwarding limitations. The transmitting of large volumes of data can require a large amount of time and power. This has led researchers to focus on the new technology of underwater computing systems, in which information is extracted under the water using embedded processors via data mining and/or data compression. In this...
The state of art secure digital computing systems heavily rely on secure hardware as the Trusted Computing Base to build upon the chain of trust for trusted computing. Attack Protection Blocks are added to the hardware to prevent an adversary from bypassing the security provided by hardware using various side channel, voltage, frequency, temperature, and other attacks. However, attackers can target...
In-air handwriting is becoming a new human-computer interaction way. It is a challenging task to accurately recognizing in-air handwritten Chinese characters. In this paper, we present an end-to-end recognizer for in-air handwritten Chinese characters by using recurrent neural networks (RNN). Compared with the existing methods, the proposed RNN based methods does not need to explicitly extract features...
We address the challenge of learning good video representations by explicitly modeling the relationship between visual concepts in time space. We propose a novel Temporal Preserving Recurrent Neural Network (TPRNN) that extracts and encodes visual dynamics with frame-level features as input. The proposed network architecture captures temporal dynamics by keeping track of the ordinal relationship of...
It is the aim of IoT-Testware to supply a rich set of TTCN-3 test suites and test cases for IoT technologies to enable developers in setting up a comprehensive test environment of their own, if needed from the beginning of a project. Initially, IoT-Testware will focus on protocols like CoAP and MQTT. To ensure test and implementation technology independence, the test suites will be realized in TTCN-3...
Existing transistor-level monolithic 3D (T-M3D) standard cell layouts are based on the folding scheme, in which the pull-down network is simply folded and placed on top of the pull-up network. In this paper, we propose a new layout method, the stitching scheme, targeted towards improved cell performance and power integrity. We perform extensive analysis on each layout scheme and evaluate the timing/power...
In this paper, a low power 4-bit 400 MS/s standard cell based flash Analog-to-Digital Converter (ADC) is presented. The proposed flash ADC uses comparators based on the logic gates. Relationship between the input voltage and comparator reference voltage defines the output of comparator to be '1' or '0'. The comparator is followed by the gain booster and encoder. Low power consumption is achieved by...
This paper proposes the different way of designing standard-cell based flash ADC in order to increase its input dynamic range. It includes implementation of 5-bit flash ADC for fully automated digital synthesis. The input dynamic range is increased by including 5-input logic gates. The proposed architecture results in Differential Non-Linearity (DNL) of ±0.206 LSB and Integral Non-Linearity (INL)...
A high-performance and energy-efficient 256-bit CMOS priority encoder is presented and realized on transistor level using 32 nm predictive technology. The new circuit is designed with a full custom approach and incorporates 2 novel logic styles: the Multiple-Output Monotonic CMOS (M2CMOS) and the Dynamic Inversion technique (DI). The achieved performance is in the order of O(log2(N)), with respect...
Adders are basic building blocks of any processor or data path application. For the design of high performance processing units high speed adders with low power consumption is a requirement. Carry Select Adder (CSA) is known to be one of the fastest adders used in many data processing applications. In this paper, we present a new CSA architecture using Manchester carry chain(MCC) in multioutput domino...
Memristor technology is a promising alternative to CMOS due to its high integration density, near-zero standby power, and ability to implement novel resistive computing. One of the major limitations of these architectures is the limited endurance of memristor devices, especially when a logic gate requires multiple steps/switching to execute the logic operations. To alleviate the endurance requirement...
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