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Abu-Zitar, R.; Nuseirat, A.M. Al-Fahed

doi:10.1023/a:1012094400369

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…For future work, our research will focus more on efficient techniques for training the (feedforward Neural Network) NN such as levenberg-marquardt training algorithm [ 50 ], backpropagation through time [ 64 ], genetic assisted rule-based training [ 65 ], polynomial networks training [ 66 ], artificial immune system optimization [ 67 ], ant colony optimization [ 68 ], nonlinear complementarity optimization [ 69 ], and different neural network architecture [ 70 ]. More hybrid encryption/decryption will be used utilizing more layers of the NN to minimize any correlations between the inputs and the outputs.…”

Section: Discussionmentioning

confidence: 99%

Hybrid encryption technique: Integrating the neural network with distortion techniques

Zitar

Al-Muhammed²

2022

PLoS ONE

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This paper proposes a hybrid technique for data security. The computational model of the technique is grounded on both the non-linearity of neural network manipulations and the effective distortion operations. To accomplish this, a two-layer feedforward neural network is trained for each plaintext block. The first layer encodes the symbols of the input block, making the resulting ciphertext highly uncorrelated with the input block. The second layer reverses the impact of the first layer by generating weights that are used to restore the original plaintext block from the ciphered one. The distortion stage imposes further confusion on the ciphertext by applying a set of distortion and substitution operations whose functionality is fully controlled by random numbers generated by a key-based random number generator. This hybridization between these two stages (neural network stage and distortion stage) yields a very elusive technique that produces ciphertext with the maximum confusion. Furthermore, the proposed technique goes a step further by embedding a recurrent neural network that works in parallel with the first layer of the neural network to generate a digital signature for each input block. This signature is used to maintain the integrity of the block. The proposed method, therefore, not only ensures the confidentiality of the information but also equally maintains its integrity. The effectiveness of the proposed technique is proven through a set of rigorous randomness testing.

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Section: Discussionmentioning

confidence: 99%

Hybrid encryption technique: Integrating the neural network with distortion techniques

Zitar

Al-Muhammed²

2022

PLoS ONE

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“…After estimating the wrinkle's radius, its height can be calculated as equation (14), and these values are summarized in the following table. By estimating the radius and height of the wrinkle, the wrinkle's shape with different activated pressure values can be asses and is depicted in Figure . 14.…”

Section: Deformation Modelling and Analysis Of The Soft Padsmentioning

confidence: 99%

“…This method involved optimizing both the chosen parameters of the gripper mechanism and the parameters of the finger geometry by utilizing task-specific finger designs acquired through dynamic simulation. Several methods to control and optimize the grasping force were presented in [13][14][15]. However, these grippers have intricate structures, sensor systems, and advanced control algorithms, and are only suitable for gripping rigid objects.…”

Section: Introductionmentioning

confidence: 99%

A novel rigid-soft gripper for safe and reliable object handling

Trinh,

Nguyen,

Pham

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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This research paper proposes a novel robotic gripper that combines the advantages of both rigid and soft grippers. The challenge of developing robotic grippers that can handle a wide variety of objects with high precision, especially under harsh vibration is addressed. The gripper uses a main rigid structure for maintaining the stiffness of the robot hand and soft pads under air pressurization for handling the softly and flexibly the objects. The novel conceptual design and structural design are introduced. The gripper design and fabrication were validated through mathematical modelling, analysis, and experimental procedures. In particular, the deformation of the soft element is estimated using numerical simulation and mathematical calculation. Based on the estimation of the soft element's deformation, the contact force is analysed. The experiment confirms that the proposed gripper allows for safe interaction with a variety of grasped objects while maintaining diversity and stability during gripping movement, even under harsh vibration conditions with high frequency. This gripper design can be applied for handling a wide range of objects with varying sizes, masses, fragility, and placement positions. It is particularly useful when the gripping process necessitates both safe interaction and high reliability.

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Neural network approach to firm grip in the presence of small slips

Nuseirat

Abu-Zitar

2001

J. Robotic Syst.

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This paper presents a two stage method for constructing a firm grip that can tolerate small slips of the fingertips. The fingers are assumed to be of frictionless contact type. The first stage was to formulate the interaction in the gripper–object system as a linear complementarity problem (LCP). Then it was solved using a special neural network to find minimal fingers forces. The second stage was to use the obtained results in the first stage as a static mapping in training another neural network. The second neural network training included emulating the slips by random noise in the form of changes in the positions of the contact points relative to the reference coordinate system. This noisy training increased robustness against unexpected changes in fingers positions. Genetic algorithms were used in training the second neural network as global optimization techniques. The resulting neural network is a robust, reliable, and stable controller for rigid bodies that can be handled by a robot gripper. © 2001 John Wiley & Sons, Inc.

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Untitled

Cited by 12 publications

References 21 publications

Hybrid encryption technique: Integrating the neural network with distortion techniques

Hybrid encryption technique: Integrating the neural network with distortion techniques

A novel rigid-soft gripper for safe and reliable object handling

Neural network approach to firm grip in the presence of small slips

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