We propose a forearm-worn haptic device that integrates tactile skin-stretch feedback with kinaesthetic force feedback from a grounded haptic device. The wearable module generates lateral skin deformation corresponding to the tangential components of the applied kinaesthetic force, moving the skin in the opposite direction to produce coherent tactile cues that effectively convey sensations of surface friction and tangential shear, reinforcing the perception of contact with virtual surfaces. This integration aims to enhance both perceptual realism and transparency during haptic interaction. A series of preliminary experiments were conducted in a Chai3D-based virtual environment, where users interacted with virtual walls and corners while receiving combined feedback. The results demonstrate that the proposed device can render directionally accurate and temporally synchronized skin-stretch cues consistent with the kinaesthetic forces, validating its potential for multimodal haptic interaction.

Food handling is a challenging task for robotic grippers, as it requires to manipulate highly deformable and fragile items, that can be easily damaged. Moreover, ingredients for the preparation of the different dishes are usually stored in small containers that are often not easily accessible. This paper introduces an innovative soft-rigid, tendon-driven gripper: the Double-Scoop Gripper (DSG). Its two-fingered design exploits a specialized structure to cope with constrained spaces (e.g., containers in narrow shelves). The DSG can delicately grasp objects of various shapes by employing two scoop-shaped fingertips that can form a single plate when fingers are flexed. Data obtained from an on-board camera are used to detect the food item features and plan the grasping strategy that better exploits the possible environmental constraints regulating the opening of the two fingers and the approaching direction of the gripper. DSG capabilities are verified with experiments conducted using real food ingredients within a pick-and-place setup to evaluate both the grasping and the releasing capability of the gripper. Obtained results are promising and suggest that this approach could be particularly advantageous in the context of automated food serving.

The teleoperation of complex, kinematically redundant robots with loco-manipulation capabilities represents a challenge for human operators, who have to learn how to operate the many degrees of freedom of the robot to accomplish a desired task. In this context, developing an easy-to-learn and easy-to-use human-robot interface is paramount. Recent works introduced a novel teleoperation concept, which relies on a virtual physical interaction interface between the human operator and the remote robot equivalent to a "Marionette" control, but whose feedback was limited to only visual feedback on the human side. In this paper, we propose extending the "Marionette" interface by adding a wearable haptic interface to cope with the limitations given by the previous works. Leveraging the additional haptic feedback modality, the human operator gains full sensorimotor control over the robot, and the awareness about the robot’s response and interactions with the environment is greatly improved. We evaluated the proposed interface and the related teleoperation framework with naive users, assessing the teleoperation performance and the user experience with and without haptic feedback. The conducted experiments consisted in a loco-manipulation mission with the CENTAURO robot, a hybrid leg-wheel quadruped with a humanoid dual-arm upper body.

Supernumerary Robotic Limbs (SRL) represent a new class of wearable robots that can augment human manipulation capabilities. SRL can be controlled through input interfaces worn on the user body and can interact with the environment. Such interaction can be measured and feedback to the human wearer through wearable haptic interfaces. However, human somatotopic arrangement on the central nervous system lacks a location for artificially added limbs. Where is the best location for feedback coming from a robot not directly associated with a part of the wearer’s body?This paper sheds light on the problem of the best body location for the feedback coming from an SRL as well as on the relation between the position of the input interface and the haptic interface. We have tested four different body locations - shoulder, wrist, hip, and ankle - for vibrotactile feedback coming from the simulated interaction with a robotic extra limb activated using an interface consisting of an accelerometer worn on the user’s shoulder. Results from the experiment involving 14 participants demonstrated that the ankle feedback position led to significantly worse performances when having inputs from the shoulder, whereas the other three locations led to comparable results.

Clinicians are interested in the estimation of robust and relevant genetic signatures from gene sequencing data. Many machine learning approaches have been proposed trying to address well-known issues of this complex task (feature or gene selection, classification or model selection, and prediction assessment). Addressing this problem often requires a deep knowledge of these methods and some of them demand high computational resources that may not be affordable. In this paper, an exhaustive study that includes different types of feature selection methods and classifiers is presented, providing clinicians an useful insight of the most suitable methods for this purpose. Predictions assessment is performed using a bootstrap cross-validation strategy as an honest validation scheme. The results of this study for six benchmark datasets show that filter or embedded methods are preferred, in general, to wrapper methods according to their better statistical significant results, in terms of accuracy, and lower demand for computational resources.

The well-known backpropagation learning algorithm is implemented in a field-programmable gate array (FPGA) board and a microcontroller, focusing in obtaining efficient implementations in terms of a resource usage and computational speed. The algorithm was implemented in both cases using a training/validation/testing scheme in order to avoid overfitting problems. For the case of the FPGA implementation, a new neuron representation that reduces drastically the resource usage was introduced by combining the input and first hidden layer units in a single module. Further, a time-division multiplexing scheme was implemented for carrying out product computations taking advantage of the built-in digital signal processor cores. In both implementations, the floating-point data type representation normally used in a personal computer (PC) has been changed to a more efficient one based on a fixed-point scheme, reducing system memory variable usage and leading to an increase in computation speed. The results show that the modifications proposed produced a clear increase in computation speed in comparison with the standard PC-based implementation, demonstrating the usefulness of the intrinsic parallelism of FPGAs in neurocomputational tasks and the suitability of both implementations of the algorithm for its application to the real world problems.

A two-dimensional Ising model with nearest-neighbors ferromagnetic interactions is implemented in a Field Programmable Gate Array (FPGA) board. Extensive Monte Carlo simulations were carried out using an efficient hardware representation of individual spins and a combined global-local LFSR random number generator. Consistent results regarding the descriptive properties of magnetic systems, like energy, magnetization and susceptibility are obtained while a speed-up factor of approximately six times is achieved in comparison to previous FPGA-based published works and almost$10^4$times in comparison to a standard CPU simulation. A detailed description of the logic design used is given together with a careful analysis of the quality of the random number generator used. The obtained results confirm the potential of FPGAs for analyzing the statistical mechanics of magnetic systems.

Competitive majority network trained by error correction (C-Mantec), a recently proposed constructive neural network algorithm that generates very compact architectures with good generalization capabilities, is implemented in a field programmable gate array (FPGA). A clear difference with most of the existing neural network implementations (most of them based on the use of the backpropagation algorithm) is that the C-Mantec automatically generates an adequate neural architecture while the training of the data is performed. All the steps involved in the implementation, including the on-chip learning phase, are fully described and a deep analysis of the results is carried on using the two sets of benchmark problems. The results show a clear increase in the computation speed in comparison to the standard personal computer (PC)-based implementation, demonstrating the usefulness of the intrinsic parallelism of FPGAs in the neurocomputational tasks and the suitability of the hardware version of the C-Mantec algorithm for its application to real-world problems.

A new constructive approach for creating all linearly separable functions is introduced. Balanced and unate functions on N+1 variables are created and then projected to N variables permitting to create straightforward all the linearly separable functions without needing to check for linearly separability. The method is supported by the demonstration of a theorem and numerical simulation results for small number of variables. If the results extrapolates the method may permit to test the linear separability of any function on N variables by checking monotonicity and unaleness in N+1 dimension. Furthermore, the generalization complexity of the linear threshold functions is analyzed.

In this paper, we analyze Boolean functions using a recently proposed measure of their complexity. This complexity measure, motivated by the aim of relating the complexity of the functions with the generalization ability that can be obtained when the functions are implemented in feed-forward neural networks, is the sum of a number of components. We concentrate on the case in which we use the first two of these components. The first is related to the "average sensitivity" of the function and the second is, in a sense, a measure of the "randomness" or lack of structure of the function. In this paper, we investigate the importance of using the second term in the complexity measure, and we consider to what extent these two terms suffice as an indicator of how difficult it is to learn a Boolean function. We also explore the existence of very complex Boolean functions, considering, in particular, the symmetric Boolean functions.

We analyze Boolean functions using a recently proposed measure of their complexity. This complexity measure, motivated by the aim of relating the complexity of the functions with the generalization ability that can be obtained when the functions are implemented in feed-forward neural networks, is the sum of two components. The first of these is related to the 'average sensitivity' of the function and the second is, in a sense, a measure of the 'randomness' or lack of structure of the function. In this paper, we investigate the importance of using the second term in the complexity measure. We also explore the existence of very complex Boolean functions, considering, in particular, the symmetric Boolean functions.

The parity function is one of the most used Boolean function for testing learning algorithms because both of its simple definition and its great complexity. We construct a family of modular architectures that implement the parity function in which, every member of the family can be characterized by the fan-in max of the network, i.e., the maximum number of connections that a neuron can receive. We analyze the generalization ability of the modular networks first by computing analytically the minimum number of examples needed for perfect generalization and then by numerical simulations. Both results show that the generalization ability of these networks is systematically improved by the degree of modularity of the network. We also analyze the influence of the selection of examples in the emergence of generalization ability, by comparing the learning curves obtained through a random selection of examples to those obtained through examples selected accordingly to a general algorithm we (2000) recently proposed.

In this work, we study how the selection of examples affects the learning procedure in a boolean neural network and its relationship with the complexity of the function under study and its architecture. We analyze the generalization capacity for different target functions with particular architectures through an analytical calculation of the minimum number of examples needed to obtain full generalization (i.e., zero generalization error). The analysis of the training sets associated with such parameter leads us to propose a general architecture-independent criterion for selection of training examples. The criterion was checked through numerical simulations for various particular target functions with particular architectures, as well as for random target functions in a nonoverlapping receptive field perceptron. In all cases, the selection sampling criterion lead to an improvement in the generalization capacity compared with a pure random sampling. We also show that for the parity problem, one of the most used problems for testing learning algorithms, only the use of the whole set of examples ensures global learning in a depth two architecture. We show that this difficulty can be overcome by considering a tree-structured network of depth 2log2(N)-1.