- Swarm Intelligence, Compressed Sensing, Machine Learning, Computational Intelligence, Robotics (Computer Science), Social automata, and 16 moreFractal Interfaces and related regularizations, Content-Based Information Retrieval (CBIR), Spatial Memory, Intelligent Space, Human Computer Interaction, Time Series, Compressive Sensing, Generalized Linear models, Human Activity Recognition, Conditional Random Fields, Nonparametric Inference, Self Organized Map, Computer Science, Multimedia, Hypermedia, and Artificial Neural Networksedit
- Professor in Machine Learning at Universidad Panamericana.Interested in research on Bioinformatics, Machine Learning and Astronomy.edit
- Christopher Hamiltonedit
Abstract Detecting human activities in a controlled room such as Hashimoto Laboratory Intelligent Space is an important part in researching the interaction between robots and humans within the space. A number of algorithms is required in... more
Abstract Detecting human activities in a controlled room such as Hashimoto Laboratory Intelligent Space is an important part in researching the interaction between robots and humans within the space. A number of algorithms is required in order to detect and classify effectively human activity. In this paper we implement a self organized map structure to data obtained from a sensor network in order to classify human activity in the space for a latter classification and use of it.
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When dealing with an elevated sensor number we may have to reduce our sampling time or the sensing features in order to achieve real time sensing. In this paper we propose a make use of the recently developed theory of Compressive Sensing... more
When dealing with an elevated sensor number we may have to reduce our sampling time or the sensing features in order to achieve real time sensing. In this paper we propose a make use of the recently developed theory of Compressive Sensing to try and reduce the number of sensing as well as the sample size without loosing quality in
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ABSTRACT
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The ability to understand what humans are doing is crucial for any intelligent system to autonomously support human daily activities. Technologies to enable such ability, however, are still undeveloped due to the many challenges in human... more
The ability to understand what humans are doing is crucial for any intelligent system to autonomously support human daily activities. Technologies to enable such ability, however, are still undeveloped due to the many challenges in human activity analysis. Among them are the difficulties in extracting human poses and motions from raw sensor data, either recorded from visual sensor or wearable sensor and the need to recognize activities not seen before using unsupervised learning. Furthermore, human activity ...
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This paper proposes a paradigm in the forensic area for detecting and categorizing human activities. The presented approach uses five base variables, referred to as 4W1H (“Who,” “When,” “What,” “Where,” and “How”) to describe the context... more
This paper proposes a paradigm in the forensic area for detecting and categorizing human activities. The presented approach uses five base variables, referred to as 4W1H (“Who,” “When,” “What,” “Where,” and “How”) to describe the context in an environment. The proposed system uses self-organizing maps to classify movements for the “How” variable of 4W1H, as well as particle swarm optimization clustering techniques for the grouping (clustering) of data obtained from observations. The paper describes the hardware settings required for detecting these variables and the system designed to do the sensing.
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The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus... more
The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus on crater detection, which represents only one of many geological landforms of scientific interest. In this work, we use Convolutional Neural Networks (ConvNets) to detect both volcanic rootless cones and transverse aeolian ridges. Our system, named MarsNet, consists of five networks, each of which is trained to detect landforms of different sizes. We compare our detection algorithm with a widely used method for image recognition, Support Vector Machines (SVMs) using Histogram of Oriented Gradients (HOG) features. We show that ConvNets can detect a wide range of landforms and has better accuracy and recall in testing data than traditional classifiers based on SVMs.
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Objective, Approach. A growing number of prototypes for diagnosing and treating neurological and psychiatric diseases are predicated on access to high-quality brain signals, which typically requires surgically opening the skull. Where... more
Objective, Approach. A growing number of prototypes for diagnosing and treating neurological and psychiatric diseases are predicated on access to high-quality brain signals, which typically requires surgically opening the skull. Where endovascular navigation previously transformed the treatment of cerebral vascular malformations, we now show that it can provide access to brain signals with substantially higher signal quality than scalp recordings. While endovascular signals were known to be larger in amplitude than scalp signals, our analysis in rabbits borrows a standard technique from communication theory to show endovascular signals also have up to 100× better signal-to-noise ratio. With a viable minimally-invasive path to high-quality brain signals, patients with brain diseases could one day receive potent electroceuticals through the bloodstream, in the course of a brief outpatient procedure.
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ABSTRACT Human activity recognition is an important functionality in any intelligent system designed to support human daily activities. While majority of human activity recognition systems use supervised learning, these systems lack the... more
ABSTRACT Human activity recognition is an important functionality in any intelligent system designed to support human daily activities. While majority of human activity recognition systems use supervised learning, these systems lack the ability to detect new activities by themselves. In this paper, we report the results of our investigation of unsupervised human activity detection with features extracted from skeleton data obtained from RGBD sensor. Unlike activity recognition, activity detection does not provide the label however attempts to distinguish one activity from another. This paper demonstrates a suitable set of features to be used with K-means clustering to distinguish different activities from a pool of unlabeled observations. The results show 100% F0.5-score were achieved for six out of nine activities for one of the subjects at low frame rate, while F0.5-score of 71.9% was achieved on average for all activities by four subjects.
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Recurrent Neural Network (RNN) is a comparatively newer modeling approach to gene regulation process. Several reverse engineering algorithms came out since its proposal and many of them are evolutionary algorithm based approaches. Almost... more
Recurrent Neural Network (RNN) is a comparatively newer modeling approach to gene regulation process. Several reverse engineering algorithms came out since its proposal and many of them are evolutionary algorithm based approaches. Almost all of these works have used mean square error (MSE) function for fitness evaluation of the alternative gene network models. Akaike's Information Criteria (AIC) is a well established technique for discriminating the true and the estimated models. In this work we systematically compare the these two ...
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ABSTRACT The sibling disciplines, systems and synthetic biology, are engaged in unraveling the complexity of the biological networks. One is trying to understand the design principle of the existing networks while the other is trying to... more
ABSTRACT The sibling disciplines, systems and synthetic biology, are engaged in unraveling the complexity of the biological networks. One is trying to understand the design principle of the existing networks while the other is trying to engineer artificial gene networks with predicted functions. The significant and important role that computational intelligence can play to steer the life engineering discipline towards its ultimate goal, has been acknowledged since its time of birth. However, as the field is facing many challenges in building complex modules/systems from the simpler parts/devices, whether from scratch or through redesign, the role of computational assistance becomes even more crucial. Evolutionary computation, falling under the broader domain of artificial intelligence, is well-acknowledged for its near optimal solution seeking capability for poorly known and partially understood problems. Since the post genome period, these natural-selection simulating algorithms are playing a noteworthy role in identifying, analyzing and optimizing different types of biological networks. This article calls attention to how evolutionary computation can help synthetic biologists in assembling larger network systems from the lego-like parts.
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Research Interests: Engineering, Genetics, Algorithms, Artificial Intelligence, Biomedical Engineering, and 15 moreGene regulation, Gene expression, DNA, Computer Simulation, Gene Network, Gene Regulatory Networks, Expectation Maximization, Bayesian Network, dynamic Bayesian network, Experimental Data, Electrical And Electronic Engineering, Biochemistry and cell biology, Gene Expression Regulation, Gene expression profiling, and Gene Expression Data
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Abstract Finding interactions among genes is one of the main problems in molecular biology. In this paper, we use a novel approach to model the gene's regulations, or Gene Regulatory Networks (GRNs). We use a Recursive Neural Network... more
Abstract Finding interactions among genes is one of the main problems in molecular biology. In this paper, we use a novel approach to model the gene's regulations, or Gene Regulatory Networks (GRNs). We use a Recursive Neural Network (RNN) to model the networks, and then use Population Based Incremental Learning (PBIL) enhanced with K-means to find the optimum parameters of the Neural Network.
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Abstract The ability to understand what humans are doing is crucial for any intelligent system to autonomously support human daily activities. Technologies to enable such ability, however, are still undeveloped due to the many challenges... more
Abstract The ability to understand what humans are doing is crucial for any intelligent system to autonomously support human daily activities. Technologies to enable such ability, however, are still undeveloped due to the many challenges in human activity analysis. Among them are the difficulties in extracting human poses and motions from raw sensor data, either recorded from visual sensor or wearable sensor and the need to recognize activities not seen before using unsupervised learning.
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The Portfolio Optimization problem is an example of a resource allocation problem with money as the resource to be allocated to assets. We first have to select the assets from a pool of them available in the market and then assign proper... more
The Portfolio Optimization problem is an example of a resource allocation problem with money as the resource to be allocated to assets. We first have to select the assets from a pool of them available in the market and then assign proper weights to them to maximize the return and minimize the risk associated with the Portfolio. In our work, we have introduced a new “greedy coordinate ascent mutation operator” and we have also included the trading volumes concept. We performed simulations with the past data of NASDAQ100 and DowJones30, concentrating mainly on the 2008 recession period. We also compared our results with the indices and the simple Genetic Algorithms approach
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Research in the areas of localization, mapping and path planning for single mobile robots has been carried out extensively. Nevertheless, relatively little of its work is applied to multiple robot systems. Moreover, when these robots... more
Research in the areas of localization, mapping and path planning for single mobile robots has been carried out extensively. Nevertheless, relatively little of its work is applied to multiple robot systems. Moreover, when these robots coexist with human, complex and unpredictable human environments make the above navigational tasks even more challenging. To address this problem we propose intelligent assistance, a novel scheme to assist mobile robots by providing localization information externally. This scheme aims to combine the research fields of autonomous mobile robot navigation and target tracking. The mobile robots are detected using a laser range finder and camera based sensor unit. Using Rao-Blackwellized particle filter technique, the sensory information is integrated in a probabilistic manner and the mobile robots that need assistance are continuously tracked. We maneuver the non-holonomic constraint of mobile robots together with suitable state variables to obtain the heading angle of the robot. The preliminary experiments show the validity of the proposed scheme for simultaneous localization assistance for multiple mobile robots.
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Regulatory Networks (GRNs) describe the interactions between different genes. One of the most important tasks in biology is to find the right regulations in a GRN given observed data. The problem, is that the data is often noisy and... more
Regulatory Networks (GRNs) describe the interactions between different genes. One of the most important tasks in biology is to find the right regulations in a GRN given observed data. The problem, is that the data is often noisy and scarce, and we have to use models robust to noise and scalable to hundreds of genes.
Recently, Recursive Neural Networks (RNNs) have been presented as a viable model for GRNs, which is robust to noise and can be scaled to larger networks. In this paper, to optimize the parameters of the RNN, we implement a classic Population Based Incremental Learning (PBIL), which in certain scenarios has outperformed classic GA and other evolutionary techniques like Particle Swarm Optimization (PSO). We test this implementation on a small and a large artificial networks. We further study the optimal tunning parameters and discuss the advantages of the method.
Recently, Recursive Neural Networks (RNNs) have been presented as a viable model for GRNs, which is robust to noise and can be scaled to larger networks. In this paper, to optimize the parameters of the RNN, we implement a classic Population Based Incremental Learning (PBIL), which in certain scenarios has outperformed classic GA and other evolutionary techniques like Particle Swarm Optimization (PSO). We test this implementation on a small and a large artificial networks. We further study the optimal tunning parameters and discuss the advantages of the method.
Research Interests:
Detecting human activity has been one of the main focuses in intelligent spaces. This is achieved by using a large number of sensors attached both to the humans and the environment. Yet, these systems are prone to failure due to the... more
Detecting human activity has been one of the main focuses in intelligent spaces. This is achieved by using a large number of sensors attached both to the humans and the environment. Yet, these systems are prone to failure due to the parallel sensing when miss firings occur. We propose a method to test and prevent the miss firings using conditional random fields, since they provide us with a tool that allows us to confirm whether the
expected output or activity is likely to happen in the space or not, given the inputs of the system, which are provided by the 4W1H paradigm, that allows us to segment every piece of information in the space into 5 simple variables (Who, When, What, Where and How).
expected output or activity is likely to happen in the space or not, given the inputs of the system, which are provided by the 4W1H paradigm, that allows us to segment every piece of information in the space into 5 simple variables (Who, When, What, Where and How).
Research Interests:
In this paper we propose the use of a widely known paradigm in the forensic area to detect and categorize human activities. 4W1H describes the context in any environment by describing it using 5 base variables, Who, When, What, Where and... more
In this paper we propose the use of a widely known paradigm in the forensic area to detect and categorize human activities. 4W1H describes the context in any environment by describing it using 5 base variables, Who, When, What, Where and How. We make use of this description plus an intention variable known as “Why” to be able to predict and react accordingly to the current user’s situation. We show the hardware setting required to detect these variables as well as some approaches to sense them from the environment using a minimal hardware setting. We also look into the current work in categorizing the data using Self Organizing Maps and Clustering Techniques.
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In human – human communication we use verbal, vocal and non-verbal signals to communicate with others. Facial expressions are a form of non-verbal communication, recognizing them helps to improve the human – machine interaction. This... more
In human – human communication we use verbal, vocal and non-verbal signals to communicate with others. Facial expressions are a form of non-verbal communication, recognizing them helps to improve the human – machine interaction. This paper proposes an approach for recognizing the facial expressions using camera images. The proposed system tracks the human face using high-speed cameras, extracts the pose-normalized image and it is able to learn a dynamic model of the facial expressions.
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The human action recognition problem, for real time implementation have always been part of the research interest in the iSpace. Different techniques have been used in approaching a real time sensing and processing of the information to... more
The human action recognition problem, for real time implementation have always been part of the research interest in the iSpace. Different techniques have been used in approaching a real time sensing and processing of the information to be able to deliver feedback to the user as well to monitoring systems. In this paper we apply wavelet processing techniques to solve the problem of real time processing, as well as to filter the original signal in order to have better classification.
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Abstract. Tracking and recording human activities have been a major interest in the iSpace, for this purpose different recognition and clustering techniques have been used, like using a Learning Classifier System and data Mining... more
Abstract. Tracking and recording human activities have been a major interest in the iSpace, for this purpose different recognition and clustering techniques have been used, like using a Learning Classifier System and data Mining Techniques. These techniques share the common factor of database dependence and there was actually little effort into making the system to understand the way human were behaving in a given time in the space. Using Artificial Intelligence techniques, we present a work that reads and classifies user object activity
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People tracking and movement recognition are widely needed features in the Intelligent Space, this paper proposes a method to integrate wavelet signal processing into the current people tracking architectures in order to simplify and... more
People tracking and movement recognition are
widely needed features in the Intelligent Space, this paper
proposes a method to integrate wavelet signal processing into the
current people tracking architectures in order to simplify and
optimize the processes regarding background subtraction and
movement pattern matching. The results presented give a good
insight on the potential of wavelet processing techniques in the
iSpace
widely needed features in the Intelligent Space, this paper
proposes a method to integrate wavelet signal processing into the
current people tracking architectures in order to simplify and
optimize the processes regarding background subtraction and
movement pattern matching. The results presented give a good
insight on the potential of wavelet processing techniques in the
iSpace
The 4W1H technique has been a subject of study in the ISpace, presenting multiple problems that had to be solved individually in order to improve performance and number of sensors. The current paper presents a proposed algorithm that... more
The 4W1H technique has been a subject of study in the ISpace, presenting multiple problems that had to be solved individually in order to improve performance and number of sensors. The current paper presents a proposed algorithm that makes use of sensing and processing tools compressive sensing and swarm optimization to pose a feasible solution to this problem.
When detecting human activities in a controlled room such as Hashimoto Laboratory Intelligent Space, a number of algorithms is required in order to detect and classify effectively human activity within an specified part of the room. In... more
When detecting human activities in a controlled room such as Hashimoto Laboratory Intelligent Space, a number of algorithms is required in order to detect and classify effectively human activity within an specified part of the room. In this paper we implement a self organized map structure to data obtained from a sensor network in order to classify human activity in the space.
Detecting human activities in a controlled room such as Hashimoto Laboratory Intelligent Space is an important part in researching the interaction between robots and humans within the space.A number of algorithms is required in... more
Detecting human activities in a controlled room
such as Hashimoto Laboratory Intelligent Space is an important
part in researching the interaction between robots and humans
within the space.A number of algorithms is required in order to
detect and classify effectively human activity. In this paper we
implement a self organized map structure to data obtained from
a sensor network in order to classify human activity in the space
for a latter classification and use of it.
such as Hashimoto Laboratory Intelligent Space is an important
part in researching the interaction between robots and humans
within the space.A number of algorithms is required in order to
detect and classify effectively human activity. In this paper we
implement a self organized map structure to data obtained from
a sensor network in order to classify human activity in the space
for a latter classification and use of it.
Research Interests:
When dealing with an elevated sensor number we may have to reduce our sampling time or the sensing features in order to achieve real time sensing. In this paper we propose a make use of the recently developed theory of Compressive... more
When dealing with an elevated sensor number we may
have to reduce our sampling time or the sensing features in order to achieve real time sensing. In this paper we propose a make use of the recently developed theory of Compressive Sensing to try and reduce the number of sensing as well as the sample size without loosing quality in our sampling for doing a good feature recognition.
have to reduce our sampling time or the sensing features in order to achieve real time sensing. In this paper we propose a make use of the recently developed theory of Compressive Sensing to try and reduce the number of sensing as well as the sample size without loosing quality in our sampling for doing a good feature recognition.