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NAND flash memory is widely used for data storage due to low power consumption, high throughput, short random access latency, and high density. The storage density of the NAND flash memory devices increases from one generation to the next, albeit at the expense of storage reliability.
Our objective in this dissertation is to improve the reliability of the NAND flash memory with a low hard implementation cost. We investigate the error characteristic, i.e. the various noises of the NAND flash memory. Based on the error behavior at different life-aging stages, we develop offset calibration techniques that minimize the bit error rate (BER).
Furthermore, we introduce data compression to reduce the write amplification effect and support the error correction codes (ECC) unit. In the first scenario, the numerical results show that the data compression can reduce the wear-out by minimizing the amount of data that is written to the flash. In the ECC scenario, the compression gain is used to improve the ECC capability. Based on the first scenario, the write amplification effect can be halved for the considered target flash and data model. By combining the ECC and data compression, the NAND flash memory lifetime improves three fold compared with uncompressed data for the same data model.
In order to improve the data reliability of the NAND flash memory, we investigate different ECC schemes based on concatenated codes like product codes, half-product codes, and generalized concatenated codes (GCC). We propose a construction for high-rate GCC for hard-input decoding. ECC based on soft-input decoding can significantly improve the reliability of NAND flash memories. Therefore, we propose a low-complexity soft-input decoding algorithm for high-rate GCC.
Despite the importance of Social Life Cycle Sustainability Assessment (S-LCSA), little research has addressed its integration into Product Lifecycle Management (PLM) systems. This paper presents a structured review of relevant research and practice. Also, to address practical aspects in more detail, it focuses on challenges and potential for adoption of such an integrated system at an electronics company.
We began by reviewing literature on implementations of Social-LCSA and identifying research needs. Then we investigated the status of Social-LCSA within the electronics industry, both by reviewing literature and interviewing decision makers, to identify challenges and the potential for adopting S-LCSA at an electronics company. We found low maturity of Social-LCSA, particularly difficulty in quantifying social sustainability. Adoption of Social-LCSA was less common among electronics industry suppliers, especially mining & smelting plants. Our results could provide a basis for conducting case studies that could further clarify issues involved in integrations of Social-LCSA into PLM systems.
A residual neural network was adapted and applied to the Physionet/Computing data in Cardiology Challenge 2020 to detect 24 different classes of cardiac abnormalities from 12-lead. Additive Gaussian noise, signal shifting, and the classification of signal sections of different lengths were applied to prevent the network from overfitting and facilitating generalization. Due to the use of a global pooling layer after the feature extractor, the network is independent of the signal’s length. On the hidden test set of the challenge, the model achieved a validation score of 0.656 and a full test score of 0.27, placing us 15th out of 41 officially ranked teams (Team name: UC_Lab_Kn). These results show the potential of deep neural networks for ap- plication to raw data and a complex multi-class multi-label classification problem, even if the training data is from di- verse datasets and of differing lengths.
In previous studies, we used a method for detecting stress that was based exclusively on heart rate and ECG for differentiation between such situations as mental stress, physical activity, relaxation, and rest. As a response of the heart to these situations, we observed different behavior in the Root Mean Square of the Successive differences heartbeats (RMSSD). This study aims to analyze Virtual Reality via a virtual reality headset as an effective stressor for future works. The value of the Root Mean Square of the Successive Differences is an important marker for the parasympathetic effector on the heart and can provide information about stress. For these measurements, the RR interval was collected using a breast belt. In these studies, we can observe the Root Mean Square of the successive differences heartbeats. Additional sensors for the analysis were not used. We conducted experiments with ten subjects that had to drive a simulator for 25 minutes using monitors and 25 minutes using virtual reality headset. Before starting and after finishing each simulation, the subjects had to complete a survey in which they had to describe their mental state. The experiment results show that driving using virtual reality headset has some influence on the heart rate and RMSSD, but it does not significantly increase the stress of driving.
The reliability of flash memories suffers from various error causes. Program/erase cycles, read disturb, and cell to cell interference impact the threshold voltages and cause bit errors during the read process. Hence, error correction is required to ensure reliable data storage. In this work, we investigate the bit-labeling of triple level cell (TLC) memories. This labeling determines the page capacities and the latency of the read process. The page capacity defines the redundancy that is required for error correction coding. Typically, Gray codes are used to encode the cell state such that the codes of adjacent states differ in a single digit. These Gray codes minimize the latency for random access reads but cannot balance the page capacities. Based on measured voltage distributions, we investigate the page capacities and propose a labeling that provides a better rate balancing than Gray labeling.
Dieses besondere Lehrbuch führt in die Berechnung von Verbrennungsmotoren ein, geht dabei von aktuellen Fragestellungen z. B. zur Fahrzeugdynamik oder Motorthermodynamik aus und stellt bei der Lösung die notwendige Theorie mit bereit. Damit auch Quereinsteiger erfolgreich sind, ist in einem Verzeichnis aufgeführt, welche theoretischen Kenntnisse man für die Lösung der jeweiligen Aufgabe benötigt und in welchem Abschnitt des Buches diese hergeleitet werden. Alle Berechnungen werden in Excel durchgeführt.
In der vorliegenden Auflage wurden aktuelle Beispiele zur Motoreffizienz, zum Motorkennfeld, zur Lastpunktanhebung, zum Fahrzyklus WLTC und zu realen Fahrzyklen (RDE) ergänzt.
Der Inhalt:
- Fahrwiderstand und Motorleistung
- Kraftstoffe und Stöchiometrie
- Motorleistung und Mitteldruck
- Motorthermodynamik
- Motormechanik
- Fahrzeugdynamik
- Hybrid- und Elektrofahrzeuge
- Aufladung von Verbrennungsmotoren
Die Zielgruppen:
Studierende des Maschinenbaus und der Kfz-Technik an Hochschulen in Bachelor- und Masterstudiengängen; Motoreningenieure, die sich mit verbrennungsmotorischen Fragestellungen beschäftigen; Absolventen von Weiterbildungskursen auf Meisterniveau
Der Autor Dr.-Ing. Klaus Schreiner ist Professor an der HTWG Konstanz und Leiter des Labors für Verbrennungsmotoren in der Fakultät Maschinenbau. Er war viele Jahre lang Didaktikbeauftragter seiner Hochschule. Im Jahr 2008 erhielt er den Lehrpreis des Landes Baden-Württemberg.