Overflow Errors Research Articles

Implementation of polyphase digital down converter for wireless applications

This paper briefs the hardware-efficient polyphase digital down converter (DDC), which reduces the input sampling frequency to 3.64 GHz and produces a complex output to meet the IEEE 802.11ah wireless network standard. The design comprises a polyphase mixer (PM) filter and a lowpass polyphase finite impulse response (PFIR) filter. The PM filter uses a cascaded integrator comb (CIC) decimation filter based on multiplexer technology to potentially process parallel input interfaces and considerably reduce the sampling rate. The time-sharing mechanism of the CIC filter performs an efficient hardware implementation. Again, the improved design technique of the PFIR filter can effectively increase the operating speed and optimize power consumption. The proposed design offers high flexibility due to programmable sampling rate factors that can support multistandard communication systems. In addition, the design includes the truncation mechanism to protect against overflow errors and the fixed-point data type in each filter node to minimize the area requirements. The presented polyphase DDC is tested using the field-programmable gate array (FPGA) platform targeting the Xilinx Kintex-7 device. According to experimental results, the described DDC significantly reduces the logical resources and power dissipation associated with earlier design structures. The functional verification is tested to ensure the validation of the suggested design.

An Accurate and Numerically Stable Formulation for Computing the Electromagnetic Fields in Uniform Bend Rectangular Waveguides

This article describes a numerically stable formulation for the analysis of electromagnetic fields in rectangular cross section waveguides with a curved longitudinal axis. A novel set of scaled Hankel functions for real-valued arguments and complex-valued orders is introduced for rescaling the characteristic equations associated with the transverse electric (TE) and magnetic (TM) fields of the exact boundary value problem. The exponentially scaled cylindrical functions presented here prevent numerical underflow and overflow errors associated with large real and large imaginary orders without sacrificing accuracy. The proposed methodology is validated against variable-precision arithmetic (VPA) results. Numerical results are also presented for waveguides with large radii of curvature, where the present methodology is compared with perturbation ones in several examples. Dielectric-filled waveguides with small radii of curvature are investigated, and our solutions are compared with finite-integration technique (FIT) results.

Quantum‐dot cellular automata based design for overflow detection in two's complement arithmetic operation

AbstractThis article describes designing a low‐power novel circuit for overflow detection in two's complement arithmetic operations. The quantum‐dot cellular automata (QCA) is employed to achieve the nanoscale design. For designing the circuit of the proposed arithmetic overflow detector, a new layout of the QCA full adder is utilized. Two different designs of arithmetic overflow detectors are outlined in this article. The designs are compared based on latency, cell count, and device area. Based on the comparison, the best design for an arithmetic overflow detector is presented. The circuit cost for all the designs is also shown in this work. Besides, thermal power's effect on the output cell's polarization has also been investigated. The power dissipation of all proposed QCA layouts is estimated.

Predicting Integer Overflow Errors via Supervised Learning

An integer overflow error occurs when an integer operation in computer software evaluates a value out of the integer range. It can lead to a fatal system failure. The existing approaches to detecting integer overflow errors rely on data/control-flow analysis of the code or execution of the code with test cases. This paper presents a supervised learning approach to predicting whether each method in a given Java program has an integer overflow error by treating the source code as text. Built upon real-world programs, our Java dataset covers all integer data types and operations in Java, the methods for preventing integer overflow errors, and adversarial samples. We have evaluated six classification models, BERT, DistilBERT, codeBERT, Code2Vec, fastText, and NBSVM. They represent different text embedding techniques for dealing with source code. The experiment results show that BERT and its variants have outperformed other models. We have applied the resultant BERT model to eleven real-world projects, including JDK13.0 and ten top-ranked GitHub projects, and revealed 181 integer overflow errors. In addition, we have evaluated the classification models with a C/C++ dataset. The result is similar to that of the Java dataset.

SolType: refinement types for arithmetic overflow in solidity

As smart contracts gain adoption in financial transactions, it becomes increasingly important to ensure that they are free of bugs and security vulnerabilities. Of particular relevance in this context are arithmetic overflow bugs, as integers are often used to represent financial assets like account balances. Motivated by this observation, this paper presents SolType, a refinement type system for Solidity that can be used to prevent arithmetic over- and under-flows in smart contracts. SolType allows developers to add refinement type annotations and uses them to prove that arithmetic operations do not lead to over- and under-flows. SolType incorporates a rich vocabulary of refinement terms that allow expressing relationships between integer values and aggregate properties of complex data structures. Furthermore, our implementation, called Solid, incorporates a type inference engine and can automatically infer useful type annotations, including non-trivial contract invariants. To evaluate the usefulness of our type system, we use Solid to prove arithmetic safety of a total of 120 smart contracts. When used in its fully automated mode (i.e., using Solid's type inference capabilities), Solid is able to eliminate 86.3% of redundant runtime checks used to guard against overflows. We also compare Solid against a state-of-the-art arithmetic safety verifier called VeriSmart and show that Solid has a significantly lower false positive rate, while being significantly faster in terms of verification time.

Representing Increasing Virtual Machine Security Strategy in Cloud Computing Computations

This paper proposes algorithm for Increasing Virtual Machine Security Strategy in Cloud Computing computations. Imbalance between load and energy has been one of the disadvantages of old methods in providing server and hosting, so that if two virtual severs be active on a host and energy load be more on a host, it would allocated the energy of other hosts (virtual host) to itself to stay steady and this option usually leads to hardware overflow errors and users dissatisfaction. This problem has been removed in methods based on cloud processing but not perfectly, therefore,providing an algorithm not only will implement a suitable security background but also it will suitably divide energy consumption and load balancing among virtual severs. The proposed algorithm is compared with several previously proposed Security Strategy including SC-PSSF, PSSF and DEEAC. Comparisons show that the proposed method offers high performance computing, efficiency and consumes lower energy in the network.

Spark solutions for discovering fuzzy association rules in Big Data

The high computational impact when mining fuzzy association rules grows significantly when managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is in these cases when the application of Big Data techniques can help to achieve the experiment completion. Therefore, in this paper several Spark algorithms are proposed to handle with massive fuzzy data and discover interesting association rules. For that, we based on a decomposition of interestingness measures in terms of α-cuts, and we experimentally demonstrate that it is sufficient to consider only 10 equidistributed α-cuts in order to mine all significant fuzzy association rules. Additionally, all the proposals are compared and analysed in terms of efficiency and speed up, in several datasets, including a real dataset comprised of sensor measurements from an office building.

Additional methods for the stable calculation of isotropic Gaussian filter coefficients: The case of a truncated filter kernel

The isotropic Gaussian filter is frequently used as a post-processing method for mitigating errors in Gravity Recovery and Climate Experiment (GRACE) time-variable gravity field solutions. It is known that the recurrent calculation of isotropic Gaussian filter coefficients in the spherical harmonic domain results in numerical instabilities. This issue has been recently resolved by Piretzidis & Sideris (Computers & Geosciences 133:104303) for the case of a global Gaussian filter. In this paper, we extend this work for the case of a regional Gaussian filter (i.e., a Gaussian filter with a truncated kernel). We show that instabilities also occur when the coefficients of a regional Gaussian filter are calculated using a conventional recurrence relation, and we provide methods for their proper treatment. We also briefly present some theoretical preliminaries related to recurrence relations and examine algorithms (i.e., initial value and boundary value algorithms) for their stable evaluation. Our analysis indicates that the initial value algorithms are ineffective for the calculation of regional Gaussian filter coefficients, contrary to the global Gaussian filter. The comparison of the boundary value algorithms shows that the Thomas and lower-upper (LU) decomposition algorithms are the most suitable for this case. Their main advantage is their insusceptibility to numerical limitations, such as overflow errors. The Thomas algorithm is also the most efficient in terms of computational speed.

Integration of the original software applications for mechanical engineering

The paper presents some of the progresses recorded in the latest 35 years regarding the concepts, developments and applications of my original computer based research instruments. The identification of the original modelling concepts in mechanical engineering used original computer based instruments whose components are employed for: data visualisation, large matrices processing, parameterized calculus domains, parameterized solvers, arbitrary precision, interfaces between the software instruments and metaprogramming instruments. Data visualization was an important direction of development being used for the data validation and for the results’ interpretation. Several ‘sources’ of graphical facilities were identified and used over the time. Processing of the large matrices allowed the analyst to focus on the scientific problem to be solved, the data processing techniques being provided in background and leading to finer discretization and consequently, to more accurate results. Parameterized domains were used to define in a flexible way the calculus domain and the other input data. Moreover, the original interfaces allow the definition of the ‘common database’ concept, according to which several types of solvers may use the same ‘database’, i.e. the same set of input data. The parameterized solvers are original general software reusable solutions. The multiple precision allows the analyst to avoid some of the runtime errors, such as the overflow error and to get high accuracy results. The metaprogramming allows the user to easily integrate directly into the code the results of the previous data processing stage, which are expressed as a computer code. Several examples are given and some of the directions of future development are also presented.

An instrumentation based algorithm for stack overflow detection

Despite all the efforts made by the scientific community in terms of computer security, buffer overflow vulnerabilities continue being the biggest security flaw in applications, since they compromise the security of the system through memory corruption. To tackle this problem, there are different techniques based on the binary analysis of the application in question. With this objective in mind, the present paper proposes an algorithm based on the dynamic instrumentation of binaries, that is, dynamic local variables belonging to the functions of the program are detected, and a check is performed to see whether there is an overflow of memory between them. The results obtained show how the proposed algorithm is able to detect buffer overflow errors in the stack frames of a function.

Algorithm-based fault tolerance for discrete wavelet transform implemented on GPUs

An important application for discrete wavelet transform (DWT) is the image compression such as the JPEG2000 Standard, and GPU is a good choice for the implementation of the DWT. For the application of image compression in the space environment, this paper has discussed the fault tolerance for the forward DWT (FDWT) implemented on GPUs, to deal with the silent data corruption (SDC) errors caused by single event upsets (SEUs). This paper has analyzed the matrix descriptions for the procedures of the forward DWT in the JPEG2000 Standard. Using the matrix descriptions, the propagation of SDC errors in the multi-level forward DWT has been analyzed, and an algorithm-based fault tolerance (ABFT) method has been proposed, in addition, by analyzing the relationship between the data type and the SDC errors, an on-line fast error detection and correction method has been proposed to suppress the propagation of the logical value overflow errors. Finally, the performance of the program with ABFT has been evaluated and the overhead of memory space caused by the checksum data has been further analyzed.

A Why3 proof of GMP algorithms

Large-integer arithmetic algorithms are used in contexts where both their performance and their correctness are critical, such as cryptographic software. The fastest algorithms are complex enough that formally verifying them is desirable but challenging. We have formally verified a comprehensive arbitrary-precision integer arithmetic library that implements many state-of-the-art algorithms from the GMP library. The algorithms we have verified include addition, subtraction, Toom-Cook multiplication, division and square root. We use the Why3 platform to perform the proof semi-automatically. We obtain an efficient and formally verified C library of low-level functions on arbitrary-precision natural integers. This verification covers the functional correctness of the algorithms, as well as the memory safety and absence of arithmetic overflows of their C implementation. Using detailed pseudocode, we present the algorithms that we verified and outline their proofs.

Analysis to Heap Overflow Exploit in Linux with Symbolic Execution

Heap overflow is a common error of buffer overflow in Linux. The control flow of a program may be hijacked when the program satisfies several specific conditions. The existing automatic exploit generation technologies for buffer overflow find vulnerability trigger point and generate exploit by checking the control flow state. However, the heap overflow data rarely lead to a control flow hijacking as well as protection mechanisms limit the trigger condition. It is difficult to analyze the exploitability of heap overflow automatically through the existing analysis technology. For the heap overflow errors in Linux, we summarize the features of exploit on the basis of analyzing the instances, building the detection model of the exploitability of heap overflow, and proposing a method for analyzing the exploitability of heap overflow based on the model. The proposed method monitors the input data and insecurity functions of the program by using taint analysis; builds the path constraints and data constraints which satisfy the conditions of heap overflow exploit through selective symbolic execution; solves the abovementioned constraints and generates the test case automatically. All the steps of our method can be finished automatically by using the symbolic execution tool S2E. The experiments show that this method can automatically analyze and detect the exploitability of heap overflow errors.

Establishing Reliability for Efficient Routing in Opportunistic Networks

Opportunistic network (Oppnet) is a class of networks where connections between the nodes are not permanent. The nodes are continuously moving and some nodes even switch off their batteries to conserve energy. Reliable delivery of messages in Opportunistic network is one major inherent issue. It is unreliable in the sense that once the source node has forwarded its message, then it will never get to know about its status in the network like whether the message has got discarded at an intermediate node or at the destination node (due to buffer overflow) or the successful delivery of the message has taken place. This work tries to make Oppnet as much reliable as possible. It proposes a reliability protocol named as “Reliability in Oppnet” (RIO). RIO improves the routing in Oppnet and works in parallel with the existing routing protocols. It makes the source node aware about the status of message so that if an error occurs then the source node can take suitable action to resend the message. It considers the redirection error, buffer overflow error, Time Limit Exceeded (TLE), parameter problem and destination unreachable errors that may occur inside the network. RIO has been tested using ONE simulator and implemented with Spray and Wait routing protocol. Results show that the RIO with Spray and Wait protocol outperforms normal Spray and Wait protocol in terms of average message delivery probability.

A Note on Parameters Estimation for Nonlinear Wiener Processes With Measurement Errors

The nonlinear Wiener process has been widely used as a model for the degradation process. This note concerns parameters estimation of nonlinear Wiener processes with measurement errors (WPME) by the maximum likelihood estimation method. Firstly, we prove a rule that the estimated results based on the sample likelihood function developed through observations at each point are equal to the results from the first differences of the observations. This rule indicates that for reducing computation complexity the first differences of the observations may develop the sample likelihood function. Then we present a simple method to calculate the determinant and the inverse matrix of the covariance matrix of the WPME. This simple method could avoid the overflow error when calculating the determinant of the covariance matrix and the case that the inverse matrix is close to be singular, which could result in wrong estimation results. Secondly, we highlight the unit-specific assumption, which has a significant impact on parameters estimation but has been neglected in many papers. Then, we propose a modified expectation maximization algorithm for parameters estimation with random effects. Finally, to demonstrate the application and superiority of the proposed method, we provide a numerical example and a case study with comparison to several representative methods in the literature.

Accurate and Efficient Explicit Approximations of the Colebrook Flow Friction Equation Based on the Wright ω-Function

The Colebrook equation is a popular model for estimating friction loss coefficients in water and gas pipes. The model is implicit in the unknown flow friction factor, f . To date, the captured flow friction factor, f , can be extracted from the logarithmic form analytically only in the term of the Lambert W -function. The purpose of this study is to find an accurate and computationally efficient solution based on the shifted Lambert W -function also known as the Wright ω-function. The Wright ω-function is more suitable because it overcomes the problem with the overflow error by switching the fast growing term, y = W ( e x ) , of the Lambert W -function to series expansions that further can be easily evaluated in computers without causing overflow run-time errors. Although the Colebrook equation transformed through the Lambert W -function is identical to the original expression in terms of accuracy, a further evaluation of the Lambert W -function can be only approximate. Very accurate explicit approximations of the Colebrook equation that contain only one or two logarithms are shown. The final result is an accurate explicit approximation of the Colebrook equation with a relative error of no more than 0.0096%. The presented approximations are in a form suitable for everyday engineering use, and are both accurate and computationally efficient.

DSVerifier-Aided Verification Applied to Attitude Control Software in Unmanned Aerial Vehicles

During the last decades, model checking techniques have been applied to improve overall system reliability, in unmanned aerial vehicle (UAV) approaches. Nonetheless, there is little effort focused on applying those methods to the control-system domain, especially when it comes to the investigation of low-level implementation errors, which are related to digital controllers and hardware compatibility. The present study addresses the mentioned problems and proposes the application of a bounded model checking tool, named as Digital System Verifier (DSVerifier), to the verification of digital-system implementation issues, in order to investigate problems that emerge in digital controllers designed for UAV attitude systems. A verification methodology to search for implementation errors related to finite word-length effects (e.g., arithmetic overflows and limit cycles), in UAV attitude controllers, is presented, along with its evaluation, which aims to ensure correct-by-design systems. Experimental results show that low-level failures in UAV attitude control software used in aerial surveillance are identified by DSVerifier, which can also be used for developing sound and correct implementations, through its integration into development processes. Finally, given that the proposed approach handles C code and takes into account hardware specifications, it is suitable for verifying final controller implementations, which is a more practical scenario.

Self-reconfigurable implementation for a switched beam smart antenna

We present a self-reconfigurable embedded system for a switched beam smart antenna that can steer the beam pattern into a desired direction, handle arithmetic overflow, and respond to user-specified constraints on accuracy and resources. The main component (adaptive beamforming) is implemented as a fully customized hardware in fixed-point arithmetic. By varying the numerical formats and desired beam patterns, we generate a set of hardware configurations, which can then be selected at run-time via a dynamic manager implemented in software. The self-reconfigurable embedded system is implemented and tested on a Programmable System-on-Chip (PSoC). Results are presented in terms of resources, accuracy, and run-time hardware adaptation. This work illustrates the benefits of run-time reconfiguration technology on PSoCs for the implementation of switched-beam smart antennas.

BMCLua

Lua is a programming language designed as scripting language, which is fast, lightweight, and suitable for embedded applications. Due to its features, Lua is widely used in the development of games and interactive applications for digital TV. However, during the development phase of such applications, some errors may be introduced, such as deadlock, arithmetic overflow, and division by zero. This paper describes a novel verification approach for software written in Lua, using as backend the Efficient SMTBased Context-Bounded Model Checker (ESBMC). Such an approach, called bounded model checking - Lua (BMCLua), consists in translating Lua programs into ANSI-C source code, which is then verified with ESBMC. Experimental results show that the proposed verification methodology is effective and efficient, when verifying safety properties in Lua programs. The performed experiments have shown that BMCLua produces an ANSI-C code that is more efficient for verification, when compared with other existing approaches. To the best of our knowledge, this work is the first that applies bounded model checking to the verification of Lua programs.

REAL TIME VLSI IWT DENOSING ARCHITECTURE

In the last decade many efficient VLSI architectures were designed to implement the discrete wavelet transform using the lifting scheme. Most of these architectures do not operate in real-time. This paper presents a scalable real time VLSI architectural to compute an integer wavelet transform (IWT) using the lifting scheme for (5/3) biorthogonal filter. The proposed architecture is projected on (XC3S700A FG484) FPGA chip embedded on a Spartan 3A starter kit board. An efficient formula is driven for controlling the delay introduced between tandem architectures that are adapted to work in real-time. The layout of the integrated VLSI structure is simple and can be connected easily in tandem for computing IWT in real time. A hard threshold module is designed and attached with proposed architecture to implement real time denosing application. The achievement of the proposed architecture along with supplement hard threshold module is assessed by denoising a four benchmarks signals corrupted by adding white Gaussian noise. The result outcomes show that the proposed IWT architecture has powerful performance in the real time wavelet based signal denoising process. The architecture data word length is selected as 11-bits to avoided arithmetic overflow for two’s complement 8-bit integer data input. The maximum operating frequency of the proposed architecture varies from 26 MHz in 1-level to 14MHz in 5-level for decomposition/ reconstruction with hard threshold module in an FPGA implementation. The hardware utilization varies from (50%) in 1-level to (97%) for 5-level.