Query: Unraveling the Anxious Mind: Anxiety, Worry, and Frontal Engagement in Sustained Attention Versus Off-Task Processing

Relevant passages:
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion <=SEP=> Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.

Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution <=SEP=> The analysis of functional magnetic resonance imaging (fMRI) time-series data can provide information not only about task-related activity, but also about the connectivity (functional or effective) among regions and the influences of behavioral or physiologic states on that connectivity. Similar analyses have been performed in other imaging modalities, such as positron emission tomography. However, fMRI is unique because the information about the underlying neuronal activity is filtered or convolved with a hemodynamic response function. Previous studies of regional connectivity in fMRI have overlooked this convolution and have assumed that the observed hemodynamic response approximates the neuronal response. In this article, this assumption is revisited using estimates of underlying neuronal activity. These estimates use a parametric empirical Bayes formulation for hemodynamic deconvolution.

Common regions of the human frontal lobe recruited by diverse cognitive demands <=SEP=> Though many neuroscientific methods have been brought to bear in the search for functional specializations within prefrontal cortex, little consensus has emerged. To assess the contribution of functional neuroimaging, this article reviews patterns of frontal-lobe activation associated with a broad range of different cognitive demands, including aspects of perception, response selection, executive control, working memory, episodic memory and problem solving. The results show a striking regularity: for many demands, there is a similar recruitment of mid-dorsolateral, mid-ventrolateral and dorsal anterior cingulate cortex. Much of the remainder of frontal cortex, including most of the medial and orbital surfaces, is largely insensitive to these demands. Undoubtedly, these results provide strong evidence for regional specialization of function within prefrontal cortex. This specialization, however, takes an unexpected form: a specific frontal-lobe network that is consistently recruited for solution of diverse cognitive problems.

Prefrontal cortical function and anxiety: controlling attention to threat-related stimuli <=SEP=> Threat-related stimuli are strong competitors for attention, particularly in anxious individuals. We used functional magnetic resonance imaging (fMRI) with healthy human volunteers to study how the processing of threat-related distractors is controlled and whether this alters as anxiety levels increase. Our work builds upon prior analyses of the cognitive control functions of lateral prefrontal cortex (lateral PFC) and anterior cingulate cortex (ACC). We found that rostral ACC was strongly activated by infrequent threat-related distractors, consistent with a role for this area in responding to unexpected processing conflict caused by salient emotional stimuli. Participants with higher anxiety levels showed both less rostral ACC activity overall and reduced recruitment of lateral PFC as expectancy of threat-related distractors was established. This supports the proposal that anxiety is associated with reduced top-down control over threat-related distractors. Our results suggest distinct roles for rostral ACC and lateral PFC in governing the processing of task-irrelevant, threat-related stimuli, and indicate reduced recruitment of this circuitry in anxiety.

Other minds in the brain: a functional imaging study of “theory of mind” in story comprehension <=SEP=> The ability of normal children and adults to attribute independent mental states to self and others in order to explain and predict behaviour ("theory of mind") has been a focus of much recent research. Autism is a biologically based disorder which appears to be characterised by a specific impairment in this "mentalising" process. The present paper reports a functional neuroimaging study with positron emission tomography in which we studied brain activity in normal volunteers while they performed story comprehension tasks necessitating the attribution of mental states. The resultant brain activity was compared with that measured in two control tasks: "physical" stories which did not require this mental attribution, and passages of unlinked sentences. Both story conditions, when compared to the unlinked sentences, showed significantly increased regional cerebral blood flow in the following regions: the temporal poles bilaterally, the left superior temporal gyrus and the posterior cingulate cortex. Comparison of the "theory of mind" stories with "physical" stores revealed a specific pattern of activation associated with mental state attribution: it was only this task which produced activation in the medial frontal gyrus on the left (Brodmann's area 8). This comparison also showed significant activation in the posterior cingulate cortex. These surprisingly clear-cut findings are discussed in relation to previous studies of brain activation during story comprehension. The localisation of brain regions involved in normal attribution of mental states and contextual problem solving is feasible and may have implication for the neural basis of autism.

Daemo: A Self-Governed Crowdsourcing Marketplace <=SEP=> Crowdsourcing marketplaces provide opportunities for autonomous and collaborative professional work as well as social engagement. However, in these marketplaces, workers feel disrespected due to unreasonable rejections and low payments, whereas requesters do not trust the results they receive. The lack of trust and uneven distribution of power among workers and requesters have raised serious concerns about sustainability of these marketplaces. To address the challenges of trust and power, this paper introduces Daemo, a self-governed crowdsourcing marketplace. We propose a prototype task to improve the work quality and open-governance model to achieve equitable representation. We envisage Daemo will enable workers to build sustainable careers and provide requesters with timely, quality labor for their businesses.

Discriminative shape from shading in uncalibrated illumination <=SEP=> Estimating surface normals from just a single image is challenging. To simplify the problem, previous work focused on special cases, including directional lighting, known reflectance maps, etc., making shape from shading impractical outside the lab. To cope with more realistic settings, shading cues need to be combined and generalized to natural illumination. This significantly increases the complexity of the approach, as well as the number of parameters that require tuning. Enabled by a new large-scale dataset for training and analysis, we address this with a discriminative learning approach to shape from shading, which uses regression forests for efficient pixel-independent prediction and fast learning. Von Mises-Fisher distributions in the leaves of each tree enable the estimation of surface normals. To account for their expected spatial regularity, we introduce spatial features, including texton and silhouette features. The proposed silhouette features are computed from the occluding contours of the surface and provide scale-invariant context. Aside from computational efficiency, they enable good generalization to unseen data and importantly allow for a robust estimation of the reflectance map, extending our approach to the uncalibrated setting. Experiments show that our discriminative approach outperforms state-of-the-art methods on synthetic and real-world datasets.

System Integration and Power-Flow Management for a Series Hybrid Electric Vehicle Using Supercapacitors and Batteries <=SEP=> In this paper, system integration and power-flow management algorithms for a four-wheel-driven series hybrid electric vehicle (HEV) having multiple power sources composed of a diesel-engine-based generator, lead acid battery bank, and supercapacitor bank are presented. The super-capacitor is utilized as a short-term energy storage device to meet the dynamic performance of the vehicle, while the battery is utilized as a mid-term energy storage for the electric vehicle (EV) mode operation due to its higher energy density. The generator based on an interior permanent magnet machine (IPMM), run by a diesel engine, provides the average power for the normal operation of the vehicle. Thanks to the proposed power-flow management algorithm, each of the energy sources is controlled appropriately and also the dynamic performance of the vehicle has been improved. The proposed power-flow management algorithm has been experimentally verified with a full-scale prototype vehicle.

Linear AC LED driver with the multi-level structure and variable current regulator <=SEP=> This paper proposes a linear AC LED driver for LED lighting applications. The proposed circuit is small in size because the circuit structure consists of only semiconductors and resistors without any reactors and electrolytic capacitors. The current bypass circuit which is connected in parallel to the LED string consists of single MOSFET, single zener diode and two resistors. The MOSFET is operated in an active state by a self-bias circuit. Thus, an external controller and high voltage gate drivers are not required. The proposed circuit is experimentally validated by using a 7 W prototype. From the experimental results, the THD of input current is 2.1% and the power factor is 0.999. In addition, the simulation loss analysis demonstrates an efficiency of 87% for a 7 W prototype.

Space-Vector PWM Control Synthesis for an H-Bridge Drive in Electric Vehicles <=SEP=> This paper deals with a synthesis of space-vector pulsewidth modulation (SVPWM) control methods applied for an H-bridge inverter feeding a three-phase permanent-magnet synchronous machine (PMSM) in electric-vehicle (EV) applications. First, a short survey of existing architectures of power converters, particularly those adapted to degraded operating modes, is presented. Standard SVPWM control methods are compared with three innovative methods using EV drive specifications in the normal operating mode. Then, a rigorous analysis of the margins left in the control strategy is presented for a semiconductor switch failure to fulfill degraded operating modes. Finally, both classic and innovative strategies are implemented in numerical simulation; their results are analyzed and discussed.

WALNUT: Waging Doubt on the Integrity of MEMS Accelerometers with Acoustic Injection Attacks <=SEP=> Cyber-physical systems depend on sensors to make automated decisions. Resonant acoustic injection attacks are already known to cause malfunctions by disabling MEMS-based gyroscopes. However, an open question remains on how to move beyond denial of service attacks to achieve full adversarial control of sensor outputs. Our work investigates how analog acoustic injection attacks can damage the digital integrity of a popular type of sensor: the capacitive MEMS accelerometer. Spoofing such sensors with intentional acoustic interference enables an out-of-spec pathway for attackers to deliver chosen digital values to microprocessors and embedded systems that blindly trust the unvalidated integrity of sensor outputs. Our contributions include (1) modeling the physics of malicious acoustic interference on MEMS accelerometers, (2) discovering the circuit-level security flaws that cause the vulnerabilities by measuring acoustic injection attacks on MEMS accelerometers as well as systems that employ on these sensors, and (3) two software-only defenses that mitigate many of the risks to the integrity of MEMS accelerometer outputs. We characterize two classes of acoustic injection attacks with increasing levels of adversarial control: output biasing and output control. We test these attacks against 20 models of capacitive MEMS accelerometers from 5 different manufacturers. Our experiments find that 75% are vulnerable to output biasing, and 65% are vulnerable to output control. To illustrate end-to-end implications, we show how to inject fake steps into a Fitbit with a $5 speaker. In our self-stimulating attack, we play a malicious music file from a smartphone's speaker to control the on-board MEMS accelerometer trusted by a local app to pilot a toy RC car. In addition to offering hardware design suggestions to eliminate the root causes of insecure amplification and filtering, we introduce two low-cost software defenses that mitigate output biasing attacks: randomized sampling and 180 degree out-of-phase sampling. These software-only approaches mitigate attacks by exploiting the periodic and predictable nature of the malicious acoustic interference signal. Our results call into question the wisdom of allowing microprocessors and embedded systems to blindly trust that hardware abstractions alone will ensure the integrity of sensor outputs.

Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem <=SEP=> In this paper we introduce Ant-Q, a family of algorithms which present many similarities with Q-learning (Watkins, 1989), and which we apply to the solution of symmetric and asymmetric instances of the traveling salesman problem (TSP). Ant-Q algorithms were inspired by work on the ant system (AS), a distributed algorithm for combinatorial optimization based on the metaphor of ant colonies which was recently proposed in (Dorigo, 1992; Dorigo, Maniezzo and Colorni, 1996). We show that AS is a particular instance of the Ant-Q family, and that there are instances of this family which perform better than AS. We experimentally investigate the functioning of Ant-Q and we show that the results obtained by Ant-Q on symmetric TSP's are competitive with those obtained by other heuristic approaches based on neural networks or local search. Finally, we apply Ant-Q to some difficult asymmetric TSP's obtaining very good results: Ant-Q was able to find solutions of a quality which usually can be found only by very specialized algorithms.

All-at-once Optimization for Coupled Matrix and Tensor Factorizations <=SEP=> Joint analysis of data from multiple sources has the potential to improve our understanding of the underlying structures in complex data sets. For instance, in restaurant recommendation systems, recommendations can be based on rating histories of customers. In addition to rating histories, customers’ social networks (e.g., Facebook friendships) and restaurant categories information (e.g., Thai or Italian) can also be used to make better recommendations. The task of fusing data, however, is challenging since data sets can be incomplete and heterogeneous, i.e., data consist of both matrices, e.g., the person by person social network matrix or the restaurant by category matrix, and higher-order tensors, e.g., the “ratings” tensor of the form restaurant by meal by person. In this paper, we are particularly interested in fusing data sets with the goal of capturing their underlying latent structures. We formulate this problem as a coupled matrix and tensor factorization (CMTF) problem where heterogeneous data sets are modeled by fitting outer-product models to higher-order tensors and matrices in a coupled manner. Unlike traditional approaches solving this problem using alternating algorithms, we propose an all-at-once optimization approach called CMTF-OPT (CMTF-OPTimization), which is a gradient-based optimization approach for joint analysis of matrices and higher-order tensors. We also extend the algorithm to handle coupled incomplete data sets. Using numerical experiments, we demonstrate that the proposed all-at-once approach is more accurate than the alternating least squares approach.

A Tagged Corpus and a Tagger for Urdu <=SEP=> In this paper, we describe a release of a sizeable monolingual Urdu corpus automatically tagged with part-of-speech tags. We extend the work of Jawaid and Bojar (2012) who use three different taggers and then apply a voting scheme to disambiguate among the different choices suggested by each tagger. We run this complex ensemble on a large monolingual corpus and release the tagged corpus. Additionally, we use this data to train a single standalone tagger which will hopefully significantly simplify Urdu processing. The standalone tagger obtains the accuracy of 88.74% on test data.

Learning as search optimization: approximate large margin methods for structured prediction <=SEP=> Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., linear chains) in which search and parameter estimation can be performed exactly. Unfortunately, in many complex problems, it is rare that exact search or parameter estimation is tractable. Instead of learning exact models and searching via heuristic means, we embrace this difficulty and treat the structured output problem in terms of approximate search. We present a framework for learning as search optimization, and two parameter updates with convergence the-orems and bounds. Empirical evidence shows that our integrated approach to learning and decoding can outperform exact models at smaller computational cost.

Bricolage versus breakthrough : distributed and embedded agency in technology entrepreneurship <=SEP=> We develop a perspective on technology entrepreneurship as involving agency that is distributed across different kinds of actors. Each actor becomes involved with a technology, and, in the process, generates inputs that result in the transformation of an emerging technological path. The steady accumulation of inputs to a technological path generates a momentum that enables and constrains the activities of distributed actors. In other words, agency is not only distributed, but it is embedded as well. We explicate this perspective through a comparative study of processes underlying the emergence of wind turbines in Denmark and in United States. Through our comparative study, we flesh out “bricolage” and “breakthrough” as contrasting approaches to the engagement of actors in shaping technological paths. © 2002 Elsevier Science B.V. All rights reserved.

Threshold selection using Renyi's entropy <=SEP=> 

UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes <=SEP=> The 2016 Clinical TempEval challenge addresses temporal information extraction from clinical notes. The challenge is composed of six sub-tasks, each of which is to identify: (1) event mention spans, (2) time expression spans, (3) event attributes, (4) time attributes, (5) events’ temporal relations to the document creation times (DocTimeRel), and (6) narrative container relations among events and times. In this article, we present an end-to-end system that addresses all six sub-tasks. Our system achieved the best performance for all six sub-tasks when plain texts were given as input. It also performed best for narrative container relation identification when gold standard event/time annotations were given.

Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks <=SEP=> Functional groups (FGs) serve as a foundation for analyzing chemical properties of organic molecules. Automatic discovery of FGs will impact various fields of research, including medicinal chemistry, by reducing the amount of lab experiments required for discovery or synthesis of new molecules. Here, we investigate methods based on graph convolutional neural networks (GCNNs) for localizing FGs that contribute to specific chemical properties. Molecules are modeled as undirected graphs with atoms as nodes and bonds as edges. Using this graph structure, we trained GCNNs in a supervised way on experimentally-validated molecular training sets to predict specific chemical properties, e.g., toxicity. Upon learning a GCNN, we analyzed its activation patterns to automatically identify FGs using four different methods: gradient-based saliency maps, Class Activation Mapping (CAM), gradient-weighted CAM (Grad-CAM), and Excitation Back-Propagation. We evaluated the contrastive power of these methods with respect to the specificity of the identified molecular substructures and their relevance for chemical functions. GradCAM had the highest contrastive power and generated qualitatively the best FGs. This work paves the way for automatic analysis and design of new molecules.

How to make large self-organizing maps for nonvectorial data <=SEP=> The self-organizing map (SOM) represents an open set of input samples by a topologically organized, finite set of models. In this paper, a new version of the SOM is used for the clustering, organization, and visualization of a large database of symbol sequences (viz. protein sequences). This method combines two principles: the batch computing version of the SOM, and computation of the generalized median of symbol strings.

Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising <=SEP=> Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the X-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of low-dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio, leading to strong noise and artifacts that down-grade the CT image quality. In this paper, we propose a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the LDCT image quality. Specifically, we incorporate 3-D volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve the structural and textural information in reference to the normal-dose CT images and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information and outperforms competing methods.

Massive parallelization of approximate nearest neighbor search on KD-tree for high-dimensional image descriptor matching <=SEP=> 

REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems <=SEP=> The package REGULARIZATION TOOLS consists of 54 Matlab routines for analysis and solution of discrete ill-posed problems, i.e., systems of linear equations whose coefficient matrix has the properties that its condition number is very large, and its singular values decay gradually to zero. Such problems typically arise in connection with discretization of Fredholm integral equations of the first kind, and similar ill-posed problems. Some form of regularization is always required in order to compute a stabilized solution to discrete ill-posed problems. The purpose of REGULARIZATION TOOLS is to provide the user with easy-to-use routines, based on numerical robust and efficient algorithms, for doing experiments with regularization of discrete ill-posed problems. By means of this package, the user can experiment with different regularization strategies, compare them, and draw conclusions from these experiments that would otherwise require a major programming effert. For discrete ill-posed problems, which are indeed difficult to treat numerically, such an approach is certainly superior to a single black-box routine. This paper describes the underlying theory gives an overview of the package; a complete manual is also available.

Gradient Regularization Improves Accuracy of Discriminative Models <=SEP=> Regularizing the gradient norm of the output of a neural network with respect to its inputs is a powerful technique, rediscovered several times. This paper presents evidence that gradient regularization can consistently improve classification accuracy on vision tasks, using modern deep neural networks, especially when the amount of training data is small. We introduce our regularizers as members of a broader class of Jacobian-based regularizers. We demonstrate empirically on real and synthetic data that the learning process leads to gradients controlled beyond the training points, and results in solutions that generalize well.

Transparent and Efficient CFI Enforcement with Intel Processor Trace <=SEP=> Current control flow integrity (CFI) enforcement approaches either require instrumenting application executables and even shared libraries, or are unable to defend against sophisticated attacks due to relaxed security policies, or both, many of them also incur high runtime overhead. This paper observes that the main obstacle of providing transparent and strong defense against sophisticated adversaries is the lack of sufficient runtime control flow information. To this end, this paper describes FlowGuard, a lightweight, transparent CFI enforcement approach by a novel reuse of Intel Processor Trace (IPT), a recent hardware feature that efficiently captures the entire runtime control flow. The main challenge is that IPT is designed for offline performance analysis and software debugging such that decoding collected control flow traces is prohibitively slow on the fly. FlowGuard addresses this challenge by reconstructing applications' conservative control flow graphs (CFG) to be compatible with the compressed encoding format of IPT, and labeling the CFG edges with credits in the help of fuzzing-like dynamic training. At runtime, FlowGuard separates fast and slow paths such that the fast path compares the labeled CFGs with the IPT traces for fast filtering, while the slow path decodes necessary IPT traces for strong security. We have implemented and evaluated FlowGuard on a commodity Intel Skylake machine with IPT support. Evaluation results show that FlowGuard is effective in enforcing CFI for several applications, while introducing only small performance overhead. We also show that, with minor hardware extensions, the performance overhead can be further reduced.

Predictive coding as a model of cognition <=SEP=> Previous work has shown that predictive coding can provide a detailed explanation of a very wide range of low-level perceptual processes. It is also widely believed that predictive coding can account for high-level, cognitive, abilities. This article provides support for this view by showing that predictive coding can simulate phenomena such as categorisation, the influence of abstract knowledge on perception, recall and reasoning about conceptual knowledge, context-dependent behavioural control, and naive physics. The particular implementation of predictive coding used here (PC/BC-DIM) has previously been used to simulate low-level perceptual behaviour and the neural mechanisms that underlie them. This algorithm thus provides a single framework for modelling both perceptual and cognitive brain function.

Fine-tuning Deep Convolutional Networks for Plant Recognition <=SEP=> This paper describes the participation of the ECOUAN team in the LifeCLEF 2015 challenge. We used a deep learning approach in which the complete system was learned without hand-engineered components. We pre-trained a convolutional neural network using 1.8 million images and used a fine-tuning strategy to transfer learned recognition capabilities from general domains to the specific challenge of Plant Identification task. The classification accuracy obtained by our method outperformed the best result obtained in 2014. Our group obtained the 4th position among all teams and the 10th position among 18 runs.

A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines <=SEP=> Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

Interpreting random forest models using a feature contribution method <=SEP=> Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For “black box” models, such as random forest, this information is hidden inside the model structure. This work presents an approach for computing feature contributions for random forest classification models. It allows for the determination of the influence of each variable on the model prediction for an individual instance. Interpretation of feature contributions for two UCI benchmark datasets shows the potential of the proposed methodology. The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models.

Methodology of band rejection / addition for microstrip antennas design using slot line theory and current distribution analysis <=SEP=> Radio or wireless communication means to transfer information over long distance without using any wires. Millions of people exchange information every day using pager, cellular, telephones, laptops, various types of personal digital assistance (PDAs) and other wireless communication products. The worldwide interoperability for microwave access (Wi-Max) aims to provide wireless data over a long distance in variety of ways. It was based on IEEE 802.16 standard. It is an effective metropolitan area access technique with many favorable features like flexibility, cost, efficiency and fast networking, which not only provides wireless access, but also serves to expand the access to wired network. The coverage area of Wi-Max is around 30-50 km. it can provide high 100 Mbps data rates in 20 MHz bandwidth on fixed and nomadic applications in the 2-11 GHz frequencies. In this paper, a methodology of band rejection/addition for microstrip antennas design using slot line theory and current distribution analysis has been introduced and analyzed. The analysis and design are done by a commercial software. The radiation characteristics, such as; return loss, VSWR, input impedance, and the surface current densities have been introduced and discussed. Finally, the proposed optimum antenna design structure has been fabricated and the measured S-parameters of the proposed structure can be analyzed with network analyzer and compared with simulation results to demonstrate the excellent performance and meet the requirements for wireless communication applications.