Cognitive radio has been recently considered a promising technology to improve spectrum utilization by enabling secondary access to licensed bands that are not used by primary users temporarily or spatially. A prerequisite to this secondary access is the lack of interference to the primary system. This requirement makes spectrum sensing a key process for cognitive radio. In this study, we consider amplify and forward (AF)-based cooperative spectrum sensing for cognitive radio networks where multiple relay nodes are utilized to amplify and forward the primary user signal for better spectrum sensing, and maximum ratio combining is used for fusion detection by a cognitive coordinator. Further, the detection probability and the bit error rate of AF-based cooperative spectrum sensing are analyzed in fading multiple cognitive relay channels. The simulation results show that the AF-based cooperative spectrum sensing scheme outperforms the conventional scheme.

Diversity-multiplexing tradeoff (DMT) characterizes the fundamental relationship between the diversity gain in terms of outage probability and the multiplexing gain as the normalized rate parameter r, where the limiting transmission rate is give by rlog SNR (here, SNR denote the received signal-to-noise ratio). In this paper, we analyze the DMT and performance of an underwater network with a cooperative relay. Since over an acoustic channel, the propagation delay is commonly considerably higher than the processing delay, the existing transmission protocols need to be explained accordingly. For this underwater network, we briefly describe two well-known relay transmissions: decode-and-forward (DF) and amplify-and-forward (AF). As our main result, we then show that an instantaneous DF relay scheme achieves the same DMT curve as that of multiple-input single-output channels and thus guarantees the DMT optimality, while using an instantaneous AF relay leads at most only to the DMT for the direct transmission with no cooperation. To validate our analysis, computer simulations are performed in terms of outage probability.

Cloud systems are popular computing environment because they can provide easy access to computing resources for users as well as efficient use of resources for companies. The resources of cloud computing are heterogeneous and jobs have various characteristics. One such issue is effective job scheduling. Scheduling in the cloud system may be defined as a multiple criteria decision model. To address this issue, this paper proposes a priority-based two-step job scheduling algorithm. On the first level, jobs are classified based on preference. Resources are dedicated to a job if a deadline failure would cause severe results or critical business losses. In case of only minor discomfort or slight functional impairment, the job is scheduled using a best effort approach. On the second level, jobs are allocated to adequate resources through their priorities that are calculated by the analytic hierarchic process model. We then analyze the proposed algorithm and make a scheduling example to confirm its efficiency.

A classic document clustering technique may incorrectly classify documents into different clusters when documents that should belong to the same cluster do not have any shared terms. Recently, to overcome this problem, internal and external knowledge-based approaches have been used for text document clustering. However, the clustering results of these approaches are influenced by the inherent structure and the topical composition of the documents. Further, the organization of knowledge into an ontology is expensive. In this paper, we propose a new enhanced text document clustering method using non-negative matrix factorization (NMF) and WordNet. The semantic terms extracted as cluster labels by NMF can represent the inherent structure of a document cluster well. The proposed method can also improve the quality of document clustering that uses cluster labels and term weights based on term mutual information of WordNet. The experimental results demonstrate that the proposed method achieves better performance than the other text clustering methods.

An intelligent light emitting diode (LED) dimming system is designed and implemented for energy-saving lighting systems. An LED light bulb is powered by an LED driver controlled by a microcontroller using pulse width modulation (PWM) signals. By changing the duty cycle of the PWM signals, the LED driver generates a driving current of up to 1,000 mA. The current consumption by the LED light bulb exhibits a very linear characteristic that indicates that the level of LED dimming can be finely tuned. Multiple sensors-lighting intensity and ultrasonic range sensors-are combined with the LED dimming system to realize an automatically controllable LED lighting system. The light intensity sensor is capable of sensing ambient light. The ultrasonic range sensor can detect objects from 0.15 to 5.6 m at a resolution of 0.0254 m. The collected information by the light intensity and ultrasonic range sensors is processed by the microcontroller that in turn automatically controls the brightness of the LED light bulb. The algorithm of the software for the LED dimming system is also described.

Mobile computers can process large amounts of data at high speeds, and it is feasible and easy to implement a remote monitoring system utilizing mobile computers. Because of their portability and convenience, these computers have been employed in various research areas to develop such monitoring systems. Existing monitoring system is a bit difficult to real time monitoring the scattered offshore facilities. So this paper compensate the existing system by using mobile computers such as a tablet PC-based monitoring system. Also, the scattered offshore facilities can be monitored in real-time through the tablet PC. The developed monitoring system is a fully Internet-based monitoring platform that enables one to monitor and control remote oceanic applications at any time and any place where it is possible to access the Internet. It can be applied to many oceanic applications as well as the unmanned systems and remote monitoring systems on land.

New incremental learning algorithm using extended data expression, based on probabilistic compounding, is presented in this paper. Incremental learning algorithm generates an ensemble of weak classifiers and compounds these classifiers to a strong classifier, using a weighted majority voting, to improve classification performance. We introduce new probabilistic weighted majority voting founded on extended data expression. In this case class distribution of the output is used to compound classifiers. UChoo, a decision tree classifier for extended data expression, is used as a base classifier, as it allows obtaining extended output expression that defines class distribution of the output. Extended data expression and UChoo classifier are powerful techniques in classification and rule refinement problem. In this paper extended data expression is applied to obtain probabilistic results with probabilistic majority voting. To show performance advantages, new algorithm is compared with Learn++, an incremental ensemble-based algorithm.

In this paper, we propose a document classification model using Web documents as a part of the training corpus in order to resolve the imbalance of the training corpus size per category. For the purpose of retrieving the Web documents closely related to each category, the proposed document classification model calculates the matching score between word features and each category, and generates a Web search query by combining the higher-ranked word features and the category title. Then, the proposed document classification model sends each combined query to the open application programming interface of the Web search engine, and receives the snippet results retrieved from the Web search engine. Finally, the proposed document classification model adds these snippet results as Web documents to the training corpus. Experimental results show that the method that considers the balance of the training corpus size per category exhibits better performance in some categories with small training sets.

In this study, we consider the challenges in motion planning for automated driving systems. Most of the existing online motion-planning algorithms, which take dynamics into account, find it difficult to operate in an environment with narrow passages. Some of the existing algorithms overcome this by offline preprocessing if environment is known. In this work an online algorithm for motion planning with dynamics in an unknown cluttered environment with narrow passages is presented. It utilizes an idea of hybrid planning with sampling- and discretization-based motion planners, which run simultaneously in a full configuration space and a derived reduced space. The proposed algorithm has been implemented and tested with a real autonomous vehicle. It provides significant improvements in computational time performance over basic planning algorithms and allows the generation of smoother paths than those generated by the recently developed hybrid motion planners.

A real-time digital holographic display is the core technology for the next-generation 3DTV. Holographic display requires a considerably large amount of calculation. If generating a large number of digital holograms is intended, the amount of calculation and the time required increase exponentially. This is a significant obstacle in a real-time hologram service. This paper proposes an algorithm that increases the speed of generating a Fresnel hologram by using a recursive addition operation covering the entire coordinate array of a digital hologram. The 3D object designed to calculate the digital hologram uses a depth-map image produced by computer graphics. The proposed algorithm is a technique that performs the computer-generated holography (CGH) operation with only recursive addition of all of the hologram's coordinates by analyzing the regularity between the 3D object and the digital hologram coordinates. The experimental results show that the proposed algorithm increases the operation speed by 70% over the technique using the conventional CGH equation and by more than 30% over the previously proposed recursive technique.

In this paper, a carbon implant is investigated in detail from the perspectives of performance advantages and side effects for the thick n-type metal-oxide-semiconductor field-effect transistor (n-MOSFET). Threshold voltage ($V_{th}$) adjustment using a carbon implant significantly improves the $V_{th}$ mismatch performance in a thick (3.3-V) n-MOS transistor. It has been reported that a bad mismatch occurs particularly in the case of 0.11-${\mu}m$ $V_{th}$ node technology. This paper investigates a carbon implant process as a promising candidate for the optimal $V_{th}$ roll-off curve. The carbon implant makes the $V_{th}$ roll-off curve perfectly flat, which is explained in detail. Further, the mechanism of hot carrier injection lifetime degradation by the carbon implant is investigated, and new process integration involving the addition of a nitrogen implant in the lightly doped drain process is offered as its solution. This paper presents the critical side effects, such as Isub increases and device performance shifts caused by the carbon implant and suggests an efficient method to avoid these issues.

Delivery of high quality video over a wide area network with large number of users poses great challenges for the video communication system. To ensure video quality, multiple descriptions have recently attracted various attention as a way of encoding and visual information delivery over wireless network. We propose a new efficient multiple description coding (MDC) technique. Quincunx lattice sub-sampling is used for generating multiple descriptions of an image. In this paper, we propose the application of a directional discrete cosine transform (DCT) to a sub-sampled quincunx lattice to create an MDC representation. On the decoder side, the image is decoded from the received side information. If all the descriptions arrive successfully, the image is reconstructed by combining the descriptions. However, if only one side description is received, decoding is executed using an interpolation process. The experimental results show that such the directional DCT can achieve a better coding gain as well as energy packing efficiency than the conventional DCT with re-alignment.

The scale invariant feature transform (SIFT) is an effective algorithm used in object recognition, panorama stitching, and image matching. However, due to its complexity, real-time processing is difficult to achieve with current software approaches. The increasing availability of parallel computers makes parallelizing these tasks an attractive approach. This paper proposes a novel parallel approach for SIFT algorithm implementation using a block filtering technique in a Gaussian convolution process on the SIMD Pixel Processor. This implementation fully exposes the available parallelism of the SIFT algorithm process and exploits the processing and input/output capabilities of the processor, which results in a system that can perform real-time image and video compression. We apply this implementation to images and measure the effectiveness of such an approach. Experimental simulation results indicate that the proposed method is capable of real-time applications, and the result of our parallel approach is outstanding in terms of the processing performance.