A novel and energy-efficient algorithm with Quality-of-Service (QoS) guarantee is proposed for cooperative spectrum sensing (CSS) with soft information fusion and hard information fusion. By weighting the sensing performance and the consumption of system resources in a utility function that is maximized with respect to the number of secondary users (SUs), it is shown that the optimal number of SUs is related to the price of these QoS requirements.

The key weakness of Low-Density Parity Check codes is the complexity of the encoding scheme. The generator matrices can be made by Gaussian elimination of parity check matrices for normal block codes. Richardson succeeded in making parity bits from parity check matrices by the low density computation. In this letter, we focus on the execution of numerical experiments which show that even if the matrix D, which is the part of the Richardson's LDPC matrix, is restricted, proposed LDPC codes is lower complexity than Richardson's LDPC codes. The constraint of D results in reducing complexity from O(n + g2) to O(n) due to the omission of computing inverse matrices of φ and T in Richardson's encoding scheme. All the sub-matrices in parity check matrix are composed of Circulant Permutation Matrices based on Galois Fields.

A new approach is proposed in this paper for the tracking of the optimal operating frequency in a Class E backlight inverter using the phase-locked loop (PLL) technique. First, a new single-stage backlight module is introduced to simplify the circuit and to raise the system efficiency. A piezoelectric transformer (PT) is used to drive the cold cathode fluorescent lamp (CCFL) to eliminate the downside of a conventional transformer and to reduce the dimension of the backlight module. Next, a PLL is embedded in the backlight system, as a feedback mechanism, to track the optimal operating frequency of the PT so that the PT's temperature effect is removed and, hence, the system efficiency and stability is improved. The feedback variable proposed is a phase angle rather than a lamp current amplitude traditionally used. A simplified model, along with its design procedure, is next presented. The complete analysis and design considerations are detailed. Finally, it is rather encouraging to observe that the experimental results match our analytical solutions closely.

An efficient algorithm for reconstructing all polyhedral 3D objects from two orthographic views is presented. Since the two-view orthographic representation of a 3D object is ambiguous, it requires a numerous amount of combinatorial searches in the process of reconstruction. Also, multiple number of solutions in contrast to the designers intention can be existed in the problem. This paper proposes a two phase algorithm to reduce the search space and to select the most plausible solution described by the given projections. First, the partially constructed objects are reconstructed from the restricted candidate faces corresponding to each area on the two-view drawings in its first phase. Then the complete objects are obtained from the partially constructed objects by adding other candidates with geometrical validity in the second phase. The algorithm performs a combinatorial search based on the face decision rules along with two heuristics. Decision rules check geometrical validity and heuristic rules enhance the search speed. In addition, the reconstruction finds the most plausible 3D object that human observers are most likely to select first among the given multiple solutions. Several examples from a working implementation are given to show the completeness of the algorithm.

In this work, we investigate a joint transmit beamforming and artificial noise (AN) covariance matrix design in a multiple-input multiple-output (MIMO) cognitive radio (CR) downlink network with simultaneous wireless information and power transfer (SWIPT), where the malicious energy receivers (ERs) may decode the desired information and hence can be treated as potential eavesdroppers (Eves). In order to improve the secure performance of the transmission, AN is embedded to the information-bearing signal, which acts as interference to the Eves and provides energy to all receivers. Specifically, this joint design is studied under a practical non-linear energy harvesting (EH) model, our aim is to maximize the secrecy rate at the SR subject to the transmit power budget, EH constraints and quality of service (QoS) requirement. The original problem is not convex and challenging to be solved. To circumvent its intractability, an equivalent reformulation of this secrecy rate maximization (SRM) problem is introduced, wherein the resulting problem is primal decomposable and thus can be handled by alternately solving two convex subproblems. Finally, numerical results are presented to verify the effectiveness of our proposed scheme.

This paper introduces an energy-efficient transmit design for multiple-input single-output (MISO) energy-harvesting cognitive radio (CR) networks in the presence of external eavesdroppers (Eves). Due to the inherent characteristics of CR network with simultaneous wireless information and power transfer (SWIPT), Eves may illegitimately access the primary user (PU) bands, and the confidential message is prone to be intercepted in wireless communications. Assuming the channel state information (CSI) of the Eves is not perfectly known at the transmitter, our approach to guaranteeing secrecy is to maximize the secrecy energy efficiency (SEE) by jointly designing the robust beamforming and the power splitting (PS) ratio, under the constraints of total transmit power, harvested energy at secondary receiver (SR) and quality of service (QoS) requirement. Specifically, a non-linear energy harvesting (EH) model is adopted for the SR, which can accurately characterize the property of practical RF-EH circuits. To solve the formulated non-convex problem, we first employ fractional programming theory and penalty function to recast it as an easy-to-handle parametric problem, and then deal with the non-convexity by applying S-Procedure and constrained concave convex procedure (CCCP), which enables us to exploit the difference of concave functions (DC) programming to seek the maximum worst-case SEE. Finally, numerical results are presented to verify the performance of the proposed scheme.

The cascode NMOS architecture has been tested by the Human Body Model (HBM), Machine Model (MM) and Transmission Line Pulse Generator (TLP) in this paper. For the TLP, detailed silicon data have been analyzed well in many parameters, such as the first triggering-on voltage (Vt1), the first triggering-on current (It1), the holding voltage (Vh), and the TLP I-V curve. Besides the above three kinds of Electrostatic Discharge (ESD) events, the device gate oxide breakdown voltage is also taken into consideration and the correlations between HBM, MM and TLP are also observed. In order to explain the bipolar transistor turning-on mechanisms, two kinds of models have been proposed in this paper. In typical cases, substrate resistance decreases as the technology advances. On the one hand, for processes older than the 0.35 µm process, such as 0.5 µm and 1 µm, ESD designers can use pick-up insertions to trigger integrated circuits (IC) turn on uniformly. The NPN Side Model can dominate ESD performances in such old processes. On the other hand, in 0.18 µm and newer processes, such as 0.15 µm, 0.13 µm, 90 nm, etc., ESD designers must use non-pick-up insertion structures. The NPN Central Model can dominate ESD performances in such processes. After combining both models together, the bipolar turning-on mechanisms can be explained as "ESD currents occur from side regions to central regions." Besides ESD parasitic bipolar transistor turning-on concerns, another reason that ESD designers should use non-pick-up insertions in deep sub-micron processes is the decreasing of the gate oxide breakdown voltage. As IC size scales down, the gate oxide thickness lessens. The thinner gate oxide thickness will encounter a smaller gate oxide breakdown voltage. In order to avoid gate oxide damage under ESD stresses, ESD designers should endeavor to decrease ESD device turn-on resistances. ESD protecting devices with low turn-on resistances can endure larger currents for the same TLP voltage. In this paper, silicon data show that the non-pick-up insertion cascode NMOS transistor's turning on resistance is smaller than the pick-up insertion cascode NMOS transistor's turning on resistance. Although this paper discovers NPN turning-on mechanisms based on the cascode NMOS structure, ESD designers can adopt the same theories for other kinds of ESD protecting structures, such as one single poly Gate-Grounded NMOS transistor (GGNMOST). ESD designers can use pick-up insertion architecture for NMOS transistors in the low-end processes, but utilize the non-pick-up insertion architecture for GGNMOST in the high-end processes. Then they can obtain the optimized ESD performances.

The prediction of peak power load is a critical factor directly impacting the stability of power supply, characterized significantly by its time series nature and intricate ties to the seasonal patterns in electricity usage. Despite its crucial importance, the current landscape of power peak load forecasting remains a multifaceted challenge in the field. This study aims to contribute to this domain by proposing a method that leverages a combination of three primary models - the GRU model, self-attention mechanism, and Transformer mechanism - to forecast peak power load. To contextualize this research within the ongoing discourse, it’s essential to consider the evolving methodologies and advancements in power peak load forecasting. By delving into additional references addressing the complexities and current state of the power peak load forecasting problem, this study aims to build upon the existing knowledge base and offer insights into contemporary challenges and strategies adopted within the field. Data preprocessing in this study involves comprehensive cleaning, standardization, and the design of relevant functions to ensure robustness in the predictive modeling process. Additionally, recognizing the necessity to capture temporal changes effectively, this research incorporates features such as “Weekly Moving Average” and “Monthly Moving Average” into the dataset. To evaluate the proposed methodologies comprehensively, this study conducts comparative analyses with established models such as LSTM, Self-attention network, Transformer, ARIMA, and SVR. The outcomes reveal that the models proposed in this study exhibit superior predictive performance compared to these established models, showcasing their effectiveness in accurately forecasting electricity consumption. The significance of this research lies in two primary contributions. Firstly, it introduces an innovative prediction method combining the GRU model, self-attention mechanism, and Transformer mechanism, aligning with the contemporary evolution of predictive modeling techniques in the field. Secondly, it introduces and emphasizes the utility of “Weekly Moving Average” and “Monthly Moving Average” methodologies, crucial in effectively capturing and interpreting seasonal variations within the dataset. By incorporating these features, this study enhances the model’s ability to account for seasonal influencing factors, thereby significantly improving the accuracy of peak power load forecasting. This contribution aligns with the ongoing efforts to refine forecasting methodologies and addresses the pertinent challenges within power peak load forecasting.