Call Data Records (CDR) are important evidence in cases involving cybercrime and crimes such as murder and robbery. Nonetheless, analyzing CDR is arduous work because the amount of data is enormous, and if a person do...
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In this paper, we are presenting a new architecture to directly compute the 'Percent-free PSA' diagnostics index to enhance the differentiation of prostate cancer from benign prostatic disease. The proposed ci...
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ISBN:
(纸本)9798350385434;9798350385427
In this paper, we are presenting a new architecture to directly compute the 'Percent-free PSA' diagnostics index to enhance the differentiation of prostate cancer from benign prostatic disease. The proposed circuit uses silicon nanowires-based memristive biosensors by implementing a combination of sensing and computing carried out inside the sensing architecture and not near the sensor. These types of biosensor configurations that are able to directly provide the desired result without any intermediate operations can open up a new branch of research in the area of in-memory sensing and edge computing.
This research develops and optimizes an optical character recognition (OCR) system for goat weighing scales using intelligent computer vision and distributedcomputing to enhance accuracy and efficiency in real-world ...
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The measured output current data of distributed energy resources is crucial in realizing cyber-physical DC microgrids (DCMG) and achieving distributed control objectives. This paper proposes an artificial neural netwo...
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ISBN:
(纸本)9798350363029;9798350363012
The measured output current data of distributed energy resources is crucial in realizing cyber-physical DC microgrids (DCMG) and achieving distributed control objectives. This paper proposes an artificial neural network-based output current estimation at the secondary control level. The estimated output current value is used to implement distributed control successfully for the DCMG. The proposed artificial neural network (ANN) acts as a virtual sensor to alleviate problems associated with physical sensors, such as faults and biasing effects. The ANN's input features and training performance are detailed for the considered DCMG. The performance of the proposed virtual sensor-based distributed controlled DCMG is validated on an experimental hardware setup under multiple load changes and communication delays.
Accrued incidental complexity in software systems lowers their resilience. This thesis proposes means for understanding and evolving complex situated systems through broadening the scope of what is considered to be th...
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ISBN:
(数字)9781665471770
ISBN:
(纸本)9781665471770
Accrued incidental complexity in software systems lowers their resilience. This thesis proposes means for understanding and evolving complex situated systems through broadening the scope of what is considered to be the act and medium of programming. The key idea of this thesis is a universal data model based on accreting assertions and retractions of atomic facts over time to represent code and state in a single level, ephemeral and persistent. Changes to both code and data together with provenance are immutably reified in the same model. A minimal set of interactions is expected to support live inspection and controlled modification of a running system by coupling the editing environment to the runtime and extending the language of REPL (read-evaluate-print-loop) interactions with means of direct manipulation. Combining algebraic effect systems with object capabilities allows safe experimentation on a live production system. By way of Design Science Research, this thesis will iteratively yield designs, implementations, and evaluations of usable proof-of-concept artifacts comprising a resilient interactive programming system.
With the proliferation of battery-operated systems, reducing power consumption in integrated circuits is increasingly critical. This paper introduces a comprehensive approach to reducing both static and dynamic power ...
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While distributed multi-cell resource allocation (D-MCRA) is promising for improving the spectral efficiency of cellular systems, it is challenging to realize in practice due to the large overhead for exchanging the c...
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ISBN:
(纸本)9798350344868;9798350344851
While distributed multi-cell resource allocation (D-MCRA) is promising for improving the spectral efficiency of cellular systems, it is challenging to realize in practice due to the large overhead for exchanging the channel state information (CSI) between base stations (BSs) and limited backhaul bandwidth. Inspired by the emerging integrated sensing and communication (ISAC) technique for mmWave systems, we propose in this paper a new distributed user scheduling and beamforming framework with a small signaling overhead. Specifically, we employ the ISAC signal for the BSs to track the kinematic parameters of the served users, which not only can be used to estimate the line-of-sight (LoS) CSI of served users but also can be exchanged with other BSs to construct cross-cell CSI. Based on this, we propose an enhanced proportional fairness zero-forcing greedy (PFZFG) scheduler for BSs to determine the set of users for ISAC signal transmission distributively. Afterward, each BS optimizes the ISAC beamformers based on a signal-to-average-leakage-plus-interference-plus-noise ratio (SALINR) criterion and subject to sensing error constraints. Simulation results show that our proposed design can approach the performance of the centralized algorithm.
Edge computing has evolved as a decentralised technology for processing data closer to its source, thus reducing latency and improving bandwidth utilisation. However, this shift also introduces new security challenges...
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Matrix multiplication is crucial in scientific computing, but it demands substantial resources. We propose a framework for effectively utilizing heterogeneous GPUs to large matrix multiplication. By splitting matrices...
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The primary objective of this study is to introduce an innovative framework for privacy-preserving federated learning (FL) by utilizing distributed differential privacy through secure aggregation (DDP-SA) to tackle th...
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ISBN:
(纸本)9798350387452;9798350387445
The primary objective of this study is to introduce an innovative framework for privacy-preserving federated learning (FL) by utilizing distributed differential privacy through secure aggregation (DDP-SA) to tackle the issue of privacy breaches during the FL training phase. Recent studies have demonstrated that traditional FL methods do not offer adequate privacy assurances, as malicious entities can deduce sensitive information about the training data from local model parameters or gradients, known as privacy inference attacks. The core concept behind DDP-SA is to integrate local differential privacy (LDP) and secure multi-party computation (MPC) to safeguard the gradients of local clients throughout the FL training process. Comprehensive experiments have been conducted to demonstrate that the proposed DDP-SA approach can deliver enhanced privacy safeguards in the FL process in comparison to either a solely LDP or solely MPC approach, while still maintaining a reasonable level of efficiency and accuracy.
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