This study reports the robust sideslip angle estimation of vehicles with an uncertain tire cornering stiffness and fading measurements. The missing measurement and possible inaccuracy in the measurement of the vehicle's yaw rate are considered by using a random variable distributed over [0, 1]. Norm-bounded uncertainties are considered in the vehicle's tire cornering stiffness. Next, the Lyapunov stability theory is used to design a sideslip angle estimator such that the filtering error dynamics is stochastically stable and the$H_{\infty}$performance criterion is met. The desired parameters of the proposed$H_{\infty}$sideslip angle estimator are gained by solving a linear matrix inequality (LMI) problem. Simulation results show that the proposed novel estimator can efficiently estimate the sideslip angle while it demonstrates robust performance to uncertainties and fading measurements.

The kernels of operating systems such as Windows, Linux, and MacOS are vulnerable to control-flow hijacking. Defenses exist, but many require efficient intra-address-space isolation. Execute-only memory, for example, requires read protection on code segments, and shadow stacks require protection from buffer overwrites. Intel’s Protection Keys for Userspace (PKU) could, in principle, provide the intra-kernel isolation needed by such defenses, but, when used as designed, it applies only to user-mode application code.

Memcached is a widely used key-value store. It is structured as a multithreaded user-level server, accessed over socket connections by a potentially distributed collection of clients. Because socket communication is so much more expensive than a single operation on a K-V store, much of the client library is devoted to batching of requests. Batching is not always feasible, however, and the cost of communication seems particularly unfortunate when—as is often the case—clients are co-located on a single machine with the server, and have access to the same physical memory.

The proliferation of fast, dense, byte-addressable nonvolatile memory suggests that data might be kept in pointer-rich "in-memory" format across program runs and even process and system crashes. For full generality, such data requires dynamic memory allocation, and while the allocator could in principle be "rolled into" each data structure, it is desirable to make it a separate abstraction.

The proliferation of fast, dense, byte-addressable nonvolatile memory suggests the possibility of keeping data in pointer-rich "in-memory" format across program runs and even crashes. For full generality, such data requires dynamic memory allocation. Toward this end, we introduce recoverability, a correctness criterion for persistent allocators, together with a nonblocking allocator, Ralloc, that satisfies this criterion. Ralloc is based on LRMalloc [8], with three key innovations. First, we persist just enough information during normal operation to permit reconstruction of the heap after a full-system crash. Our reconstruction mechanism performs garbage collection (GC) to identify and remedy any failure-induced memory leaks. Second, in support of GC, we introduce the notion of filter functions, which identify the locations of pointers within persistent blocks. Third, to allow persistent regions to be mapped at an arbitrary address, we employ the position-independent pointer representation of Chen et al. [4], both in data and in allocator metadata.