Showing papers by "Andreas Spanias published in 2012"

Porphyrins-Functionalized Single-Walled Carbon Nanotubes Chemiresistive Sensor Arrays for VOCs.

Abstract: Single-walled carbon nanotubes (SWNTs) have been used extensively for sensor fabrication due to their high surface-to-volume ratio, nanosized structure, and interesting electronic property. Lack of selectivity is a major limitation for SWNT-based sensors. However, surface modification of SWNTs with a suitable molecular recognition system can enhance the sensitivity. On the other hand, porphyrins have been widely investigated as functional materials for chemical sensor fabrication due to their several unique and interesting physicochemical properties. Structural differences between free-base and metal-substituted porphyrins make them suitable for improving the selectivity of sensors. However, their poor conductivity is an impediment in the fabrication of prophyrin-based chemiresistor sensors. The present attempt is to resolve these issues by combining free-base and metallo-porphyrins with SWNTs to fabricate SWNT–porphyrin hybrid chemiresistor sensor arrays for monitoring volatile organic compounds in the a...

Signal processing for fault detection in photovoltaic arrays

Abstract: Photovoltaics (PV) is an important and rapidly growing area of research With the advent of power system monitoring and communication technology collectively known as the “smart grid,” an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simulated instances of arc and ground faults is evaluated The algorithm is found to perform well on many types of faults commonly occurring in PV arrays

On the Effectiveness of Multiple Antennas in Distributed Detection over Fading MACs

Abstract: A distributed detection problem over fading Gaussian multiple-access channels is considered. Sensors observe a phenomenon and transmit their observations to a fusion center using the amplify and forward scheme. The fusion center has multiple antennas with different channel models considered between the sensors and the fusion center, and different cases of channel state information are assumed at the sensors. The performance is evaluated in terms of the error exponent for each of these cases, where the effect of multiple antennas at the fusion center is studied. When there is channel information at the sensors, the gain in error exponent due to having multiple antennas at the fusion center is shown to be limited to a factor of 8/π for Rayleigh fading channels between the sensors and the fusion center, and independent of the number of antennas at the fusion center. Simple practical schemes and numerical methods using semidefinite relaxation techniques are presented that utilize the limited possible gains available. Simulations are used to establish the accuracy of the results.

Signal Processing for Solar Array Monitoring, Fault Detection, and Optimization

Abstract: Although the solar energy industry has experienced rapid growth recently, high-level management of photovoltaic (PV) arrays has remained an open problem. As sensing and monitoring technology continues to improve, there is an opportunity to deploy sensors in PV arrays in order to improve their management. In this book, we examine the potential role of sensing and monitoring technology in a PV context, focusing on the areas of fault detection, topology optimization, and performance evaluation/data visualization. First, several types of commonly occurring PV array faults are considered and detection algorithms are described. Next, the potential for dynamic optimization of an array's topology is discussed, with a focus on mitigation of fault conditions and optimization of power output under non-fault conditions. Finally, monitoring system design considerations such as type and accuracy of measurements, sampling rate, and communication protocols are considered. It is our hope that the benefits of monitoring presented here will be sufficient to offset the small additional cost of a sensing system, and that such systems will become common in the near future.

Lifelogging: Archival and retrieval of continuously recorded audio using wearable devices

Abstract: We propose a complete system for lifelogging where audio is continuously recorded using a smartphone or a wearable recorder. Recorded audio includes speech, music and environmental sounds. First, we describe a feature-based segmentation algorithm for breaking down a long piece of audio into smaller clips. In order to archive clips into a large database, we present methods for automatically indexing and annotating audio with relevant acoustic and semantic tags. Retrieval is performed using a Query-By-Example based approach. To support our claims, the results are demonstrated via a smart-phone application on the popular Android platform. Finally, we also propose a novel virtualization-based design framework to rapidly develop and test such systems for signal processing.

Signal processing for photovoltaic applications

Abstract: The need for the usage of signal processing and pattern recognition techniques to monitor photovoltaic (PV) arrays and to detect and respond to faults with minimal human involvement is increasing. The data obtained from the array can be used to dynamically modify the array topology and improve array power output. This is beneficial especially when module mismatches such as shading, soiling and aging occur in the PV array. A robust statistics-based fault detection algorithm to find faulty modules is presented. Further, topology optimization of PV arrays using module level data is considered. Various topologies such as the series-parallel (SP), the total cross-tied (TCT), the bridge link (BL) and their bypassed versions are considered. The performance associated with these topologies for a possible shading pattern is analyzed and a topology reconfiguration algorithm is employed to find an optimal configuration. The results demonstrate the benefit of having an electrically re-configurable array topology. Results were generated in a SPICE simulator using synthetic and real data obtained from the APS experimental PV array facility.

Distributed SNR Estimation With Power Constrained Signaling Over Gaussian Multiple-Access Channels

Abstract: A sensor network is used for distributed signal-to-noise ratio (SNR) estimation in a single-time snapshot. Sensors observe a signal embedded in noise, and each observation is phase modulated using a constant-modulus scheme and transmitted over a Gaussian multiple-access channel to a fusion center. At the fusion center, the mean and variance are estimated jointly, using an asymptotically minimum-variance estimator. It is shown that this joint estimator decouples into simple individual estimators of the mean and the variance. The constant-modulus phase modulation scheme ensures a fixed transmit power, robust estimation across several sensing noise distributions, as well as an SNR estimate that requires a single set of transmissions from the sensors to the fusion center. The estimators are evaluated in terms of asymptotic variance, which are then used to evaluate the performance of the SNR estimator with Gaussian and Cauchy sensing noise distributions in the cases of total transmit power constraint as well as a per-sensor power constraint. For each sensing noise distribution, the optimal phase transmission parameters are also determined. The asymptotic relative efficiency of the estimators is evaluated. It is shown that among the noise distributions considered, the estimators are asymptotically efficient only when the noise distribution is Gaussian. Simulation results corroborate analytical results.

Interactive DSP laboratories on mobile phones and tablets

Abstract: The use of mobile devices and tablets in engineering education has been gaining lot of interest, due to its interactive capabilities and its ability to stimulate student interest. On the other hand, this technology can also enable instructors to broaden the scope of their curriculum and increase student participation. In this paper, we describe an interactive application to perform signal processing simulations on iOS devices such as the iPhone and the iPad. Furthermore, we describe two laboratory exercises to introduce continuous/discrete convolution and filter design. The exercises and the proposed application will be evaluated by students of an undergraduate DSP course at Arizona State University during Fall 2011. Finally, we describe the planned assessment methodology which will enable us to provide prescriptive recommendations for using i-JDSP in DSP courses.

Supervised local sparse coding of sub-image features for image retrieval

Abstract: The success of sparse representations in image modeling and recovery has motivated its use in computer vision applications. Image retrieval and classification tasks require extracting features that discriminate different image classes. State-of-the-art object recognition methods based on sparse coding use spatial pyramid features obtained from dense descriptors. In this paper, we develop a feature extraction method that uses multiple global/local features extracted from large overlapping regions of an image, which we refer to as sub-images. We propose a procedure for dictionary design and supervised local sparse coding of sub-image heterogeneous features. We perform image retrieval on the Microsoft Research Cambridge image dataset and show that the proposed features outperform the spatial pyramid features obtained using dense descriptors.

Electronic-nose for detecting environmental pollutants: signal processing and analog front-end design

Abstract: Environmental monitoring relies on compact, portable sensor systems capable of detecting pollutants in real-time. An integrated chemical sensor array system is developed for detection and identification of environmental pollutants in diesel and gasoline exhaust fumes. The system consists of a low noise floor analog front-end (AFE) followed by a signal processing stage. In this paper, we present techniques to detect, digitize, denoise and classify a certain set of analytes. The proposed AFE reads out the output of eight conductometric sensors and eight amperometric electrochemical sensors and achieves 91 dB SNR at 23.4 mW quiescent power consumption for all channels. We demonstrate signal denoising using a discrete wavelet transform based technique. Appropriate features are extracted from sensor data, and pattern classification methods are used to identify the analytes. Several existing pattern classification algorithms are used for analyte detection and the comparative results are presented.

Performance comparison of localization techniques for sequential WSN discovery

Abstract: In this paper, the performance of different localization algorithms are compared in the context of the sequential Wireless Sensor Network (WSN) discovery problem. Here, all sensor nodes are at unknown locations except for a very small number of so called anchor nodes at known locations. The locations of nodes are sequentially estimated such that when the location of a given node is found, it may be used to localize others. The underlying performance of such an approach is largely dependent upon the localization technique employed. In this paper, several well-known localization techniques are presented using a united notation. These methods are time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), direction of arrival (DOA) and large aperture array (LAA) localization. The performance of a sequential network discovery process is then compared when using each of these localization algorithms. These algorithms are implemented in the Java-DSP software package as part of a localization toolbox. (5 pages)

Learning dictionaries with graph embedding constraints

Abstract: Several supervised, semi-supervised, and unsupervised machine learning schemes can be unified under the general framework of graph embedding. Incorporating graph embedding principles into sparse representation based learning schemes can provide an improved performance in several learning tasks. In this work, we propose a dictionary learning procedure for computing discriminative sparse codes that obey graph embedding constraints. In order to compute the graph-embedded sparse codes, we integrate a modified version of the sequential quadratic programming procedure with the feature sign search method. We demonstrate, using simulations with the AR face database, that the proposed approach performs better than several baseline methods in supervised and semi-supervised classification.

Undergraduate Signal Processing Laboratories for the Android Operating System

Abstract: We present a DSP simulation environment that will enable students to perform laboratory exercises using Android mobile devices and tablets. Due to the pervasive nature of the mobile technology, education applications designed for mobile devices have the potential to stimulate student interest in addition to offering convenient access and interaction capabilities. This paper describes a portable signal processing laboratory for the Android platform. This software is intended to be an educational tool for students and instructors in DSP, and signals and systems courses. The development of Android JDSP (A-JDSP) is carried out using the Android SDK, which is a Java-based open source development platform. The proposed application contains basic DSP functions for convolution, sampling, FFT, filtering and frequency domain analysis, with a convenient graphical user interface. A description of the architecture, functions and planned assessments are presented in this paper.

Implementation of a fast image coding and retrieval system using a GPU

Abstract: Sparse coding of image patches is a compact but computationally expensive method of representing images. As part of our SenSIP consortium industry projects, we implement the Orthogonal Matching Pursuit algorithm using a single CUDA kernel on a GPU and sparse codes for image patches are obtained in parallel. Image-based “exact search” and “visually similar search” using the image patch sparse codes are performed. Results demonstrate large speed-up over CPU implementations and good retrieval performance is also achieved.

Sequential wireless sensor network discovery using wide aperture array signal processing

Abstract: In this paper, a novel wireless sensor network discovery algorithm is presented which estimates the locations of a large number of low powered, randomly distributed sensor nodes. Initially, all nodes are at unknown locations except for a small number which are termed the “anchor” nodes. The remaining nodes are to be located as part of the discovery procedure. As the locations of sensor nodes are estimated, they can be used in the localization of other nodes. The locations of transmitting nodes are estimated in a decentralized manner by using a set of receiving sensor nodes at known or estimated locations within its coverage area to form an array. Initially a coarse localization of all nodes is performed to identify their approximate positions. A fine grained localization procedure then follows for enhancement. This paper will focus on the coarse localization approach. Simulations demonstrate the effectiveness of the proposed method.

A top-down design methodology using virtual platforms for concept development

Abstract: Virtual platforms are widely used for system-level modeling, design and simulation In this paper, we propose a virtual platform-based, top-down, system-level design methodology for developing and testing hardware/software right from the concept level and even before the architecture is finalized The methodology is based on using tools such as QEMU, SystemC and TLM20 that starts with a functional, high-level description of the system and gradually refines the intricate architectural details We present our results by testing a novel concept aimed at performing audio blogging The system under consideration involves the design of a low-power wearable audio recorder, an Android application for user interface and a server for audio analysis A virtual system consisting of three instances of QEMU and other tools was created to demonstrate the concept and to test this approach Finally, we describe a suite of tools useful for quickly validating concepts and creating virtual platforms for early hardware/software codesign

Work in progress: Performing signal analysis laboratories using Android devices

Abstract: In this paper, we present a graphical-programming application to support signal processing education on the Android operating system This application features a simulation environment and a palette of DSP functions, which will allow students to perform laboratories using Android smartphones and tablets In order to demonstrate the application of the software in a classroom setting, a number of laboratories which incorporate the proposed functionalities have been developed A set of assessments designed to evaluate the effectiveness of the software is also presented

Automated tumor segmentation using kernel sparse representations

Abstract: In this paper, we describe a pixel based approach for automated segmentation of tumor components from MR images. Sparse coding with data-adapted dictionaries has been successfully employed in several image recovery and vision problems. Since it is trivial to obtain sparse codes for pixel values, we propose to consider their non-linear similarities to perform kernel sparse coding in a high dimensional feature space. We develop the kernel K-lines clustering procedure for inferring kernel dictionaries and use the kernel sparse codes to determine if a pixel belongs to a tumorous region. By incorporating spatial locality information of the pixels, contiguous tumor regions can be efficiently identified. A low complexity segmentation approach, which allows the user to initialize the tumor region, is also presented. Results show that both of the proposed approaches lead to accurate tumor identification with a low false positive rate, when compared to manual segmentation by an expert.

De-noising and event extraction for silicon pore sensors using matrix decomposition

Abstract: Silicon pores with diameters in the range of micro/nano-meters can be used to detect an array of analytes. Silica beads are used as carriers of biomolecules through the pores. Passage of beads through the pores are termed as translocation events. In the presence of certain pairs of biomolecules, the pores exhibit trapping behaviour where the pores gets partially blocked. Such behaviour is termed as a trapping event. In this paper, we analyze simulated data of silicon-pore sensors and propose methods to perform signal de-noising and extraction of translocation/trapping events. In the first approach, we use the Discrete Wavelet based de-noising (DWT) as a preprocessing step. We window the signal and stack the segment into a matrix. The data matrix is decomposed into low rank and non-positive sparse components using the modified RPCA (Robust Principal Component Analysis) algorithm. In the second approach, we decompose the noisy signal matrix obtained without DWT. A GoDec (Go Decomposition) based approach is used here, with an explicit noise component and additionally a smoothness constraint. We compare both approaches and show results for signal de-noising and translocation/trapping event extraction. (5 pages)

Shortest path techniques for annotation and retrieval of environmental sounds

Abstract: Many techniques for text-based retrieval and automatic annotation of music and sound effects rely on learning with explicit generalization, training individual classifiers for each tag. Non-parametric approaches, where queries are individually compared to training instances, can provide added flexibility, both in terms of robustness to shifts in database content and support for foreign queries, such as concepts not yet included in the database. In this paper, we build upon prior work in designing an ontological framework for annotation and retrieval of environmental sounds, where shortest paths are used to navigate a network containing edges that represent content-based similarity, semantic similarity, and user tagging data. We evaluate novel techniques for ordering query results using weights of both shortest paths and minimum cost paths of specified lengths, pruning outbound edges by nodes’ K nearest neighbors, and adjusting edge weights depending on type (acoustic, semantic, or user tagging). We evaluate these methods both through traditional cross-validation and through simulation of live systems containing a complete collection of sounds and tags but incomplete tagging data.

Distributed average consensus using bounded transmissions

Abstract: A distributed consensus algorithm in which every sensor maps its state value through a bounded function before transmission is proposed. It is shown that when the step size of the algorithm is chosen appropriately, the state values of all the nodes converge exponentially to the sample average of the initial observations provided that the transmission function has a bounded first derivative. The convergence factor is shown to depend on the derivative of the transmission function. The performance of various bounded transmission functions are studied through simulations. It is shown that by appropriately choosing the step size, the proposed algorithm could achieve the same speed of convergence as that of the best case linear consensus algorithm based on the Laplacian heuristic.

Search-Free Codebook Mapping for Artificial Bandwidth Extension

Abstract: A new codebook mapping algorithm for artificial bandwidth extension (ABE) is introduced in this paper. We design a wideband line spectrum pair (LSP) codebook which is coupled with the same index as the LSP codebook of a narrowband speech codec. The received narrowband LSP codebook indices are used to directly induce wideband LSP codewords. Thus, the proposed scheme eliminates codebook search processing to estimate the wideband spectrum envelope. We apply the proposed scheme to bandwidth extension in adaptive multi-rate (AMR) compressed domain. Its performance is assessed via the perceptual evaluation of speech quality (PESQ), informal listening tests, and weighted million operations per second (WMOPS) calculations.

Workshop: Interactive education tools for earth systems and sustainability applications

Abstract: Earth system signals include indicators of climate change. In this workshop, the participants will use the Java-DSP/Earth Systems Edition in order to analyze and understand the components and drivers of climate change in the twentieth century. The session will be interactive and will be useful to researchers, practitioners and instructors with interests in Earth systems signal analysis. People with interests in general STEM related areas will also find this workshop useful as an important interdisciplinary application of signal processing.

Learning Multilevel Dictionaries for Compressed Sensing Using Discriminative Clustering

Abstract: The performance of sparse recovery using compressed measurements improves when dictionaries learned from training data are used in place of predefined dictionaries. In this paper, we propose to learn incoherent multilevel dictionaries using discriminative clustering in each level. To this end, we present the discriminative K-lines clustering that iterates between identifying the cluster centers and computing the discriminant directions. A scheme for computing representations using the proposed dictionary is also developed. Simulation results for compressed sensing using standard images demonstrate that incorporating incoherence in the dictionary results in improved recovery performance. Furthermore, we implement the proposed algorithms as part of a sparse representations toolbox for the J-DSP software package.

Ac2012-5201: work-in-progress:usingmodernmobiletech- nologies in stem education

Abstract: With advances in modern mobile devices and the increasing use of smart phones and tablets by students, there is an opportunity to provide enhanced and personalized educational experiences and convenient remote access to course material. This paper describes an education research endeavor aimed at developing, packaging and assessing course content, interactive learning modules, educational simulations, and visualization examples for smart phones and tablets. Our efforts focus on integrating existing and new tools, optimized for smart phones and tablets, in undergraduate Science, Technology, Engineering and Mathematics (STEM) courses. The project brings together several technologies including a Java simulation environment, an Android operating system with several dedicated apps, Blackboard Mobile Learn, Sprint ID and others. The hypothesis here is that the creation of a “central” education site with maximum mobility will be compelling to undergraduate students that seem fascinated by the exciting multi-tasking possibilities that smart phones and tablets bring these days. In this paper, we will work on the development and assessment of a portal for mobile devices that will enable students to operate several state of the art education mobile applications (apps) seamlessly. These apps include visualization modules, mobile labs, lecture video viewing, quiz taking, homework/lab submission applets, and customized educational web site access. An assessment task will be a senior level signal processing class to examine several aspects of the mobile student learning experience.

Ac 2012-4755: undergraduate signal processing laborato- ries for the android operating system

Abstract: We present a DSP simulation environment that will enable students to perform laboratory exercises using Android mobile devices and tablets. Due to the pervasive nature of the mobile technology, education applications designed for mobile devices have the potential to stimulate student interest in addition to offering convenient access and interaction capabilities. This paper describes a portable signal processing laboratory for the Android platform. This software is intended to be an educational tool for students and instructors in DSP, and signals and systems courses. The development of Android JDSP (A-JDSP) is carried out using the Android SDK, which is a Java-based open source development platform. The proposed application contains basic DSP functions for convolution, sampling, FFT, filtering and frequency domain analysis, with a convenient graphical user interface. A description of the architecture, functions and planned assessments are presented in this paper. Introduction Mobile technologies have grown rapidly in recent years and play a significant role in modern day computing. The pervasiveness of mobile devices opens up new avenues for developing applications in education, entertainment and personal communications. Understanding the effectiveness of smartphones and tablets in classroom instruction have been a subject of considerable research in recent years. The advantages of handheld devices over personal computers in K-12 education have been investigated 1 . The study has found that the easy accessibility and maneuverability of handheld devices lead to an increase in student interest. By incorporating mobile technologies into mathematics and applied mathematics courses, it has been shown that smartphones can broaden the scope and effectiveness of technical education in classrooms 2 . Fig 1: Splash screen of the AJDSP Android application Designing interactive applications to complement traditional teaching methods in STEM education has also been of considerable interest. The role of interactive learning in knowledge dissemination and acquisition has been discussed and it has been found to assist in the development of cognitive skills 3 . It has been showed learning potential is enhanced when education tools that possess a higher degree of interactivity are employed 4 . Software applications that incorporate visual components in learning, in order to simplify the understanding of complex theoretical concepts, have been also been developed 5-9 . These applications are generally characterized by rich user interaction and ease of accessibility. Modern mobile phones and tablets possess abundant memory and powerful processors, in addition to providing highly interactive interfaces. These features enable the design of applications that require intensive calculations to be supported on mobile devices. In particular, Android operating system based smartphones and tablets have large user base and sophisticated hardware configurations. Though several applications catering to elementary school education have been developed for Android devices, not much effort has been undertaken towards building DSP simulation applications 10 . In this paper, we propose a mobile based application that will enable students to perform Digital Signal Processing laboratories on their smartphone devices (Figure 1). In order to enable students to perform DSP labs over the Internet, the authors developed J-DSP, a visual programming environment 11-12 . J-DSP was designed as a zero footprint, standalone Java applet that can run directly on a browser. Several interactive laboratories have been developed and assessed in undergraduate courses. In addition to containing basic signal processing functions such as sampling, convolution, digital filter design and spectral analysis, J-DSP is also supported by several toolboxes. An iOS version of the software has also been developed and presented 13-15 . Here, we describe an Android based graphical application, A-JDSP, for signal processing simulation. The proposed tool has the potential to enhance DSP education by supporting both educators and students alike to teach and learn digital signal processing. The rest of the paper is organized as follows. We review related work in Section 2 and present the architecture of the proposed application in Section 3. In Section 4 we describe some of the functionalities of the software. We describe planned assessment strategies for the proposed application in Section 5. The concluding remarks and possible directions of extending this work are discussed in Section 6. Related Work Commercial packages such as MATLAB 16 and LabVIEW 17 are commonly used in signal processing research and application development. J-DSP, a web-based graphical DSP simulation package, was proposed as a non-commercial alternative for performing laboratories in undergraduate courses 3 . Though J-DSP is a light-weight application, running J-DSP over the web on mobile devices can be data-intensive. Hence, executing simulations directly on the mobile device is a suitable alternative. A mobile application that supports functions pertinent to different areas in electrical engineering, such as circuit theory, control systems and DSP has been reported 18 . However, it does not contain a comprehensive set of functions to simulate several DSP systems. In addition to this, a mobile interface for the MATLAB package has been released 19 . However, this requires an active version of MATLAB on a remote machine and a high speed internet connection to access the remote machine from the mobile device. In order to circumvent these problems, i-JDSP, an iOS version of the J-DSP software was proposed 13-15 . It implements DSP functions and algorithms optimized for mobile devices, thereby removing the need for internet connectivity. Our work builds upon J-DSP 11-12 and the iOS version of J-DSP 13-15 , and proposes to build an application for the Android operating system. Presently, to the best of our knowledge, there are no freely available Android applications that focus on signal processing education. Architecture The proposed application is implemented using Android-SDK 22 , which is a Java based development framework. The user interfaces are implemented using XML as it is well suited for Android development. The architecture of the proposed system is illustrated in Figure 2. It has five main components: (i) User Interfaces, (ii) Part Object, (iii) Part Calculator, (iv) Part View, and (v) Parts Controller. The role of each of them is described below in detail. The blocks in A-JDSP can be accessed through a function palette (user interface) and each block is associated with a view using which the function properties can be modified. The user interfaces obtain the user input data and pass them to the Part Object. Furthermore, every block has a separate Calculator function to perform the mathematical and signal processing algorithms. The Part Calculator uses the data from the input pins of the block, implements the relevant algorithms and updates the output pins. Figure 2. Architecture of AJDSP. Parts Controller Part Calculator Part Object User Interface Part View All the configuration information, such as the pin specifications, the part name and location of the block is contained in the Part Object class. In addition, the Part Object can access the data from each of the input pins of the block. When the user adds a particular block in the simulation, an instance of the Part Object class is created and is stored by a list object in the Parts Controller. The Parts Controller is an interface between the Part Object and the Part View. One of the main functions of Parts Controller is supervising block creation. The process of block creation by the Parts Controller can be described as follows: The block is configured by the user through the user interface and the block data is passed to an instance of the Part Object class. The Part Object then sends the block configuration information through the Parts Controller to the Part View, which finally renders the block. The Part View is the main graphical interface of the application. This displays the blocks and connections on the screen. It contains functionalities for selecting, moving and deleting blocks. Examples of block diagrams in the A-JDSP application for different simulations are illustrated in Figure 3(a), Figure 4(a) and Figure 5(a) respectively. Functionalities In this section, we describe some of the DSP functionalities that have been developed as part of A-JDSP. Android based Signal Generator block This generates the various input signals necessary for A-JDSP simulations. In addition to deterministic signals such as square, triangular and sinusoids; random signals from Gaussian Rayleigh and Uniform distributions can be generated. The signal related parameters such as signal frequency, time shift, mean and variance can be set through the user interface.